diff --git a/CHANGELOG.md b/CHANGELOG.md
index b97904b0..283ff5a8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,7 @@
### Added
- **Module-level skill introspection API**: `STATE_TYPE`, `STATE_NAMESPACE`, `skill_metadata()`, `instructions()`, and `state_metadata()` on `haiku.rag.skills.rag` and `haiku.rag.skills.rlm` — allows introspecting skill configuration without calling `create_skill()`
+- **Automatic structured output detection**: Native JSON schema output is used automatically when the model supports it, with tool-call fallback otherwise. No configuration needed.
### Changed
diff --git a/haiku_rag_slim/haiku/rag/agents/qa/agent.py b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
index 11de13ce..d19220df 100644
--- a/haiku_rag_slim/haiku/rag/agents/qa/agent.py
+++ b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
@@ -1,7 +1,6 @@
from dataclasses import dataclass
from pydantic_ai import Agent
-from pydantic_ai.output import ToolOutput
from haiku.rag.agents.qa.prompts import QA_SYSTEM_PROMPT
from haiku.rag.agents.research.models import (
@@ -14,7 +13,7 @@ from haiku.rag.config import Config
from haiku.rag.config.models import AppConfig, ModelConfig
from haiku.rag.store.models import SearchResult
from haiku.rag.tools.search import create_search_toolset
-from haiku.rag.utils import get_model
+from haiku.rag.utils import get_model, structured_output_type
@dataclass
@@ -57,10 +56,11 @@ class QuestionAnswerAgent:
# Agent created per-call: toolset varies with filter, and Agent
# construction is pure Python (no IO).
+ model = get_model(self._model_config, self._config)
agent: Agent[_QARunDeps, RawSearchAnswer] = Agent( # ty: ignore[invalid-assignment]
- model=get_model(self._model_config, self._config),
+ model=model,
deps_type=_QARunDeps,
- output_type=ToolOutput(RawSearchAnswer, max_retries=3),
+ output_type=structured_output_type(RawSearchAnswer, model),
instructions=self._system_prompt,
toolsets=[search_toolset],
retries=3,
diff --git a/haiku_rag_slim/haiku/rag/agents/research/graph.py b/haiku_rag_slim/haiku/rag/agents/research/graph.py
index d6996dcb..0278e3c0 100644
--- a/haiku_rag_slim/haiku/rag/agents/research/graph.py
+++ b/haiku_rag_slim/haiku/rag/agents/research/graph.py
@@ -1,7 +1,6 @@
import asyncio
from pydantic_ai import Agent, RunContext, format_as_xml
-from pydantic_ai.output import ToolOutput
from pydantic_graph.beta import Graph, GraphBuilder, StepContext
from haiku.rag.agents.research.dependencies import ResearchContext, ResearchDependencies
@@ -20,7 +19,7 @@ from haiku.rag.agents.research.prompts import (
from haiku.rag.agents.research.state import ResearchDeps, ResearchState
from haiku.rag.config import Config
from haiku.rag.config.models import AppConfig
-from haiku.rag.utils import build_prompt, get_model
+from haiku.rag.utils import build_prompt, get_model, structured_output_type
def format_context_for_prompt(context: ResearchContext) -> str:
@@ -66,9 +65,10 @@ async def _iterative_plan_logic(
else:
effective_prompt = build_prompt(ITERATIVE_PLAN_PROMPT, config)
+ model = get_model(model_config, config)
plan_agent: Agent[ResearchDependencies, IterativePlanResult] = Agent( # type: ignore[assignment]
- model=get_model(model_config, config),
- output_type=ToolOutput(IterativePlanResult, max_retries=3),
+ model=model,
+ output_type=structured_output_type(IterativePlanResult, model),
instructions=effective_prompt,
retries=3,
deps_type=ResearchDependencies,
@@ -115,9 +115,10 @@ async def _search_one_step_logic(
deps.semaphore = asyncio.Semaphore(state.max_concurrency)
async with deps.semaphore:
+ model = get_model(model_config, config)
agent: Agent[ResearchDependencies, RawSearchAnswer] = Agent( # type: ignore[assignment]
- model=get_model(model_config, config),
- output_type=ToolOutput(RawSearchAnswer, max_retries=3),
+ model=model,
+ output_type=structured_output_type(RawSearchAnswer, model),
instructions=search_prompt,
retries=3,
deps_type=ResearchDependencies,
@@ -216,9 +217,10 @@ def build_research_graph(
state = ctx.state
deps = ctx.deps
+ model = get_model(model_config, config)
agent: Agent[ResearchDependencies, ResearchReport] = Agent( # type: ignore[assignment]
- model=get_model(model_config, config),
- output_type=ToolOutput(ResearchReport, max_retries=3),
+ model=model,
+ output_type=structured_output_type(ResearchReport, model),
instructions=synthesis_prompt,
retries=3,
deps_type=ResearchDependencies,
diff --git a/haiku_rag_slim/haiku/rag/agents/rlm/agent.py b/haiku_rag_slim/haiku/rag/agents/rlm/agent.py
index 19ab3c4a..55ff5c3f 100644
--- a/haiku_rag_slim/haiku/rag/agents/rlm/agent.py
+++ b/haiku_rag_slim/haiku/rag/agents/rlm/agent.py
@@ -1,11 +1,10 @@
from pydantic_ai import Agent, RunContext
-from pydantic_ai.output import ToolOutput
from haiku.rag.agents.rlm.dependencies import RLMDeps
from haiku.rag.agents.rlm.models import CodeExecution, RLMResult
from haiku.rag.agents.rlm.prompts import RLM_SYSTEM_PROMPT
from haiku.rag.config.models import AppConfig
-from haiku.rag.utils import get_model
+from haiku.rag.utils import get_model, structured_output_type
def create_rlm_agent(config: AppConfig) -> Agent[RLMDeps, RLMResult]:
@@ -26,7 +25,7 @@ def create_rlm_agent(config: AppConfig) -> Agent[RLMDeps, RLMResult]:
agent: Agent[RLMDeps, RLMResult] = Agent( # type: ignore[invalid-assignment]
model,
deps_type=RLMDeps,
- output_type=ToolOutput(RLMResult, max_retries=3),
+ output_type=structured_output_type(RLMResult, model),
instructions=RLM_SYSTEM_PROMPT,
retries=3,
)
diff --git a/haiku_rag_slim/haiku/rag/utils.py b/haiku_rag_slim/haiku/rag/utils.py
index 0c89f49c..ca6bfa7a 100644
--- a/haiku_rag_slim/haiku/rag/utils.py
+++ b/haiku_rag_slim/haiku/rag/utils.py
@@ -306,6 +306,20 @@ def get_model(
return f"{provider}:{model}"
+def structured_output_type(
+ result_type: type,
+ model: Any,
+ max_retries: int = 3,
+) -> Any:
+ """Return a NativeOutput or ToolOutput wrapper based on model capability."""
+ from pydantic_ai.models import Model
+ from pydantic_ai.output import NativeOutput, ToolOutput
+
+ if isinstance(model, Model) and model.profile.supports_json_schema_output:
+ return NativeOutput(result_type)
+ return ToolOutput(result_type, max_retries=max_retries)
+
+
def format_bytes(num_bytes: int) -> str:
"""Format bytes as human-readable string."""
size = float(num_bytes)
diff --git a/tests/agents/rlm/test_agent.py b/tests/agents/rlm/test_agent.py
index ba1cdf6c..b5893d0f 100644
--- a/tests/agents/rlm/test_agent.py
+++ b/tests/agents/rlm/test_agent.py
@@ -2,7 +2,6 @@ from pathlib import Path
import pytest
from pydantic_ai import Agent
-from pydantic_ai.output import ToolOutput
from haiku.rag.agents.rlm.agent import create_rlm_agent
from haiku.rag.agents.rlm.dependencies import RLMDeps
@@ -16,10 +15,22 @@ def vcr_cassette_dir():
class TestCreateRLMAgent:
- def test_creates_agent_with_correct_types(self):
+ def test_creates_agent_native_output_when_supported(self):
+ from pydantic_ai.output import NativeOutput
+
agent = create_rlm_agent(Config)
assert isinstance(agent, Agent)
assert agent.deps_type is RLMDeps
+ assert isinstance(agent.output_type, NativeOutput)
+ assert agent.output_type.outputs is RLMResult
+
+ def test_creates_agent_tool_output_when_not_supported(self):
+ from pydantic_ai.output import ToolOutput
+
+ config = AppConfig()
+ config.rlm.model.name = "qwen3"
+ agent = create_rlm_agent(config)
+ assert isinstance(agent, Agent)
assert isinstance(agent.output_type, ToolOutput)
assert agent.output_type.output is RLMResult
diff --git a/tests/cassettes/test_qa/test_qa_ollama.yaml b/tests/cassettes/test_qa/test_qa_ollama.yaml
index 3a0f6930..e0af991c 100644
--- a/tests/cassettes/test_qa/test_qa_ollama.yaml
+++ b/tests/cassettes/test_qa/test_qa_ollama.yaml
@@ -86,7 +86,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '2778'
+ - '2953'
content-type:
- application/json
host:
@@ -138,34 +138,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: high
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Search the knowledge base for relevant documents.
-
- Returns results with chunk IDs and rank positions.
- Reference results by their chunk_id in cited_chunks.
- name: search_documents
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -189,12 +166,39 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: |-
+ Search the knowledge base for relevant documents.
+
+ Formatted search results with content and metadata.
+
+ name: search_documents
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ description: 'Number of results to return (default: from config).'
+ query:
+ description: The search query (what to search for).
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1430'
+ - '865'
content-type:
- application/json
parsed_body:
@@ -203,28 +207,27 @@ interactions:
index: 0
message:
content: ''
- reasoning: |-
- We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" This is a specific question about some innovative civic engagement introduced by "Bintang" for gathering feedback from citizens during Jakarta's election. Bintang could refer to a person or a company. Might be a brand? Or maybe it's a short for Bintang, maybe a government official? The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" So the answer: likely something like "interactive polling booth", "online civic engagement platform", "live voting kiosks" or something.
-
- We need to search the knowledge base. Use search_documents with query: 'Bintang innovative form of civic engagement gathering feedback citizens Jakarta election'.
+ reasoning: 'We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback
+ from citizens during Jakarta''s election?" This asks about a specific innovative form of civic engagement introduced
+ by Bintang during Jakarta''s election. We need to look into the knowledge base. Let''s search.'
role: assistant
tool_calls:
- function:
- arguments: '{"limit":5,"query":"Bintang innovative form of civic engagement gathering feedback citizens Jakarta
- election"}'
+ arguments: '{"query":"Bintang innovative form of civic engagement gathering feedback citizens during Jakarta
+ election","limit":10}'
name: search_documents
- id: call_pg1twsq1
+ id: call_zzsyoh9m
index: 0
type: function
- created: 1769001375
- id: chatcmpl-679
+ created: 1772626873
+ id: chatcmpl-900
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 203
- prompt_tokens: 561
- total_tokens: 764
+ completion_tokens: 97
+ prompt_tokens: 510
+ total_tokens: 607
status:
code: 200
message: OK
@@ -237,7 +240,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '158'
+ - '165'
content-type:
- application/json
host:
@@ -246,7 +249,7 @@ interactions:
parsed_body:
encoding_format: base64
input:
- - Bintang innovative form of civic engagement gathering feedback citizens Jakarta election
+ - Bintang innovative form of civic engagement gathering feedback citizens during Jakarta election
model: qwen3-embedding:4b
uri: http://localhost:11434/v1/embeddings
response:
@@ -257,14 +260,14 @@ interactions:
- chunked
parsed_body:
data:
- - embedding: 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
index: 0
object: embedding
model: qwen3-embedding:4b
object: list
usage:
- prompt_tokens: 14
- total_tokens: 14
+ prompt_tokens: 15
+ total_tokens: 15
status:
code: 200
message: OK
@@ -277,7 +280,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8898'
+ - '8507'
content-type:
- application/json
host:
@@ -327,22 +330,19 @@ interactions:
- content: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during
Jakarta's election?
role: user
- - content: |-
-
- We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" This is a specific question about some innovative civic engagement introduced by "Bintang" for gathering feedback from citizens during Jakarta's election. Bintang could refer to a person or a company. Might be a brand? Or maybe it's a short for Bintang, maybe a government official? The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" So the answer: likely something like "interactive polling booth", "online civic engagement platform", "live voting kiosks" or something.
-
- We need to search the knowledge base. Use search_documents with query: 'Bintang innovative form of civic engagement gathering feedback citizens Jakarta election'.
-
+ - content: null
+ reasoning: 'We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback
+ from citizens during Jakarta''s election?" This asks about a specific innovative form of civic engagement introduced
+ by Bintang during Jakarta''s election. We need to look into the knowledge base. Let''s search.'
role: assistant
tool_calls:
- function:
- arguments: '{"limit":5,"query":"Bintang innovative form of civic engagement gathering feedback citizens Jakarta
- election"}'
+ arguments: '{"query":"Bintang innovative form of civic engagement gathering feedback citizens during Jakarta election","limit":10}'
name: search_documents
- id: call_pg1twsq1
+ id: call_zzsyoh9m
type: function
- content: |-
- [e6d3622c-0d09-4caa-a7ab-ef01949233c8] [rank 1 of 1]
+ [2a4d127c-da6d-40dc-b02f-4c012653b40f] [rank 1 of 1]
Type: text
Content:
Jakarta Election Campaigns Heat Up: Here's How to Understand the System
@@ -387,37 +387,14 @@ interactions:
Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
role: tool
- tool_call_id: call_pg1twsq1
+ tool_call_id: call_zzsyoh9m
model: gpt-oss
reasoning_effort: high
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Search the knowledge base for relevant documents.
-
- Returns results with chunk IDs and rank positions.
- Reference results by their chunk_id in cited_chunks.
- name: search_documents
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -441,44 +418,67 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: |-
+ Search the knowledge base for relevant documents.
+
+ Formatted search results with content and metadata.
+
+ name: search_documents
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ description: 'Number of results to return (default: from config).'
+ query:
+ description: The search query (what to search for).
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1666'
+ - '1785'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
+ content: '{"query":"Jakarta election Bintang interactive app","answer":"Bintang introduced an interactive app that
+ let citizens give real‑time feedback on their daily commute challenges, which was praised as an innovative civic‑engagement
+ tool.","cited_chunks":["2a4d127c-da6d-40dc-b02f-4c012653b40f"]}'
reasoning: |-
- We have the chunk from source: "Jakarta Election Campaigns Heat Up: Here's How to Understand the System". It mentions "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". That appears to be the answer: Bintang introduced an interactive app for gathering real-time feedback from citizens about daily commute challenges. The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" The answer: an interactive app for collecting real-time feedback about daily commute challenges.
+ Search result shows content that indicates Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges as an innovative form of civic engagement. The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" The answer: Bintang introduced an interactive app for gathering real-time feedback from citizens about daily commute challenges. According to the snippet: "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". So that is the answer. Provide with citation.
- We need to format answer with cited_chunks: chunk id e6d3622c-0d09-4caa-a7ab-ef01949233c8. The answer: Bintang introduced an interactive app. Provide answer concise. Use final_result.
+ But we must cite chunk IDs. The chunk ID is [2a4d127c-da6d-40dc-b02f-4c012653b40f]. That is the relevant chunk. I can only include that chunk ID. Let's use that. The instruction: "In your response, include the chunk IDs you used in cited_chunks."
+
+ Hence answer: "Bintang introduced an interactive app for gathering real-time feedback on daily commute challenges".
+
+ Add citation. Also maybe mention "interactive app" or "digital platform". I'll keep it concise.
role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"Bintang introduced an interactive app that allowed voters to give real‑time feedback
- on daily commute challenges during the Jakarta election.","cited_chunks":["e6d3622c-0d09-4caa-a7ab-ef01949233c8"],"confidence":0.93,"query":"What
- innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta''s
- election?"}'
- name: final_result
- id: call_2tcii5ad
- index: 0
- type: function
- created: 1769001382
- id: chatcmpl-355
+ created: 1772626882
+ id: chatcmpl-570
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 278
- prompt_tokens: 1657
- total_tokens: 1935
+ completion_tokens: 76
+ prompt_tokens: 1735
+ total_tokens: 1811
status:
code: 200
message: OK
@@ -491,7 +491,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '1823'
+ - '1856'
content-type:
- application/json
host:
@@ -525,7 +525,7 @@ interactions:
- content: |-
QUESTION: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?
- GENERATED ANSWER: Bintang introduced an interactive app that allowed voters to give real‑time feedback on daily commute challenges during the Jakarta election.
+ GENERATED ANSWER: Bintang introduced an interactive app that let citizens give real‑time feedback on their daily commute challenges, which was praised as an innovative civic‑engagement tool.
EXPECTED ANSWER: Bintang introduced an interactive app for real-time feedback on daily commute challenges.
role: user
@@ -551,7 +551,7 @@ interactions:
response:
headers:
content-length:
- - '571'
+ - '556'
content-type:
- application/json
parsed_body:
@@ -560,25 +560,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need to evaluate equivalence. Both mention interactive app for real-time feedback on daily commute challenges.
- So yes equivalent.
+ reasoning: We compare. Both mention interactive app, real-time feedback, daily commute challenges. They match. So
+ equivalent.
role: assistant
tool_calls:
- function:
arguments: '{"equivalent":true}'
name: final_result
- id: call_hvd0sams
+ id: call_3tgu31go
index: 0
type: function
- created: 1769001384
- id: chatcmpl-355
+ created: 1772626883
+ id: chatcmpl-801
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 48
- prompt_tokens: 387
- total_tokens: 435
+ completion_tokens: 47
+ prompt_tokens: 395
+ total_tokens: 442
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_research_graph/test_graph_end_to_end.yaml b/tests/cassettes/test_research_graph/test_graph_end_to_end.yaml
index e4631378..a86e9c96 100644
--- a/tests/cassettes/test_research_graph/test_graph_end_to_end.yaml
+++ b/tests/cassettes/test_research_graph/test_graph_end_to_end.yaml
@@ -86,7 +86,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '1786'
+ - '1708'
content-type:
- application/json
host:
@@ -97,8 +97,6 @@ interactions:
- content: |-
You are the research orchestrator planning the investigation.
- If a section is provided, use it to understand the conversation context.
-
Your task:
1. Analyze the original question
2. Propose the first question to investigate
@@ -126,13 +124,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Output from iterative planning step.
- name: final_result
- parameters:
+ name: IterativePlanResult
+ schema:
additionalProperties: false
properties:
is_complete:
@@ -151,44 +147,39 @@ interactions:
- is_complete
- reasoning
type: object
- type: function
+ strict: false
+ type: json_schema
+ stream: false
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1141'
+ - '842'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: 'Need first question: identify the upstart candidate known for social activism in Jakarta election. Likely
- Anies Baswedan? No upstart candidate: maybe "Kusno" no. Upstart candidate known for activism: "Reza Rahadian"?
- Actually "Siti Wirastuti"? Not sure. Let''s ask: "Who is the upstart candidate in Jakarta''s 2024 mayoral election
- that is known for social activism?"'
+ content: |-
+ {
+ "is_complete": false,
+ "reasoning": "The original question asks for the identity of the upstart Jakarta election candidate noted for social activism. To investigate this, we first need to gather basic information about who that candidate is.",
+ "next_question": "Which upstart candidate in Jakarta's current election is known for social activism?"
+ }
+ reasoning: 'Need first question: identify candidate in Jakarta election known for social activism. So ask: "Which
+ upstart candidate in Jakarta''s election is known for social activism?"'
role: assistant
- tool_calls:
- - function:
- arguments: '{"is_complete":false,"next_question":"What is the name of the upstart candidate in Jakarta''s 2024
- mayoral election who is known for social activism?","reasoning":"The question needs to be precise; identifying
- the candidate requires specifying the election year and that the candidate is known for social activism to
- resolve ambiguity."}'
- name: final_result
- id: call_z1eo6ehj
- index: 0
- type: function
- created: 1769800539
- id: chatcmpl-728
+ created: 1772626888
+ id: chatcmpl-629
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 174
- prompt_tokens: 376
- total_tokens: 550
+ completion_tokens: 73
+ prompt_tokens: 307
+ total_tokens: 380
status:
code: 200
message: OK
@@ -201,7 +192,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '2900'
+ - '2911'
content-type:
- application/json
host:
@@ -254,34 +245,15 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
+ - content: Which upstart candidate in Jakarta's current election is known for social activism?
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -305,12 +277,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '610'
+ - '534'
content-type:
- application/json
parsed_body:
@@ -319,24 +312,24 @@ interactions:
index: 0
message:
content: ''
- reasoning: We need upstart candidate in Jakarta 2024 mayoral election known for social activism. Search.
+ reasoning: Need search.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"upstart candidate Jakarta 2024 mayoral election social activism","limit":5}'
+ arguments: '{"limit":3,"query":"Jakarta current election upstart candidate known for social activism"}'
name: search_and_answer
- id: call_6gzq7v5v
+ id: call_pzmpok4y
index: 0
type: function
- created: 1769800543
- id: chatcmpl-328
+ created: 1772626890
+ id: chatcmpl-212
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 60
- prompt_tokens: 640
- total_tokens: 700
+ completion_tokens: 42
+ prompt_tokens: 550
+ total_tokens: 592
status:
code: 200
message: OK
@@ -349,7 +342,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '133'
+ - '138'
content-type:
- application/json
host:
@@ -358,7 +351,7 @@ interactions:
parsed_body:
encoding_format: base64
input:
- - upstart candidate Jakarta 2024 mayoral election social activism
+ - Jakarta current election upstart candidate known for social activism
model: qwen3-embedding:4b
uri: http://localhost:11434/v1/embeddings
response:
@@ -369,14 +362,14 @@ interactions:
- chunked
parsed_body:
data:
- - embedding: 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wzvTuSSAGT2CpEs8m3qcO6m7ArsnbSs96+N3Pdm+jTxFO408OwNdO/yXizx8Q4e88T3UOROY6LsFzM48S2ZKO9F+QDxQefy8ezu5vC1lvbsyEEy84IglPPblmz1NjP08Q8JTvLz4w7vtRZG8pnDIvXXDMDskwgE9JYluva2CWr0vWOu6qgeVPPmOmTtXzia8RGuhvKOTeLu1t1O8Q3fnO43g2TyqgUE77LqGPKHESrtoDgY98WRcu9GAk7rU3oU7Rpc7uzVpIb01Gfk7bOugO44UzryYDwK9lgfHujlksDyTBhs9wUpKvMTqwrv6i8a8ACeou1uFLzqxPB+7hAy7vK9KE7wCH6+87jE0vUI9Jbx7mp26w1M8PCU7P70cXoA7LWm5unzjKjzyvKA88Ib6vBImSbyHg/s8C1SZPHiH/jzSKTW8ZsJ3PK6CcTw+1cE8AJVjPCbGH7w3p9c8kAkNuRBs67m20g88vQtkPLjt3Tz2YCI8TckaPaN1Jzx0bII8BHKDuyuq27zke/K8gpqUPLlAPbt/Z868sVjzPDC8KDvC+ZM5y2G4vPKcs7zxTga8KHmzuvlrcTsrmYO7eVuEu3NXQjwzPBC9eu+fu2523zueq8c7dEDBPNoPyruXwgG8xcpwuzW7UzwUR4s8BvnfO2vqw7ueO6I6MEZOvM4Yt7pzhDI80w6HPPYLxDva9yu81eIZvDCdTbzjtLk8HyQdPPqCE7q6oUW8JCWJvM3cPTyaXmE6heArO0ZpKzyo0Ig7O8ievN3hiLwnwWq8o67gOxfArjynE9Q7TJSgPI3UAryyn5Y7HbMVPWENobuJ5rI8ZS2GvPxZ3bvI5l885SRhPAecWDv3fLg8Rkr5vNFw9TzCHwo7a21dPMpogLyVyq28dKi/vOjgljg1B6+6bb4cvNHBBrznv0m8Rg6Cu96o3DrS9ri854AlvRmJj7zsOgW6DvvTu0ukVLx0C2K8FvzaO7O1wzsKlcK7KfNpvJ2oETzvlIU8r1Beu0pjnLzxjjK8foUDPHXcfzwuLhY7cQTaO/QEyTuitLW8tUaqPIaoBzwOOl48f83Ru3cY7bt1DOY7D1MevL0NxrrCWp879hBUvPeLADwg3OC8HqHiO+wbgLzlTC28d06dvDo8fjw3Goc8vM9JvKS/tLuPnGg88PbrPNKzhTxCZ0I7NOnDvKN3yDws78K8dJ3CO72qkjvtOOo8j4D0u5/rLbpyq1g7roHMPDdIHbysPCG80gY2PDFlmLp5Sre8+F0DuzAv6DxhpHm8R0IkvSaTrrwRzyA81H6FPHXgJjnkHQu9jvnJO5xDNrw4BDK7ehMEveYxJrxylBS8AXqkPOSoGb1o/GI7f9i4uRaty7zEjVK8Hgmpu6cPWz1ZYUM7WoyMPO6kX7whrzq7PywhvD24N7wjbeo8dkW4Ol1qeDx+CHc74mcpPXZt8zrpdCe46xSGO3iyeLxhzIa82V7AOuxw+DgFDsA8RUXQPD1xqbwDLYw7KZ6Cuwt88zz2Sle7D7gvvO0++DwzswK85FPyvK5Kxjsz/ak8oPSyPJ0e3DraQUe8j8WwO7Lmb7zxV1s8heNbvEK7lLsc8tC8qptEvLfXmrs2/qQ7zclovMD6vDkbuaA8oDEZukvSwDvHlJ68OAfGuwPFO7x/WW+76+YGPFKzkTyGnVK81ePPvCIIkTxiSoI8Z70QvC77o7xv5Kg8lZbAvfrUBb1tWxC8gimTvKMinDyQh2M8bfExPDEsxzzYUik8+PU7vL1rtDwojQW88e0LPJZ2JzwP+jm7+gWFOUGQSz3LB287JdJWOCKPTbz7T4A8EHljPG8fcTuMOh87ZQKyOrLBhzxqP/C7s00LvRYEDb3QiY08jIcevPC5Ar2KaFu8Kmm6O+Y85zyLrpC83OZZO0bnAz2M18G7tNQ2uSA9arplYIs8Sn/QuhnCdbwW8D67r2lAvHjvcjw2v9s7zk19vGNt4TzdKNS7uzclPSo4EbyrIwW96duJuy1k+Tyq8Tm8DtcNu5XMlDxGCNQ86HcsvIT1Qbxlv8A8ka5NPD1PJTzS1mu6NesxPGSXtLwesqA7O1QsPEBU17yqH4s8WCT4O/GUPryN81A8Iin9O66BhbuN2q08jCHSvF3jijxV+Fi8o/tLvemtMDygjr07x8c0vOFxM7zTQJM99AHJPDn0zzv7WTM78XXuPCCQsztcIZE8gWPTOyGVwbqpO4c5dmINvczmlrzU6gQ5kqJRvBcCkbwe4Ky75S6gPILeWzx1hWc828gxvNgK3DwStrE7N9CMu9pW2LuRBy88JOAWPLpZJz0oUfE6XTcqPZsF5zw1LMK8scDAvDr7/Lz9pqI8rJt0OxqFdDwLWDw9nA9wvWU1K7wE1cA86FMrO+OuVbvq/U88urhMu0hKfDuLgz67Kixhun7HobxX0hO7x0+BPI7dIjwtZY28odHQPMu9J717WII7ICeTuy3zXLzfZYC82FRJvXxjGbxrj/u7BgHOvGGZBj0z2MO8zoXLvHILg7wJ3xC8oRVWO3jFZTzJhGo863z4OhkaIDz/CDI9kdmYO7wMwructLu8zdCfuytiNborVCk8gxgou2tjN7qeVAA8zoy7PGjYPTy16lA8n8AZPE7h5rqAN0y7/poVvLws6jwhjs67dXCpu9yBkTylcjO9lCfPOzuOYjxGZ526vux+O+pAHT1tJgo9j+PBvKOzArwQnRE9npWMO2EVcDy2m3s7/QEvvdFNVLsBN4+8GKTVO2EeyDugnIU8MWK2OvaYP72dcbI6UcgbO1ZllTk3KUW8dFzMPFGDazypUAO9KBlpPC3bDrprleq8yK8Nu1cFdDyJrx27qlvLvMpDJzw1DYu7GwYTPcBLnDwthLc7eMsuvP0o4rsDrzi7YSCRu00sMj25Oio8hjRmPDnBdDzDcb+8WRHdPFQe5Dy3Po+7nCJwu2cp5bxKVIE8dCooPDcOjDwedYI8Z/qDO4fNebs4tOq8tPDiO0HTx7tiJog8x5EdPCUXS7qUIoG8s2JzPEibhDzK8qi7ssf2vNbGyDzVP5o8LpuXPAcj6zsJ6E69mTWdvJSvn7ydXoC8ACt2O6mnkLyX5LI8IpM4u6fPnrzGB2Y8cZnMvNytwDxHA+o8Are9PG9RDrtT44K8HGMIPBFYcjuwoNW5Tf7APP9rIb0Jefw8XoOCvI64F7xhpOk5jYT3PI0zmTqEiJq7sqkiPK2wcztOU128ryN/uw1h0Tw5ATG9oIbGPBO8oLxEBo076flRPFEc6btaukM8Zxm6uz59mLyxm6i58bZgvLdS5TtO5My8lT6fvBedTLwivRK9m5zXvF0UT703/k+6pT8NvQ0ARbwFAhy8BozOvJozrTyF2kS8WGLYvHrojrwjiFi86jfvvC9zuTyTeR89lMcovX5sEz3U0fA8bJzcPKiet7zck3c8r1UnvSq4FrzFUxI8YL7TPHhIHT2hsvs7ghksPOIeMLxCawS8LldgPO/C1Dwk61Q8q7oRvdymLLsTq4q8vg5VPBPGQbxqG0c7mZnNPB3tlL2s/da8eQlmvVSv0LzEU3Y9STpDPHcIxDomhxw7vE76OdkkDryJINw8n7s8vXVnFzwLZ2c7zcbqur8mOL3eoky71zrpO9XOeTy0j1I8oiVvOz6IZbyJOtO7B9rnvOWXG7xjpo88Ir4Iu863JjxjFpG8NneKPD0v2TxY1vI8eqzfvBBtXTx3eTe9vd4ZvNxBYbyBON08yx4tPL1SnrvyneG8M5ZovFQTXjy2nLk6o7rfuW03Ij1Ibg09HenRO17utLx4mrC86oKAPJ2qITyvSDG9Waehu3vgerrxhUc7lyHbPIBSkjwvmaC8+ZxEPAke8Lt8lEi7up7JPLdc1rg6qgW9p7EbPcgyPjysAya7O4G5vF05xTz2zBO93CAlPGBt/byv6Is8IMkSvaMLhLy3Uos87nABvR39qzyAVKY87AgMvV2fPz3gHMO7sJq0vFOd9jwgpT07PG+fuw5NozxJaQ099HIFuj0TOry9hTQ7xpR1vAgqCz3+Sto8VEXFPGGipLt/hIq8wOGGPOZDAb1bHdS81yyOPH0VCzmyVti6m+BQvSwMCT0KzAK7az2APJepgjp6fw49tMYoPZDxw7zg0va8YkwZvRoFQD2n+I677/XOPIMTSrxcYv06YfqZvPn68LtPyju8OmoKvQauj7w86IQ80RFRvOn6dzw55wI8TltTvElJxbzODQK9B7cjvLn4ALwIV9O6U7GevGP7Ir16Q3k8t+PfvMCb3rwrtsw8PLeMuviIkDtIyzg880i0u7cJwbwflrM8ZKJqPD0Bprw9+yy9Fc+mPCP81rsmr8W87fqTu4VQlTxQ7oo7EaYfPRylWTzofZs853vRvNNWNLyeOJ06mgJEuspgFz1chQC8UTkQvGLSn7wBjLK8n5qkPLvYNjzbDXc8JP5wvCdpOjyqoLK8q1CLvU97H73Egic8uGCdPOCuBDwYKf88pIpkvTxHtTzgEDE8yWWxuzNhBDwjzhQ600KpO8AwuDyLSo89zuu6uyMt5DvKd048hQM6PagSEj3mnCa8pTowPYUvTLvECoM8kcB1vYw/yLzDCja7s2FKPMcHZrmk6BM8E1AovQ1Dgjsn1DO9SeNgPKHH4Ty7kbI7/umlO+TViDlLnf473WDIO0Kyiry6rne5FRDQOyAqpTwJUa88X6tOPazNhTxPPVe7QoXeu8pWJTtWMyc7+ix5PNfQ4rwW0Vi6c35aOpBxBbsvRBU9+istPcwVFLxTWIO8zi2rvKUYurq/82+9fZPaOffXGDzfnXe8eTb7uyWumLtmf3U8+CSMu5T+L7yn2Ok8YA16Ozm2Er0ym9K767TbO3HMpjzT2ki9DQwCu2ny5jxhNLo8Rbfguw1Gkjyt2uk8HWEzPI9VdLxUbi+8BSIMPcThKLyqs9W8e6b4O6/F37xYRZu7XzccvUy2Bj1eq7s79lOhvGSW+jlCJtG72RPbPBojs7uF94w7H0XzO9xo2byKAhm8PPiYPD7/ED1TrX47ZWGHPDrCHTyB6GG8xIqdO3do6jxYqmq8RxhlvP7stbxH89q8WYG4Ow4t+jsH30a8H8iEPFMW0DxNjBA9ZQtNvP/nSToSigI6m4ObPFuLoTyGu9G8JvRVPER/kjwc8bk8IWszPHuzu7ypVFS8lq94OsCUnrwFBSe87YIGO4k9SDwwyrM8pz9FPWSTYDzgro+8DkHxOxOJjzxDIr68JbmVPGhTE72yagG7Xz0Du9HAgbn/x2c8IGd0vCj52zwZqSo8CirsPJx+oDyqPvw8YY4wvffOlTyPIk08MO5MvKU/6bzOR8g85/hCOzmDIr3NEOe8L0ZCvPf5wTpaw428N0DaPE4oGjw8ZZ88cFHYvBi5MTzW23e8JobNvN3PjzwttvQ8Xb0wvNs2zbuH7Q88enicuwDHcLyXc6a8nKN9udaBiTzItta8jCIbPfNU9Tswxx47zassPPqRnDxulB087QJ1PB33e7rUGn87AaRHvIm3ybyfyhq7FADsOn++1bsIRYu8Ay+4OzwsfTxc7YO7197EvDZmhbxJKoE8ziLRvCAEBL0yQEE6j5ECvR9DFT3wCgs8ch1LPMV+0jwgrpa8w6KhvKyKaDxfUj88uPmwPJmPITySnze650eDPB4t8bx4+Iy8A9aHvCwaFTwRxZW8WvahvA0zlby5lCS932aGPB22yDvNoDe828idO3DevzzTkgU8VhFTu3MQELsPMgC9o4XXvJHAfLwi8MO8su/avBELoDshJY+8Xjt+PGIbYTyQosc6fwEmPIcHzjyRng08fHTxOn4tbjtr1mI9ArzCvNDuq7wYvZg8HBVJvJurY7wWtVS8KCXQugD2tLwC/d67LNZhvMtWDL0WOd876t8RPe/LC7wbM2A8xeELvZU8SDyudVq8YaOMPKHZUDowPbM6QOeMu8EeyjxkkT+8ajIvvXlde7xEITA8ANamvIJqlLucVGm7oFIWPS0DZrqOeE08vNQCvaLRmTsphnC8gn3KPNUD7jyriUA51ej7PFMOeTz/MSm81zzsOnORxbwimIg8RPsSvJD9gTz9OhS8hFBSvPYVkLwBGnY9U66QvO2/wjwANt481X8rPAJkJ7zYedg6IVGdO+hrprzQiHQ6l0McPcF7dzzA/RA9MQf2uNmpPjvjhoU7r+3IPJ7p+zwU2hE9OEkpvTXenzpY5Z28oeISPRCkeTyWiw08fmUpvV+Chby/G8A8tfSwPOwWNDy7AuY7F3J+PMkf3Ttp0Y68P4H/uqEmgjzKGya74P81PUWZ8Tx62Wy85jODPCbCXDs7lkK85gPKvC+vtbxWFas86vbHPHaJoTv6Gmg8XV/5PO+ghLwxEQa7sBYgvM9S37wFLMK8QE5rPNE2WL3RjRM86C4jOzT+/jpV2D+5ID6Vuhmg3DvGIRy9ah4mPU9A3DyY0H481Mj5OUJO5Tw1zsO7fVqQvOjlnDxtY8a80Fh5PNyLoTxqXok8lqcKPavu37td1M68YAKgu43XL7zqJJg5VC1UPGGVgLya/I28WD1qvPtuhbf7nEG84uKuPLLfUrxMtQQ8RdMHOwKxIb1ANV08JXAPvKarzTx3+IM7C/z0O6WidDw7Q2U9JHAOPTbAi7yaszs8iimmuWBZw7zc6IO84GlHvKK6eTzNUuW8lOQLvK3Xhrw4qQA9QckQvEDF+TuQUSw9c1tGvFHMqLx4Axe7QM+tvGe7sbwqtMi7/OoUPe+pMTsJpqA8FPocvO8uyjxG4/8876k0vMRYMD2X9Wm8RMejPGoa+jteByS9ibpXPDTIkrywYaI8nZp9PFz9TTxVZXG8s0YZPYchNryXPMK8L/EHu6jOFL0i8u28i/aeO1g3pDtLB+286nQFvaW/0jr2DbO82L6PPGN09TuVGYE83PRzOUw+T7xZuug7uvydO3plEL3aYQy9TDY4vbyjRD3ioN889Kvou/lWojxrjOI84DwRO3z7hLxhVFU8BYKBOyS+IbxxRF07TGTHvLMpAL0TKX484s/OuKwPoLt5fCW8M1a3vLVYnjzLaCq8zjYtOk0bsryVeLe7xhikvD92qjroyFw8LVCNvDVCILuvNFe6hG2YPHPKZTzAZ687FlfhPLg46LxHgeq8eSNRvJdo3LuQ8Zc8XUe2PPc4rDzveKi8xRHAPGbZ+DuRtw291/i4O2o9W7y4xY87rTzIPBWjT7lp7Kc8fBy8PPw+57zixhk8r+a2POaB/bs1T0O82pZrPJX0gLxjdyq9qWRCu0KlHj3OPfs8T2oCvESHCj02sXE8d/dbPYkAETy2KIQ8TuEPPbxKDD2ZxI677dBLPO9CWbxmCkc8eXlcPNYBBbx2K788Mg6DPMabAT0m3j49R54TvZ3D3LpwD248+hMDPXspGzqzcli8j2XEvFUdoDy3OMu8S3PMvKujQjyT/7m8oDnFvHRQfzwxdmc6Si22un8DQbwgRks8eFyAO0UamTviDs88Ss2vu2OB67uSEtM5XxINvZlNGT3c3028XYwWvFZbVLqV34O7wWWTvEgI4bzwVfk7dqrHvDM4dLzZK5Y8sXjsPBcvYjx8hRg7WvCzvMjg+TwQkJ87q9A1OmSfi7sChV87MvsSvKQmc7zkcFS8wr45vBhU0zv401E9wEUJPfI7Pj2zdWk8bpIhvADJH71K/mc8DzR9vBMWWzw4dn88UMgmu+L4ALuPOza7rGQQvZsQabxAaWi7EjGqPFKVhTu5uqW8KaxgvAgo1DsZuTG8pEPOPES7rrtpwMQ5FwwbPJ7+mrzk74o8T5MlPShC3zmK1PO8wtMfvZ7Cjrwakw+9DwhfvLH6OTwNcRO8CkhWPFHa47zE9Hu70GEiPeDJJTqTIfa7ODRJvPG1jroPIS087s67OrkhUzudNEC9vHQiO8wqNLy0niO990qKu9gU/TxPFYI7+d8jvK5ci7xfPx28ruOFPIKtSjvhdeK8pOLUu9EDDj0L9Ae9OYWLPC8K87xXE5A8USxfPAxSYTuUaba8diojPfHAX7zaoOI8iCAyvAudbjuV5q67pNd2PNwQKDxkaKK7Yk2LvJfP97v5jIU8L6wyvBZkATxcDj67Dw1AvGT/YDzesYy8zwtBPJ5mD730pds8CugNPdk8ELuFQJ48a9gHOy8qGzwXERs8LnjnO8DjpLs0OcQ8fLUeOpBUlTyOn3q87cMxPPojsLxuQV07VokWvHjCvztXfxg8Z6+ZvBc/nztyGNQ8e7QkO41Flbwt+i68wPoPPMQ9BD1RtoM8UbNevAd4AzkjUX88bMsnPGHDwTy4HHU8X25aunj8BTw7f728g6hNPJAUqDwf1s28psjePCyiOD3AVV06ZspIOrsLqbwzWgC8gH6EO/cDCD3Iek2878QuPbWWUTw76NE85tDgunKoBT1wNdU8yM1mu5rSOLrrQS68+NiPOyeruLzpDR48bK4vOlHeiTw7Qb28DLGJu1sLKDvfNaI68TCJvLPDO7zZHrC7q2MRPStEdDxQ2HQ6wtL7PGQWpLxlZ1m8Qq51PJguBTxO5/e8AnUwO1rZubuoi/e75VKDvKsjAjzeG+47QDb2u5dP0LuovYi8XX+ou1b+Bb07GMm5BqkAPcrWLTpWH8o87UKOPOnq+DuoDq84AvhOvBFjQbpswd06NAlnPC2svTiBav+8dk64vPa8kLs25SK8IqU4PEc2ezo1RTK9vJ1Curacq7zILy08KHuou5gWk7yNbZC8eZSdOzfztDrCJIq61V1yvNleXbw0Ez+8DMUBPVFswjyJHDO8we0OPe+vhzmH4NM8pVZMPEPB6Lsf6+O7oCjSuTRzCb1tnoa8pYotPEsMa7w49ru8nO1pPaXnrDt8AQ68VE4WO6BSkLtWRgQ8lv69vA1UR7zbtpO7IFLUvOhJk7pksdI8TMSMvNEhizzF6/W6ZXTsPMVwRr0jlLu8ODr9vP2svbyu3Og7W1vYO66We7y39KW8rKYmPJTB5TxRHdK7tznQO4ZEjjw9ne08rzk+uqFIgjwwLAc8/kBfPBzGYriUo6+8lqv6vFbJpzwgAiq85GgRu1FOJDy0uai8KceaOzat0zxa26u8tck6vDe7LrxhKdy8fVRYO0kl6jwKk9e8vCkLvPi1rrot3Vg8nbd6Oa9abDxSwM88acGqPONcHTsYVJ07h6uwPGlAf7vvrOC87xZhPKI8dzzSB0c84GR3PGvGlTzbhig8262OOYQ0Kz14Sqm83NPpuq+EEr1kcgs9Dm3KPHdOZbye1Sc8ASOyOzvG0Dwz9Wy8+5h4u/LZ9rvF0YK8t7eNvBG6GjwxxSU8yE5mOsQ5kLt2z4W8QRv4u+gGKryzGgG9omokOhPRujrEze88cLmBPC8tUjzDuc07/LDbvG40wLzzihW8zFcJvboKATx0JU28USKDPCQtKDqPwX28GJQ2u+1+zDxQ4FW8oOQ0PUK7gjzlKRC8HIBsvEMZPTyHxdy8L6HwO4WICL0BbRi8DUivvG+bmTxtRI48dT8wPIoFq7y3Y1e7GKJWPP36dLu5Cn85K2hsPLGLTDqRa2E8Ch2wvC3yET0UNoa8o4bdOGrQBrym94g8Qn3iO3/f07wxicW7fw+PvEiqAT0n3wA778WzuzuTArwzHf67ojVmvOCFtTxKJVM6JayVvHkoGjvUnhY7yDXEO3PcULtLqRo8TSuYvDA9ybxTfgC9+e7rvL2n4rxfm0Y8mzAIvTaWgbxttPM7HaCJOlUBgbxKp5Y8F/NivNtAgLzTwia9E3UFvaJ4BTzLGqC7pzWvPBU0mry7V6M8nQu6vPoEXrw/LOo7OvY0vbgbwjwKVUO5H70NPPcl3rqpXt67N1D3vJVVnjys55g83x6UvNEPMLxaz1W8o1UMvUw1x7kZnlg79OvkPMd9xbrQF7E8BS9LO4vt2zv3KFY8IS2/PCGW8zytE8S7caeYuzHhILwNoJ07TPOLvBjko7zFjAC8+esGvKKNs7zdQPS668iMO9Ptvjzcia88wJs2PdYYD7zhmka84wcJPfJkV7xpLws92Ag6PcSTXrwk0q28rqTpPJUegzz1PgC9IXJIu7spJDw0Pzw8xEKzvPwLiDwJ55q8Ti2OuhtCTT3wle67BHaZPObjGLwZwCK84GT7vNFPnLzp4B88Ua6pOzLSt7sYKNq5Z1CxvGbNDrzLS848yXIuvFGO3zv5jPA7teDSu7UurjumgxS8GWm1vGK3uTxG7468nBSgvPRNmjxUdxo9+2FYOx+d0TzCF9o8LlSQugEiB71vgEO8x8R0PNQRQbzG84+8UcvOvKPT4Dyq7Vu84FUbPEDk1LqEAK67h2ZWPPcnD72vDyq8aFWNu1gv+br8kM45iI7XvFAFPrslZVU7CYcsO9XXPjxT/9u8wrgmvT1JgjrP4Vu8+iS6O++YCrwv0W87znxLvF+Kmry+Tlw7qudwuwcA7rsmlVM8GFLGvHT1uLyssKu67vO2O9clejz/lny9upkRPTVhlbzovm884IuGPE/g9Dus9BW9UCW/Og5fdjxa+Im8pNd/vON5cLuyyQW9IKUCPee007vA+Sy83A93vHZLDTw5w5W8yZTzPG018rw2A8a8K8amPPK6AjyfrNm7Xzt/utSF9LrvhtI8usnJvBxt9zykZz09NwegOy0Tf7oZZue8JWGCvHFBdzwDCxU8LyIcvcB7Ab07b6G8vAPJPPVQMbyEq5O88MNpO36HGbpQUCw8ozoNvftKG72Xduw7HeOqO/Z/Ejye2Gu8/NrcvBbyQLtu88C7+4Y5t2SDg7xljwq8k1avvIrcdzsE9Hq8hZunO0x+lLrSnP+5aoPcumanmrwLo8u8eRs6PKLZe7zAqpK8zO09O8higDywIDU8x8WNvPKHBrtL2GI84csZOVpLQLyqy8u86yURPMalETx+3dS7wzzIO1dqeLuzEWM8WWowPGO0Brxh2sO8pQHAuyLR7zyI43E8+0COvELyWbxnLtm8fmXjPKNLpDqmiAW9iLZcOV/2ObyKuqA89D45vPaRhztMTTS9AREyvY2bmDyc4+w7YwnNvDY4e7w7SpE7GOuvuknUhjw/21q8uxUmvJ5VkjtmGAG9MJ5pOztX1ztopyG7k0gbvVHOprxnWgI9icRGu0Vb6bx+krY6U6E/PcZbijxQDFA81XtvPLmWUz3d6Gi7YPFLvCR+2jzw5uS8O/sAO609IjzidtY7aKCivMpjBbztuu26AwvxPA9Hdbz/V0+7lY74OkeYh7y561e80v4bPD0JrDzI3mA7cgKrvGMzHrzhMw29+vk3PIystzut6Be8o4mDvF1g7bvHWMK6PptIOvGNcrzFueE8ECaJvIC9ubwkCK673B3jvBfAbTyI/aW7fFBOuUxGPjyGqBm9rIOJO5/0RLxrnmm87c2LPAQHKTy2Vuy8D0gpvNrWgDo6V3y7a1wQvIn46zzuTzu6Xib0PLmnNjwkR9+8KKGSPDErBL3j1Ss8IvoHPIxS87wBR4m6X/w7PHwm2LxZmJQ8sr9bPPVp2buk5YE84TfPuodCnbuEaA69Lbm4vJa0vrx/uCa8GH8/vdxdsbz68ta89RYqvB7cQbzAkXA7Qhohu7uncrxue/K7UzpZO260MDxCMYm7zJhDPAGbAry8AKE7r++DPL8BP7zc+A68NV/Cu84KNz3LHtQ8cAy2OywxBr1Npd88j//bvJi9Qbvy2E48tmbFOxagJzmAq0W8A9bhO6ZhAr2fJJI8yl2IvAFliDtIgn874/exvEziaDxD7Pu8EUccPJoT2LvGTqi752MKvF7pLr2UOnc8Q2lGvMQXHz1fF2q8NipYvBLpujvX2/e60feDPGUPsjrZZoA8BUSrvKVS5johxUS8Ig/+O1VmzjureMA6eQOJO3O1FrxsnJy86cYzPIL03bzINCQ8Lq7dPHRZ7byfV4c8Fiw6PPvAorygZES8uj/JuM8REb04pq486veMPM9EtzlpRiy9LkY1vPKitDx3Ixo8h1YLvajw8rxoBZ68gWi5vJ1YSjwI2wq8sVUuPBZqHDo0LtM8n4tQu3yDoTsxXeU7DWy2PBIzfDy5vFm7mKkPvDL7VDxRaNq8DgusvOJay7yAOCM8ErROO7mVm7wYsaU7o94PPde39Tsfn6o7g2BjvCADNjxz9CG7ApoBPNB10rvTM+48Xz0IPYxnE73Fc9285yVrOwa0YrvS0Vk8AJlMOh7I4DlKvvE8sbLyu2d2vzwWpc480n/2u7bfnLzj6EK8oMkDvdC9uzwfHos8VsmOPAcdJDxcZdK7DW7bvMwByjyLhay8jVVpOpy+M72chQk9BOtgPEtpFDyK9PO8WVN8uo/IZrxYS6k83DnEvFxqBz2q/iM9iXDWvMHQM7p5uAO8SLeiPMPAWDvDkcE8oEbyu1TWdDyYxNO86nkOPE/7tTxeCfK6NxObvEWwMDun+zk7WZuSu39PDDsb7mk8J935u1ps8zwzBSo840iyPGJDkjyOSwC9VjuvuGnSLjyC67S8nfclvV/NVDrgyLW8WSNoPD6KmLwOdI67fukvOtzuUbyOI8a84JDfvDIRSLv703q8JA5LPCfFGTud0Ia864OkOngAVbsTmpM8LgdnPG68ATt7wrA6s/AcPcs9Ob3oV8E74EkEPI8qBLyT1xe9qKuGPGMFQzs8G2G89zIGPTy53Tm2bhS8BXA4PZEUlzqlJT49MrM+vH7HqzzhvqS7/ykgveqfY7yNrpi88vg+u/BowDyzUEO8bP1APNsbiLzrV1880HW2PK52LbqYEr68G0T3vLtk0DpgA3m7sX58O4KCfbxT/Ku7F5n9vJ9o1zzhcQM8E52mvJSFoLyjNIU85lonO4PEFjtTUpM87bu0vCiLOjwGFxe8IARnPOhgkTyACFY8cYKoPBojVLwdyBc8d+d/vNjCLrzvU448KdpvuyipDb0s5ai72lOVPJGlkLz8nIO8eU3IPBo16rw8/Cc9GBssu5yduzvy0qa8pznQOj2+6DmuH1C8GbQbPZlSfTz5pH05/yTiPGqoNTzZIbM7D62GuwD+Lrs796s8vo90PKYvuzux3uI7AXiVvJMKSTy/Tu68DjyzvBxlpLtJEh68lWC3vMfyajwQ1TU8TrdyPBHepjym0DK8AwdQuywmsTz0C5Q8YX5dOk1mtbwbDhG8BIQfPDGps7sRMhk8be0iOuLHUrxU+ai8rfsmPHacqjxJJYu7L9AqvBv+jDslg6W80o1dvDTJlbtp6Cm8VXtSPGANy7s2RC+87kWsvOQ/HDzIvks8c3u2PKUT+rs1aog8jK2QPFoXADv9sLA8rV38Orel5bvkj6Y8mK0gO8wAF7xzFRs74Qjdu3CsWjx1qsG82hqDvOQNAzxKRJe4NSYmO5RFCTxVpjG8p/7sPC/8FbwNtoM8BeOfu09psDwWsSA7TqjtO4EQObyDrW48E3XZuvZmTzwPmo472lGTvMqFbjwk7aa8nyu3OtE0ersxYaK8GD9FPPGTsDzklKI7yoi/O5AyRrvrNDa8x9UhO9lhrbvOkec75pPDO8vZDDzm7ow5oqEkvPhKfrwkmz68Hy6Lu8auBDyjaty6pWGsvDsBpzxEnbw71fGwOw3R7DuSkq67DqrVPOebAD0E2Jg7mjamO9VlXzzmv1S8/EzEOw==
index: 0
object: embedding
model: qwen3-embedding:4b
object: list
usage:
- prompt_tokens: 15
- total_tokens: 15
+ prompt_tokens: 12
+ total_tokens: 12
status:
code: 200
message: OK
@@ -389,7 +382,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8201'
+ - '6148'
content-type:
- application/json
host:
@@ -442,21 +435,19 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
+ - content: Which upstart candidate in Jakarta's current election is known for social activism?
role: user
- - content: |-
-
- We need upstart candidate in Jakarta 2024 mayoral election known for social activism. Search.
-
+ - content: null
+ reasoning: Need search.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"upstart candidate Jakarta 2024 mayoral election social activism","limit":5}'
+ arguments: '{"limit":3,"query":"Jakarta current election upstart candidate known for social activism"}'
name: search_and_answer
- id: call_6gzq7v5v
+ id: call_pzmpok4y
type: function
- content: |-
- [b9711035-47af-4f3a-8607-d41b1cfd4aba] [rank 1 of 1]
+ [5c0350d5-dd0c-483f-aa6d-fd105a8915bc] [rank 1 of 3]
Type: text
Content:
Jakarta Election Campaigns Heat Up: Here's How to Understand the System
@@ -471,14 +462,9 @@ interactions:
Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
- Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
- Rallies and Persuasion
-
- Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
- Debates: Clashes Between Visions and Policies
-
+ [b0415699-c082-4b41-bf53-2dd0a21af86c] [rank 2 of 3]
+ Type: text
+ Content:
Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
Voter Engagement: Making Every Vote Count
@@ -487,47 +473,19 @@ interactions:
Campaign Financing: Transparency and Accountability
- Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
- undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
- Before Election Day: Submit Final Appeals Now
-
- As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
- Polling Day: The Final Act of Campaign Activities
-
- On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
+ [d18fb450-cc25-45a1-a311-62bde95a5ca9] [rank 3 of 3]
+ Type: text
+ Content:
Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
role: tool
- tool_call_id: call_6gzq7v5v
+ tool_call_id: call_pzmpok4y
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -551,12 +509,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '547'
+ - '612'
content-type:
- application/json
parsed_body:
@@ -564,22 +543,19 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: |-
- - query: What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
- - answer: Amira Bintang
- - cited_chunks: ["b9711035-47af-4f3a-8607-d41b1cfd4aba"]
- - confidence: 0.97
+ content: '{"query":"Which upstart candidate in Jakarta''s current election is known for social activism?","answer":"Amira Bintang
+ is the upstart candidate noted for her extensive social‑activism background.","cited_chunks":["5c0350d5-dd0c-483f-aa6d-fd105a8915bc"],"confidence":0.95}'
reasoning: 'Answer: Amira Bintang.'
role: assistant
- created: 1769800548
- id: chatcmpl-631
+ created: 1772626893
+ id: chatcmpl-736
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 89
- prompt_tokens: 1596
- total_tokens: 1685
+ completion_tokens: 76
+ prompt_tokens: 1193
+ total_tokens: 1269
status:
code: 200
message: OK
@@ -592,230 +568,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8632'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a search and question-answering specialist.
-
- Process:
- 1. Call search_and_answer with relevant keywords from the question.
- 2. Review the results ordered by relevance.
- 3. If needed, perform follow-up searches with different keywords (max 3 total).
- 4. Provide a concise answer based strictly on the retrieved content.
-
- The search tool returns results like:
- [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
- Source: "Document Title" > Section > Subsection
- Type: paragraph
- Content:
- The actual text content here...
-
- [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
- Source: "Another Document"
- Type: table
- Content:
- | Column 1 | Column 2 |
- ...
-
- Each result includes:
- - chunk_id in brackets and rank position (rank 1 = most relevant)
- - Source: document title and section hierarchy (when available)
- - Type: content type like paragraph, table, code, list_item (when available)
- - Content: the actual text
-
- Output format:
- - query: Echo the question you are answering
- - answer: Your concise answer based on the retrieved content
- - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
- - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
- IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
- Guidelines:
- - Base answers strictly on retrieved content - do not use external knowledge.
- - Use the Source and Type metadata to understand context.
- - If multiple results are relevant, synthesize them coherently.
- - If information is insufficient, say so clearly.
- - Be concise and direct; avoid meta commentary about the process.
- - Results are ordered by relevance, with rank 1 being most relevant.
- role: system
- - content: What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
- role: user
- - content: |-
-
- We need upstart candidate in Jakarta 2024 mayoral election known for social activism. Search.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"query":"upstart candidate Jakarta 2024 mayoral election social activism","limit":5}'
- name: search_and_answer
- id: call_6gzq7v5v
- type: function
- - content: |-
- [b9711035-47af-4f3a-8607-d41b1cfd4aba] [rank 1 of 1]
- Type: text
- Content:
- Jakarta Election Campaigns Heat Up: Here's How to Understand the System
-
- As election day in Jakarta draws nearer, candidates are out in force using various strategies to woo voters and prepare to exercise their democratic rights. Citizens make their voices heard as the city vibrates with life. This coverage offers valuable insight into campaign activities while offering an in-depth guide for understanding electoral processes in Jakarta.
-
- Initial Launch of Candidates' Campaign Plans on September 1
-
- After September 1st, when campaign season officially kicked off, candidates have moved swiftly to engage their bases. Amira Bintang, an upstart candidate with extensive social activism experience and promising urban development and public transportation reform as key platforms of her candidacy speech in Jakarta; incumbent Rizal Harahap relies heavily on his track record and highlights all infrastructure projects completed during his term.
-
- Campaign Strategies: From Digital Battlegrounds to Door-toDoor Outreach
-
- Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
-
- Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
- Rallies and Persuasion
-
- Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
- Debates: Clashes Between Visions and Policies
-
- Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
-
- Voter Engagement: Making Every Vote Count
-
- Ensuring every eligible voter is engaged and informed remains an ongoing challenge for Jakartans. Civil society groups and independent bodies host workshops and publish voter guides to inform voters of their rights and choices, while an annual democracy festival such as that held on November 20 featured interactive exhibits on Jakarta's electoral history as well as mock voting booths for first-time voters.
-
- Campaign Financing: Transparency and Accountability
-
- Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
- undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
- Before Election Day: Submit Final Appeals Now
-
- As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
- Polling Day: The Final Act of Campaign Activities
-
- On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
- Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
- role: tool
- tool_call_id: call_6gzq7v5v
- - content: |-
-
- Answer: Amira Bintang.
-
-
- - query: What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
- - answer: Amira Bintang
- - cited_chunks: ["b9711035-47af-4f3a-8607-d41b1cfd4aba"]
- - confidence: 0.97
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
- description: Answer to a search query with chunk references.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the question
- type: string
- cited_chunks:
- description: IDs of chunks used to form the answer
- items:
- type: string
- type: array
- confidence:
- default: 1.0
- description: Confidence score for this answer (0-1)
- maximum: 1.0
- minimum: 0.0
- type: number
- query:
- description: The question that was answered
- type: string
- required:
- - query
- - answer
- type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '690'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to output via final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"Amira Bintang","cited_chunks":["b9711035-47af-4f3a-8607-d41b1cfd4aba"],"confidence":0.97,"query":"What
- is the name of the upstart candidate in Jakarta''s 2024 mayoral election who is known for social activism?"}'
- name: final_result
- id: call_h37dorqy
- index: 0
- type: function
- created: 1769800552
- id: chatcmpl-541
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 97
- prompt_tokens: 1710
- total_tokens: 1807
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '3258'
+ - '3318'
content-type:
- application/json
host:
@@ -860,9 +613,9 @@ interactions:
Who is the upstart candidate in Jakarta's election known for social activism?
- What is the name of the upstart candidate in Jakarta's 2024 mayoral election who is known for social activism?
- Amira Bintang
- 0.97
+ Which upstart candidate in Jakarta's current election is known for social activism?
+ Amira Bintang is the upstart candidate noted for her extensive social‑activism background.
+ 0.95mljudppg
@@ -872,13 +625,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Final research report structure.
- name: final_result
- parameters:
+ name: ResearchReport
+ schema:
additionalProperties: false
properties:
conclusions:
@@ -919,48 +670,49 @@ interactions:
- conclusions
- sources_summary
type: object
- type: function
+ strict: false
+ type: json_schema
+ stream: false
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1796'
+ - '1335'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: 'We need to produce final_result. The answer known: Amira Bintang. Provide report.'
+ content: |-
+ {
+ "title": "Jakarta Election Upstart Candidate – Social Activist",
+ "executive_summary": "The upstart candidate in Jakarta's election known for social activism is Amira Bintang.",
+ "main_findings": [
+ "Amira Bintang is identified as the upstart candidate.",
+ "She has an extensive background in social activism.",
+ "No other upstart candidate with a comparable activist profile is mentioned.",
+ "The information is drawn from a reliable election‑analysis source."
+ ],
+ "conclusions": [
+ "Amira Bintang is the sole upstart candidate in the Jakarta election who is specifically recognized for her social‑activism work.",
+ "Her activist credentials distinguish her within the candidate field."
+ ],
+ "sources_summary": "mljudppg (question/answer reference, page not specified – source document)"
+ }
+ reasoning: We need to answer who is the upstart candidate in Jakarta election known for social activism. Prior answer
+ says Amira Bintang. Must cite source. Provide report.
role: assistant
- tool_calls:
- - function:
- arguments: '{"title":"Jakarta Upstart Candidate with Social Activism","executive_summary":"The upstart candidate
- in Jakarta''s 2024 mayoral election who is known for social activism is Amira Bintang.","main_findings":["Amira
- Bintang is a prominent Jakarta-based social activist.","She ran as an independent in the 2024 mayoral race.","Her
- platform focuses on community engagement and anti-corruption.","She gained national media attention for grassroots
- initiatives.","She is recognized for advocacy on housing and environmental issues."],"conclusions":["Amira
- Bintang represents the social activism faction in Jakarta politics.","She leverages her activist background
- to engage voters.","Her candidacy reflects a trend toward grassroots leadership in Indonesian elections."],"recommendations":["Monitor
- her campaign developments for shifts in voter sentiment.","Analyze her policy proposals for practical feasibility.","Engage
- with her grassroots networks to assess on-the-ground support."],"limitations":["Information reflects early
- 2024 campaign coverage; later developments may alter context.","Sources are limited to pre‑election reporting;
- post‑election data not yet available."],"sources_summary":"mljudppg – internal dataset, page not applicable"}'
- name: final_result
- id: call_ntatrps2
- index: 0
- type: function
- created: 1769800563
- id: chatcmpl-46
+ created: 1772626899
+ id: chatcmpl-165
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 269
- prompt_tokens: 642
- total_tokens: 911
+ completion_tokens: 185
+ prompt_tokens: 547
+ total_tokens: 732
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_aggregation.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_aggregation.yaml
index c248b869..a0a7d82e 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_aggregation.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_aggregation.yaml
@@ -128,7 +128,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7720'
+ - '7737'
content-type:
- application/json
host:
@@ -276,6 +276,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -302,29 +321,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '683'
+ - '690'
content-type:
- application/json
parsed_body:
@@ -333,25 +334,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: We need total revenue across all quarterly reports. Likely documents contain quarterly reports. Let's
- search for "quarterly report revenue" or "Revenue" with limit.
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
name: execute_code
- id: call_l8kk2in2
+ id: call_no1egdsi
index: 0
type: function
- created: 1772549330
- id: chatcmpl-995
+ created: 1772626955
+ id: chatcmpl-2
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 76
- prompt_tokens: 1732
- total_tokens: 1808
+ completion_tokens: 85
+ prompt_tokens: 1688
+ total_tokens: 1773
status:
code: 200
message: OK
@@ -404,7 +405,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '9191'
+ - '8682'
content-type:
- application/json
host:
@@ -551,28 +552,42 @@ interactions:
- content: What is the total revenue across all quarterly reports?
role: user
- content: null
- reasoning: We need total revenue across all quarterly reports. Likely documents contain quarterly reports. Let's search
- for "quarterly report revenue" or "Revenue" with limit.
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
name: execute_code
- id: call_l8kk2in2
+ id: call_no1egdsi
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)","stdout":"[{''chunk_id'':
- ''4caa01a9-80d3-4d7d-bfab-ca11a66318d7'', ''content'': ''Sales report Q1: Revenue was $100,000.'', ''document_id'':
- ''330c5184-910f-4515-b99b-72e3e8a229bf'', ''document_title'': ''Q1 Report'', ''document_uri'': None, ''score'':
- 0.03226645663380623, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''93295467-8f49-4268-8701-d0f84a17e5d9'',
- ''content'': ''Sales report Q2: Revenue was $150,000.'', ''document_id'': ''30e9d013-29b1-46bb-b578-bfc260572dda'',
- ''document_title'': ''Q2 Report'', ''document_uri'': None, ''score'': 0.03226645663380623, ''page_numbers'': [],
- ''headings'': None}, {''chunk_id'': ''efc76976-d951-4c74-a881-979e1382a6ae'', ''content'': ''Sales report Q3: Revenue
- was $200,000.'', ''document_id'': ''3a6a60f5-3467-40cf-80b0-1d7a76bea1dd'', ''document_title'': ''Q3 Report'', ''document_uri'':
- None, ''score'': 0.032258063554763794, ''page_numbers'': [], ''headings'': None}]\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor r in results[:5]:\n print(r[''document_title''],
+ r[''score''], r[''content''][:200])","stdout":"3\nQ1 Report 0.03226645663380623 Sales report Q1: Revenue was $100,000.\nQ2
+ Report 0.03226645663380623 Sales report Q2: Revenue was $150,000.\nQ3 Report 0.032258063554763794 Sales report Q3:
+ Revenue was $200,000.\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_l8kk2in2
+ tool_call_id: call_no1egdsi
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -599,10 +614,233 @@ interactions:
type: object
strict: true
type: function
- - function:
+ uri: http://localhost:11434/v1/chat/completions
+ response:
+ headers:
+ content-length:
+ - '689'
+ content-type:
+ - application/json
+ parsed_body:
+ choices:
+ - finish_reason: tool_calls
+ index: 0
+ message:
+ content: ''
+ reasoning: Three reports with revenue numbers. Sum = 450k. Need citation chunks. Retrieve chunks.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())"}'
+ name: execute_code
+ id: call_sg5vtxpb
+ index: 0
+ type: function
+ created: 1772626957
+ id: chatcmpl-37
+ model: gpt-oss
+ object: chat.completion
+ system_fingerprint: fp_ollama
+ usage:
+ completion_tokens: 85
+ prompt_tokens: 1922
+ total_tokens: 2007
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '9437'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ messages:
+ - content: |-
+ You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
+
+ You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
+
+ Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
+ - results = await search("query") ✓ CORRECT
+ - import search ✗ WRONG - will fail
+ - results = search("query") ✗ WRONG - must use await
+
+ ## Available Functions
+
+ ### await search(query, limit=10) -> list[dict]
+ Search the knowledge base using hybrid search (vector + full-text).
+ Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
+
+ ### await list_documents(limit=10, offset=0) -> list[dict]
+ List available documents in the knowledge base.
+ Returns list of dicts with keys: id, title, uri, created_at
+
+ ### await get_document(id_or_title) -> str | None
+ Get the full text content of a document by ID, title, or URI.
+ Returns the document content as a string, or None if not found.
+
+ ### await get_chunk(chunk_id) -> dict | None
+ Get a specific chunk by its ID (from search results).
+ Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
+ Use this to retrieve full chunk details and metadata for citation.
+
+ ### await get_docling_document(document_id) -> dict | None
+ Get the full document structure as a dict (DoclingDocument format).
+ Use `list_documents()` or search results to get document IDs first.
+ - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
+ - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
+ - `pictures`: list of figures/images with metadata
+ - `pages`: page dimensions and metadata
+
+ ### await regex_findall(pattern, text) -> list[str]
+ Find all non-overlapping matches of a regular expression pattern in text.
+
+ ### await regex_sub(pattern, repl, text) -> str
+ Replace all occurrences of a regular expression pattern with a replacement string.
+
+ ### await regex_search(pattern, text) -> dict | None
+ Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
+
+ ### await regex_split(pattern, text) -> list[str]
+ Split text by a regular expression pattern.
+
+ ### await llm(prompt) -> str
+ Call an LLM directly with the given prompt. Returns the response as a string.
+ Use this for classification, summarization, extraction, or any task where you
+ already have the content and just need LLM reasoning.
+
+ ## Pre-loaded Documents Variable
+
+ If documents were pre-loaded for this session, a `documents` variable is available:
+ ```python
+ # documents is a list of dicts with keys: id, title, uri, content
+ for doc in documents:
+ print(doc['title'], len(doc['content']))
+ ```
+ Check if it exists with: `try: documents ... except NameError: ...`
+
+ ## Available Python Features
+
+ The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
+
+ Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
+
+ For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
+
+ ## Strategy Guide
+
+ 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
+ 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
+ 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
+ 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
+ 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
+
+ ## Example Patterns
+
+ ### Counting documents matching a condition
+ ```python
+ docs = await list_documents(limit=100)
+ count = 0
+ for doc in docs:
+ content = await get_document(doc['id'])
+ if content and 'keyword' in content.lower():
+ count += 1
+ print(f"Found in: {doc['title']}")
+ print(f"Total: {count}")
+ ```
+
+ ### Extracting data with regex
+ ```python
+ numbers = []
+ results = await search("financial data", limit=20)
+ for r in results:
+ amounts = await regex_findall(r'\$([\d,]+)', r['content'])
+ for a in amounts:
+ numbers.append(int(a.replace(',', '')))
+ if numbers:
+ print(f"Average: {sum(numbers) / len(numbers)}")
+ ```
+
+ ### Extracting tables from a document
+ ```python
+ docs = await list_documents(limit=10)
+ for d in docs:
+ doc = await get_docling_document(d['id'])
+ if doc:
+ tables = doc.get('tables', [])
+ if tables:
+ print(f"{d['title']}: {len(tables)} table(s)")
+ for i, table in enumerate(tables):
+ grid = table.get('data', {}).get('grid', [])
+ for row in grid:
+ cells = [cell.get('text', '') for cell in row]
+ print(f" Table {i}: {cells}")
+ ```
+
+ ## Output Format
+
+ Your final response MUST be valid JSON matching this exact schema:
+ ```json
+ {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
+ ```
+
+ - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
+ - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
+
+ Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
+
+ You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
+ role: system
+ - content: What is the total revenue across all quarterly reports?
+ role: user
+ - content: null
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
+ name: execute_code
+ id: call_no1egdsi
+ type: function
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor r in results[:5]:\n print(r[''document_title''],
+ r[''score''], r[''content''][:200])","stdout":"3\nQ1 Report 0.03226645663380623 Sales report Q1: Revenue was $100,000.\nQ2
+ Report 0.03226645663380623 Sales report Q2: Revenue was $150,000.\nQ3 Report 0.032258063554763794 Sales report Q3:
+ Revenue was $200,000.\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_no1egdsi
+ - content: null
+ reasoning: Three reports with revenue numbers. Sum = 450k. Need citation chunks. Retrieve chunks.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())"}'
+ name: execute_code
+ id: call_sg5vtxpb
+ type: function
+ - content: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())","stdout":"","stderr":"NameError: name ''results'' is not defined","success":false}'
+ role: tool
+ tool_call_id: call_sg5vtxpb
+ model: gpt-oss
+ reasoning_effort: low
+ response_format:
+ json_schema:
description: Result from RLM agent execution.
- name: final_result
- parameters:
+ name: RLMResult
+ schema:
additionalProperties: false
properties:
answer:
@@ -616,12 +854,38 @@ interactions:
- program
type: object
strict: true
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: |-
+ Execute Python code in a sandboxed interpreter.
+
+ The code has access to haiku.rag functions (search, list_documents,
+ get_document, get_chunk, llm).
+
+ Use print() to output results.
+
+ Structured result with success status, stdout, and stderr.
+
+ name: execute_code
+ parameters:
+ additionalProperties: false
+ properties:
+ code:
+ description: Python code to execute.
+ type: string
+ required:
+ - code
+ type: object
+ strict: true
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '710'
+ - '737'
content-type:
- application/json
parsed_body:
@@ -630,25 +894,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: We see 3 chunks. Need total revenue sum. Extract amounts.
+ reasoning: Need to keep results variable. Use same scope. Let's re-run with storing results.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor
- r in results:\n m = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())"}'
name: execute_code
- id: call_5erv45jj
+ id: call_h6pfbenb
index: 0
type: function
- created: 1772549332
- id: chatcmpl-421
+ created: 1772626960
+ id: chatcmpl-20
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 103
- prompt_tokens: 2185
- total_tokens: 2288
+ completion_tokens: 98
+ prompt_tokens: 2084
+ total_tokens: 2182
status:
code: 200
message: OK
@@ -701,7 +965,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '10014'
+ - '10405'
content-type:
- application/json
host:
@@ -848,43 +1112,72 @@ interactions:
- content: What is the total revenue across all quarterly reports?
role: user
- content: null
- reasoning: We need total revenue across all quarterly reports. Likely documents contain quarterly reports. Let's search
- for "quarterly report revenue" or "Revenue" with limit.
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
name: execute_code
- id: call_l8kk2in2
+ id: call_no1egdsi
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)","stdout":"[{''chunk_id'':
- ''4caa01a9-80d3-4d7d-bfab-ca11a66318d7'', ''content'': ''Sales report Q1: Revenue was $100,000.'', ''document_id'':
- ''330c5184-910f-4515-b99b-72e3e8a229bf'', ''document_title'': ''Q1 Report'', ''document_uri'': None, ''score'':
- 0.03226645663380623, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''93295467-8f49-4268-8701-d0f84a17e5d9'',
- ''content'': ''Sales report Q2: Revenue was $150,000.'', ''document_id'': ''30e9d013-29b1-46bb-b578-bfc260572dda'',
- ''document_title'': ''Q2 Report'', ''document_uri'': None, ''score'': 0.03226645663380623, ''page_numbers'': [],
- ''headings'': None}, {''chunk_id'': ''efc76976-d951-4c74-a881-979e1382a6ae'', ''content'': ''Sales report Q3: Revenue
- was $200,000.'', ''document_id'': ''3a6a60f5-3467-40cf-80b0-1d7a76bea1dd'', ''document_title'': ''Q3 Report'', ''document_uri'':
- None, ''score'': 0.032258063554763794, ''page_numbers'': [], ''headings'': None}]\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor r in results[:5]:\n print(r[''document_title''],
+ r[''score''], r[''content''][:200])","stdout":"3\nQ1 Report 0.03226645663380623 Sales report Q1: Revenue was $100,000.\nQ2
+ Report 0.03226645663380623 Sales report Q2: Revenue was $150,000.\nQ3 Report 0.032258063554763794 Sales report Q3:
+ Revenue was $200,000.\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_l8kk2in2
+ tool_call_id: call_no1egdsi
- content: null
- reasoning: We see 3 chunks. Need total revenue sum. Extract amounts.
+ reasoning: Three reports with revenue numbers. Sum = 450k. Need citation chunks. Retrieve chunks.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r
- in results:\n m = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)"}'
+ arguments: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())"}'
name: execute_code
- id: call_5erv45jj
+ id: call_sg5vtxpb
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r in results:\n m
- = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
+ - content: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())","stdout":"","stderr":"NameError: name ''results'' is not defined","success":false}'
role: tool
- tool_call_id: call_5erv45jj
+ tool_call_id: call_sg5vtxpb
+ - content: null
+ reasoning: Need to keep results variable. Use same scope. Let's re-run with storing results.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())"}'
+ name: execute_code
+ id: call_h6pfbenb
+ type: function
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())","stdout":"Q1
+ Report Sales report Q1: Revenue was $100,000.\nQ2 Report Sales report Q2: Revenue was $150,000.\nQ3 Report Sales
+ report Q3: Revenue was $200,000.\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_h6pfbenb
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -911,10 +1204,262 @@ interactions:
type: object
strict: true
type: function
- - function:
+ uri: http://localhost:11434/v1/chat/completions
+ response:
+ headers:
+ content-length:
+ - '614'
+ content-type:
+ - application/json
+ parsed_body:
+ choices:
+ - finish_reason: tool_calls
+ index: 0
+ message:
+ content: Now compute sum.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"total = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for
+ n in nums:\n total += int(n.replace('','', ''''))\nprint(total)"}'
+ name: execute_code
+ id: call_rlqh271l
+ index: 0
+ type: function
+ created: 1772626962
+ id: chatcmpl-924
+ model: gpt-oss
+ object: chat.completion
+ system_fingerprint: fp_ollama
+ usage:
+ completion_tokens: 83
+ prompt_tokens: 2312
+ total_tokens: 2395
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '11090'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ messages:
+ - content: |-
+ You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
+
+ You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
+
+ Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
+ - results = await search("query") ✓ CORRECT
+ - import search ✗ WRONG - will fail
+ - results = search("query") ✗ WRONG - must use await
+
+ ## Available Functions
+
+ ### await search(query, limit=10) -> list[dict]
+ Search the knowledge base using hybrid search (vector + full-text).
+ Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
+
+ ### await list_documents(limit=10, offset=0) -> list[dict]
+ List available documents in the knowledge base.
+ Returns list of dicts with keys: id, title, uri, created_at
+
+ ### await get_document(id_or_title) -> str | None
+ Get the full text content of a document by ID, title, or URI.
+ Returns the document content as a string, or None if not found.
+
+ ### await get_chunk(chunk_id) -> dict | None
+ Get a specific chunk by its ID (from search results).
+ Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
+ Use this to retrieve full chunk details and metadata for citation.
+
+ ### await get_docling_document(document_id) -> dict | None
+ Get the full document structure as a dict (DoclingDocument format).
+ Use `list_documents()` or search results to get document IDs first.
+ - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
+ - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
+ - `pictures`: list of figures/images with metadata
+ - `pages`: page dimensions and metadata
+
+ ### await regex_findall(pattern, text) -> list[str]
+ Find all non-overlapping matches of a regular expression pattern in text.
+
+ ### await regex_sub(pattern, repl, text) -> str
+ Replace all occurrences of a regular expression pattern with a replacement string.
+
+ ### await regex_search(pattern, text) -> dict | None
+ Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
+
+ ### await regex_split(pattern, text) -> list[str]
+ Split text by a regular expression pattern.
+
+ ### await llm(prompt) -> str
+ Call an LLM directly with the given prompt. Returns the response as a string.
+ Use this for classification, summarization, extraction, or any task where you
+ already have the content and just need LLM reasoning.
+
+ ## Pre-loaded Documents Variable
+
+ If documents were pre-loaded for this session, a `documents` variable is available:
+ ```python
+ # documents is a list of dicts with keys: id, title, uri, content
+ for doc in documents:
+ print(doc['title'], len(doc['content']))
+ ```
+ Check if it exists with: `try: documents ... except NameError: ...`
+
+ ## Available Python Features
+
+ The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
+
+ Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
+
+ For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
+
+ ## Strategy Guide
+
+ 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
+ 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
+ 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
+ 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
+ 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
+
+ ## Example Patterns
+
+ ### Counting documents matching a condition
+ ```python
+ docs = await list_documents(limit=100)
+ count = 0
+ for doc in docs:
+ content = await get_document(doc['id'])
+ if content and 'keyword' in content.lower():
+ count += 1
+ print(f"Found in: {doc['title']}")
+ print(f"Total: {count}")
+ ```
+
+ ### Extracting data with regex
+ ```python
+ numbers = []
+ results = await search("financial data", limit=20)
+ for r in results:
+ amounts = await regex_findall(r'\$([\d,]+)', r['content'])
+ for a in amounts:
+ numbers.append(int(a.replace(',', '')))
+ if numbers:
+ print(f"Average: {sum(numbers) / len(numbers)}")
+ ```
+
+ ### Extracting tables from a document
+ ```python
+ docs = await list_documents(limit=10)
+ for d in docs:
+ doc = await get_docling_document(d['id'])
+ if doc:
+ tables = doc.get('tables', [])
+ if tables:
+ print(f"{d['title']}: {len(tables)} table(s)")
+ for i, table in enumerate(tables):
+ grid = table.get('data', {}).get('grid', [])
+ for row in grid:
+ cells = [cell.get('text', '') for cell in row]
+ print(f" Table {i}: {cells}")
+ ```
+
+ ## Output Format
+
+ Your final response MUST be valid JSON matching this exact schema:
+ ```json
+ {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
+ ```
+
+ - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
+ - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
+
+ Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
+
+ You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
+ role: system
+ - content: What is the total revenue across all quarterly reports?
+ role: user
+ - content: null
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
+ name: execute_code
+ id: call_no1egdsi
+ type: function
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor r in results[:5]:\n print(r[''document_title''],
+ r[''score''], r[''content''][:200])","stdout":"3\nQ1 Report 0.03226645663380623 Sales report Q1: Revenue was $100,000.\nQ2
+ Report 0.03226645663380623 Sales report Q2: Revenue was $150,000.\nQ3 Report 0.032258063554763794 Sales report Q3:
+ Revenue was $200,000.\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_no1egdsi
+ - content: null
+ reasoning: Three reports with revenue numbers. Sum = 450k. Need citation chunks. Retrieve chunks.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())"}'
+ name: execute_code
+ id: call_sg5vtxpb
+ type: function
+ - content: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())","stdout":"","stderr":"NameError: name ''results'' is not defined","success":false}'
+ role: tool
+ tool_call_id: call_sg5vtxpb
+ - content: null
+ reasoning: Need to keep results variable. Use same scope. Let's re-run with storing results.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())"}'
+ name: execute_code
+ id: call_h6pfbenb
+ type: function
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())","stdout":"Q1
+ Report Sales report Q1: Revenue was $100,000.\nQ2 Report Sales report Q2: Revenue was $150,000.\nQ3 Report Sales
+ report Q3: Revenue was $200,000.\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_h6pfbenb
+ - content: Now compute sum.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"total = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for
+ n in nums:\n total += int(n.replace('','', ''''))\nprint(total)"}'
+ name: execute_code
+ id: call_rlqh271l
+ type: function
+ - content: '{"code":"total = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for
+ n in nums:\n total += int(n.replace('','', ''''))\nprint(total)","stdout":"","stderr":"NameError: name ''chunks''
+ is not defined","success":false}'
+ role: tool
+ tool_call_id: call_rlqh271l
+ model: gpt-oss
+ reasoning_effort: low
+ response_format:
+ json_schema:
description: Result from RLM agent execution.
- name: final_result
- parameters:
+ name: RLMResult
+ schema:
additionalProperties: false
properties:
answer:
@@ -928,12 +1473,38 @@ interactions:
- program
type: object
strict: true
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: |-
+ Execute Python code in a sandboxed interpreter.
+
+ The code has access to haiku.rag functions (search, list_documents,
+ get_document, get_chunk, llm).
+
+ Use print() to output results.
+
+ Structured result with success status, stdout, and stderr.
+
+ name: execute_code
+ parameters:
+ additionalProperties: false
+ properties:
+ code:
+ description: Python code to execute.
+ type: string
+ required:
+ - code
+ type: object
+ strict: true
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '747'
+ - '819'
content-type:
- application/json
parsed_body:
@@ -942,25 +1513,27 @@ interactions:
index: 0
message:
content: ''
- reasoning: We cannot import re. Use regex_findall etc. Use await regex_findall.
+ reasoning: Variable scope lost. Let's combine.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\ntotal=0\nfor r in results:\n amounts
- = await regex_findall(r''\\$([\\d,]+)'', r[''content''])\n for a in amounts:\n total+=int(a.replace('','',''''))\nprint(total)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n\ntotal = 0\nfor c in chunks:\n nums = await
+ regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for n in nums:\n total += int(n.replace('','',
+ ''''))\nprint(total)"}'
name: execute_code
- id: call_otkvyqic
+ id: call_kbsx3dxm
index: 0
type: function
- created: 1772549335
- id: chatcmpl-944
+ created: 1772626965
+ id: chatcmpl-319
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 109
- prompt_tokens: 2391
- total_tokens: 2500
+ completion_tokens: 132
+ prompt_tokens: 2483
+ total_tokens: 2615
status:
code: 200
message: OK
@@ -1013,7 +1586,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '10867'
+ - '12120'
content-type:
- application/json
host:
@@ -1160,57 +1733,102 @@ interactions:
- content: What is the total revenue across all quarterly reports?
role: user
- content: null
- reasoning: We need total revenue across all quarterly reports. Likely documents contain quarterly reports. Let's search
- for "quarterly report revenue" or "Revenue" with limit.
+ reasoning: Need revenue from quarterly reports. Search for "quarterly report revenue".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''document_title''], r[''score''], r[''content''][:200])"}'
name: execute_code
- id: call_l8kk2in2
+ id: call_no1egdsi
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)","stdout":"[{''chunk_id'':
- ''4caa01a9-80d3-4d7d-bfab-ca11a66318d7'', ''content'': ''Sales report Q1: Revenue was $100,000.'', ''document_id'':
- ''330c5184-910f-4515-b99b-72e3e8a229bf'', ''document_title'': ''Q1 Report'', ''document_uri'': None, ''score'':
- 0.03226645663380623, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''93295467-8f49-4268-8701-d0f84a17e5d9'',
- ''content'': ''Sales report Q2: Revenue was $150,000.'', ''document_id'': ''30e9d013-29b1-46bb-b578-bfc260572dda'',
- ''document_title'': ''Q2 Report'', ''document_uri'': None, ''score'': 0.03226645663380623, ''page_numbers'': [],
- ''headings'': None}, {''chunk_id'': ''efc76976-d951-4c74-a881-979e1382a6ae'', ''content'': ''Sales report Q3: Revenue
- was $200,000.'', ''document_id'': ''3a6a60f5-3467-40cf-80b0-1d7a76bea1dd'', ''document_title'': ''Q3 Report'', ''document_uri'':
- None, ''score'': 0.032258063554763794, ''page_numbers'': [], ''headings'': None}]\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nprint(len(results))\nfor r in results[:5]:\n print(r[''document_title''],
+ r[''score''], r[''content''][:200])","stdout":"3\nQ1 Report 0.03226645663380623 Sales report Q1: Revenue was $100,000.\nQ2
+ Report 0.03226645663380623 Sales report Q2: Revenue was $150,000.\nQ3 Report 0.032258063554763794 Sales report Q3:
+ Revenue was $200,000.\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_l8kk2in2
+ tool_call_id: call_no1egdsi
- content: null
- reasoning: We see 3 chunks. Need total revenue sum. Extract amounts.
+ reasoning: Three reports with revenue numbers. Sum = 450k. Need citation chunks. Retrieve chunks.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r
- in results:\n m = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)"}'
+ arguments: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())"}'
name: execute_code
- id: call_5erv45jj
+ id: call_sg5vtxpb
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r in results:\n m
- = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
+ - content: '{"code":"chunks = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n chunks.append(chunk)\n print(chunk[''document_title''],
+ chunk[''content''].strip())","stdout":"","stderr":"NameError: name ''results'' is not defined","success":false}'
role: tool
- tool_call_id: call_5erv45jj
+ tool_call_id: call_sg5vtxpb
- content: null
- reasoning: We cannot import re. Use regex_findall etc. Use await regex_findall.
+ reasoning: Need to keep results variable. Use same scope. Let's re-run with storing results.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\ntotal=0\nfor r in results:\n amounts
- = await regex_findall(r''\\$([\\d,]+)'', r[''content''])\n for a in amounts:\n total+=int(a.replace('','',''''))\nprint(total)"}'
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())"}'
name: execute_code
- id: call_otkvyqic
+ id: call_h6pfbenb
type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\ntotal=0\nfor r in results:\n amounts
- = await regex_findall(r''\\$([\\d,]+)'', r[''content''])\n for a in amounts:\n total+=int(a.replace('','',''''))\nprint(total)","stdout":"450000\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n print(c[''document_title''], c[''content''].strip())","stdout":"Q1
+ Report Sales report Q1: Revenue was $100,000.\nQ2 Report Sales report Q2: Revenue was $150,000.\nQ3 Report Sales
+ report Q3: Revenue was $200,000.\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_otkvyqic
+ tool_call_id: call_h6pfbenb
+ - content: Now compute sum.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"total = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for
+ n in nums:\n total += int(n.replace('','', ''''))\nprint(total)"}'
+ name: execute_code
+ id: call_rlqh271l
+ type: function
+ - content: '{"code":"total = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for
+ n in nums:\n total += int(n.replace('','', ''''))\nprint(total)","stdout":"","stderr":"NameError: name ''chunks''
+ is not defined","success":false}'
+ role: tool
+ tool_call_id: call_rlqh271l
+ - content: null
+ reasoning: Variable scope lost. Let's combine.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n\ntotal = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'',
+ c[''content''])\n for n in nums:\n total += int(n.replace('','', ''''))\nprint(total)"}'
+ name: execute_code
+ id: call_kbsx3dxm
+ type: function
+ - content: '{"code":"results = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c
+ = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n\ntotal = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'',
+ c[''content''])\n for n in nums:\n total += int(n.replace('','', ''''))\nprint(total)","stdout":"450000\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_kbsx3dxm
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -1237,29 +1855,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '766'
+ - '880'
content-type:
- application/json
parsed_body:
@@ -1267,321 +1867,21 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: '{"answer":"The total revenue reported across all quarterly reports is $450,000.","program":"# Find all
- quarterly report revenue snippets and sum them\nresults = await search(''quarterly report revenue'', limit=10)\ntotal
- = 0\nfor r in results:\n # Find dollar amounts, allowing commas\n amounts = await regex_findall(r''\\$([\\d,]+)'',
- r[''content''])\n for a in amounts:\n total += int(a.replace('','', ''''))\nprint(f\"Total revenue:
- ${total}\")"}'
+ content: '{"answer":"The total revenue across all quarterly reports is $450,000. This figure comes from three reports:\n-
+ Q1 Report: $100,000\n- Q2 Report: $150,000\n- Q3 Report: $200,000\nSumming these gives the stated total.\n", "program":"results
+ = await search(\"quarterly report revenue\", limit=20)\nchunks = []\nfor r in results:\n c = await get_chunk(r[''chunk_id''])\n chunks.append(c)\n\ntotal
+ = 0\nfor c in chunks:\n nums = await regex_findall(r''\\$([\\d,]+)'', c[''content''])\n for n in nums:\n total
+ += int(n.replace('','', ''''))\nprint(total)"}'
role: assistant
- created: 1772549338
- id: chatcmpl-206
+ created: 1772626970
+ id: chatcmpl-772
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 121
- prompt_tokens: 2596
- total_tokens: 2717
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '11508'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: What is the total revenue across all quarterly reports?
- role: user
- - content: null
- reasoning: We need total revenue across all quarterly reports. Likely documents contain quarterly reports. Let's search
- for "quarterly report revenue" or "Revenue" with limit.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)"}'
- name: execute_code
- id: call_l8kk2in2
- type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nprint(results)","stdout":"[{''chunk_id'':
- ''4caa01a9-80d3-4d7d-bfab-ca11a66318d7'', ''content'': ''Sales report Q1: Revenue was $100,000.'', ''document_id'':
- ''330c5184-910f-4515-b99b-72e3e8a229bf'', ''document_title'': ''Q1 Report'', ''document_uri'': None, ''score'':
- 0.03226645663380623, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''93295467-8f49-4268-8701-d0f84a17e5d9'',
- ''content'': ''Sales report Q2: Revenue was $150,000.'', ''document_id'': ''30e9d013-29b1-46bb-b578-bfc260572dda'',
- ''document_title'': ''Q2 Report'', ''document_uri'': None, ''score'': 0.03226645663380623, ''page_numbers'': [],
- ''headings'': None}, {''chunk_id'': ''efc76976-d951-4c74-a881-979e1382a6ae'', ''content'': ''Sales report Q3: Revenue
- was $200,000.'', ''document_id'': ''3a6a60f5-3467-40cf-80b0-1d7a76bea1dd'', ''document_title'': ''Q3 Report'', ''document_uri'':
- None, ''score'': 0.032258063554763794, ''page_numbers'': [], ''headings'': None}]\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_l8kk2in2
- - content: null
- reasoning: We see 3 chunks. Need total revenue sum. Extract amounts.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r
- in results:\n m = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)"}'
- name: execute_code
- id: call_5erv45jj
- type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\nimport re\ntotal=0\nfor r in results:\n m
- = re.search(r''\\$(\\d+)'', r[''content''])\n if m:\n total+=int(m.group(1))\nprint(total)","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
- role: tool
- tool_call_id: call_5erv45jj
- - content: null
- reasoning: We cannot import re. Use regex_findall etc. Use await regex_findall.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''quarterly report revenue'', limit=10)\ntotal=0\nfor r in results:\n amounts
- = await regex_findall(r''\\$([\\d,]+)'', r[''content''])\n for a in amounts:\n total+=int(a.replace('','',''''))\nprint(total)"}'
- name: execute_code
- id: call_otkvyqic
- type: function
- - content: '{"code":"results = await search(''quarterly report revenue'', limit=10)\ntotal=0\nfor r in results:\n amounts
- = await regex_findall(r''\\$([\\d,]+)'', r[''content''])\n for a in amounts:\n total+=int(a.replace('','',''''))\nprint(total)","stdout":"450000\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_otkvyqic
- - content: '{"answer":"The total revenue reported across all quarterly reports is $450,000.","program":"# Find all quarterly
- report revenue snippets and sum them\nresults = await search(''quarterly report revenue'', limit=10)\ntotal = 0\nfor
- r in results:\n # Find dollar amounts, allowing commas\n amounts = await regex_findall(r''\\$([\\d,]+)'',
- r[''content''])\n for a in amounts:\n total += int(a.replace('','', ''''))\nprint(f\"Total revenue: ${total}\")"}'
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '954'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to wrap answer in tool call. Use final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"The total revenue reported across all quarterly reports is $450,000.","program":"# Find
- all quarterly report revenue snippets and sum them\nresults = await search(''quarterly report revenue'', limit=10)\ntotal
- = 0\nfor r in results:\n # Find dollar amounts, allowing commas\n amounts = await regex_findall(r''\\$([\\d,]+)'',
- r[''content''])\n for a in amounts:\n total += int(a.replace('','', ''''))\nprint(f\"Total revenue:
- ${total}\")"}'
- name: final_result
- id: call_ne7i4w3g
- index: 0
- type: function
- created: 1772549342
- id: chatcmpl-743
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 148
- prompt_tokens: 2661
- total_tokens: 2809
+ completion_tokens: 178
+ prompt_tokens: 2744
+ total_tokens: 2922
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_count_documents.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_count_documents.yaml
index 1d7205df..9ff42329 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_count_documents.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_count_documents.yaml
@@ -128,7 +128,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7704'
+ - '7721'
content-type:
- application/json
host:
@@ -276,6 +276,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -302,29 +321,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '516'
+ - '522'
content-type:
- application/json
parsed_body:
@@ -333,24 +334,24 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need list_documents.
+ reasoning: We need to list documents.
role: assistant
tool_calls:
- function:
arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
name: execute_code
- id: call_oyaoz18v
+ id: call_cthflnpr
index: 0
type: function
- created: 1772549310
- id: chatcmpl-325
+ created: 1772626945
+ id: chatcmpl-979
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 43
- prompt_tokens: 1730
- total_tokens: 1773
+ completion_tokens: 45
+ prompt_tokens: 1686
+ total_tokens: 1731
status:
code: 200
message: OK
@@ -363,7 +364,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8140'
+ - '8163'
content-type:
- application/json
host:
@@ -510,19 +511,38 @@ interactions:
- content: How many documents are in the database?
role: user
- content: null
- reasoning: Need list_documents.
+ reasoning: We need to list documents.
role: assistant
tool_calls:
- function:
arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
name: execute_code
- id: call_oyaoz18v
+ id: call_cthflnpr
type: function
- content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))","stdout":"3\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_oyaoz18v
+ tool_call_id: call_cthflnpr
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -549,29 +569,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '416'
+ - '459'
content-type:
- application/json
parsed_body:
@@ -579,272 +581,18 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: '{"answer":"There are 3 documents in the database.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
+ content: '{"answer":"There are 3 documents in the database.","program":"docs = await list_documents(limit=1000)\nprint(f''There
+ are {len(docs)} documents in the database.'')" }'
role: assistant
- created: 1772549311
- id: chatcmpl-670
+ created: 1772626947
+ id: chatcmpl-490
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 38
- prompt_tokens: 1815
- total_tokens: 1853
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '8432'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: How many documents are in the database?
- role: user
- - content: null
- reasoning: Need list_documents.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- name: execute_code
- id: call_oyaoz18v
- type: function
- - content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_oyaoz18v
- - content: '{"answer":"There are 3 documents in the database.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '610'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to return via tool call? We should use final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"There are 3 documents in the database.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- name: final_result
- id: call_46d25765
- index: 0
- type: function
- created: 1772549312
- id: chatcmpl-412
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 63
- prompt_tokens: 1865
- total_tokens: 1928
+ completion_tokens: 49
+ prompt_tokens: 1773
+ total_tokens: 1822
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_extract.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_extract.yaml
index 76741f2b..99c5aa9d 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_extract.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_extract.yaml
@@ -182,7 +182,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7799'
+ - '7816'
content-type:
- application/json
host:
@@ -331,6 +331,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -357,10 +376,218 @@ interactions:
type: object
strict: true
type: function
- - function:
+ uri: http://localhost:11434/v1/chat/completions
+ response:
+ headers:
+ content-length:
+ - '573'
+ content-type:
+ - application/json
+ parsed_body:
+ choices:
+ - finish_reason: tool_calls
+ index: 0
+ message:
+ content: ''
+ reasoning: We need to search.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"import json\nresults = await search(\"document element types\", limit=20)\nprint(json.dumps(results,
+ indent=2))"}'
+ name: execute_code
+ id: call_asxbylxn
+ index: 0
+ type: function
+ created: 1772628395
+ id: chatcmpl-532
+ model: gpt-oss
+ object: chat.completion
+ system_fingerprint: fp_ollama
+ usage:
+ completion_tokens: 54
+ prompt_tokens: 1702
+ total_tokens: 1756
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '8408'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ messages:
+ - content: |-
+ You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
+
+ You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
+
+ Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
+ - results = await search("query") ✓ CORRECT
+ - import search ✗ WRONG - will fail
+ - results = search("query") ✗ WRONG - must use await
+
+ ## Available Functions
+
+ ### await search(query, limit=10) -> list[dict]
+ Search the knowledge base using hybrid search (vector + full-text).
+ Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
+
+ ### await list_documents(limit=10, offset=0) -> list[dict]
+ List available documents in the knowledge base.
+ Returns list of dicts with keys: id, title, uri, created_at
+
+ ### await get_document(id_or_title) -> str | None
+ Get the full text content of a document by ID, title, or URI.
+ Returns the document content as a string, or None if not found.
+
+ ### await get_chunk(chunk_id) -> dict | None
+ Get a specific chunk by its ID (from search results).
+ Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
+ Use this to retrieve full chunk details and metadata for citation.
+
+ ### await get_docling_document(document_id) -> dict | None
+ Get the full document structure as a dict (DoclingDocument format).
+ Use `list_documents()` or search results to get document IDs first.
+ - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
+ - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
+ - `pictures`: list of figures/images with metadata
+ - `pages`: page dimensions and metadata
+
+ ### await regex_findall(pattern, text) -> list[str]
+ Find all non-overlapping matches of a regular expression pattern in text.
+
+ ### await regex_sub(pattern, repl, text) -> str
+ Replace all occurrences of a regular expression pattern with a replacement string.
+
+ ### await regex_search(pattern, text) -> dict | None
+ Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
+
+ ### await regex_split(pattern, text) -> list[str]
+ Split text by a regular expression pattern.
+
+ ### await llm(prompt) -> str
+ Call an LLM directly with the given prompt. Returns the response as a string.
+ Use this for classification, summarization, extraction, or any task where you
+ already have the content and just need LLM reasoning.
+
+ ## Pre-loaded Documents Variable
+
+ If documents were pre-loaded for this session, a `documents` variable is available:
+ ```python
+ # documents is a list of dicts with keys: id, title, uri, content
+ for doc in documents:
+ print(doc['title'], len(doc['content']))
+ ```
+ Check if it exists with: `try: documents ... except NameError: ...`
+
+ ## Available Python Features
+
+ The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
+
+ Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
+
+ For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
+
+ ## Strategy Guide
+
+ 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
+ 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
+ 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
+ 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
+ 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
+
+ ## Example Patterns
+
+ ### Counting documents matching a condition
+ ```python
+ docs = await list_documents(limit=100)
+ count = 0
+ for doc in docs:
+ content = await get_document(doc['id'])
+ if content and 'keyword' in content.lower():
+ count += 1
+ print(f"Found in: {doc['title']}")
+ print(f"Total: {count}")
+ ```
+
+ ### Extracting data with regex
+ ```python
+ numbers = []
+ results = await search("financial data", limit=20)
+ for r in results:
+ amounts = await regex_findall(r'\$([\d,]+)', r['content'])
+ for a in amounts:
+ numbers.append(int(a.replace(',', '')))
+ if numbers:
+ print(f"Average: {sum(numbers) / len(numbers)}")
+ ```
+
+ ### Extracting tables from a document
+ ```python
+ docs = await list_documents(limit=10)
+ for d in docs:
+ doc = await get_docling_document(d['id'])
+ if doc:
+ tables = doc.get('tables', [])
+ if tables:
+ print(f"{d['title']}: {len(tables)} table(s)")
+ for i, table in enumerate(tables):
+ grid = table.get('data', {}).get('grid', [])
+ for row in grid:
+ cells = [cell.get('text', '') for cell in row]
+ print(f" Table {i}: {cells}")
+ ```
+
+ ## Output Format
+
+ Your final response MUST be valid JSON matching this exact schema:
+ ```json
+ {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
+ ```
+
+ - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
+ - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
+
+ Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
+
+ You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
+ role: system
+ - content: Search for content about document element types or labels. What are all the different document element types
+ mentioned? List them all.
+ role: user
+ - content: null
+ reasoning: We need to search.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"import json\nresults = await search(\"document element types\", limit=20)\nprint(json.dumps(results,
+ indent=2))"}'
+ name: execute_code
+ id: call_asxbylxn
+ type: function
+ - content: '{"code":"import json\nresults = await search(\"document element types\", limit=20)\nprint(json.dumps(results,
+ indent=2))","stdout":"","stderr":"ModuleNotFoundError: No module named ''json''","success":false}'
+ role: tool
+ tool_call_id: call_asxbylxn
+ model: gpt-oss
+ reasoning_effort: low
+ response_format:
+ json_schema:
description: Result from RLM agent execution.
- name: final_result
- parameters:
+ name: RLMResult
+ schema:
additionalProperties: false
properties:
answer:
@@ -374,12 +601,38 @@ interactions:
- program
type: object
strict: true
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: |-
+ Execute Python code in a sandboxed interpreter.
+
+ The code has access to haiku.rag functions (search, list_documents,
+ get_document, get_chunk, llm).
+
+ Use print() to output results.
+
+ Structured result with success status, stdout, and stderr.
+
+ name: execute_code
+ parameters:
+ additionalProperties: false
+ properties:
+ code:
+ description: Python code to execute.
+ type: string
+ required:
+ - code
+ type: object
+ strict: true
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '756'
+ - '648'
content-type:
- application/json
parsed_body:
@@ -388,26 +641,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: We need to search for content about document element types or labels. Likely many documents mention e.g.
- "title", "section_header", "list_item", etc. We should search for terms like "element types" or "labels". Let's
- search.
+ reasoning: We cannot import json. But we can print. Use simple print.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n"}'
+ arguments: '{"code":"results = await search(\"document element types\", limit=20)\nfor r in results:\n print(r[''document_title''],
+ r[''chunk_id''], r[''content''][:200])"}'
name: execute_code
- id: call_4k3t2n10
+ id: call_tnybh8wo
index: 0
type: function
- created: 1772548402
- id: chatcmpl-870
+ created: 1772628397
+ id: chatcmpl-481
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 92
- prompt_tokens: 1746
- total_tokens: 1838
+ completion_tokens: 76
+ prompt_tokens: 1817
+ total_tokens: 1893
status:
code: 200
message: OK
@@ -460,7 +712,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '18052'
+ - '13241'
content-type:
- application/json
host:
@@ -608,102 +860,88 @@ interactions:
mentioned? List them all.
role: user
- content: null
- reasoning: We need to search for content about document element types or labels. Likely many documents mention e.g.
- "title", "section_header", "list_item", etc. We should search for terms like "element types" or "labels". Let's
- search.
+ reasoning: We need to search.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n"}'
+ arguments: '{"code":"import json\nresults = await search(\"document element types\", limit=20)\nprint(json.dumps(results,
+ indent=2))"}'
name: execute_code
- id: call_4k3t2n10
+ id: call_asxbylxn
type: function
- - content: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n","stdout":"[{''chunk_id'':
- ''4b9cb6a5-f203-4070-8b2f-ab3ef12dde1b'', ''content'': ''Phase 2: Label selection and guideline. We reviewed the
- collected documents and identified the most common structural features they exhibit. This was achieved by identifying
- recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$,
- $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$,
- $_{Table}$, $_{Text}$, and $_{Title}$. Critical factors that were considered for the choice of these class labels
- were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single
- page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures
- that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated.
- We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific
- Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such
- as Author and'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.032786883413791656,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''fdc2266a-b812-48c4-a49a-ece08a348ead'', ''content'':
- ''Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large
- effort went into ensuring that all documents are free to use. The data sources include publication repositories
- such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports
- and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would
- not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.'',
- ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.0320020467042923, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''0b6492ef-bece-4486-98ce-85280c3b2667'',
- ''content'': ''$_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on\\nPreparation
- work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native
- platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation
- interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was
- achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include
- the title page of each document and bias the remaining page selection to those with figures or tables. The latter
- was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many
- figures and tables a given page contains.\\n$^{3}$https://arxiv.org/'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.03036576882004738, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''940f11c1-5028-4dd4-9515-781f1b9cdc2a'',
- ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
- the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
- is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
- we obtain accuracy ranges.'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''eba516e5-277f-487b-bcb9-3caea945ac54'',
- ''content'': ''Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header,
- % of Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple
- inter-annotator mAP @ 0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 66-76.
- Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP @ 0.5-0.95
- (%).Sci = 98-100. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator
- mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.015625,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''63d5d7a0-e9e6-4258-9c76-d97689acffb0'', ''content'':
- ''0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header,
- triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table,
- % of Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @ 0.5-0.95 (%).All = 77-81.
- Table, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Man
- = 83-86. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-99. Table, triple'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.015384615398943424, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''48c416f5-db1e-47c9-9f5c-0caf7b36e568'',
- ''content'': ''Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption,
- % of Total.Val = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator
- mAP @ 0.5-0.95 (%).Fin = 40-61. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 86-92. Caption, triple
- inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-99. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 95-99.
- Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- ='', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.01515151560306549, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''f931ae1e-5413-4b26-a8cc-14ce5ce7bdc1'',
- ''content'': ''inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- = 59-76. Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test
- = 15.77. Section-header, % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).All =
- 83-84. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator
- mAP @ 0.5-0.95 (%).Man = 90-92. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-95. Section-header,
- triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.014925372786819935, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''b8056bae-cc3b-4674-a7df-44d918eb4aea'',
- ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
- Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
- inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
- triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
- 85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
- (%).Ten'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.014705882407724857,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''917de523-f069-49fa-ad71-1f0886db460c'', ''content'':
- ''185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
- List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @ 0.5-0.95
- (%).Fin = 74-83. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator
- mAP @ 0.5-0.95 (%).Sci = 97-97. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 81-85. List-item, triple
- inter-annotator mAP @ 0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.014285714365541935, ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
+ - content: '{"code":"import json\nresults = await search(\"document element types\", limit=20)\nprint(json.dumps(results,
+ indent=2))","stdout":"","stderr":"ModuleNotFoundError: No module named ''json''","success":false}'
role: tool
- tool_call_id: call_4k3t2n10
+ tool_call_id: call_asxbylxn
+ - content: null
+ reasoning: We cannot import json. But we can print. Use simple print.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"document element types\", limit=20)\nfor r in results:\n print(r[''document_title''],
+ r[''chunk_id''], r[''content''][:200])"}'
+ name: execute_code
+ id: call_tnybh8wo
+ type: function
+ - content: '{"code":"results = await search(\"document element types\", limit=20)\nfor r in results:\n print(r[''document_title''],
+ r[''chunk_id''], r[''content''][:200])","stdout":"None d5720da3-0a74-4750-ba03-3db361e81173 Phase 2: Label selection
+ and guideline. We reviewed the collected documents and identified the most common structural features they exhibit.
+ This was achieved by identifying recurrent layout elements \nNone e0d09a22-db60-4997-9b6d-fe941955779e Phase 1:
+ Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort
+ went into ensuring that all documents are free to use. The data sources includ\nNone d08ece43-ef73-43dc-bcc4-98aaa669cfa1
+ $_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on\nPreparation work included
+ uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CC\nNone c30bbfbf-6626-417c-a67e-c1396693e987
+ \nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence
+ (as % of row \"Total\") in the train, test and validation sets. The inter-annotator\nNone 6f6b4653-20f8-4907-bf49-8b3aed8027df
+ Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header, % of
+ Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val =\nNone f23f85c0-169d-43db-a1ed-de049b9ff73e
+ 0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header,
+ triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % o\nNone 1e1a7caf-ce36-40f9-8a0d-d6fc3ce5c8aa
+ Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption, % of Total.Val
+ = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, trip\nNone 7079c771-a705-4188-b183-639f38e9e41d
+ inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 59-76.
+ Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header\nNone 3387ce95-57b9-44c3-8187-0c5039ab8d3e
+ n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31. Footnote, % of
+ Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Foot\nNone 6652c53a-5654-4db8-ab6a-28ec5ea42534
+ 185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
+ List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple \nNone 89a3d27b-070c-45e8-95be-a3d9670f1f9f
+ 0.5-0.95 (%).Ten = 93-95. Page-footer, Count = 70878. Page-footer, % of Total.Train = 6.51. Page-footer, % of Total.Test
+ = 5.58. Page-footer, % of Total.Val = 6.00. Page-footer, triple inter-annotator\nNone 33c86a46-b88b-4492-bc22-c049b6be6712
+ 89-93. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 87-92. Text, triple inter-annotator mAP @ 0.5-0.95
+ (%).Pat = 71-79. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 87-95. Title, Cou\nNone 9101a029-0c92-4e1c-b427-53a69afaa2a3
+ 0.5-0.95 (%).Pat = 97-99. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 81-86. Picture, Count = 45976.
+ Picture, % of Total.Train = 4.21. Picture, % of Total.Test = 2.78. Picture, % of T\nNone b7284b9f-3ec7-405c-b6a1-0a01c08bd29c
+ (%).Sci = 94-100. Title, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 82-96. Title, triple inter-annotator mAP
+ @ 0.5-0.95 (%).Pat = 68-79. Title, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 24-\nNone 646b0b0b-625f-44dc-a3af-98ef180fbd35
+ inter-annotator mAP @ 0.5-0.95 (%).Law = 58-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 79-84. Table,
+ triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 70-85. Text, Count = 510377. Text, \nNone 7e518b7d-7418-4b46-a879-acd3217151dc
+ = 82-97. Formula, Count = 25027. Formula, % of Total.Train = 2.25. Formula, % of Total.Test = 1.90. Formula, % of
+ Total.Val = 2.96. Formula, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-85. Form\nNone f2c88b81-fc96-42a8-93bc-27fbaae2db5d
+ mAP @ 0.5-0.95 (%).Sci = 89-94. Total, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 86-91. Total, triple inter-annotator
+ mAP @ 0.5-0.95 (%).Pat = 71-76. Total, triple inter-annotator mAP @ 0.5-0.95\n","stderr":"","success":true}'
+ role: tool
+ tool_call_id: call_tnybh8wo
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -730,840 +968,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1473'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: 'We got list of chunks with labels info. The element types mentioned in that chunk: Caption, Footnote,
- Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also maybe others like
- Affiliation but not part of 11 class labels. So answer list unique types. Let''s list: Caption, Footnote, Formula,
- List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also mention that these are
- the 11 distinct class labels. Provide answer and program summarizing retrieval of labels. Let''s write program:
- search for that chunk, extract from content using regex to find labels. Use regex pattern r''\b[_\{]([A-Za-z-]+)[_\}]''
- or simpler from known labels list. But we can hardcode extraction: search patterns like ''$_{Caption}$'', etc.
- We''ll collect unique.'
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n"}'
- name: execute_code
- id: call_jxgy76dr
- index: 0
- type: function
- created: 1772548413
- id: chatcmpl-986
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 275
- prompt_tokens: 5108
- total_tokens: 5383
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '85'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- encoding_format: base64
- input:
- - 11 class labels
- model: qwen3-embedding:4b
- uri: http://localhost:11434/v1/embeddings
- response:
- headers:
- content-type:
- - application/json
- transfer-encoding:
- - chunked
- parsed_body:
- data:
- - embedding: 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
- index: 0
- object: embedding
- model: qwen3-embedding:4b
- object: list
- usage:
- prompt_tokens: 5
- total_tokens: 5
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '19656'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Search for content about document element types or labels. What are all the different document element types
- mentioned? List them all.
- role: user
- - content: null
- reasoning: We need to search for content about document element types or labels. Likely many documents mention e.g.
- "title", "section_header", "list_item", etc. We should search for terms like "element types" or "labels". Let's
- search.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n"}'
- name: execute_code
- id: call_4k3t2n10
- type: function
- - content: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n","stdout":"[{''chunk_id'':
- ''4b9cb6a5-f203-4070-8b2f-ab3ef12dde1b'', ''content'': ''Phase 2: Label selection and guideline. We reviewed the
- collected documents and identified the most common structural features they exhibit. This was achieved by identifying
- recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$,
- $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$,
- $_{Table}$, $_{Text}$, and $_{Title}$. Critical factors that were considered for the choice of these class labels
- were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single
- page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures
- that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated.
- We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific
- Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such
- as Author and'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.032786883413791656,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''fdc2266a-b812-48c4-a49a-ece08a348ead'', ''content'':
- ''Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large
- effort went into ensuring that all documents are free to use. The data sources include publication repositories
- such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports
- and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would
- not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.'',
- ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.0320020467042923, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''0b6492ef-bece-4486-98ce-85280c3b2667'',
- ''content'': ''$_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on\\nPreparation
- work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native
- platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation
- interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was
- achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include
- the title page of each document and bias the remaining page selection to those with figures or tables. The latter
- was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many
- figures and tables a given page contains.\\n$^{3}$https://arxiv.org/'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.03036576882004738, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''940f11c1-5028-4dd4-9515-781f1b9cdc2a'',
- ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
- the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
- is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
- we obtain accuracy ranges.'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''eba516e5-277f-487b-bcb9-3caea945ac54'',
- ''content'': ''Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header,
- % of Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple
- inter-annotator mAP @ 0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 66-76.
- Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP @ 0.5-0.95
- (%).Sci = 98-100. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator
- mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.015625,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''63d5d7a0-e9e6-4258-9c76-d97689acffb0'', ''content'':
- ''0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header,
- triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table,
- % of Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @ 0.5-0.95 (%).All = 77-81.
- Table, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Man
- = 83-86. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-99. Table, triple'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.015384615398943424, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''48c416f5-db1e-47c9-9f5c-0caf7b36e568'',
- ''content'': ''Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption,
- % of Total.Val = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator
- mAP @ 0.5-0.95 (%).Fin = 40-61. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 86-92. Caption, triple
- inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-99. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 95-99.
- Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- ='', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.01515151560306549, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''f931ae1e-5413-4b26-a8cc-14ce5ce7bdc1'',
- ''content'': ''inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- = 59-76. Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test
- = 15.77. Section-header, % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).All =
- 83-84. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator
- mAP @ 0.5-0.95 (%).Man = 90-92. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-95. Section-header,
- triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.014925372786819935, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''b8056bae-cc3b-4674-a7df-44d918eb4aea'',
- ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
- Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
- inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
- triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
- 85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
- (%).Ten'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.014705882407724857,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''917de523-f069-49fa-ad71-1f0886db460c'', ''content'':
- ''185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
- List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @ 0.5-0.95
- (%).Fin = 74-83. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator
- mAP @ 0.5-0.95 (%).Sci = 97-97. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 81-85. List-item, triple
- inter-annotator mAP @ 0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.014285714365541935, ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_4k3t2n10
- - content: null
- reasoning: 'We got list of chunks with labels info. The element types mentioned in that chunk: Caption, Footnote,
- Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also maybe others like
- Affiliation but not part of 11 class labels. So answer list unique types. Let''s list: Caption, Footnote, Formula,
- List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also mention that these are the
- 11 distinct class labels. Provide answer and program summarizing retrieval of labels. Let''s write program: search
- for that chunk, extract from content using regex to find labels. Use regex pattern r''\b[_\{]([A-Za-z-]+)[_\}]''
- or simpler from known labels list. But we can hardcode extraction: search patterns like ''$_{Caption}$'', etc. We''ll
- collect unique.'
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n"}'
- name: execute_code
- id: call_jxgy76dr
- type: function
- - content: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
- role: tool
- tool_call_id: call_jxgy76dr
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '721'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Cannot import re. Use regex_* functions. Use regex_findall.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''11 class labels'', limit=5)\nlabels=set()\nfor r in results:\n matches
- = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(sorted(labels))\n"}'
- name: execute_code
- id: call_oyc93cba
- index: 0
- type: function
- created: 1772548416
- id: chatcmpl-210
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 103
- prompt_tokens: 5486
- total_tokens: 5589
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '85'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- encoding_format: base64
- input:
- - 11 class labels
- model: qwen3-embedding:4b
- uri: http://localhost:11434/v1/embeddings
- response:
- headers:
- content-type:
- - application/json
- transfer-encoding:
- - chunked
- parsed_body:
- data:
- - embedding: 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
- index: 0
- object: embedding
- model: qwen3-embedding:4b
- object: list
- usage:
- prompt_tokens: 5
- total_tokens: 5
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '20587'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Search for content about document element types or labels. What are all the different document element types
- mentioned? List them all.
- role: user
- - content: null
- reasoning: We need to search for content about document element types or labels. Likely many documents mention e.g.
- "title", "section_header", "list_item", etc. We should search for terms like "element types" or "labels". Let's
- search.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n"}'
- name: execute_code
- id: call_4k3t2n10
- type: function
- - content: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n","stdout":"[{''chunk_id'':
- ''4b9cb6a5-f203-4070-8b2f-ab3ef12dde1b'', ''content'': ''Phase 2: Label selection and guideline. We reviewed the
- collected documents and identified the most common structural features they exhibit. This was achieved by identifying
- recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$,
- $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$,
- $_{Table}$, $_{Text}$, and $_{Title}$. Critical factors that were considered for the choice of these class labels
- were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single
- page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures
- that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated.
- We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific
- Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such
- as Author and'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.032786883413791656,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''fdc2266a-b812-48c4-a49a-ece08a348ead'', ''content'':
- ''Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large
- effort went into ensuring that all documents are free to use. The data sources include publication repositories
- such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports
- and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would
- not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.'',
- ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.0320020467042923, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''0b6492ef-bece-4486-98ce-85280c3b2667'',
- ''content'': ''$_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on\\nPreparation
- work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native
- platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation
- interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was
- achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include
- the title page of each document and bias the remaining page selection to those with figures or tables. The latter
- was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many
- figures and tables a given page contains.\\n$^{3}$https://arxiv.org/'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.03036576882004738, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''940f11c1-5028-4dd4-9515-781f1b9cdc2a'',
- ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
- the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
- is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
- we obtain accuracy ranges.'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''eba516e5-277f-487b-bcb9-3caea945ac54'',
- ''content'': ''Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header,
- % of Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple
- inter-annotator mAP @ 0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 66-76.
- Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP @ 0.5-0.95
- (%).Sci = 98-100. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator
- mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.015625,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''63d5d7a0-e9e6-4258-9c76-d97689acffb0'', ''content'':
- ''0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header,
- triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table,
- % of Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @ 0.5-0.95 (%).All = 77-81.
- Table, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Man
- = 83-86. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-99. Table, triple'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.015384615398943424, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''48c416f5-db1e-47c9-9f5c-0caf7b36e568'',
- ''content'': ''Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption,
- % of Total.Val = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator
- mAP @ 0.5-0.95 (%).Fin = 40-61. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 86-92. Caption, triple
- inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-99. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 95-99.
- Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- ='', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.01515151560306549, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''f931ae1e-5413-4b26-a8cc-14ce5ce7bdc1'',
- ''content'': ''inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- = 59-76. Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test
- = 15.77. Section-header, % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).All =
- 83-84. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator
- mAP @ 0.5-0.95 (%).Man = 90-92. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-95. Section-header,
- triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.014925372786819935, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''b8056bae-cc3b-4674-a7df-44d918eb4aea'',
- ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
- Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
- inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
- triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
- 85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
- (%).Ten'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.014705882407724857,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''917de523-f069-49fa-ad71-1f0886db460c'', ''content'':
- ''185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
- List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @ 0.5-0.95
- (%).Fin = 74-83. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator
- mAP @ 0.5-0.95 (%).Sci = 97-97. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 81-85. List-item, triple
- inter-annotator mAP @ 0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.014285714365541935, ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_4k3t2n10
- - content: null
- reasoning: 'We got list of chunks with labels info. The element types mentioned in that chunk: Caption, Footnote,
- Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also maybe others like
- Affiliation but not part of 11 class labels. So answer list unique types. Let''s list: Caption, Footnote, Formula,
- List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also mention that these are the
- 11 distinct class labels. Provide answer and program summarizing retrieval of labels. Let''s write program: search
- for that chunk, extract from content using regex to find labels. Use regex pattern r''\b[_\{]([A-Za-z-]+)[_\}]''
- or simpler from known labels list. But we can hardcode extraction: search patterns like ''$_{Caption}$'', etc. We''ll
- collect unique.'
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n"}'
- name: execute_code
- id: call_jxgy76dr
- type: function
- - content: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
- role: tool
- tool_call_id: call_jxgy76dr
- - content: null
- reasoning: Cannot import re. Use regex_* functions. Use regex_findall.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''11 class labels'', limit=5)\nlabels=set()\nfor r in results:\n matches
- = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(sorted(labels))\n"}'
- name: execute_code
- id: call_oyc93cba
- type: function
- - content: '{"code":"results = await search(''11 class labels'', limit=5)\nlabels=set()\nfor r in results:\n matches
- = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(sorted(labels))\n","stdout":"[''Caption'',
- ''Footnote'', ''Formula'', ''List-item'', ''Page-header'', ''Picture'', ''Section-header'', ''Table'', ''Text'',
- ''Title'', ''footer'']\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_oyc93cba
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '1530'
+ - '1552'
content-type:
- application/json
parsed_body:
@@ -1571,422 +980,27 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: '{"answer":"The documents mention eleven distinct document element types (class labels). They are:\n1.
- Caption\n2. Footnote\n3. Formula\n4. List‑item\n5. Page‑header\n6. Page‑footer\n7. Picture\n8. Section‑header\n9.
- Table\n10. Text\n11. Title\n\nThese labels are used as the primary structural categories for annotating content
- in the dataset.\n","program":"import json\n\n# Retrieve the relevant chunk that lists the 11 class labels\nresults
- = await search(\"11 class labels\", limit=5)\n\n# Extract the label names using a regex that captures words inside
- \\{\\}\\_\nlabels = set()\nfor r in results:\n matches = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'',
- r[''content''])\n labels.update(matches)\n\n# Convert to a sorted list for consistent display\nlabel_list =
- sorted(labels)\n\n# Print the labels in a numbered list (JSON friendly)\noutput = \"\\n\".join(f\"{i+1}. {name}\"
- for i, name in enumerate(label_list))\nprint(output)\n"}'
- reasoning: 'It extracted ''footer'' separately. But Page-footer might be footer? Keep list: Caption, Footnote, Formula,
- List-item, Page-header, Page-footer (footer), Picture, Section-header, Table, Text, Title. Provide answer.'
+ content: '{"answer":"The document element types (labels) mentioned across the retrieved documents are:\n- Title\n-
+ Text\n- Page-header\n- Page-footer\n- Section-header\n- Footnote\n- List-item\n- Table\n- Caption\n- Formula\n-
+ Picture\n\nThese are the distinct element types that appear in the search results.","program":"results = await
+ search(\"document element types\", limit=50)\nlabels = set()\nfor r in results:\n txt = r.get(''content'',
+ '''')\n # Find labels of form \"Label,\"\n for match in await regex_findall(r\"([A-Za-z-]+),\", txt):\n labels.add(match)\nif
+ labels:\n print(\"Detected labels:\")\n for lbl in sorted(labels):\n print(lbl)\n# Return JSON answer\nans
+ = {\n \"answer\": \"The document element types (labels) mentioned across the retrieved documents are:\\n- \"
+ + \"\\n- \".join(sorted(labels)) + \"\\n\",\n \"program\": \"\"\"\n# The code above prints the labels and constructs
+ the answer string.\n\"\"\"\n}\nprint(ans)"}'
+ reasoning: 'We saw labels: Page-header, Page-footer, Section-header, Table, Caption, Picture, Footnote, List-item,
+ Text, Title, Formula. Also maybe List-item. Also maybe Section-header. So list unique.'
role: assistant
- created: 1772548424
- id: chatcmpl-943
+ created: 1772628408
+ id: chatcmpl-687
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 316
- prompt_tokens: 5718
- total_tokens: 6034
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '21992'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Search for content about document element types or labels. What are all the different document element types
- mentioned? List them all.
- role: user
- - content: null
- reasoning: We need to search for content about document element types or labels. Likely many documents mention e.g.
- "title", "section_header", "list_item", etc. We should search for terms like "element types" or "labels". Let's
- search.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n"}'
- name: execute_code
- id: call_4k3t2n10
- type: function
- - content: '{"code":"results = await search(\"document element types\", limit=10)\nprint(results)\n","stdout":"[{''chunk_id'':
- ''4b9cb6a5-f203-4070-8b2f-ab3ef12dde1b'', ''content'': ''Phase 2: Label selection and guideline. We reviewed the
- collected documents and identified the most common structural features they exhibit. This was achieved by identifying
- recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$,
- $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$,
- $_{Table}$, $_{Text}$, and $_{Title}$. Critical factors that were considered for the choice of these class labels
- were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single
- page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures
- that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated.
- We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific
- Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such
- as Author and'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.032786883413791656,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''fdc2266a-b812-48c4-a49a-ece08a348ead'', ''content'':
- ''Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large
- effort went into ensuring that all documents are free to use. The data sources include publication repositories
- such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports
- and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would
- not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.'',
- ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.0320020467042923, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''0b6492ef-bece-4486-98ce-85280c3b2667'',
- ''content'': ''$_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on\\nPreparation
- work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native
- platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation
- interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was
- achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include
- the title page of each document and bias the remaining page selection to those with figures or tables. The latter
- was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many
- figures and tables a given page contains.\\n$^{3}$https://arxiv.org/'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.03036576882004738, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''940f11c1-5028-4dd4-9515-781f1b9cdc2a'',
- ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
- the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
- is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
- we obtain accuracy ranges.'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''eba516e5-277f-487b-bcb9-3caea945ac54'',
- ''content'': ''Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header,
- % of Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple
- inter-annotator mAP @ 0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 66-76.
- Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP @ 0.5-0.95
- (%).Sci = 98-100. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator
- mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.015625,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''63d5d7a0-e9e6-4258-9c76-d97689acffb0'', ''content'':
- ''0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header,
- triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table,
- % of Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @ 0.5-0.95 (%).All = 77-81.
- Table, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Man
- = 83-86. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-99. Table, triple'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.015384615398943424, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''48c416f5-db1e-47c9-9f5c-0caf7b36e568'',
- ''content'': ''Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption,
- % of Total.Val = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator
- mAP @ 0.5-0.95 (%).Fin = 40-61. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 86-92. Caption, triple
- inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-99. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 95-99.
- Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- ='', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.01515151560306549, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''f931ae1e-5413-4b26-a8cc-14ce5ce7bdc1'',
- ''content'': ''inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
- = 59-76. Section-header, Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test
- = 15.77. Section-header, % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).All =
- 83-84. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator
- mAP @ 0.5-0.95 (%).Man = 90-92. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-95. Section-header,
- triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None,
- ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'':
- 0.014925372786819935, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''b8056bae-cc3b-4674-a7df-44d918eb4aea'',
- ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
- Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
- inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
- triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
- 85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
- (%).Ten'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'', ''document_title'': None, ''document_uri'':
- ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'', ''score'': 0.014705882407724857,
- ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''917de523-f069-49fa-ad71-1f0886db460c'', ''content'':
- ''185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
- List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @ 0.5-0.95
- (%).Fin = 74-83. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator
- mAP @ 0.5-0.95 (%).Sci = 97-97. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 81-85. List-item, triple
- inter-annotator mAP @ 0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @'', ''document_id'': ''b6b9012d-56d5-4aba-a9c7-e8ab027b40db'',
- ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-fix-tool-calls/tests/data/doclaynet.pdf'',
- ''score'': 0.014285714365541935, ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_4k3t2n10
- - content: null
- reasoning: 'We got list of chunks with labels info. The element types mentioned in that chunk: Caption, Footnote,
- Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also maybe others like
- Affiliation but not part of 11 class labels. So answer list unique types. Let''s list: Caption, Footnote, Formula,
- List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. Also mention that these are the
- 11 distinct class labels. Provide answer and program summarizing retrieval of labels. Let''s write program: search
- for that chunk, extract from content using regex to find labels. Use regex pattern r''\b[_\{]([A-Za-z-]+)[_\}]''
- or simpler from known labels list. But we can hardcode extraction: search patterns like ''$_{Caption}$'', etc. We''ll
- collect unique.'
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n"}'
- name: execute_code
- id: call_jxgy76dr
- type: function
- - content: '{"code":"results = await search(\"11 class labels\" , limit=5)\nlabels=set()\nfor r in results:\n import
- re\n matches=re.findall(r''\\b_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(labels)\n","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''re''","success":false}'
- role: tool
- tool_call_id: call_jxgy76dr
- - content: null
- reasoning: Cannot import re. Use regex_* functions. Use regex_findall.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"results = await search(''11 class labels'', limit=5)\nlabels=set()\nfor r in results:\n matches
- = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(sorted(labels))\n"}'
- name: execute_code
- id: call_oyc93cba
- type: function
- - content: '{"code":"results = await search(''11 class labels'', limit=5)\nlabels=set()\nfor r in results:\n matches
- = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\nprint(sorted(labels))\n","stdout":"[''Caption'',
- ''Footnote'', ''Formula'', ''List-item'', ''Page-header'', ''Picture'', ''Section-header'', ''Table'', ''Text'',
- ''Title'', ''footer'']\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_oyc93cba
- - content: '{"answer":"The documents mention eleven distinct document element types (class labels). They are:\n1. Caption\n2.
- Footnote\n3. Formula\n4. List‑item\n5. Page‑header\n6. Page‑footer\n7. Picture\n8. Section‑header\n9. Table\n10.
- Text\n11. Title\n\nThese labels are used as the primary structural categories for annotating content in the dataset.\n","program":"import
- json\n\n# Retrieve the relevant chunk that lists the 11 class labels\nresults = await search(\"11 class labels\",
- limit=5)\n\n# Extract the label names using a regex that captures words inside \\{\\}\\_\nlabels = set()\nfor r
- in results:\n matches = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'', r[''content''])\n labels.update(matches)\n\n#
- Convert to a sorted list for consistent display\nlabel_list = sorted(labels)\n\n# Print the labels in a numbered
- list (JSON friendly)\noutput = \"\\n\".join(f\"{i+1}. {name}\" for i, name in enumerate(label_list))\nprint(output)\n"}'
- reasoning: 'It extracted ''footer'' separately. But Page-footer might be footer? Keep list: Caption, Footnote, Formula,
- List-item, Page-header, Page-footer (footer), Picture, Section-header, Table, Text, Title. Provide answer.'
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '1592'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to wrap answer in execute_code? We need to output JSON via tool call? Must return tool call. Use
- function final_result. We'll provide final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"The documents mention eleven distinct document element types (class labels). They are:\n1.
- Caption\n2. Footnote\n3. Formula\n4. List‑item\n5. Page‑header\n6. Page‑footer\n7. Picture\n8. Section‑header\n9.
- Table\n10. Text\n11. Title\n\nThese labels are used as the primary structural categories for annotating content
- in the dataset.\n","program":"import json\n\n# Retrieve the relevant chunk that lists the 11 class labels\nresults
- = await search(\"11 class labels\", limit=5)\n\n# Extract the label names using a regex that captures words
- inside \\{\\}\\_\nlabels = set()\nfor r in results:\n matches = await regex_findall(r''_{\\s*([A-Za-z-]+)\\s*}'',
- r[''content''])\n labels.update(matches)\n\n# Convert to a sorted list for consistent display\nlabel_list
- = sorted(labels)\n\n# Print the labels in a numbered list (JSON friendly)\noutput = \"\\n\".join(f\"{i+1}.
- {name}\" for i, name in enumerate(label_list))\nprint(output)\n"}'
- name: final_result
- id: call_sgf8xfq2
- index: 0
- type: function
- created: 1772548439
- id: chatcmpl-437
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 308
- prompt_tokens: 5737
- total_tokens: 6045
+ completion_tokens: 255
+ prompt_tokens: 3658
+ total_tokens: 3913
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_get_chunk.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_get_chunk.yaml
index 2746a19c..d3e714ed 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_get_chunk.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_search_and_get_chunk.yaml
@@ -48,7 +48,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7738'
+ - '7755'
content-type:
- application/json
host:
@@ -196,6 +196,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -222,29 +241,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '625'
+ - '574'
content-type:
- application/json
parsed_body:
@@ -253,25 +254,24 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need to search for "animals".
+ reasoning: We need to search for "animals".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(\"animals\", limit=5)\nprint(len(results))\nfor r in results:\n print(r[''document_title''],
- r[''score''])\nprint(results[:5])"}'
+ arguments: '{"code":"results = await search(\"animals\", limit=10)\nimport json\nprint(json.dumps(results, indent=2))"}'
name: execute_code
- id: call_4vtaz637
+ id: call_1b91tqi2
index: 0
type: function
- created: 1772549356
- id: chatcmpl-763
+ created: 1772626980
+ id: chatcmpl-326
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 72
- prompt_tokens: 1736
- total_tokens: 1808
+ completion_tokens: 55
+ prompt_tokens: 1692
+ total_tokens: 1747
status:
code: 200
message: OK
@@ -324,7 +324,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8713'
+ - '8333'
content-type:
- application/json
host:
@@ -471,24 +471,39 @@ interactions:
- content: Search for content about animals and tell me which document it came from.
role: user
- content: null
- reasoning: Need to search for "animals".
+ reasoning: We need to search for "animals".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(\"animals\", limit=5)\nprint(len(results))\nfor r in results:\n print(r[''document_title''],
- r[''score''])\nprint(results[:5])"}'
+ arguments: '{"code":"results = await search(\"animals\", limit=10)\nimport json\nprint(json.dumps(results, indent=2))"}'
name: execute_code
- id: call_4vtaz637
+ id: call_1b91tqi2
type: function
- - content: '{"code":"results = await search(\"animals\", limit=5)\nprint(len(results))\nfor r in results:\n print(r[''document_title''],
- r[''score''])\nprint(results[:5])","stdout":"1\nAnimal Facts 0.016393441706895828\n[{''chunk_id'': ''69018e55-d141-4f97-89bb-7d7a19ffc273'',
- ''content'': ''The quick brown fox jumps over the lazy dog.'', ''document_id'': ''7a3553dd-19ff-4660-8ff1-23c7e9b3aa89'',
- ''document_title'': ''Animal Facts'', ''document_uri'': None, ''score'': 0.016393441706895828, ''page_numbers'':
- [], ''headings'': None}]\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"animals\", limit=10)\nimport json\nprint(json.dumps(results, indent=2))","stdout":"","stderr":"ModuleNotFoundError:
+ No module named ''json''","success":false}'
role: tool
- tool_call_id: call_4vtaz637
+ tool_call_id: call_1b91tqi2
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -515,55 +530,37 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1314'
+ - '577'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: stop
+ - finish_reason: tool_calls
index: 0
message:
- content: '{"answer":"The search result for the query “animals” came from the document titled **\"Animal Facts\"**.
- The relevant chunk excerpted is: “The quick brown fox jumps over the lazy dog.”.","program":"import asyncio\n\nasync
- def main():\n # Search for the term \"animals\" in the knowledge base\n results = await search(\"animals\",
- limit=5)\n # Print the number of results found\n print(f\"Found {len(results)} result(s).\")\n # Output
- the source document for each result\n for r in results:\n print(f\"Chunk ID: {r[''chunk_id'']}\")\n print(f\"Document
- ID: {r[''document_id'']}\")\n print(f\"Document Title: {r[''document_title'']}\")\n print(f\"Score:
- {r[''score'']:.6f}\")\n print(f\"Excerpt: {r[''content''][:200]}\\n\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())"}'
- reasoning: The only result is chunk about fox and dog from document Animal Facts. Provide answer and program that
- runs search.
+ content: ''
+ reasoning: We can't import json. Need to use print with default. Just print results.
role: assistant
- created: 1772549361
- id: chatcmpl-58
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"animals\", limit=10)\nprint(results)"}'
+ name: execute_code
+ id: call_v8mqb6kj
+ index: 0
+ type: function
+ created: 1772626981
+ id: chatcmpl-834
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 265
- prompt_tokens: 2001
- total_tokens: 2266
+ completion_tokens: 54
+ prompt_tokens: 1806
+ total_tokens: 1860
status:
code: 200
message: OK
@@ -576,7 +573,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '9903'
+ - '77'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - animals
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: c1YDOcck2zxYaqa8Lu0RvZMWKDr+LgA9RY1TPUhmIThQTQ89fxacPGMbT7xvkjg84j2HuGR5IL1XDFs9PJcGu2WjXD1IfUK9MCJEvd7UDbznYuO8a4ePPLSlJr3uFrg8dXIQPVLN/LxYsMy8lZhJvcv/tTziShi7FAH7vNRolLynnDI9iQJFvMw3yzsv+IW8K+qGPKQDzbvNEDQ8OZeGvXe9CbvLghS9goL1PCLKBrsMoiS801mqvG7Zu7pL6q08BPDOvDa2t7ytn/E7jbqAO/5u4zv0vpC8fg9MPOg5ALwweZw9//z2u4C4sry8+QY9JVs7vHW417z+MJW8MpyCvLdtI7vWWUK8/6e5vE/Qjr03cpM8hc8qvL4mUrwZNlQ9twpFvJKmYbwoJeI8RmCIvB9MvrrhUgE9d8DMOyOm0Tyqi588Mv3vOXbuXjsnhTw98ZkgPUfRnjwJG7I8qUa2O3EAVby6Kho7BUqfOy8e3Tr3C8q7boGoPAhQA7w5MgI8sl2Gu3uwkrxTHZu7LtBjPARIHru1AqS7b0iCPRRC4rt4sCI8rUUfvZx3i7xh/ok71hhFvErUmLzmuHG8uvHOu8XJmjwxy928QYU0vHWhx7s2oK+69fI+PVbHdLvzYpY8+Tr6u7/eejwcxtM7D9ayPOQNizu4xmS8M2kyvOLMTbxpvqi8dJJ1PD4EkTz1gcu8dR5xPCvUsrsP4js6jnyEvIMYl7s8LKW6krH4uog3JTy8rRy6I1umuxgbDDj29nE8YH1UvEwqFTllQmm8E6i/uCy9mTyuCwu8l3sTPKS5K7yHvyw8T0G4PKphTTxkSus7c5FxvNQVBrs2yc87qg5mu2irBzxWauE7KgXiuwDrHT2LqX08RES7OweuCr2ZxSq86pz8u9efAb3aVUK8N4q/vKS9TzyABJu8SWutvJc8qTubCbq8cpDNOworSbnHaRq8J2STu5vINbtG35i81WbBu9ZzgzyYQF+6rBYTu3nrIzzrKxo8YkZVunQDbb0LGYQ7+i3bOraTtbxz3M67SvCzvNAkw7xI61Q8U2VVvLTK8Dy08ZK8anhFvMd+8ryrNvS7EYAYPMcARbvSFMk4/yGcvKIJEjo0F4w4ML8uPHgPPrzfEnK8yLbivD77JDzYaCO8PbanvLqqyryBGwM9dCzsPLFRKzsYILk8B8LDu3Q9zjsmUB+947KFOoUJeTzVPF08QBugPAVaEDupSXm7V7svPE8sBrxnL+y7pcW5u2ZOjDuhbNg76T80uzAqSbyb9ou8uVRvvFgmdLxE/we83uzPu3/j3jvME7e8ZbqJuPzWa7xIhhK8F9QHvNjvQryKONM6fjN/PJSh0rw1Vdu8HwMRPJkqqrxVaiK9K6KRvJ6kpDy0u2S7pz4nPLl/j7sJZOe7bPvMO9o2CrxexAS8BuOwvCBXqzwW1Z68guAtPTDYvDsva9I7uk8APOJWeLsW6Ia8Mo8nu92ot7tPd/U79bn/PFo067y4hC+8AwcHO0NNmDy8OMs7YdKKPADoSLwIpNc8yTGPvC7BiDvWn0A83tBhOz3iFD069J+7E4UJOx/zQTwTl6E7z1ECuqEjmTyymte88NOKvP/UJLyg7Qm71f6oPONZSzzj5/o8QiKluu9WjDwmDGy9ntspOvBcgLvnkwq9KHWeOxC5DDt+ef675FEOvUkshLwGRaw7idvmvEw7lLw41/I7AA3TvGRO67v40Q87R6iUO3d08brhTig8GOmFPJxt4jtTnaY9yiXgvDvAbzwxpni8XqhsvMYI/bt84d+8ngS8u+XyNjw50vg8cHNlPBMdJDw7h2C8eMOhPEYbDL0wrSy8e82CPOcUILtfwKI7T1sxvYx/xLy42hm8rlfmvFMRkTsaNZI7092zOEcV1Tu7K9Q6/P5pPGryOrwGqIq8fGF/vPtlpTobDz67/5I0O/nzQTxMnyU8REEKvAtBHD0lli29Fth1PLvSLbzvsFm8/OfxPBqYAbzfWDY8jXjCvIaIyDwO9hu91fKru//OQLts6Ms8/NS9PC38E7z8UlY8NZiEuyEFvzy4wKE7l0EzO++iS73SBG67nBpeu+iNCDzk9Su7g8UjPdhBJDwjvNM8Uv4jPNmYAT2Qfa482Kelu8Zn17wuUz68QR8HvScN1LwLIeQ7C/DbvPoSl7zgyZI9RjgLPKo6oDuNYh68BMq1PCzkNTz25WC8lL2DPCsBwzvM9ac8msTqvHp59rw6a38804B6PAM1FLzvQhy7Qv8bPBgcNj27YBs4esxmvJUCzjuFg6e7wQ2qvFPFFzpwPEm8GcXLPPrk8zz/qjy8B1g4O9lWYLsPPo68tFvCul/04DibiKC662Q/vCtaLTy0GYA8ykMUvBNJ8rtV19I7pCgOvAW5aTzTyia94HlIvHQAn7zlpJu7z4ALPB14Pr12aZ08DcoqPNT65bzJmKC7h2R2vD7psb0qHZw83YCouuVKpLrFfGK8n7z+vPJu57yktWS8yFPKPFuCbDtywxu9QL0fvQPluLwb5208K/+lughPsDsVL2k8A+iIO86T4TuYT5s87ANtuf477TmtrXo8yAvZPKyxUzxjlos8Mt/4O3LXkjzizRq924JZvPSfhTwJ/167sp32OxRmvzz01+g7PSqAOVPHMT2MsLi8qidkvJ36g7t6U9a5EffQPPEllrnBBJ+8OovXvBvywTzResM8GfIaPMGlBzz8/HQ8S7atPC2tOzwnxIu7viFIvVEbAj1STS69IIZKvJcrTLuNd4w6l+SYu2nMpbww62C8vRsNPMVZhrzAVQW9kFWTPAWDuzxUtR29invJOx2eKjznnsq8yCeWvJaPwLy1R5o7CoH+vAvENzwLy3Q88zxSPZrFojyyjIY6OmsAPfyQH7wYYQs9TdvXO4Wz1juuwUM8UR1hPObAQ7scFBS9WtKhPH9RNrxejxC8VoxEO+CKqbw5Wr08s161O7jXMDz8N6g8daHCvJ1jfDy1S9a87EtUPG/Bhrxk5ei7EE/KvK0FRr0dGvu7dn87vLyYwLziCd87FYVzu5X0gTwquaA8gqRtvJMH1LwUHRW9TxAsPIJbkbw66US9Ckbju7AjaTzIHqE7Xxk0POEsqrwPhiY9+IdDPGqRIj0dP2W7WYiAPLyCI7x/4Ki80i4iPc7zTLxkNTS9TECJPIed7LqWHDi62uCpu14QUDrjjg87eyStPNnaG70AMI+8WdHLPIUnO7zoMBC7koxxvNXuuDxmGrS7FuMlPDxHjLwQ8fS8ZZCcu/Gq6zxSnn88/ergvPbiA716d1y7XbC6vF1ADr1zXdu8fM4ovGhgibuWNxE9+eOJPEn+sL0RGrE7GU+GPKmMmTx1kRy9dlKWOtYiYruo2Dg9xGgHvHGeIjyGd1y8zK02vZreYT2g4DU7xljuuyYV8Dzd/C+8ybMxPPbwAbwjZQU9dodYvPjIDb3F6hu8nJP6uj6CzrsqwSI7UngWvIzAgrwel4M8f+xAPPYArjwuy5M8wvhrPMusiDzvCeQ68uvkvCptgjx0QBs8wzI5u6qBIbxNEr03LbnMvOqhbLyrLAM8fdiJOxQ2dzyfC+Y7k1Gbu91ZoLvETTK72/lbvZOjBzy13n27qsA1PD2MajudRTS9ejbau/gzWjxfya28unhcOlbfwDxuMxA8zFlnun0GaTuNcrA8PxxUPFTwULyhHxa8MsP0Oz2JPzw+dSa8M8DMO62B0jwCVxe99MFZvP/aibw145M4HexiPMDOCLiRkCW8/3X+PEkArjxKHkU9k1eePKBNQD3GY6y8yFD4vDXxFL2XSOA7Rh65O0eDpLw1cyK91WCJvHU/wruXIvy6Q7tqu54uMTwNK8K8xvNtuxsn+Lv6YSo8Hb6RPHOtZrtkjy28bGMTPeCoODzwHmK7lB/zuwMa5jwJ1a686+pUPFZ/sDvEo6w8f/9wPM1oJLuv1AG6COfLu3OUZ7srO/08q3cKveA11rruS4C9OEW0uXazLLr43mQ83ZWcPFt1cDx8QHq5Y69LvD+pb7wxmmW610VbPB5KM7y+R948gUwEvNmY6rx04Cu8SGTduZqt7ryPc6e8V6J9OwSQgDy7dvM6eKcIvWHKqDyRGQG9ociFPJjmWrwV7xw8GcqhPO/pczzvh5i77OZqvIMJEz0As8y8MT5KPAWo9rxZHBO8dYQJvCTnj7wnZxq8Vq1qvJS8Cr2Oc8y8W4oovYKWmTwU8Ys9XrbNvDPNN71g/vg80MdVvI5/kbzgwLa8Vbvqu46VYDwmz9e7njxRvIlQt7u0Tzs9EaVXu5MiHT0VZ0q7QnRZPLCxdbx6b648/nr2ugYyFLxXjLK7x+3iO/E0IDy7lYa8q+rEuPq1kby/DZE7kk2PPBxR7zxLaqg8hayJu+mVAzzpaoi8lhajO3ZDarzfUIe7VWBVPN1Fuju2giY8faDAvMcAajyvI9e7sT7tvGrPpLwOnh88Z+HuvFsFB71d3qO8FJEnPMIqJzxVQW+7gwehvE68mjsasLq8P1zVOnFBhbyOQS06MdbwPCySLDzfgEk9rxGHPA+0sTiCXBE8ZWRVPP/PzTwSeIQ8xI9dvMlMEjw301w8pdkHvX/qU7zXWAC9t+xEPC8rUbw32Da8rJf+O7m0LzzcSKa8LXKWPFGK/zxNoAQ9UgIOvKX9izyI5Gi8G4bQPB05HbyfKpI8vkSCvEJ7mjz+is47tfnPPH78jzxdcHu8NuNVPLBHObrrWlK8RvsMPJS17bxDrMQ8Nm6Du2DcA71gPxs7ldrcvGe7trzRT2g8jf0bve6W3zyz8da8B+CMvFaIxjtvEQg8MiMrPNHF5DwYOSO89AcCvRSio7yItFc8OEVnu/j2tLwjqrK8+t77POSkA7zwZ0m9xisqPFbkOTwmTLk8SuCHvJwh6rusvy08lKYkvYJkCb0K8g+9XQGzPeU+WrwuHpQ8UDg3ure1Z701HOi7Xpx6vHFpnTqi1iM8BHIFO4Vhs7y1Vhc8tvnpurdek7wzHgM90iFLvGmI/rvTO9+7enDAOlJccj0vNg687eYLPdskgzwK0Y68NSYJPEkdzjvRNpE7IyQtOopWurx91h28H0xAPPnsv7ylYJk8xI+wPKr3hrzBEoQ8iXh6O5Qz7rtuUGS6U3uwO4WBZLxDzD48EXumPI8CejuViDS8r+3MuwoX9zskuC68XwzKPKjBJzyTMiU8bnLFPHIBjrywLfE8hObePMPp3DzqMM+8Ht0tvLZ+UjxMmmO88w17vP1j87w1+ro7XyaOvNcznjzqHui7GkCwvN090bvG3Uw8/+51vPUIGjwcZuA8SBGMu1y67Lsh3/E6Msz7u/L1O714u4w8crU2PPJVvTtZCRE7ngi3vMyu3jxYFKC8vNr0PEIM7Lx/F3+8a3wLPIlq0LziHsW6L3MUuRiZgDzlLci7E0AMvFNfF70w0B89A2TkvGDN2TtE5pM8g1nRO5Y/SDx4PDO9hu9CPbjCirxzIf68xNfQuymHFDwxvJ285thevAfnFD2jx7k828rwvPhGwjt6y627UBwqOplLFrhW1c68RwJxvLXLhrxc3xI7HE8APGzWWDwhUTU7jiydO0gHWLwXGDK8BJ7HvANu9Dz9bue7H73cu0m11btGiGi7HUlqPMG2RTzDWPY8u8NqPLb2UjzOyPW8zqYIvBbgb7vwS8w8U38LvEqxnDu3vne8pb61vH/CCT3zWYi8KO2YPBVLOTi3WEw63nKxOvwQ/7tuH2k6vR9BvZ6nt7sdQ0m9TH6JvAzqrDr4CGa8QiGIvBmbrTuaPB27z2axPEB8nrwU5i8875EYPUNVhzxL/5i85fiCvCrQtzwjrx49uR/QuVBhY7z4Y2m74cynPI+BO7wk3DQ9Bh/2usemTbxBO2K8LUsWPMA3Cb0gcrm7ydnlO1UuRjuBJtQ80aDfuPO2FLzWbgK8YUzLPJYmnby2jJE86HaXu50VgrxAxlq8CxHfvLLEGblDiD+7jn4rvG7iWrxrMXk8XoyPu9iA6zzvVVi7Vs0dvV64DjwWAik8dPsmPHMRJDzbnvA6qbNuPZx83rtRjDe7DQecO9J1ajxgZoA8Ykl0vPd+rju0cwO9zjxsPDy2gTsnQmk8+cvyuxqmjDxdhZc8S3Q0PCmC/ryGCWM8e7LqOsvhiDz4LAg8XV5zvBrkfzwyBZQ7y0s8vF7Y1DyGPtS80J8UO54ImLy75PY8cz3YvEBP9Ty5K566BANAPaiiqDtL7PC8fCq+PEy/zrvTibK7RRiZu1CnWbzep+Q8I1YXPdEAWDz4ZD286A0NvZDJuzwUxyg7HKMYPUFoybxvgKa8+NnEu4rsIT1i31o8teZvvMIffDyhVOc8Al/9uwUChLyi9i68eK4muyairruM1OO80uRLvEQXY7uc4L28crY6vD9J7bxBuFa7pDfou4qImbxGxyS8Xuitu8At/zz+Riq69aQ2PVV987xNTUM8FoQ0PE/wuTwcOyO89N7GOiSBqjwQ2oU4Mda2PHv1Fjus6oe84rWTOwlFYjy50jK8B3KPO6EV8jtndIC8Lay6PCGeBjxlH7y8sRB1vCnYTzyw4te7KMkVPR5dAb2Btw08bLvOvGrB87xsZ5s8ewWfPCdFjrvU73I8rhYPOyLjNDx62HY9lFZuOrDKJbzl4u67Rja8u/XE8LsMp2m8RvlSvHX8nLxmMGA8WsJAO3YcYLw9fBw8g4PmOoUUWrv9iD48qGFHPMwLubuftHy8qogpvNSdzLqZZ6m7bkAPPJxNyTpKpom5itAkOxZKGTwIi5o876QvPJSMUbzYD4Y78HQTvGsAhzyrYja9Xjfhu8ZZobzy6aE8yz0sPbPGnTmX1qi7x4IMvQeqqLzwJvW7b+hEPahlgjuO0QC9F+4jPOwYDzzQAmC8QaNsvEUVIrw9C928jZlWO9i6Ab2G06e6dp5NvMHyi7ykoqc66vkbvNZD+Lu+qRk8nuPsvEZiyzs9GcM7QEL5vOUHpbwqsCs8PrUIvD/GfbwTl6A7ujKkPHUpGDxqT4y8jh4bvFKPiTxAy748XrqEPGF9vTtVCGs8svDXPELCnjycxZC8RO9yPG5vILz32sk7QSAHPT04izulpaE8n8YLvMc7Cj2w0uC4s49cvOIQwzw7Ws48tYEHPU4S1bwSdHK8LhtwvOp8P7wsTks8m6OFu3knarwDQz+9/wAkPfnxijzAOEu82D+6u1st47rDKKc8EtCcPDzvXjx9oRg8f428PGLnaTpHkI88mQ2lPIN6WTwlFdC8dQcLPCu/XrwTU467M4BLutI5Yrrhs/s61yE3PMHw0TyMJxM8HFmwPHBkNrxMtJQ6sykSPBWWC7wFKoa8O2P7vFbspLyYhQG9gSElvPHhr7y7GQq8EmKDutCp1TtmQW48aA7dvPOtVzs31rg8/k0LPU+ggDpogLA7mtOdvOn1Dj2xRBS8meyEO2k6fzxdNoa87uqTvP01bTzqEig8fb12O4uYuLrEqII80CmSu3tqELxt92k8c5fNPFI/a7xcVPK8I9a8PCypVj3Y0qq6tVcNvTEAE706lpe8TJyEPIUsrbxTyKK8Ki1GPBIpJjxeV2E7EFHIvDV497sQE/G7v8K9u7johjxI4JI8wMikvOF+5rsDgp889RA8u9LuCbxNGSq8eyoBPFkokjx90AQ9H8jQPAX8JD0Q/BW7dMU/vAViI71NZpE8W7gMvQZjmbwm2mO87wnuOdLezTtqZro87q8MO71onryPjTS8m8oNPc2uQTxuZYw7+wGVO0PgjLoiGAU8lpKbPMbVpbuEjFm8Wy/dugbE+jsLlKw77Jc4PDsuwLxZgjC8i1Pgu47InzxWsQS9WDJKvP4fJLxwELS7akYgvDW8iLsMrPM8UcFrPKe54LyBTZM7KYWmvBITtDysKwc96jjzOxbb8zsis1m9Av54u3TEl7wKkh68mQq2PEDefTzoSX88uRDxvDuOL7w28ps7hfdQPQ01FzyBXIO8v0jrugRFMr2CTNW8vhI5vP3AvLwEOHy8nZ0BPLl297ydfC29FOLHPKoM7rz2wK48saPzuzpVUTum4Mo7omSwPDy9V7vgViq8rIWCPG7POLue2uU6n4Tqu0SAybvak4a8MU9svcHIFbz+Akm6kNaCPJCaBTxIa4Q8tzTnO9RDWDxsoWC8EHmjOyKKy7vwXRo9Th52vJ6KPjtWQj66jG34uzB6mroLTNK8qgbWPKtoBrxNyoE829H+OnC9jjzRw5Y8ZYlFvLUED7xS+PM8Onbpu6CwxzznbCe8CPsHPSEIJjspBDe8DXogvaP35TyfrQM9CHIxPGbKbbzOluw7LsmCvBQCbjpDuKC80/OKvEBtMDxhK4M7/hiqvDp2OT18Pwe8csoQPFc7G70Ik2e7uM1JPAYmQDy4hDC8doncu3S+6TsMXJI8ZN8DuxEOfrxHrTM7bXJHvLqghDzG9fe7cpWKPMmvirzWSwQ7PxM2PI010DuLaFm8aWTBO5X3ET0LigU4O7mTvO7lh7lIBii9lpzyvKJIyDx8Lke8WhhSPKkpYLpR8oi7vLE5O+/uWTwbNfW8tFrqvFAXETz+jcm86ENivJlcOLsGkRQ743Z6PLc4zjsydc68DzvkPFJLybw6OLe8JQ7NPCHwrjxZwco4MScZPZFofDyBp227UEH8u8610TyIuec8cuM3vDw1CD1K4I+8cuihu/+C9zyk9Mk7goDDPM5siLwML228EUchvOj+ML1lnjI9vY8nvHf+q7xZaAG9tTppOTI0eDzKnEC9gCTiulz7gDuLAQc8KDlKPBSNfzxd1T06aHPOPMDRarwJK0083JxZucIOlzvq8mo8j6/HOR1eHLzgdC48I68wvCkgkrxZyTC804BoPdF7kzy7iky8YbpBvJzIurwpcKG8LcOmvJQudbv6brs7WAYQvXLeTTw3LI67g19fvGmALbzcMW+8iikpPBHLzLwGY+y7SodUu0wcUbxBxhi9PCx6ujp/Izwyrum8YEgavGI6Hzwmj467TOfTu1lzQbvjVC28r1t4u5ifHT2GEKK8BNVxPAC9BL3lRYi82QVNvFTDDTyfdQq9/oLcPGjmozwKikA8Z2m8PAaGIjzuF3i8nF3wvKDV/LyxRxo786dWPGfwvTzF6da7xYSYOjCOdjvEwhE5odoDvPlqibwtcRM7eX2+PIV/gDyOBO07baHcvDSKRTxFtQy8JlT5PMhmNz0pCV08txWKvD/aNT1TTwA9e4DMvO0whTuyQjm9/7ZWu1HWK7u+lFg8KbJ7PHUyBLzT6S28bgPSvALx5TwkSea7HE3jO6AGPruljkm7QgVSu7X9BD3rilU8CynHu8fSZrvLX2Y8MI/ruzAUiL3SdYy7Xb2JO4MzmjwqkMU8lsntuxKevrw03Ba8OZdhu5ZSzLtQhyW99Mbiu2KkhztQoue89ITlPOwekjwGfoi8j3WHOimPyzsLeOs8pBxXvDy1+Dy4lIS8QJ+HvC0Shzx4lNK8ivlnu81yLrwBoAK7EvMGvaLV8rosyjy8alXlPBUVerzT70U8HUk2PFBDkby9/jW9lOCTPMfRuLiTnTY8lKjGO8rCET2YowG8D6zpPJZaMrzcufM8wzCfO7XPKb0nPHQ8f2ERO9g9dj01M1e7uYoMPGwbDzx5XPu7XRsHOsMomzzYHyY9NY0QPDRe4LxC5Ly8W7aBO1KSgbuQ9yw8XhQ6vIXRHrzXU/K7cckBve9C97suxee8hKXdvK7N6LxGzMG8OtwKu/4e0zgMX6k8h4EgOtQBNzykwNy8H2ZgO6UMrzrb/j48oqk3ukr00LygjMy6wmtJPBvbMT03VEm63RcSvEHQOTzHfNG8oJLJO/7g1rzx9GY5wbK6u76gAbwH86Q7PFtCOpa5XLwUnMy8BT/tu3KyzbzFYQ+6uaiAPDWXmTyTdlM6MeQwvMGWNjwqVaC7bg96PDNCrzuRCV88SV6SukZr3zz2yF+9v0T6OtZ8WbvQGaK8O1nIvF4hE71FTLS8pCu8PFR1ibtMxJU8/t8WPW+gELzoJiA8nsd3PIV0IzyX/6Q8JMfDO2jdjLq/LEc8jx1jPfdp9bpwrNC84MKdvGp8vzx7fjE8txWiutnEzbpa9so8uMFAOwtRU7vXCFi84QkEvCzbtLy0CUQ8dg5HveLOKrzTBxw70CkYPIIa7bhH0m68CQWQufG4KD0Wpcy790zqPGIK4jyPLsu8g7qLu/Hf6DyCHrU70p4QvPeSQz3wyqS8UnUOPAmNUzx+iH48PHIIPJ2axTwQUMI8LFmBPL39eTtVTJA7s3a+uwuJHrzU/6S8OVqwOwiDwzs1N+s7K+Tvu4oygzwf58Y8P+eJvF4v5DvkrTM7LHUmPf7MfDsGwcw8jHaIPDj8Gr2YzhM8bggAuc75FjzKqfG65h66vFQ2pbykvBS8epWcvII+FL2UxG88F0KCu0eAfzw5+lk8P84kPOCrgDxbFiS8tvDmvP5lWb1MBoA7grgyvPz1Iz1hSwy9WOOUPAUlwLxIbxI7TDUFO0P9trtlaXs8Nl+tvIhVjTuBrC88k7e1O3nxwbthixO9XhDpPARoI7xhJBm8flGAPAg64zzjrsa7wJ3pPBNHCL0E0Ge8B4BRPZcPHjt2+bu7Atd1vAI/17w0i908nyjDvDqtoDz9uBo8cEffuji8r7u25Qe9WAGbPND4Hz1d71w6RQ0FvfY4eLxm6Xu8ugieuryM1ryVtl28GRVjPFTGO73Fa4E8vrWVvHYsWr19NHC7jLxSO8t6wDtD+2s6NNmpvBhWkrt06P47ZQTAvDfKsrsg/2I7FeLnvKSI5Dz/IK08V5o2PMI6DzzvDbW6dbmgOttugTv5rhI7v13GPAWFAL3a9ha9RzsWvSjdFbxUpqy8niXKu0A4ijuNf626Ct7ZPN9o9bpjfgK987N/PKkyibu+Eac85L5Tu+mtrLxWVhM8ixkVuqrhDDtM2aK8W8dtvKmFrjxciRK86RBSu9+F7TxPXB29hs5FvNW4Nj2qEkK9u8vmOlufMLy4YJO8s/gIuhJFyDx8UNS7PxjBvPsCibx0LPy8rJwQPCFO9bx71IA8zTD7u0CN8zzWXJa8leaCvKvXvLwWKce8w2uZu70GcTyWsPm8KLF6vOudUjzqBHI8R959PKGo3rwgoOO6MbbCPJNhgzphOqc8fQRaPKxxdzwaw9m72Q5uvFP377wdrD+80KNnPC6VdjzwlYO8ewxyuuYP4TstjcY82jpDPEKFhzwOj8i7ANN1vGbDnbtSdcu8k4qzu9mTCrwwB5w79jISPcflhLpENbg7cFWJPGKejjyOHFk7s6DRvHUwjbz3eNQ8KbyHux+bi7txoPE7UC16O4DRzrxrSr07OPB4ul2lUbz/MoI8xOQbvOapyDyLUMq7a94TvJ8Mdjx/Dbm8YPC8PAUdErx7DRE7p7mSvHmKgTxqWya7Y98BPd6xBj1i+/S7Q4bUPImp0jxkaCo8tSO0PC46krwFy9e7izwTu7j3E7waksY7kmsYveCWkbwQ8oU6TbzEOwVXy7upnoe7DjIcOhGczTwZ/Le8RHc9PKy1e7w8x5G80UaqO2UmhLtS4R68j+lHvOPc3zwC7Si8FCKxvEv+qbyRGhw6A2r5umAIijwTAiG7b+JVvOR+J71XFuo8drTvuyR7jLtuBd86PAGNO2bKQDvaaA49hQ0fOwPGxDu/TB28lrlpO6Nl0btQ8qg8zFcFPV1bCDxMJaS80BnJuytGk7w5OKo8qWq4vDgqN7qX5ba8xMGIvI3ASzy6uM68oZYJPDzmbLspC7+7vXueu72G8Ly30+o8rkRCvCZfIrtk77k8MJR0vBQp0TvHG0w7AWobPffErjzOeOs7MttBvcsvTLqjQuU8d+CCPOVvsDzlu6a7l52KOqaqgDsOSA27MRXzuysLx7xls5o8n/iNPHKukDvyZ4A7/sH3u4qoEzysOOs7H8usvDR9sDvHrvq8UoruuiF7dDxvmFg8T7EMPUp5rjyYVsy7ODbbvL6nAb03pAe9qAsZvaPvpDxi/a45sbD3u5ARZzskugU9sbqXvOApl7nr6HM8DxhUPBEQYDyZOku8M1KhvLeYFTxg7uy8mGHQvC+YtbwmHAi9bhpDumH+87xroYw8K+azPFif5jtfSNC8oNLyOq3hcjwt3c07/DuqPGGj0DtWuCg7tM2cusitBzzPnaG8RpMUu0m+Nz0i70U8Xte8PBW4LrxWGvA8oFMIPO2q3TttXC48rsbeO50sW7xQHya8iy+IO4KowTqVZQu8yrJtO0EUtjy4IGM8bZLBPApPAzwL/va65ijsvOT1r7wr/nO8HTQbPEjImbvjHYq7grhUvZ5fWLy/oDo8VTMSu4T9CT1FYDo6OqzKu1DoCLzu6vs88r0MvGMXuTxopz27j1i7vPVKCz05/rm6xWg/vBEVDjwqdXS8RTMhPKQHozwc0n46uAn6vCQGKLy6GPo7yiboO9xR/Tzqchi8nsxMus5NYzskXqS8IUx8vGjejjtmRz88X6lYPMy9IDxdpRa9QXDnPAO0P7vhWJ87d5TdO5/3Aj3Fm3A8shTqvEi4BrzqJAW90D4KvFlQ/rwW5Qi9VPeBvOhRHry66K06g4fMO/IzRjy6pse8MAdvPPbD1rlv16g6zF9iOz6vwLvxF/y8pEnPPPVak7wAz1M8/TnGudGluLrhu2q6b8GmOw/bcDxPtbo8t8WNPDxazTyIkpi8tv2tvNC4kzxQtE48UzJAPHcghbuwDcs8qsArPBA4V7zSXgA9gKUyPASR+Dw5yvI8lXWhvFiogLxN42684aHFPI+TsrsgUk26w8i3vO3etryhT9I7o6I5vYZpHzxYmec5q9XaPFoUjjwnlZe77EU6u97gDz2dYx68t1NZPJMztjzJq6Y79wN3PIi3r7wVaY68N9ksvJJAhbtUEwE8AenWvP/WFL2GoJc80fc5vdus0rvuq9k7IFW2vOscubxE4SM9AYiIvKaAGzwB6Lw79p80PHKnsrxMve+8IEG1unCvVry+8HE8u/yEPEhNPzvdnsa7Pmr6PEq2sTzN4T+5gR4zvOYc4Dv2Nx08qSOMu9Xo6Ls21FO8r6niO+YgX7tIPps6ErrevLOydjst8S27IIJ+OiwLj7wf6cA7KXWBPEf97zwY6CA8MsVuPMbM3Lwd2ZU8ZCd9vEHys7tdxIW5ZR00PF9y6jxVz6u8mZtkPPxu1zq/w/q8tBWBPGzXKDxiR0Q7kI+lOyf03Lx7V5q6l+UuuusuELw8Gcu7EZ/zvKKQzTxsf5m8GF4RvG1FhrzxXhw9tQCaPFSJoznKl0S86zSePC29rbyEIL46JSZhPKrrx7vBbvy728zCvLMjkzxwhQK8Sy2NPIZGVbxIjxq8wiq3vF9fEr10VXG8PAR3PMi4+TtgFKI82iLKOwT/jbwxdZa77AzpO76rjTktVd+717xOPDk/LryANmS8nkc6uwWYVbqNrUg7dHwlPMAA17wwHxw8msNoO5jZf7wczU48slzYPCHHqzw+tLE7Zg6RPLV8q7xkmIS8z9VYvBc/GTs1lLy7zykLPOKssbyXDLO8TH7gummH8juPu5c5YiOIPHb8HL23zxY7bdSevGvltrvJKdI778T2O0REXjjKkci7tfzoO0mhHTwVxk28D/8huw==
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 2
+ total_tokens: 2
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '9130'
content-type:
- application/json
host:
@@ -723,40 +760,54 @@ interactions:
- content: Search for content about animals and tell me which document it came from.
role: user
- content: null
- reasoning: Need to search for "animals".
+ reasoning: We need to search for "animals".
role: assistant
tool_calls:
- function:
- arguments: '{"code":"results = await search(\"animals\", limit=5)\nprint(len(results))\nfor r in results:\n print(r[''document_title''],
- r[''score''])\nprint(results[:5])"}'
+ arguments: '{"code":"results = await search(\"animals\", limit=10)\nimport json\nprint(json.dumps(results, indent=2))"}'
name: execute_code
- id: call_4vtaz637
+ id: call_1b91tqi2
type: function
- - content: '{"code":"results = await search(\"animals\", limit=5)\nprint(len(results))\nfor r in results:\n print(r[''document_title''],
- r[''score''])\nprint(results[:5])","stdout":"1\nAnimal Facts 0.016393441706895828\n[{''chunk_id'': ''69018e55-d141-4f97-89bb-7d7a19ffc273'',
- ''content'': ''The quick brown fox jumps over the lazy dog.'', ''document_id'': ''7a3553dd-19ff-4660-8ff1-23c7e9b3aa89'',
+ - content: '{"code":"results = await search(\"animals\", limit=10)\nimport json\nprint(json.dumps(results, indent=2))","stdout":"","stderr":"ModuleNotFoundError:
+ No module named ''json''","success":false}'
+ role: tool
+ tool_call_id: call_1b91tqi2
+ - content: null
+ reasoning: We can't import json. Need to use print with default. Just print results.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"code":"results = await search(\"animals\", limit=10)\nprint(results)"}'
+ name: execute_code
+ id: call_v8mqb6kj
+ type: function
+ - content: '{"code":"results = await search(\"animals\", limit=10)\nprint(results)","stdout":"[{''chunk_id'': ''38a21aa0-bc2b-42a1-8b7e-3b3b58c400bc'',
+ ''content'': ''The quick brown fox jumps over the lazy dog.'', ''document_id'': ''3c76c7ee-2ff3-4344-9dad-f8dd7d0176f2'',
''document_title'': ''Animal Facts'', ''document_uri'': None, ''score'': 0.016393441706895828, ''page_numbers'':
[], ''headings'': None}]\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_4vtaz637
- - content: '{"answer":"The search result for the query “animals” came from the document titled **\"Animal Facts\"**.
- The relevant chunk excerpted is: “The quick brown fox jumps over the lazy dog.”.","program":"import asyncio\n\nasync
- def main():\n # Search for the term \"animals\" in the knowledge base\n results = await search(\"animals\",
- limit=5)\n # Print the number of results found\n print(f\"Found {len(results)} result(s).\")\n # Output
- the source document for each result\n for r in results:\n print(f\"Chunk ID: {r[''chunk_id'']}\")\n print(f\"Document
- ID: {r[''document_id'']}\")\n print(f\"Document Title: {r[''document_title'']}\")\n print(f\"Score:
- {r[''score'']:.6f}\")\n print(f\"Excerpt: {r[''content''][:200]}\\n\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())"}'
- reasoning: The only result is chunk about fox and dog from document Animal Facts. Provide answer and program that
- runs search.
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
+ tool_call_id: call_v8mqb6kj
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -783,60 +834,33 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1233'
+ - '795'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: Need to output JSON via tool call, not plain. Use function final_result.
+ content: '{"answer":"The content about animals was retrieved from the document titled \"Animal Facts\" (document_id:
+ 3c76c7ee-2ff3-4344-9dad-f8dd7d0176f2).","program":"results = await search(\"animals\", limit=10)\nfor res in results:\n doc_title
+ = res.get(''document_title'', ''Unknown Title'')\n doc_id = res.get(''document_id'')\n chunk_id = res.get(''chunk_id'')\n print(f\"Chunk
+ {chunk_id} from document ''{doc_title}'' (ID: {doc_id})\")"}'
+ reasoning: We got one. Let's output answer.
role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"The search result for the query “animals” came from the document titled *\"Animal Facts\"*.
- The relevant chunk excerpted is: “The quick brown fox jumps over the lazy dog.”.","program":"import asyncio\n\nasync
- def main():\n results = await search(\"animals\", limit=5)\n print(f\"Found {len(results)} result(s).\")\n for
- r in results:\n print(f\"Chunk ID: {r[''chunk_id'']}\")\n print(f\"Document ID: {r[''document_id'']}\")\n print(f\"Document
- Title: {r[''document_title'']}\")\n print(f\"Score: {r[''score'']:.6f}\")\n print(f\"Excerpt:
- {r[''content''][:200]}\\n\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())"}'
- name: final_result
- id: call_y5fedbyn
- index: 0
- type: function
- created: 1772549367
- id: chatcmpl-895
+ created: 1772626985
+ id: chatcmpl-701
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 231
- prompt_tokens: 2247
- total_tokens: 2478
+ completion_tokens: 134
+ prompt_tokens: 2033
+ total_tokens: 2167
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_semantic_analysis_with_llm.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_semantic_analysis_with_llm.yaml
index c329ed7d..0d6235f8 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_semantic_analysis_with_llm.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_semantic_analysis_with_llm.yaml
@@ -131,7 +131,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7766'
+ - '7783'
content-type:
- application/json
host:
@@ -279,6 +279,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -305,29 +324,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '697'
+ - '692'
content-type:
- application/json
parsed_body:
@@ -336,25 +337,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
index: 0
type: function
- created: 1772549382
- id: chatcmpl-974
+ created: 1772626991
+ id: chatcmpl-380
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 91
- prompt_tokens: 1741
- total_tokens: 1832
+ completion_tokens: 87
+ prompt_tokens: 1697
+ total_tokens: 1784
status:
code: 200
message: OK
@@ -367,7 +368,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '8487'
+ - '86'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - quarterly update
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: 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
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 4
+ total_tokens: 4
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '8754'
content-type:
- application/json
host:
@@ -514,21 +555,42 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -555,29 +617,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '668'
+ - '1255'
content-type:
- application/json
parsed_body:
@@ -586,25 +630,30 @@ interactions:
index: 0
message:
content: ''
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment
+ or simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in
- (d.get(''title'') or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content =
+ chunk[''content'']\n prompt = f\"Classify the sentiment of the following quarterly update as ''positive'',
+ ''negative'', or ''mixed''.\\nUpdate: {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if
+ sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''],
+ sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
index: 0
type: function
- created: 1772549384
- id: chatcmpl-612
+ created: 1772626995
+ id: chatcmpl-477
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 86
- prompt_tokens: 1907
- total_tokens: 1993
+ completion_tokens: 221
+ prompt_tokens: 1966
+ total_tokens: 2187
status:
code: 200
message: OK
@@ -617,7 +666,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '9165'
+ - '10880'
content-type:
- application/json
host:
@@ -764,35 +813,66 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
- content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment or
+ simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n","stdout":"","stderr":"Expected
+ name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 503..530, value: Name(ExprName { node_index:
+ NodeIndex(None), range: 503..519, id: Name(\"sentiment_counts\"), ctx: Load }), slice: Name(ExprName { node_index:
+ NodeIndex(None), range: 520..529, id: Name(\"sentiment\"), ctx: Load }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_y0fbrvbg
+ tool_call_id: call_ufxsa9nq
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -819,29 +899,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '564'
+ - '1254'
content-type:
- application/json
parsed_body:
@@ -850,24 +912,30 @@ interactions:
index: 0
message:
content: ''
- reasoning: Maybe titles None. Let's list all docs.
+ reasoning: 'Syntax error: can''t subscript sentiment_counts with variable. Use getattr? Actually Counter supports
+ update. Just do sentiment_counts[sentiment] +=1 works. The error shows misuse of attribute? maybe sentiment variable
+ name conflict. Let''s rename var.'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_ceq2v75l
+ id: call_w58ovnlw
index: 0
type: function
- created: 1772549385
- id: chatcmpl-892
+ created: 1772627001
+ id: chatcmpl-945
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 58
- prompt_tokens: 2061
- total_tokens: 2119
+ completion_tokens: 225
+ prompt_tokens: 2472
+ total_tokens: 2697
status:
code: 200
message: OK
@@ -880,7 +948,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '9821'
+ - '12881'
content-type:
- application/json
host:
@@ -1027,48 +1095,91 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
- content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment or
+ simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n","stdout":"","stderr":"Expected
+ name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 503..530, value: Name(ExprName { node_index:
+ NodeIndex(None), range: 503..519, id: Name(\"sentiment_counts\"), ctx: Load }), slice: Name(ExprName { node_index:
+ NodeIndex(None), range: 520..529, id: Name(\"sentiment\"), ctx: Load }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_y0fbrvbg
+ tool_call_id: call_ufxsa9nq
- content: null
- reasoning: Maybe titles None. Let's list all docs.
+ reasoning: 'Syntax error: can''t subscript sentiment_counts with variable. Use getattr? Actually Counter supports
+ update. Just do sentiment_counts[sentiment] +=1 works. The error shows misuse of attribute? maybe sentiment variable
+ name conflict. Let''s rename var.'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_ceq2v75l
+ id: call_w58ovnlw
type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript { node_index:
+ NodeIndex(None), range: 420..436, value: Name(ExprName { node_index: NodeIndex(None), range: 420..431, id: Name(\"sent_counts\"),
+ ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 432..435, id: Name(\"sen\"), ctx: Load
+ }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_ceq2v75l
+ tool_call_id: call_w58ovnlw
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -1095,29 +1206,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '799'
+ - '1196'
content-type:
- application/json
parsed_body:
@@ -1126,25 +1219,29 @@ interactions:
index: 0
message:
content: ''
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
+ reasoning: It appears the sandbox forbids subscript assignment? maybe need to use sent_counts.update? Instead of
+ bracket. Use sent_counts.update([sen]). Also to count, use add. Let's just use dict.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_liuy6nph
+ id: call_bnpwbfo4
index: 0
type: function
- created: 1772549389
- id: chatcmpl-838
+ created: 1772627006
+ id: chatcmpl-676
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 163
- prompt_tokens: 2247
- total_tokens: 2410
+ completion_tokens: 219
+ prompt_tokens: 2969
+ total_tokens: 3188
status:
code: 200
message: OK
@@ -1157,7 +1254,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '10814'
+ - '86'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - quarterly update
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: 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
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 4
+ total_tokens: 4
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '14556'
content-type:
- application/json
host:
@@ -1304,63 +1441,112 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
- content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment or
+ simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n","stdout":"","stderr":"Expected
+ name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 503..530, value: Name(ExprName { node_index:
+ NodeIndex(None), range: 503..519, id: Name(\"sentiment_counts\"), ctx: Load }), slice: Name(ExprName { node_index:
+ NodeIndex(None), range: 520..529, id: Name(\"sentiment\"), ctx: Load }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_y0fbrvbg
+ tool_call_id: call_ufxsa9nq
- content: null
- reasoning: Maybe titles None. Let's list all docs.
+ reasoning: 'Syntax error: can''t subscript sentiment_counts with variable. Use getattr? Actually Counter supports
+ update. Just do sentiment_counts[sentiment] +=1 works. The error shows misuse of attribute? maybe sentiment variable
+ name conflict. Let''s rename var.'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_ceq2v75l
+ id: call_w58ovnlw
type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript { node_index:
+ NodeIndex(None), range: 420..436, value: Name(ExprName { node_index: NodeIndex(None), range: 420..431, id: Name(\"sent_counts\"),
+ ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 432..435, id: Name(\"sen\"), ctx: Load
+ }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_ceq2v75l
+ tool_call_id: call_w58ovnlw
- content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
+ reasoning: It appears the sandbox forbids subscript assignment? maybe need to use sent_counts.update? Instead of bracket.
+ Use sent_counts.update([sen]). Also to count, use add. Let's just use dict.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_liuy6nph
+ id: call_bnpwbfo4
type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"ModuleNotFoundError: No module named ''collections''","success":false}'
role: tool
- tool_call_id: call_liuy6nph
+ tool_call_id: call_bnpwbfo4
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -1387,29 +1573,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '764'
+ - '1048'
content-type:
- application/json
parsed_body:
@@ -1418,25 +1586,28 @@ interactions:
index: 0
message:
content: ''
- reasoning: no import json needed. Let's just get content.
+ reasoning: collections not available? maybe only json. Use dict manually.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor
+ r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify
+ sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = await llm(pr).strip().lower()\n if
+ sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_80syxghd
+ id: call_hcn1qab7
index: 0
type: function
- created: 1772549393
- id: chatcmpl-688
+ created: 1772627011
+ id: chatcmpl-88
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 150
- prompt_tokens: 2565
- total_tokens: 2715
+ completion_tokens: 192
+ prompt_tokens: 3378
+ total_tokens: 3570
status:
code: 200
message: OK
@@ -1449,7 +1620,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '11847'
+ - '86'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - quarterly update
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: 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
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 4
+ total_tokens: 4
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '16070'
content-type:
- application/json
host:
@@ -1596,78 +1807,133 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
- content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment or
+ simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n","stdout":"","stderr":"Expected
+ name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 503..530, value: Name(ExprName { node_index:
+ NodeIndex(None), range: 503..519, id: Name(\"sentiment_counts\"), ctx: Load }), slice: Name(ExprName { node_index:
+ NodeIndex(None), range: 520..529, id: Name(\"sentiment\"), ctx: Load }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_y0fbrvbg
+ tool_call_id: call_ufxsa9nq
- content: null
- reasoning: Maybe titles None. Let's list all docs.
+ reasoning: 'Syntax error: can''t subscript sentiment_counts with variable. Use getattr? Actually Counter supports
+ update. Just do sentiment_counts[sentiment] +=1 works. The error shows misuse of attribute? maybe sentiment variable
+ name conflict. Let''s rename var.'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_ceq2v75l
+ id: call_w58ovnlw
type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript { node_index:
+ NodeIndex(None), range: 420..436, value: Name(ExprName { node_index: NodeIndex(None), range: 420..431, id: Name(\"sent_counts\"),
+ ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 432..435, id: Name(\"sen\"), ctx: Load
+ }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_ceq2v75l
+ tool_call_id: call_w58ovnlw
- content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
+ reasoning: It appears the sandbox forbids subscript assignment? maybe need to use sent_counts.update? Instead of bracket.
+ Use sent_counts.update([sen]). Also to count, use add. Let's just use dict.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_liuy6nph
+ id: call_bnpwbfo4
type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"ModuleNotFoundError: No module named ''collections''","success":false}'
role: tool
- tool_call_id: call_liuy6nph
+ tool_call_id: call_bnpwbfo4
- content: null
- reasoning: no import json needed. Let's just get content.
+ reasoning: collections not available? maybe only json. Use dict manually.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r
+ in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify
+ sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = await llm(pr).strip().lower()\n if
+ sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_80syxghd
+ id: call_hcn1qab7
type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r in results:\n chunk
+ = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify sentiment as ''positive'',
+ ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = await llm(pr).strip().lower()\n if sen
+ not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"AttributeError: ''coroutine'' object has no attribute
+ ''strip''","success":false}'
role: tool
- tool_call_id: call_80syxghd
+ tool_call_id: call_hcn1qab7
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -1694,29 +1960,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '567'
+ - '1400'
content-type:
- application/json
parsed_body:
@@ -1725,24 +1973,31 @@ interactions:
index: 0
message:
content: ''
- reasoning: Short documents. Let's print content.
+ reasoning: 'await llm returns coroutine? need await llm? I used await correctly. The result is coroutine? maybe
+ llm returns str directly. Mist: need to use await llm(pr) but it''s inside for; correct. But error says coroutine
+ object has no attribute strip implying await not used. I wrote await llm(pr).strip() - but due to precedence,
+ await applies to llm(pr).strip() incorrectly. Need parentheses: (await llm(pr)).strip().'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor
+ r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify
+ sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = (await
+ llm(pr)).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]
+ = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''], sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_khz2vm8e
+ id: call_22vyxlej
index: 0
type: function
- created: 1772549394
- id: chatcmpl-336
+ created: 1772627017
+ id: chatcmpl-559
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 58
- prompt_tokens: 2934
- total_tokens: 2992
+ completion_tokens: 277
+ prompt_tokens: 3765
+ total_tokens: 4042
status:
code: 200
message: OK
@@ -1755,7 +2010,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '12400'
+ - '86'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - quarterly update
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: 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
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 4
+ total_tokens: 4
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '348'
content-type:
- application/json
host:
@@ -1764,1027 +2059,9 @@ interactions:
parsed_body:
messages:
- content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '802'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- index: 0
- type: function
- created: 1772549398
- id: chatcmpl-466
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 164
- prompt_tokens: 3054
- total_tokens: 3218
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '14012'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '783'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- index: 0
- type: function
- created: 1772549402
- id: chatcmpl-524
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 110
- prompt_tokens: 3544
- total_tokens: 3654
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '14978'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '947'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- index: 0
- type: function
- created: 1772549407
- id: chatcmpl-490
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 199
- prompt_tokens: 3767
- total_tokens: 3966
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '370'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: "Classify the overall sentiment of the following business update as positive, negative, or mixed.\nContent:
- The new product launch exceeded expectations. Sales grew 40% and customer feedback has been overwhelmingly positive.
- Team morale is at an all-time high.\nAnswer: "
+ Classify sentiment as 'positive', 'negative', or 'mixed'.
+ Update: Mixed results this quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year but customer retention increased.
+ Sentiment:
role: user
model: gpt-oss
reasoning_effort: low
@@ -2793,7 +2070,7 @@ interactions:
response:
headers:
content-length:
- - '315'
+ - '482'
content-type:
- application/json
parsed_body:
@@ -2801,18 +2078,19 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: Positive
- reasoning: Positive.
+ content: 'Sentiment: mixed'
+ reasoning: 'We classify overall sentiment: product quality improved (positive), marketing underperformed (negative),
+ revenue flat (neutral), retention increased (positive). Mixed.'
role: assistant
- created: 1772549407
- id: chatcmpl-85
+ created: 1772627018
+ id: chatcmpl-301
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 13
- prompt_tokens: 119
- total_tokens: 132
+ completion_tokens: 43
+ prompt_tokens: 113
+ total_tokens: 156
status:
code: 200
message: OK
@@ -2825,7 +2103,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '379'
+ - '341'
content-type:
- application/json
host:
@@ -2833,9 +2111,10 @@ interactions:
method: POST
parsed_body:
messages:
- - content: "Classify the overall sentiment of the following business update as positive, negative, or mixed.\nContent:
- We faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target
- by 15%. Several key employees left the company.\nAnswer: "
+ - content: |-
+ Classify sentiment as 'positive', 'negative', or 'mixed'.
+ Update: We faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by 15%. Several key employees left the company.
+ Sentiment:
role: user
model: gpt-oss
reasoning_effort: low
@@ -2844,7 +2123,7 @@ interactions:
response:
headers:
content-length:
- - '405'
+ - '388'
content-type:
- application/json
parsed_body:
@@ -2852,18 +2131,18 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: Negative
- reasoning: 'We need to classify overall sentiment: challenges, missed target, employees left. That''s negative.'
+ content: 'Sentiment: negative'
+ reasoning: We need to classify sentiment. The passage is negative. So 'negative'.
role: assistant
- created: 1772549408
- id: chatcmpl-764
+ created: 1772627019
+ id: chatcmpl-565
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 29
- prompt_tokens: 120
- total_tokens: 149
+ prompt_tokens: 116
+ total_tokens: 145
status:
code: 200
message: OK
@@ -2876,7 +2155,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '386'
+ - '332'
content-type:
- application/json
host:
@@ -2884,9 +2163,10 @@ interactions:
method: POST
parsed_body:
messages:
- - content: "Classify the overall sentiment of the following business update as positive, negative, or mixed.\nContent:
- Mixed results this quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat
- compared to last year but customer retention increased.\nAnswer: "
+ - content: |-
+ Classify sentiment as 'positive', 'negative', or 'mixed'.
+ Update: The new product launch exceeded expectations. Sales grew 40% and customer feedback has been overwhelmingly positive. Team morale is at an all-time high.
+ Sentiment:
role: user
model: gpt-oss
reasoning_effort: low
@@ -2895,7 +2175,7 @@ interactions:
response:
headers:
content-length:
- - '516'
+ - '376'
content-type:
- application/json
parsed_body:
@@ -2903,19 +2183,18 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: Mixed
- reasoning: 'We need to give overall sentiment classification: positive, negative, or mixed. The content has mixed
- results, some positive, some negative. Overall seems balanced but might lean slightly neutral. Probably mixed.'
+ content: 'Sentiment: **positive**'
+ reasoning: We need to label overall sentiment. It's all positive.
role: assistant
- created: 1772549409
- id: chatcmpl-491
+ created: 1772627020
+ id: chatcmpl-206
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 50
- prompt_tokens: 117
- total_tokens: 167
+ completion_tokens: 27
+ prompt_tokens: 115
+ total_tokens: 142
status:
code: 200
message: OK
@@ -2928,7 +2207,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '16422'
+ - '17967'
content-type:
- application/json
host:
@@ -3075,146 +2354,157 @@ interactions:
- content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
role: user
- content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
+ reasoning: Need to search for quarterly update documents.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
+ arguments: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n"}'
name: execute_code
- id: call_b39cif5o
+ id: call_uumnbbu0
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
+ - content: '{"code":"# Search for quarterly updates\nresults = await search(\"quarterly update\", limit=20)\nprint(len(results))\nfor
+ r in results[:5]:\n print(r[''score''], r[''chunk_id''], r.get(''document_title'', ''''))\n","stdout":"3\n0.032786883413791656
+ 427522ed-8eeb-49cb-aa42-7ff86e19b1ec Q3 Update\n0.032258063554763794 1fb9ca16-a348-43cb-ba36-b421b2326826 Q2 Update\n0.01587301678955555
+ b991414f-77c4-4101-bafc-1ed09376d7c4 Q1 Update\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_b39cif5o
+ tool_call_id: call_uumnbbu0
- content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
+ reasoning: So 3 updates. We need sentiment of each. We can get chunk content then use llm to classify sentiment or
+ simple regex. Use llm to classify.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n"}'
name: execute_code
- id: call_y0fbrvbg
+ id: call_ufxsa9nq
type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsentiment_counts
+ = Counter()\nanswers = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n content = chunk[''content'']\n prompt
+ = f\"Classify the sentiment of the following quarterly update as ''positive'', ''negative'', or ''mixed''.\\nUpdate:
+ {content}\\nSentiment:\" \n sentiment = await llm(prompt).strip().lower()\n if sentiment not in [''positive'',''negative'',''mixed'']:\n sentiment
+ = ''mixed''\n sentiment_counts[sentiment]+=1\n answers.append((chunk[''document_title''], sentiment))\nprint(sentiment_counts)\nprint(answers)\n","stdout":"","stderr":"Expected
+ name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 503..530, value: Name(ExprName { node_index:
+ NodeIndex(None), range: 503..519, id: Name(\"sentiment_counts\"), ctx: Load }), slice: Name(ExprName { node_index:
+ NodeIndex(None), range: 520..529, id: Name(\"sentiment\"), ctx: Load }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_y0fbrvbg
+ tool_call_id: call_ufxsa9nq
- content: null
- reasoning: Maybe titles None. Let's list all docs.
+ reasoning: 'Syntax error: can''t subscript sentiment_counts with variable. Use getattr? Actually Counter supports
+ update. Just do sentiment_counts[sentiment] +=1 works. The error shows misuse of attribute? maybe sentiment variable
+ name conflict. Let''s rename var.'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_ceq2v75l
+ id: call_w58ovnlw
type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen]+=1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript { node_index:
+ NodeIndex(None), range: 420..436, value: Name(ExprName { node_index: NodeIndex(None), range: 420..431, id: Name(\"sent_counts\"),
+ ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 432..435, id: Name(\"sen\"), ctx: Load
+ }), ctx: Store })","success":false}'
role: tool
- tool_call_id: call_ceq2v75l
+ tool_call_id: call_w58ovnlw
- content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
+ reasoning: It appears the sandbox forbids subscript assignment? maybe need to use sent_counts.update? Instead of bracket.
+ Use sent_counts.update([sen]). Also to count, use add. Let's just use dict.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen
+ = await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_liuy6nph
+ id: call_bnpwbfo4
type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nfrom collections import Counter\nsent_counts
+ = Counter()\nans = []\nfor r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr
+ = f\"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen =
+ await llm(pr).strip().lower()\n if sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts.update([sen])\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"ModuleNotFoundError: No module named ''collections''","success":false}'
role: tool
- tool_call_id: call_liuy6nph
+ tool_call_id: call_bnpwbfo4
- content: null
- reasoning: no import json needed. Let's just get content.
+ reasoning: collections not available? maybe only json. Use dict manually.
role: assistant
tool_calls:
- function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r
+ in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify
+ sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = await llm(pr).strip().lower()\n if
+ sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_80syxghd
+ id: call_hcn1qab7
type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r in results:\n chunk
+ = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify sentiment as ''positive'',
+ ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = await llm(pr).strip().lower()\n if sen
+ not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"","stderr":"AttributeError: ''coroutine'' object has no attribute
+ ''strip''","success":false}'
role: tool
- tool_call_id: call_80syxghd
+ tool_call_id: call_hcn1qab7
- content: null
- reasoning: Short documents. Let's print content.
+ reasoning: 'await llm returns coroutine? need await llm? I used await correctly. The result is coroutine? maybe llm
+ returns str directly. Mist: need to use await llm(pr) but it''s inside for; correct. But error says coroutine object
+ has no attribute strip implying await not used. I wrote await llm(pr).strip() - but due to precedence, await applies
+ to llm(pr).strip() incorrectly. Need parentheses: (await llm(pr)).strip().'
role: assistant
tool_calls:
- function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
+ arguments: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r
+ in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify
+ sentiment as ''positive'', ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = (await llm(pr)).strip().lower()\n if
+ sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n"}'
name: execute_code
- id: call_khz2vm8e
+ id: call_22vyxlej
type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
+ - content: '{"code":"results = await search(\"quarterly update\", limit=20)\nsent_counts = {}\nans = []\nfor r in results:\n chunk
+ = await get_chunk(r[''chunk_id''])\n txt = chunk[''content'']\n pr = f\"Classify sentiment as ''positive'',
+ ''negative'', or ''mixed''.\\nUpdate: {txt}\\nSentiment:\" \n sen = (await llm(pr)).strip().lower()\n if
+ sen not in [''positive'',''negative'',''mixed'']:\n sen=''mixed''\n sent_counts[sen] = sent_counts.get(sen,0)+1\n ans.append((chunk[''document_title''],
+ sen))\nprint(sent_counts)\nprint(ans)\n","stdout":"{''mixed'': 3}\n[(''Q3 Update'', ''mixed''), (''Q2 Update'',
+ ''mixed''), (''Q1 Update'', ''mixed'')]\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- - content: null
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the
- overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- => Positive\n63b7a740-5678-4347-8d7b-37f869c0f300 => Negative\n76498bad-c7f7-4c90-a4aa-4caf826c05df => Mixed\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qwo653xg
+ tool_call_id: call_22vyxlej
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -3241,1249 +2531,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '557'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Count.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)"}'
- name: execute_code
- id: call_s8351th6
- index: 0
- type: function
- created: 1772549416
- id: chatcmpl-550
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 59
- prompt_tokens: 4240
- total_tokens: 4299
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '17262'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- - content: null
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the
- overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- => Positive\n63b7a740-5678-4347-8d7b-37f869c0f300 => Negative\n76498bad-c7f7-4c90-a4aa-4caf826c05df => Mixed\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qwo653xg
- - content: null
- reasoning: Count.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)"}'
- name: execute_code
- id: call_s8351th6
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)","stdout":"","stderr":"Expected
- name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 81..90, value: Name(ExprName { node_index:
- NodeIndex(None), range: 81..87, id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None),
- range: 88..89, id: Name(\"v\"), ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_s8351th6
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '711'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Syntax error due to using f-strings? Actually using plain code fine. The error arises from code syntax?
- Let's just print using loop.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if
- v in counts:\n counts[v] += 1\nprint(counts)"}'
- name: execute_code
- id: call_jdvk05nf
- index: 0
- type: function
- created: 1772549419
- id: chatcmpl-386
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 94
- prompt_tokens: 4450
- total_tokens: 4544
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '18290'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- - content: null
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the
- overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- => Positive\n63b7a740-5678-4347-8d7b-37f869c0f300 => Negative\n76498bad-c7f7-4c90-a4aa-4caf826c05df => Mixed\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qwo653xg
- - content: null
- reasoning: Count.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)"}'
- name: execute_code
- id: call_s8351th6
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)","stdout":"","stderr":"Expected
- name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 81..90, value: Name(ExprName { node_index:
- NodeIndex(None), range: 81..87, id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None),
- range: 88..89, id: Name(\"v\"), ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_s8351th6
- - content: null
- reasoning: Syntax error due to using f-strings? Actually using plain code fine. The error arises from code syntax?
- Let's just print using loop.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if
- v in counts:\n counts[v] += 1\nprint(counts)"}'
- name: execute_code
- id: call_jdvk05nf
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if v in
- counts:\n counts[v] += 1\nprint(counts)","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript
- { node_index: NodeIndex(None), range: 105..114, value: Name(ExprName { node_index: NodeIndex(None), range: 105..111,
- id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 112..113, id: Name(\"v\"),
- ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_jdvk05nf
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '597'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: It seems syntax not allowed? Maybe the sandbox disallows dictionary indexing? Strange. Let's instead
- collect list.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"vals = list(sentiments.values())\nprint(vals)"}'
- name: execute_code
- id: call_6emhxixc
- index: 0
- type: function
- created: 1772549421
- id: chatcmpl-67
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 56
- prompt_tokens: 4704
- total_tokens: 4760
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '18836'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- - content: null
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the
- overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- => Positive\n63b7a740-5678-4347-8d7b-37f869c0f300 => Negative\n76498bad-c7f7-4c90-a4aa-4caf826c05df => Mixed\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qwo653xg
- - content: null
- reasoning: Count.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)"}'
- name: execute_code
- id: call_s8351th6
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)","stdout":"","stderr":"Expected
- name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 81..90, value: Name(ExprName { node_index:
- NodeIndex(None), range: 81..87, id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None),
- range: 88..89, id: Name(\"v\"), ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_s8351th6
- - content: null
- reasoning: Syntax error due to using f-strings? Actually using plain code fine. The error arises from code syntax?
- Let's just print using loop.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if
- v in counts:\n counts[v] += 1\nprint(counts)"}'
- name: execute_code
- id: call_jdvk05nf
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if v in
- counts:\n counts[v] += 1\nprint(counts)","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript
- { node_index: NodeIndex(None), range: 105..114, value: Name(ExprName { node_index: NodeIndex(None), range: 105..111,
- id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 112..113, id: Name(\"v\"),
- ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_jdvk05nf
- - content: null
- reasoning: It seems syntax not allowed? Maybe the sandbox disallows dictionary indexing? Strange. Let's instead collect
- list.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"vals = list(sentiments.values())\nprint(vals)"}'
- name: execute_code
- id: call_6emhxixc
- type: function
- - content: '{"code":"vals = list(sentiments.values())\nprint(vals)","stdout":"","stderr":"NameError: name ''sentiments''
- is not defined","success":false}'
- role: tool
- tool_call_id: call_6emhxixc
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '1767'
+ - '1385'
content-type:
- application/json
parsed_body:
@@ -4491,462 +2543,25 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: |-
- {"answer":"There were 3 quarterly updates. The sentiment analysis shows:\n- **1 positive** quarter (the Q1 update).\n- **1 negative** quarter (the Q2 update).\n- **1 mixed** quarter (the Q3 update).",
- "program":"# Consolidated script to analyze quarterly update sentiments\n\n# 1. Define document IDs for the quarterly updates\nq_ids = [\n 'e3940914-d6f7-4455-889a-79c595be1760', # Q1\n '63b7a740-5678-4347-8d7b-37f869c0f300', # Q2\n '76498bad-c7f7-4c90-a4aa-4caf826c05df' # Q3\n]\n\n# 2. Retrieve contents of each document\nqs = []\nfor doc_id in q_ids:\n content = await get_document(doc_id)\n qs.append((doc_id, content))\n\n# 3. Classify sentiment of each update using the LLM\nsentiments = {}\nfor doc_id, content in qs:\n prompt = (\n \"Classify the overall sentiment of the following business update as \"\n \"positive, negative, or mixed.\\nContent: %s\\nAnswer:\" % content\n )\n sentiment = await llm(prompt)\n sentiments[doc_id] = sentiment.strip()\n\n# 4. Count the results\ncount = {\"Positive\":0, \"Negative\":0, \"Mixed\":0}\nfor s in sentiments.values():\n if s in count:\n count[s] += 1\n\n# 5. Return the counts (this example prints them; in practice you could return a dict)\nprint(\"Sentiment counts:\", count)\n"}
- reasoning: Seems the previous code didn't persist variables across calls. So need one single script that does all.
+ content: '{"answer":"All three quarterly updates (Q1, Q2, Q3) were classified as ''mixed'' sentiment by the LLM.
+ Therefore, 0 quarters were positive, 0 negative, and 3 quarters were mixed.","program":"# Analyze sentiment of
+ quarterly updates\nasync def analyze_qtr_sentiments():\n results = await search(\"quarterly update\", limit=20)\n sent_counts
+ = {}\n answers = []\n for r in results:\n chunk = await get_chunk(r[''chunk_id''])\n txt =
+ chunk[''content'']\n prompt = (\n \"Classify sentiment as ''positive'', ''negative'', or ''mixed''.\\n\"\n f\"Update:
+ {txt}\\n\"\n \"Sentiment:\"\n )\n sen = (await llm(prompt)).strip().lower()\n if
+ sen not in [''positive'', ''negative'', ''mixed'']:\n sen = ''mixed''\n sent_counts[sen] = sent_counts.get(sen,
+ 0) + 1\n answers.append((chunk[''document_title''], sen))\n print(\"Sentiment counts: \", sent_counts)\n print(\"Detailed
+ results: \", answers)\n\n# Run the analysis\nawait analyze_qtr_sentiments()\n"}'
role: assistant
- created: 1772549432
- id: chatcmpl-62
+ created: 1772627032
+ id: chatcmpl-852
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 435
- prompt_tokens: 4807
- total_tokens: 5242
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '20479'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: Analyze the sentiment of each quarterly update. How many quarters were positive, negative, and mixed?
- role: user
- - content: null
- reasoning: Need search for quarterly updates. Likely documents titled "Quarterly Update". We'll list docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly
- update'' in d[''title''].lower():\n print(d[''title''], d[''id''])"}'
- name: execute_code
- id: call_b39cif5o
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nprint(len(docs))\nfor d in docs:\n if ''quarterly update''
- in d[''title''].lower():\n print(d[''title''], d[''id''])","stdout":"3\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_b39cif5o
- - content: null
- reasoning: We got 3 documents but titles not printed? maybe title None. let's fetch each.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])"}'
- name: execute_code
- id: call_y0fbrvbg
- type: function
- - content: '{"code":"docs = await list_documents(limit=100)\nfor d in docs:\n if ''quarterly update'' in (d.get(''title'')
- or '''').lower():\n print(''Found'', d[''id''])","stdout":"","stderr":"","success":true}'
- role: tool
- tool_call_id: call_y0fbrvbg
- - content: null
- reasoning: Maybe titles None. Let's list all docs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])"}'
- name: execute_code
- id: call_ceq2v75l
- type: function
- - content: '{"code":"docs = await list_documents(limit=20)\nfor d in docs:\n print(d[''id''], d[''title''])","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- Q1 Update\n63b7a740-5678-4347-8d7b-37f869c0f300 Q2 Update\n76498bad-c7f7-4c90-a4aa-4caf826c05df Q3 Update\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_ceq2v75l
- - content: null
- reasoning: We have Q1, Q2, Q3 updates. Need sentiment per update. Get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_liuy6nph
- type: function
- - content: '{"code":"import json\nqs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"","stderr":"ModuleNotFoundError:
- No module named ''json''","success":false}'
- role: tool
- tool_call_id: call_liuy6nph
- - content: null
- reasoning: no import json needed. Let's just get content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')"}'
- name: execute_code
- id: call_80syxghd
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\n print(doc_id, len(content.split()))\nprint(''done'')","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- 23\n63b7a740-5678-4347-8d7b-37f869c0f300 25\n76498bad-c7f7-4c90-a4aa-4caf826c05df 22\ndone\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_80syxghd
- - content: null
- reasoning: Short documents. Let's print content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_khz2vm8e
- type: function
- - content: '{"code":"for doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"","stderr":"NameError:
- name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_khz2vm8e
- - content: null
- reasoning: Need to keep qs variable. Let's rerun combined.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')"}'
- name: execute_code
- id: call_v5t3zs08
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n content
- = await get_document(doc_id)\n qs.append((doc_id, content))\nfor doc_id, content in qs:\n print(''Doc'',doc_id)\n print(content)\n print(''---'')","stdout":"Doc
- e3940914-d6f7-4455-889a-79c595be1760\nThe new product launch exceeded expectations. Sales grew 40% and customer
- feedback has been overwhelmingly positive. Team morale is at an all-time high.\n---\nDoc 63b7a740-5678-4347-8d7b-37f869c0f300\nWe
- faced significant challenges this quarter. Supply chain issues caused delays, and we missed our revenue target by
- 15%. Several key employees left the company.\n---\nDoc 76498bad-c7f7-4c90-a4aa-4caf826c05df\nMixed results this
- quarter. While product quality improved, marketing campaigns underperformed. Revenue was flat compared to last year
- but customer retention increased.\n---\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_v5t3zs08
- - content: null
- reasoning: Now sentiment. Use llm to classify each content.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall
- sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_4c3q413c
- type: function
- - content: '{"code":"sentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the overall sentiment
- of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer: \")\n sentiments[doc_id]
- = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"","stderr":"NameError: name ''qs'' is not defined","success":false}'
- role: tool
- tool_call_id: call_4c3q413c
- - content: null
- reasoning: Recreate qs.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify
- the overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=\u003e'', resp.strip())"}'
- name: execute_code
- id: call_qwo653xg
- type: function
- - content: '{"code":"qs = []\nfor doc_id in [''e3940914-d6f7-4455-889a-79c595be1760'',''63b7a740-5678-4347-8d7b-37f869c0f300'',''76498bad-c7f7-4c90-a4aa-4caf826c05df'']:\n qs.append((doc_id,
- await get_document(doc_id)))\nsentiments = {}\nfor doc_id, content in qs:\n resp = await llm(f\"Classify the
- overall sentiment of the following business update as positive, negative, or mixed.\\nContent: {content}\\nAnswer:
- \")\n sentiments[doc_id] = resp.strip()\n print(doc_id, ''=>'', resp.strip())","stdout":"e3940914-d6f7-4455-889a-79c595be1760
- => Positive\n63b7a740-5678-4347-8d7b-37f869c0f300 => Negative\n76498bad-c7f7-4c90-a4aa-4caf826c05df => Mixed\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qwo653xg
- - content: null
- reasoning: Count.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)"}'
- name: execute_code
- id: call_s8351th6
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n counts[v]+=1\nprint(counts)","stdout":"","stderr":"Expected
- name, got Subscript(ExprSubscript { node_index: NodeIndex(None), range: 81..90, value: Name(ExprName { node_index:
- NodeIndex(None), range: 81..87, id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None),
- range: 88..89, id: Name(\"v\"), ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_s8351th6
- - content: null
- reasoning: Syntax error due to using f-strings? Actually using plain code fine. The error arises from code syntax?
- Let's just print using loop.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if
- v in counts:\n counts[v] += 1\nprint(counts)"}'
- name: execute_code
- id: call_jdvk05nf
- type: function
- - content: '{"code":"counts = {''Positive'':0,''Negative'':0,''Mixed'':0}\nfor v in sentiments.values():\n if v in
- counts:\n counts[v] += 1\nprint(counts)","stdout":"","stderr":"Expected name, got Subscript(ExprSubscript
- { node_index: NodeIndex(None), range: 105..114, value: Name(ExprName { node_index: NodeIndex(None), range: 105..111,
- id: Name(\"counts\"), ctx: Load }), slice: Name(ExprName { node_index: NodeIndex(None), range: 112..113, id: Name(\"v\"),
- ctx: Load }), ctx: Store })","success":false}'
- role: tool
- tool_call_id: call_jdvk05nf
- - content: null
- reasoning: It seems syntax not allowed? Maybe the sandbox disallows dictionary indexing? Strange. Let's instead collect
- list.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"vals = list(sentiments.values())\nprint(vals)"}'
- name: execute_code
- id: call_6emhxixc
- type: function
- - content: '{"code":"vals = list(sentiments.values())\nprint(vals)","stdout":"","stderr":"NameError: name ''sentiments''
- is not defined","success":false}'
- role: tool
- tool_call_id: call_6emhxixc
- - content: |-
- {"answer":"There were 3 quarterly updates. The sentiment analysis shows:\n- **1 positive** quarter (the Q1 update).\n- **1 negative** quarter (the Q2 update).\n- **1 mixed** quarter (the Q3 update).",
- "program":"# Consolidated script to analyze quarterly update sentiments\n\n# 1. Define document IDs for the quarterly updates\nq_ids = [\n 'e3940914-d6f7-4455-889a-79c595be1760', # Q1\n '63b7a740-5678-4347-8d7b-37f869c0f300', # Q2\n '76498bad-c7f7-4c90-a4aa-4caf826c05df' # Q3\n]\n\n# 2. Retrieve contents of each document\nqs = []\nfor doc_id in q_ids:\n content = await get_document(doc_id)\n qs.append((doc_id, content))\n\n# 3. Classify sentiment of each update using the LLM\nsentiments = {}\nfor doc_id, content in qs:\n prompt = (\n \"Classify the overall sentiment of the following business update as \"\n \"positive, negative, or mixed.\\nContent: %s\\nAnswer:\" % content\n )\n sentiment = await llm(prompt)\n sentiments[doc_id] = sentiment.strip()\n\n# 4. Count the results\ncount = {\"Positive\":0, \"Negative\":0, \"Mixed\":0}\nfor s in sentiments.values():\n if s in count:\n count[s] += 1\n\n# 5. Return the counts (this example prints them; in practice you could return a dict)\nprint(\"Sentiment counts:\", count)\n"}
- reasoning: Seems the previous code didn't persist variables across calls. So need one single script that does all.
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '1776'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to output JSON via tool call? Actually final response must be JSON. Use functions.final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"There were 3 quarterly updates. The sentiment analysis shows:\n- **1 positive** quarter
- (the Q1 update).\n- **1 negative** quarter (the Q2 update).\n- **1 mixed** quarter (the Q3 update).","program":"#
- Consolidated script to analyze quarterly update sentiments\n\n# Define document IDs for the quarterly updates\nq_ids
- = [\n ''e3940914-d6f7-4455-889a-79c595be1760'', # Q1\n ''63b7a740-5678-4347-8d7b-37f869c0f300'', #
- Q2\n ''76498bad-c7f7-4c90-a4aa-4caf826c05df'' # Q3\n]\n\n# Retrieve contents of each document\nqs = []\nfor
- doc_id in q_ids:\n content = await get_document(doc_id)\n qs.append((doc_id, content))\n\n# Classify
- sentiment of each update using the LLM\nsentiments = {}\nfor doc_id, content in qs:\n prompt = (\n \"Classify
- the overall sentiment of the following business update as \"\n \"positive, negative, or mixed.\\nContent:
- %s\\nAnswer:\" % content\n )\n sentiment = await llm(prompt)\n sentiments[doc_id] = sentiment.strip()\n\n#
- Count the results\ncount = {\"Positive\":0, \"Negative\":0, \"Mixed\":0}\nfor s in sentiments.values():\n if
- s in count:\n count[s] += 1\n\n# Output the counts\nprint(count)"}'
- name: final_result
- id: call_tyjck0dw
- index: 0
- type: function
- created: 1772549446
- id: chatcmpl-663
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 408
- prompt_tokens: 5010
- total_tokens: 5418
+ completion_tokens: 283
+ prompt_tokens: 4258
+ total_tokens: 4541
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_with_filter.yaml b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_with_filter.yaml
index b986f8db..e409ef10 100644
--- a/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_with_filter.yaml
+++ b/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_with_filter.yaml
@@ -128,7 +128,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '7698'
+ - '7715'
content-type:
- application/json
host:
@@ -276,6 +276,25 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -302,29 +321,11 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '519'
+ - '522'
content-type:
- application/json
parsed_body:
@@ -337,20 +338,20 @@ interactions:
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
+ arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))\n"}'
name: execute_code
- id: call_qqoyb2of
+ id: call_jsd3hga7
index: 0
type: function
- created: 1772548188
- id: chatcmpl-356
+ created: 1772626976
+ id: chatcmpl-987
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 44
- prompt_tokens: 1728
- total_tokens: 1772
+ completion_tokens: 46
+ prompt_tokens: 1684
+ total_tokens: 1730
status:
code: 200
message: OK
@@ -363,7 +364,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '8137'
+ - '8160'
content-type:
- application/json
host:
@@ -514,15 +515,34 @@ interactions:
role: assistant
tool_calls:
- function:
- arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
+ arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))\n"}'
name: execute_code
- id: call_qqoyb2of
+ id: call_jsd3hga7
type: function
- - content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))","stdout":"1\n","stderr":"","success":true}'
+ - content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))\n","stdout":"1\n","stderr":"","success":true}'
role: tool
- tool_call_id: call_qqoyb2of
+ tool_call_id: call_jsd3hga7
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Result from RLM agent execution.
+ name: RLMResult
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the user's question
+ type: string
+ program:
+ description: The final consolidated program
+ type: string
+ required:
+ - answer
+ - program
+ type: object
+ strict: true
+ type: json_schema
stream: false
tool_choice: auto
tools:
@@ -549,24 +569,6 @@ interactions:
type: object
strict: true
type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
@@ -581,270 +583,15 @@ interactions:
message:
content: '{"answer":"There is 1 document available in the knowledge base.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
role: assistant
- created: 1772548189
- id: chatcmpl-173
+ created: 1772626977
+ id: chatcmpl-959
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 40
- prompt_tokens: 1814
- total_tokens: 1854
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '8443'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
-
- You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside execute_code. Always execute code to answer questions; do not just describe what code would do.
-
- Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just call them with `await`:
- - results = await search("query") ✓ CORRECT
- - import search ✗ WRONG - will fail
- - results = search("query") ✗ WRONG - must use await
-
- ## Available Functions
-
- ### await search(query, limit=10) -> list[dict]
- Search the knowledge base using hybrid search (vector + full-text).
- Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
-
- ### await list_documents(limit=10, offset=0) -> list[dict]
- List available documents in the knowledge base.
- Returns list of dicts with keys: id, title, uri, created_at
-
- ### await get_document(id_or_title) -> str | None
- Get the full text content of a document by ID, title, or URI.
- Returns the document content as a string, or None if not found.
-
- ### await get_chunk(chunk_id) -> dict | None
- Get a specific chunk by its ID (from search results).
- Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
- Use this to retrieve full chunk details and metadata for citation.
-
- ### await get_docling_document(document_id) -> dict | None
- Get the full document structure as a dict (DoclingDocument format).
- Use `list_documents()` or search results to get document IDs first.
- - `texts`: list of text items, each with `text`, `label` (e.g. "title", "text", "section_header", "list_item"), and `prov` (provenance with page/bounding box)
- - `tables`: list of tables, each with `data` containing `grid` (list of rows, each row a list of cells with `text`), `num_rows`, `num_cols`
- - `pictures`: list of figures/images with metadata
- - `pages`: page dimensions and metadata
-
- ### await regex_findall(pattern, text) -> list[str]
- Find all non-overlapping matches of a regular expression pattern in text.
-
- ### await regex_sub(pattern, repl, text) -> str
- Replace all occurrences of a regular expression pattern with a replacement string.
-
- ### await regex_search(pattern, text) -> dict | None
- Search for the first match of a pattern. Returns a dict with keys: group, groups, start, end — or None if no match.
-
- ### await regex_split(pattern, text) -> list[str]
- Split text by a regular expression pattern.
-
- ### await llm(prompt) -> str
- Call an LLM directly with the given prompt. Returns the response as a string.
- Use this for classification, summarization, extraction, or any task where you
- already have the content and just need LLM reasoning.
-
- ## Pre-loaded Documents Variable
-
- If documents were pre-loaded for this session, a `documents` variable is available:
- ```python
- # documents is a list of dicts with keys: id, title, uri, content
- for doc in documents:
- print(doc['title'], len(doc['content']))
- ```
- Check if it exists with: `try: documents ... except NameError: ...`
-
- ## Available Python Features
-
- The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
-
- Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
-
- For pattern matching or text extraction, use the `regex_*` functions, string methods (`str.split`, `str.find`, `str.startswith`, `in` operator), or the `llm()` function.
-
- ## Strategy Guide
-
- 1. **Explore First**: Start by listing documents or searching to understand what's available. Document `title` is often None — use `uri` or `id` to identify documents instead.
- 2. **If get_document returns None**: Use `await list_documents()` to see available documents (check `uri` and `id`), or `await search()` to find relevant content.
- 3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
- 4. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
- 5. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
-
- ## Example Patterns
-
- ### Counting documents matching a condition
- ```python
- docs = await list_documents(limit=100)
- count = 0
- for doc in docs:
- content = await get_document(doc['id'])
- if content and 'keyword' in content.lower():
- count += 1
- print(f"Found in: {doc['title']}")
- print(f"Total: {count}")
- ```
-
- ### Extracting data with regex
- ```python
- numbers = []
- results = await search("financial data", limit=20)
- for r in results:
- amounts = await regex_findall(r'\$([\d,]+)', r['content'])
- for a in amounts:
- numbers.append(int(a.replace(',', '')))
- if numbers:
- print(f"Average: {sum(numbers) / len(numbers)}")
- ```
-
- ### Extracting tables from a document
- ```python
- docs = await list_documents(limit=10)
- for d in docs:
- doc = await get_docling_document(d['id'])
- if doc:
- tables = doc.get('tables', [])
- if tables:
- print(f"{d['title']}: {len(tables)} table(s)")
- for i, table in enumerate(tables):
- grid = table.get('data', {}).get('grid', [])
- for row in grid:
- cells = [cell.get('text', '') for cell in row]
- print(f" Table {i}: {cells}")
- ```
-
- ## Output Format
-
- Your final response MUST be valid JSON matching this exact schema:
- ```json
- {"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
- ```
-
- - `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
- - `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
-
- Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
-
- You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
- role: system
- - content: How many documents are available?
- role: user
- - content: null
- reasoning: Need to list documents.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- name: execute_code
- id: call_qqoyb2of
- type: function
- - content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))","stdout":"1\n","stderr":"","success":true}'
- role: tool
- tool_call_id: call_qqoyb2of
- - content: '{"answer":"There is 1 document available in the knowledge base.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: |-
- Execute Python code in a sandboxed interpreter.
-
- The code has access to haiku.rag functions (search, list_documents,
- get_document, get_chunk, llm).
-
- Use print() to output results.
-
- Structured result with success status, stdout, and stderr.
-
- name: execute_code
- parameters:
- additionalProperties: false
- properties:
- code:
- description: Python code to execute.
- type: string
- required:
- - code
- type: object
- strict: true
- type: function
- - function:
- description: Result from RLM agent execution.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the user's question
- type: string
- program:
- description: The final consolidated program
- type: string
- required:
- - answer
- - program
- type: object
- strict: true
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '620'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need to output JSON in a tool call. Use final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"answer":"There is 1 document available in the knowledge base.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
- name: final_result
- id: call_hli4bq9m
- index: 0
- type: function
- created: 1772548191
- id: chatcmpl-603
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 65
- prompt_tokens: 1865
- total_tokens: 1930
+ completion_tokens: 37
+ prompt_tokens: 1774
+ total_tokens: 1811
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_search_filter/test_research_graph_uses_search_filter.yaml b/tests/cassettes/test_search_filter/test_research_graph_uses_search_filter.yaml
index 1430d59f..02ce172d 100644
--- a/tests/cassettes/test_search_filter/test_research_graph_uses_search_filter.yaml
+++ b/tests/cassettes/test_search_filter/test_research_graph_uses_search_filter.yaml
@@ -88,7 +88,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '1730'
+ - '1652'
content-type:
- application/json
host:
@@ -99,8 +99,6 @@ interactions:
- content: |-
You are the research orchestrator planning the investigation.
- If a section is provided, use it to understand the conversation context.
-
Your task:
1. Analyze the original question
2. Propose the first question to investigate
@@ -128,13 +126,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Output from iterative planning step.
- name: final_result
- parameters:
+ name: IterativePlanResult
+ schema:
additionalProperties: false
properties:
is_complete:
@@ -153,42 +149,40 @@ interactions:
- is_complete
- reasoning
type: object
- type: function
+ strict: false
+ type: json_schema
+ stream: false
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '993'
+ - '934'
content-type:
- application/json
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: 'We need to propose first question to investigate. It should be specific: e.g., what animals? Might ask
- for types, categories. Probably "What are examples of animals and their classifications?" But standalone: maybe
- "What are the major categories of animals in biology?" Let''s choose that.'
+ content: |-
+ {
+ "is_complete": false,
+ "reasoning": "To begin a broad inquiry about animals, the most fundamental starting point is to understand the major taxonomic categories that define animal diversity. This will provide a clear framework for subsequent, more detailed questions.",
+ "next_question": "What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?"
+ }
+ reasoning: 'We need to propose first question: likely "What are some examples of animals?" but need concrete. Perhaps
+ ask "What are the main categories of animals?" Choose focused: "What are the major taxonomic groups of animals?"
+ Provide reasoning.'
role: assistant
- tool_calls:
- - function:
- arguments: '{"is_complete":false,"next_question":"What are the major taxonomic categories of animals in biological
- classification?","reasoning":"The user asked broadly about animals. To start, identify the main taxonomic
- groups (phylum, class, etc.) that define animal diversity."}'
- name: final_result
- id: call_rykigexw
- index: 0
- type: function
- created: 1769799538
- id: chatcmpl-517
+ created: 1772626907
+ id: chatcmpl-0
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 132
- prompt_tokens: 366
- total_tokens: 498
+ completion_tokens: 82
+ prompt_tokens: 313
+ total_tokens: 395
status:
code: 200
message: OK
@@ -201,7 +195,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '2870'
+ - '2910'
content-type:
- application/json
host:
@@ -254,34 +248,15 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What are the major taxonomic categories of animals in biological classification?
+ - content: What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -305,12 +280,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '535'
+ - '519'
content-type:
- application/json
parsed_body:
@@ -319,24 +315,24 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need to search.
+ reasoning: Need query.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"major taxonomic categories of animals in biological classification","limit":5}'
+ arguments: '{"query":"major taxonomic groups classify animals kingdoms phyla","limit":5}'
name: search_and_answer
- id: call_nj05050d
+ id: call_ufynikdb
index: 0
type: function
- created: 1769799540
- id: chatcmpl-928
+ created: 1772626908
+ id: chatcmpl-724
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 41
- prompt_tokens: 628
- total_tokens: 669
+ completion_tokens: 40
+ prompt_tokens: 555
+ total_tokens: 595
status:
code: 200
message: OK
@@ -349,7 +345,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '136'
+ - '124'
content-type:
- application/json
host:
@@ -358,7 +354,7 @@ interactions:
parsed_body:
encoding_format: base64
input:
- - major taxonomic categories of animals in biological classification
+ - major taxonomic groups classify animals kingdoms phyla
model: qwen3-embedding:4b
uri: http://localhost:11434/v1/embeddings
response:
@@ -369,7 +365,7 @@ interactions:
- chunked
parsed_body:
data:
- - embedding: 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
+ - embedding: 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
index: 0
object: embedding
model: qwen3-embedding:4b
@@ -389,7 +385,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '3362'
+ - '3384'
content-type:
- application/json
host:
@@ -442,53 +438,32 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What are the major taxonomic categories of animals in biological classification?
+ - content: What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?
role: user
- - content: |-
-
- Need to search.
-
+ - content: null
+ reasoning: Need query.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"major taxonomic categories of animals in biological classification","limit":5}'
+ arguments: '{"query":"major taxonomic groups classify animals kingdoms phyla","limit":5}'
name: search_and_answer
- id: call_nj05050d
+ id: call_ufynikdb
type: function
- content: |-
- [633a4240-bcd5-434a-bffb-63975abd3ee0] [rank 1 of 1]
+ [dedd022a-1771-4ea0-930b-4ada5a3f9060] [rank 1 of 1]
Source: "Cat Facts"
Type: text
Content:
Document about cats: Cats are small furry mammals that purr.
role: tool
- tool_call_id: call_nj05050d
+ tool_call_id: call_ufynikdb
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -512,12 +487,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '544'
+ - '536'
content-type:
- application/json
parsed_body:
@@ -526,24 +522,24 @@ interactions:
index: 0
message:
content: ''
- reasoning: Search not finding. Maybe refine.
+ reasoning: No relevant. Maybe need broader search.
role: assistant
tool_calls:
- function:
- arguments: '{"limit":5,"query":"kingdom phylum class order genus species animals taxonomy"}'
+ arguments: '{"limit":5,"query":"animal taxonomy kingdoms phyla major groups"}'
name: search_and_answer
- id: call_dtyqrnlk
+ id: call_qetyqik6
index: 0
type: function
- created: 1769799543
- id: chatcmpl-574
+ created: 1772626910
+ id: chatcmpl-276
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 45
- prompt_tokens: 742
- total_tokens: 787
+ completion_tokens: 43
+ prompt_tokens: 664
+ total_tokens: 707
status:
code: 200
message: OK
@@ -556,7 +552,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '127'
+ - '113'
content-type:
- application/json
host:
@@ -565,7 +561,7 @@ interactions:
parsed_body:
encoding_format: base64
input:
- - kingdom phylum class order genus species animals taxonomy
+ - animal taxonomy kingdoms phyla major groups
model: qwen3-embedding:4b
uri: http://localhost:11434/v1/embeddings
response:
@@ -576,14 +572,14 @@ interactions:
- chunked
parsed_body:
data:
- - embedding: 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
+ - embedding: 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
index: 0
object: embedding
model: qwen3-embedding:4b
object: list
usage:
- prompt_tokens: 11
- total_tokens: 11
+ prompt_tokens: 8
+ total_tokens: 8
status:
code: 200
message: OK
@@ -596,7 +592,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '3863'
+ - '3875'
content-type:
- application/json
host:
@@ -649,72 +645,49 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What are the major taxonomic categories of animals in biological classification?
+ - content: What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?
role: user
- - content: |-
-
- Need to search.
-
+ - content: null
+ reasoning: Need query.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"major taxonomic categories of animals in biological classification","limit":5}'
+ arguments: '{"query":"major taxonomic groups classify animals kingdoms phyla","limit":5}'
name: search_and_answer
- id: call_nj05050d
+ id: call_ufynikdb
type: function
- content: |-
- [633a4240-bcd5-434a-bffb-63975abd3ee0] [rank 1 of 1]
+ [dedd022a-1771-4ea0-930b-4ada5a3f9060] [rank 1 of 1]
Source: "Cat Facts"
Type: text
Content:
Document about cats: Cats are small furry mammals that purr.
role: tool
- tool_call_id: call_nj05050d
- - content: |-
-
- Search not finding. Maybe refine.
-
+ tool_call_id: call_ufynikdb
+ - content: null
+ reasoning: No relevant. Maybe need broader search.
role: assistant
tool_calls:
- function:
- arguments: '{"limit":5,"query":"kingdom phylum class order genus species animals taxonomy"}'
+ arguments: '{"limit":5,"query":"animal taxonomy kingdoms phyla major groups"}'
name: search_and_answer
- id: call_dtyqrnlk
+ id: call_qetyqik6
type: function
- content: |-
- [633a4240-bcd5-434a-bffb-63975abd3ee0] [rank 1 of 1]
+ [dedd022a-1771-4ea0-930b-4ada5a3f9060] [rank 1 of 1]
Source: "Cat Facts"
Type: text
Content:
Document about cats: Cats are small furry mammals that purr.
role: tool
- tool_call_id: call_dtyqrnlk
+ tool_call_id: call_qetyqik6
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -738,12 +711,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '345'
+ - '621'
content-type:
- application/json
parsed_body:
@@ -751,17 +745,19 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: I couldn't find relevant excerpts in the available documents.
+ content: '{"query":"What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?","answer":"I’m
+ sorry, but I don’t have enough information to answer that question based on the available records.","cited_chunks":[],"confidence":0.1}'
+ reasoning: No result. Probably no knowledge base. I'll state insufficient.
role: assistant
- created: 1769799544
- id: chatcmpl-821
+ created: 1772626912
+ id: chatcmpl-473
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 14
- prompt_tokens: 860
- total_tokens: 874
+ completion_tokens: 59
+ prompt_tokens: 795
+ total_tokens: 854
status:
code: 200
message: OK
@@ -774,326 +770,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '4086'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a search and question-answering specialist.
-
- Process:
- 1. Call search_and_answer with relevant keywords from the question.
- 2. Review the results ordered by relevance.
- 3. If needed, perform follow-up searches with different keywords (max 3 total).
- 4. Provide a concise answer based strictly on the retrieved content.
-
- The search tool returns results like:
- [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
- Source: "Document Title" > Section > Subsection
- Type: paragraph
- Content:
- The actual text content here...
-
- [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
- Source: "Another Document"
- Type: table
- Content:
- | Column 1 | Column 2 |
- ...
-
- Each result includes:
- - chunk_id in brackets and rank position (rank 1 = most relevant)
- - Source: document title and section hierarchy (when available)
- - Type: content type like paragraph, table, code, list_item (when available)
- - Content: the actual text
-
- Output format:
- - query: Echo the question you are answering
- - answer: Your concise answer based on the retrieved content
- - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
- - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
- IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
- Guidelines:
- - Base answers strictly on retrieved content - do not use external knowledge.
- - Use the Source and Type metadata to understand context.
- - If multiple results are relevant, synthesize them coherently.
- - If information is insufficient, say so clearly.
- - Be concise and direct; avoid meta commentary about the process.
- - Results are ordered by relevance, with rank 1 being most relevant.
- role: system
- - content: What are the major taxonomic categories of animals in biological classification?
- role: user
- - content: |-
-
- Need to search.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"query":"major taxonomic categories of animals in biological classification","limit":5}'
- name: search_and_answer
- id: call_nj05050d
- type: function
- - content: |-
- [633a4240-bcd5-434a-bffb-63975abd3ee0] [rank 1 of 1]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
- role: tool
- tool_call_id: call_nj05050d
- - content: |-
-
- Search not finding. Maybe refine.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"kingdom phylum class order genus species animals taxonomy"}'
- name: search_and_answer
- id: call_dtyqrnlk
- type: function
- - content: |-
- [633a4240-bcd5-434a-bffb-63975abd3ee0] [rank 1 of 1]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
- role: tool
- tool_call_id: call_dtyqrnlk
- - content: I couldn't find relevant excerpts in the available documents.
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
- description: Answer to a search query with chunk references.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the question
- type: string
- cited_chunks:
- description: IDs of chunks used to form the answer
- items:
- type: string
- type: array
- confidence:
- default: 1.0
- description: Confidence score for this answer (0-1)
- maximum: 1.0
- minimum: 0.0
- type: number
- query:
- description: The question that was answered
- type: string
- required:
- - query
- - answer
- type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '659'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: We must use function call final_result.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"query":"major taxonomic categories of animals in biological classification","answer":"I couldn’t
- find relevant excerpts in the available documents.","cited_chunks":[],"confidence":0.1}'
- name: final_result
- id: call_lak3at6h
- index: 0
- type: function
- created: 1769799546
- id: chatcmpl-66
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 64
- prompt_tokens: 899
- total_tokens: 963
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '2104'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are the research orchestrator evaluating gathered evidence.
-
- You have access to context that may include:
- - : Domain context for the conversation
- - : Previous Q&A pairs with confidence scores
-
- Your task:
- 1. Review the provided evidence carefully
- 2. Assess whether it sufficiently answers the original question
- 3. Decide whether to continue research or synthesize
-
- Decision criteria:
- - Set is_complete=True if the evidence adequately answers the question
- - Set is_complete=False with a next_question if important gaps remain
-
- If not complete, propose exactly ONE high-value follow-up question in next_question:
- - Focus on the most critical gap not covered by prior_answers
- - The question must be standalone and self-contained
- - Avoid repeating questions that have already been answered
- - Include concrete entities, scope, and any qualifiers
-
- Provide brief reasoning explaining your decision.
- role: system
- - content: |-
- Review the gathered evidence and decide whether to continue or synthesize.
-
-
- Tell me about animals
-
-
- major taxonomic categories of animals in biological classification
- I couldn’t find relevant excerpts in the available documents.
- 0.1
- null
-
-
-
- role: user
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Output from iterative planning step.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- is_complete:
- description: Whether research is complete and can be synthesized
- type: boolean
- next_question:
- anyOf:
- - type: string
- - type: 'null'
- default: null
- description: Next question to investigate, if not complete
- reasoning:
- description: Brief explanation of the decision
- type: string
- required:
- - is_complete
- - reasoning
- type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '1152'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: 'Need decide completeness. Question: "Tell me about animals". Prior answer only about major taxonomic
- categories, no info. So incomplete. Need follow-up: ask for overview of animal kingdom characteristics. Provide
- question.'
- role: assistant
- tool_calls:
- - function:
- arguments: '{"is_complete":false,"next_question":"What are the defining characteristics, major groups, and general
- diversity of the Animal Kingdom (Kingdom Animalia), including its key organ systems, reproductive strategies,
- and ecological roles?","reasoning":"The prior answer only noted a lack of information on taxonomic categories
- and did not provide any substantive overview of animals. Therefore, the question remains unanswerable and
- further research is needed to cover the basic biology of animals."}'
- name: final_result
- id: call_liiru1im
- index: 0
- type: function
- created: 1769799552
- id: chatcmpl-945
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 154
- prompt_tokens: 436
- total_tokens: 590
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '3203'
+ - '3268'
content-type:
- application/json
host:
@@ -1138,8 +815,8 @@ interactions:
Tell me about animals
- major taxonomic categories of animals in biological classification
- I couldn’t find relevant excerpts in the available documents.
+ What are the major taxonomic groups (e.g., kingdoms, phyla) that classify animals?
+ I’m sorry, but I don’t have enough information to answer that question based on the available records.0.1null
@@ -1150,13 +827,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Final research report structure.
- name: final_result
- parameters:
+ name: ResearchReport
+ schema:
additionalProperties: false
properties:
conclusions:
@@ -1197,7 +872,9 @@ interactions:
- conclusions
- sources_summary
type: object
- type: function
+ strict: false
+ type: json_schema
+ stream: false
uri: http://localhost:11434/v1/chat/completions
response:
headers:
@@ -1207,52 +884,40 @@ interactions:
- chunked
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: Need to call function.
+ content: '{"title":"Overview of Animal Classification","executive_summary":"Animals occupy a single kingdom—Animalia—and
+ are divided into several major phyla such as Chordata, Arthropoda, Mollusca, Annelida, Echinodermata, and Platyhelminthes.
+ Within each phylum, organisms are grouped into classes, orders, families, genera, and species according to shared
+ anatomical, genetic, and evolutionary traits. The classification system provides a framework for understanding
+ animal diversity, evolutionary relationships, and ecological roles.","main_findings":["Animals form the kingdom
+ Animalia, distinguished by multicellularity, lack of cell walls, and heterotrophic nutrition.","Major phyla include
+ Chordata (vertebrates), Arthropoda (insects, crustaceans), Mollusca (snails, cephalopods), Annelida (segmented
+ worms), Echinodermata (starfish, sea urchins), and Platyhelminthes (flatworms).","Each phylum is subdivided into
+ classes that reflect significant morphological and developmental differences, e.g., Mammalia, Aves, Reptilia within
+ Chordata.","Taxonomic ranks below class (orders, families, genera, species) are used to capture finer evolutionary
+ distinctions and to facilitate scientific communication.","Phylogenetic studies based on DNA sequencing increasingly
+ refine and sometimes reorganize traditional groupings, such as reshaping relationships within Arthropoda and Mollusca.","The
+ Linnaean hierarchy remains the standard for naming and classifying animals worldwide, enabling cross‑disciplinary
+ research and biodiversity conservation efforts.","Ecological and evolutionary research relies on accurate classification
+ to assess species interactions, biogeography, and response to environmental change."],"conclusions":["Animal classification
+ follows a standardized hierarchical system that enables scientists to categorize the vast diversity of animal
+ life.","Recent genetic data are refining traditional phyla and higher‑rank groupings, but the core framework remains
+ useful.","Accurate taxonomy is essential for ecological studies, conservation planning, and understanding evolutionary
+ processes."],"sources_summary":"No specific external sources accessed; information is based on the general scientific
+ consensus regarding animal taxonomy."}'
+ reasoning: Need to provide report. No sources given. So limit.
role: assistant
- tool_calls:
- - function:
- arguments: '{"title":"Overview of Animal Taxonomy","executive_summary":"Animals belong to the kingdom Animalia,
- which is subdivided into several major taxonomic groups: (1) Phylum Chordata – animals with a notochord, including
- vertebrates and some invertebrates; (2) Phylum Arthropoda – the most diverse group, encompassing insects,
- arachnids, crustaceans, and myriapods; (3) Phylum Mollusca – includes mollusks such as snails, clams, and
- cephalopods; (4) Phylum Echinodermata – marine animals like starfish and sea urchins; (5) Phylum Annelida
- – segmented worms; (6) Phylum Porifera – sponges; (7) Phylum Cnidaria – jellyfish, corals, and sea anemones;
- and (8) various other minor phyla such as Chordata, Mollusca, etc. Within each phylum, organisms are further
- classified into classes, orders, families, genera, and species. The classification is based on shared morphological
- and genetic traits, evolutionary relationships, and ecological roles.","conclusions":["The major taxonomic
- categories of animals are represented by distinct phyla within the kingdom Animalia.","Phylum Chordata includes
- all vertebrates and some invertebrates, while Arthropoda is the largest phylum.","Classification within each
- phylum follows a hierarchical system of class, order, family, genus, and species.","Evolutionary relationships
- and genetic data are essential for accurate placement of organisms in their respective categories."],"recommendations":["Compile
- a comprehensive database of animal phyla with representative species for educational purposes.","Integrate
- genetic sequencing data to refine phylogenetic trees for each phylum.","Provide accessible visual aids (charts,
- phylogenetic trees) for quick reference to major animal groups.","Encourage ongoing taxonomic revisions as
- new species are discovered and molecular techniques improve."],"limitations":["The report is based on available
- summaries and lacks specific citations from primary literature.","Information on lesser-known or recently
- discovered phyla may be incomplete.","Evolutionary relationships are dynamic and may change with future research."],"sources_summary":"No
- primary sources available; information derived from general biological taxonomy knowledge.","main_findings":["Phylum
- Chordata includes animals with a notochord, such as vertebrates and some invertebrates.","Phylum Arthropoda
- is the most diverse animal phylum, comprising insects, arachnids, crustaceans, and myriapods.","Phylum Mollusca
- contains mollusks like snails, clams, and cephalopods.","Phylum Echinodermata comprises marine animals such
- as starfish and sea urchins.","Phylum Annelida consists of segmented worms.","Phylum Porifera includes sponges.","Phylum
- Cnidaria consists of jellyfish, corals, and sea anemones."]}'
- name: final_result
- id: call_07hjnzi9
- index: 0
- type: function
- created: 1769799575
- id: chatcmpl-977
+ created: 1772626922
+ id: chatcmpl-53
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 622
- prompt_tokens: 618
- total_tokens: 1240
+ completion_tokens: 439
+ prompt_tokens: 517
+ total_tokens: 956
status:
code: 200
message: OK
diff --git a/tests/cassettes/test_search_filter/test_search_filter_none_searches_all.yaml b/tests/cassettes/test_search_filter/test_search_filter_none_searches_all.yaml
index b8bcb843..73965bd5 100644
--- a/tests/cassettes/test_search_filter/test_search_filter_none_searches_all.yaml
+++ b/tests/cassettes/test_search_filter/test_search_filter_none_searches_all.yaml
@@ -88,7 +88,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '1730'
+ - '1652'
content-type:
- application/json
host:
@@ -99,8 +99,6 @@ interactions:
- content: |-
You are the research orchestrator planning the investigation.
- If a section is provided, use it to understand the conversation context.
-
Your task:
1. Analyze the original question
2. Propose the first question to investigate
@@ -128,13 +126,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Output from iterative planning step.
- name: final_result
- parameters:
+ name: IterativePlanResult
+ schema:
additionalProperties: false
properties:
is_complete:
@@ -153,614 +149,14 @@ interactions:
- is_complete
- reasoning
type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '877'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: We need to propose first question to investigate about animals. Maybe "What are the major classifications
- of animals?" Provide reasoning.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"is_complete":false,"next_question":"What are the major taxonomic classifications of animals?","reasoning":"The
- user’s simple request about animals is broad. Begin by outlining the key taxonomic groups (kingdom, phylum,
- class, etc.) to provide a structured foundation for any further detailed inquiry."}'
- name: final_result
- id: call_b2tbvk6t
- index: 0
- type: function
- created: 1769799582
- id: chatcmpl-503
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 107
- prompt_tokens: 366
- total_tokens: 473
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '2846'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a search and question-answering specialist.
-
- Process:
- 1. Call search_and_answer with relevant keywords from the question.
- 2. Review the results ordered by relevance.
- 3. If needed, perform follow-up searches with different keywords (max 3 total).
- 4. Provide a concise answer based strictly on the retrieved content.
-
- The search tool returns results like:
- [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
- Source: "Document Title" > Section > Subsection
- Type: paragraph
- Content:
- The actual text content here...
-
- [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
- Source: "Another Document"
- Type: table
- Content:
- | Column 1 | Column 2 |
- ...
-
- Each result includes:
- - chunk_id in brackets and rank position (rank 1 = most relevant)
- - Source: document title and section hierarchy (when available)
- - Type: content type like paragraph, table, code, list_item (when available)
- - Content: the actual text
-
- Output format:
- - query: Echo the question you are answering
- - answer: Your concise answer based on the retrieved content
- - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
- - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
- IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
- Guidelines:
- - Base answers strictly on retrieved content - do not use external knowledge.
- - Use the Source and Type metadata to understand context.
- - If multiple results are relevant, synthesize them coherently.
- - If information is insufficient, say so clearly.
- - Be concise and direct; avoid meta commentary about the process.
- - Results are ordered by relevance, with rank 1 being most relevant.
- role: system
- - content: What are the major taxonomic classifications of animals?
- role: user
- model: gpt-oss
- reasoning_effort: low
+ strict: false
+ type: json_schema
stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
- description: Answer to a search query with chunk references.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the question
- type: string
- cited_chunks:
- description: IDs of chunks used to form the answer
- items:
- type: string
- type: array
- confidence:
- default: 1.0
- description: Confidence score for this answer (0-1)
- maximum: 1.0
- minimum: 0.0
- type: number
- query:
- description: The question that was answered
- type: string
- required:
- - query
- - answer
- type: object
- type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '537'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Need search.
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"major taxonomic classifications of animals taxonomy major groups animals"}'
- name: search_and_answer
- id: call_1i5os0e1
- index: 0
- type: function
- created: 1769799585
- id: chatcmpl-47
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 41
- prompt_tokens: 625
- total_tokens: 666
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '142'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- encoding_format: base64
- input:
- - major taxonomic classifications of animals taxonomy major groups animals
- model: qwen3-embedding:4b
- uri: http://localhost:11434/v1/embeddings
- response:
- headers:
- content-type:
- - application/json
- transfer-encoding:
- - chunked
- parsed_body:
- data:
- - embedding: LRABuC69iTzQThW9wnYyvLSXHDm6HhY939oIPVnpgzwqS5c891AcOhqFw7sl0u47UOOHutNQhLxqx3A7f7f/vAjZiT2FcKS80lQ6vBMyi7sfEli8UjgsPUEdebwXh0I9cScOPFHCeL11k4G8R68jO8Z/fzwBLDO7hw3MPPVO2LwWhRA8UMTiu4pm4TsY6py84fRTvPnImbuq3ma8+AkKvfIpBbvQrKy8QbeXPLD+nzvNIvi7jK/PO8u6jTvFbvE8vXF0vDL5IjqkCTM7esVDPB1lC7xHo4a8RQbgPMUaLb3NV249qLteu9fe1jrNGgM8a00GvF4Ph7zwpL+8OEaWvAKkQbtaFo+8f+H2vGqNMr3hN8k8vDgXPFnHd7zNDoE8QgVpvEzuWTsjrBO8j66avLIsB7pMnhw8U7rXvD1Fszy6WLA8ndilu8JzPbuE4hM9Il7ZPA0YfruL14I8XoWSueGj77yMWH68LPWmPGi4njuU8OS7STliOgC0F7yUvM46bkBvOdnwRrzoGu27lKW0OxReF7xLeKA7zQEAPb1Awrs1KmG70PwhvdOjAr1A/LU8F9TNO13v+7qPhZm7bu3QO82Y6roB8s28Pp4vuxBcILzCH848Nac3O07ERzxhTgK8QrApOr85CDyMGzo8EBjAu4vSxTtgQDy9C4UkPPa9RbzWGCy8yQ8lPHn63jxfYAa98q3aPIsoSbwzLOu7bDudur/pDLzlUGK7vY1nu1BLWTyUMv+6WcQYvH1kXbyH5xc9xEENu5TZRruQA4E73DsvuzpUijwNWLq8VTYeO7tvK7zFz4Y81am1PGGFFT0OZX08pTCHvCTVJDzEdVQ8APfbuutgzrtL95g8Y9D5O2DwBLw9o427D4UwPGBssLyUIak7QWHFOp1TCzz8WAu8SpQfvA0oWDw0XAy8r6rzvLd1SLrJ4eK8Zbi+PHueFLu+jaO8604SN0bRRjxryZI8LzcJu+K2qruOkw87xPsMOuQf9DvIg5o7crojOPxL+rw+vgg6MyE0vHo+VLyyX4I7rQ5bvJD9H7zop+g5k51BvEay0TxQHb27GpMou3J4azyaWMu8prrVuwvpf7t/fuc7Go/JvGHKMTxU6wS9nwlLO3FVwTydM/A7w4mwvGhsKDzroqI8+afFvOgKmLzNb6E8aX3QPPVfIrzQhrS7iukTu/S2AzxW4LK8sAiXPI10kLoIBaI7cx+1OY7mXLyEUaK8HwusOzXM5Ly4TCa7/wYsO5ggYztmZJO8Tx0YvHgPLzyAPHO8RFqmPFXQJr06Are8VhwvPByGjTqoAQ+8rx0GPD00sboILHw8sg1ovDaX8zvoC+I6+pZdPKd2xrwjxma8WK+SOxqrtbwywsS8rb52vAsmtbyh3o+67GiIvAKePLtv6ow7K0mpO1lRDDsd2Y28POCxvNPP6zycITu81qJ4PXLxyLt+r7k8XWr1uwcZRzzJCe27KGo7vNCltjunLbo7y2fWPF/nk7wrzSO7v+xyO3yVZTwqWq68Fc6TPJqJgbyScRG8bRycvPx1Bj39WsM88hHmPA0Jijx3ATu84/vCORLtjrzM46w7Pr/ou7LaGTvm1YK8s+crvGHDEbxKuLo8tdoCPZ3AkrxEIIo87vFju3FaBDullhO9Sk2NPOS9ibwaziu8sPGCOpqKEDxcGRW8p0cOOw9eg7ws9+473ZKIvAu+4Tq6uXW7oba9u4K9Q7wKFwE8zJe+PK+tZrxSeKI8kBIEPdgVKz2yhRM9+PMKvUGD0jwtxxq9rgaFvCb6RLzCxM28tGgSvY9dkjxW6aw8+MQLPV5xV7rnTo+8AifWOyoOirzxsxm8mzJLuiGv6bpqPuA7C4GfvHabuDmtCby8cEAbPJ4i1LutbPk7F33PPFUUxjy18Ku8oFkKPMiqvDsUc/a8XgEDvYi1LTxAzsG8nzyOPJFzn7wHl5S7fmxBvABfhzy57wa9i/Mmuv/gt7qAOmS6osmzPKkYGLxohjG8CRBcvJBcOT0f4Tm8shbhu7osALwVFJW7MDDQPLrfFbxaYUY8yrh8vHebzjy3yoC8q6iGvMiGKbyrbo67zT2GPBLyFD00sKQ7M5TLPAowpDtO6gK6ZVn2Oxp56TsBD+A8sz/LvBpbn7xaIpi8MoX5O+QcOr2J3tg6tObKvLnB87tdrY09MjbwOuXy1jxpY2g89Vp0u1bxI7yPg628TcJ/PEmFlrwWkd08WcrnvCBEgDtOqEE73KksO4gyRzx7fxk8LVlKO8NnlDzhPbq7Lwr0vIbM4rveRDE8iyoyvBcTxzwD9gk9XurqPAQqNT3KhRU6GT8XvADyqrxSIEQ8fnhZvDubRLyaSdI8HL6PuvDkQjwHxK86GsK4vLb8G7tPDds71vOsvIKMvzvVYFG96cawOws4mbzfQKO7RB1YPKySfjqPIBM7bNR0PE2ULb2vlNO8c7sAu4SmuL0lMco76Ak+u/iKPbxP8YE8Xy2KuxtkgrvuMhM8ezWoOsVqSrsJgDa8d9EHvaOrkbyRFBU8pS4BPerjKTv5QWS7KvKRPJx69LveiKA8fwuZPNM5xDyxVJc8bL4TPQlVvLj+AcA8gSVBPKn28LvMJFC8+naCuLxENjyTLva83NO2uTrQSbwbF6E8QNrMO0DNazyJ/KC8p4WDPBDVG7wz/LK8mhcNPbvawTznbtm8y55HvHa4CD2ah1C6OotbPPisM7wQALY6xh/nPLINmzxRUE+7j2FhvNcdzDvLwIO853TwvB8YUTxGzyw8ApaaPOo5GL1UnJU7m6W8u37q9bvKieK8JI3TPApmeDxdUQm9xYkDO44jYLx/e6C84u1pPAM7jbpRjqs8HdUwvD+yqzqmTg09bEAWPXJkljx7H5C8PkNnPKVCorxvWe476Tm9PHQdvzxUwLW8jpQJPEbB5zq33yK92HgpPY7KtTtvJeq8bX0Pu9grjbxixH48qtAFPTYwBDzLyik8XjUuPAHExDyzG2S8D2OXPD/Bkbzweqi7YPXEvKjzdL16YBW7sCwCvQLGY72tqWA8bxsjvGIkX7qdqOU8VjvVuzCh57z01b67Z6SnO07XgryVXX29GVfDvCk8pzzhkek6HC/0OKO5GbzJNIo8OjyZunHsCD1ruAc9LD46O1JCKryn10q8vJbcPHaPdbz21Aa8AAVzPDHkP7yp4g8876epu4Bn57vM7K27geG8O3bdDb0GSAW9M0yOPNWWw7z3eug8SYGBvBDVbDwsUc28XQ3eu0AMKjxmTKS8ZDQCvUDaizx0oJo8ZuwtOqk1M7y9WNW7Aoe1vIXMEr31+ku9sdTBPBArmTyJ4kg8R3cHPFtFKL1b8yq88tt0PPl2X7zxSbK8tfazvBRILbwMo5Y8e5XLOuE9gTtxgok8j1wZvVTmnjwHc/u8WqBQPDF4ET0mWz07IkVuPJBAqrr6GbU8ffMFvGRyS723+YS8mVpdPEGXyjsKZ528vM1huyfiC7xE09M8w1xMO5mSNT3zlMk84b0uPNU00zxVJ708VgnyvCVJnDzUIru7Fz/gvI9lQLscDk88SeEZvXHhTbwmK6481ePyvMdxRLzkKve7nLdEvNKiEzwyUr28Y575vAtzebwuEX48Vm4VvAV5IzxcABu9B8k2POkjuDx+yCO8JNafuu9mozwAKAe7yNUiPKJS0DubLdE8Srs4u65lybxeR1i85NEQvPga8bmZMAU6jeJHvAMeAjyFPQK9foR1OzC68LwypBo8BguoPO8LjrwUQJO8RNfYPE7eqzyP+k08JX59u6lnMDxsn9m6R9KFuxifab3KX/C7hWxCvCZMbbvId5u8UAs6u07ZWrxy0FW8AqbuPH3wcLsXUlm8VU4lvK9ayLykK8O5lwHFuy2pMLzux3I90sqHPH/KCzw8Nsw6zXM/vN8ttDzGryK9eNTlOkR45TtIZkq8jswhPKg6DzuwEPY7jLUevfi5lDwnp9M8pD+gvGtRazr8OYu9EPHPOzulLjzedes8GF6sOg8rsTtdWMe7SspvvXfghjuy9su76LXVPOqli7s6zLM8/3y1umt3rLy7Dds7WetrPE341TvEY9+7brFdvAFWxDzpCOS6lEKOvNcXIrxTVDa8xJ7cPAEq6jzMEYs7sskrPTYVgrxYTJW8GzU7vNdzAD0JbaO80Y9/O/oRP70bfYc7974GPPNhAr1BHY08W8yqvEthlLzADw+86bXivGfBSTwxhto8tQieO4X8Db0WUoy8jc/nvG6Bf7wcLtG8dBUgvacP3TzfhaQ8XswIvUazCrqcpLQ8LxpIu4CyET2bOnK5U0ZuPEe2GL29rKw8zUu0O0ZPlDzqabC7EoKgPN32uTz7OCS8PLNgPG69obzopai7Xx2LvAP0fTw6Qp08y6WBu2K7YDyN9nS7sOcKPKnHhzsybII8zqwdPKAr7DyNmS88RcpavLRP17shp4s8zDUkPHvgVrz6rkM9fbAivYm13roW3rC8pGMouyKyyDuwVfI78J8Nu4S4C7tHfF68eLWLPOQRDTyESNM7ZlOIPAGnZLkhA0E9gEuJPCzpFjwVEPi5r/nUO7FbFj2f+J27vPxuvEP3ALwQXDI8RZEVvXzSP7yNzS29TLDlO4PGdDywrGq8/BCLuukmpbwgsxK8SZzLPGx+Pby7W1g97zipOh3guzxCxXi8t82yPM7YB71DBWE7sYsiu+pKSjzPM9u8cpMKPRQiNzuEQxm8AOKMPOgm47sV7vq7kmUtvBMuGbtDoYM6nzc1vJZI3LzJ8w89AN07vOChRjwEqyu8wL7EvElW+zx6kQG9HEuUvKSGuTyq0Ry5pfyIu9XEGT2hiDs8fHcvvYD6uTkiPPG86NK9vNPuorzPuVS7R1Tcu/RfpbtGVu28GhS2PJkfz7zPfxo98SWuvBupujxDDE48lHvyvE+dpbz4/wG9A22GPVt0mryDvP06KLNhvPwtFr0XcWu8J0j3vKToHztaoVm7x6WQPAtEF7y/8Ky8vuMAPL+SBbs+Gvw8yLIbvL1G2rxceCO8S32PvP5TJz2i3gG8/LN9OysAHzx4PKo8SRBTvOk/xjvgja07c22EPKrIRbzGkoK8pK/WOg+0Crt3wtW8pCcWPTA3xrq8K9o8mFhTPBj54Lx7ule7spfKPOa46TxsSv67mTcDPUaYvrsN2uk7NOgSO5HEKruNx0C8zi9ou0K2LTza45w8Mr8QO/xh07wSt4Y9kYFQPIez9DymU0K8SZd3vOJLrrsKlx08PHnjvPQuarxj+D88AU0BvcCLST36tuU7njpWu54THTxNYW08P9EzPOO9mjzkAhA8bIfhPARsKDr7mza648TavBxkSr1Mgpo8FAjSPB09e7y8CdE6xiKQvNl5UzxMj628lATmPFsHebuJW6G8hZM0u4NogrzcsBY8D/eVvJQKUjqJhiq8izv8Ous1pbthN7o8tijnvBdpCLwTGKS7zp2LPM81Bz3yFxK9r7eaPDbDCjyiNcm8HgjvO2hWoDxR3nI8jLa4vN7U/Tx8hyC8Gc7ivEjkgjzBWg88d2KHvJWKbrsFo++8UEwyO6cnubwGttG8+X/ovHwKHD2Ep448sdVtu6vR1zxJjga8erl6vGcpr7zZ5lK7/cwxvJAzYLx/kUS8uINdux8pbTxn2UC8tk4DvDzFwzyk+zS8vjKdO5o/eTqdz+87vqAPO9cgODxv6RQ8VW9Xu3kBCD0yfmS8shbqOjbwYjyNmWU6WbdAPBHELb09k6A7GdYevWt/0jvbnVi8+4OEO+nkLzzVx4K8LdowvMxA7Dt96Fk8rinBPPZmDjxs0pA74bVJPUgR5TyH6Ac8j7fTu46ZGjuPVEU9HkIBvFuMLbzECVM8ymVCvPo0Yrl1JB097ysfu1aCrruonNu8hCE5vPWk5btEzGG7IAwhPdPEVjx7Hi09b/EnuBBiLjyBAR882uTEPGzbqjswFh2814vNvDVGDrzW6Xm8s5fCvE/vkbqUNmg8L6G4u79fTDw24Oi7meBwvMI/JD3ig9C8vVh0vGo/8LveuXq8eaXiPLuPkrtIiSW8RutTPf9mWrwtQRS8L+9du9ndr7tK2P+7NG/WOVuUKLyt8GE8McLFvFIXUrs1leQ8ybKNPAjEqDxtnqW7YK40vE8QBr1t/7+8B1GqPDXVATxQ+me8mQwzvOgMdjwNPB88jeSPvDJLHruEV7i8q8EVvAncezxQ9Io7z0rdvBTReDw10d65pmCKPHxNVjyzFoa7eeyjPL7CCL2B1Ju7y90jvM/sTrzXgZc87V6OPJDReDtgiFe8SyMDvXkkn7zM83g7IKTaPEcgpDwbDcK86BRju+nDOjzYF5c8obVtutlGgTyfat08uHsIvUPDxjySNQO90n3SPD4oabyMPxG7S+O7u+RMPL2INJe8CdvDPPhtFr3+RtW7BPEzvGgqyTr4Q087NgvhO6RC+jzJiW68Sd9lPd6Juzq+RDY8FM0gvAZBjzyLxZW8FkBZPPTu87tBLCO9wrIcPBIbMb1tgzO8mzzaO1u4VTxGR8Q7/u8+PGwGnLm+9bi82HPUPP97ljxbbxG8fnsEPGhhrTydy7Q7YIgxPaZ4Ar3udMQ7h4gtvHghrLwyIqc8nJMEPGRu7ruRo5G6WDslPFctnDwptC09FfhNvM58bTzK2Im8m4aePFCqrbyk5g69RsfyPIsVkbwTzvc7oE/cPDAXbbw0JOY8mpNPvAR7YLwPsWE8QSbuPBiu5DtYqGY6i9FdOzG0/zzWLwE6pc7DPIYa57n6L/a7sNTdO95HzjwFioA7teeXvBXuBbzKo0485qabuydfWjwQ3Re9p6IIvR95ebxEIPI89eAUPCUnsTut6LO8ZoTavGSGr7ugU287F9Y+PV4zfLwW2XG8I/MaPfwjWLu3oW68KLSJvErjyLst87y8MJaKO58eS7zbVhS7QlHsPF508LxCYYW8Uh5RvOqD2TwlwA09OLg6vJQrozoPOq07s4YlvMbk07xarBo8Wv91O+/Umbskhq67nCACPS6+DzxInyY7U8MDvUZIlzuu/tc8VpW4vGf4T7vqXZI8t0PAvBgGCj2Kt1C8jpNWvMVUDLugTh68t7XtOfKmJTx6qLk8oEwRvEK9ET0jY9I8H4lzPECEGD2TMyY9f1oYPQK9Hr3W4OS7QTQGu/A0frxiXlI8SSe4u6D2nrwdFBu9itlvPGD5iDqrDt+8TXICO6akGDy/Y1U8GXXuPLNFQzz6Ppc7BkT+PIgHXzw55Nw8/kr+O2GntDxYXSm9qjAMPBUo2zxiaIy8dzsjO++y6DzbM5i83SNyPCpa2jonttY6CB+QvOiQcrtBMIm7LwghPY+MFzyej6K8x1ryuzsR6ryANZO6fiqkPJAMi7xshpk8H15Wu2xCFz2sDVW831wSvCQkpDuTiCE9+vkXPSMHsLzKioS8w/BhvIbdTbqQM224HbcnvF6kET2RIDA8bJAwPDkWyTwAkvA8q8siO05oljy6oPY8DLbtvNxE2Lwnimg8ooTCPD2W0TuawS68yBMVPJGvbz0ZJHm8yRjhvDLGnbxBCda8dCcgPLja0rzFGz+8B80kPJwJ8zyMUd27lnWUvHlr6TtI8tO8b1cevIBeCj3lMfY8+DD6u/kwEr3Rzyc8TtG5vNbnKLwPHDe7yC1xu9eIkzpHxoU8aP+ZO+w/3jwUxaw6GKQEvQ/jzbzjK726NcevvLdFv7wSMNw7uJo8PEHkjrsaHkK7vefYPPAVJ700juq8znzrPA/YAj1fTog8MfOSvAsVabx1qZk75svTvFePxbvjYb07miY2u4GXbjz61DM8IcvKPDg6Ajp93c28204cPHpwJjv4Ht28yU5UOzD017tNBxi7k1a8vApa17yRnfU8OGvBO9yyBr0O33q8G+XgvLG3Brs46QE9yz6MPCPdQLk1AyC92b47O0x+zLy5IJo83wGlPO180bvguoo7XXMSvc0vuDyiBce8UfZaO02e7rz5OW27BDe/O++TAb37jkI7etwrvFieVDvw1ya9v8KWPBSoWDg8+wC85uK3PJvLc7q99BI8aF/guy0IajvLigk8hL9yO2Zo1Lsk5Io76oFwPI9NVLxpyT28hamLO+HbBLwKCyi6VV/Quk/a7Tp7lxm8OQy+PN0QkrtUcT+8tpHbO3eKTLvrdMI894sBu3XQh7w+5QI7k38TO6MpmbtRS4W8H5syPBAzpjtzqvw7Vi8GPCIfjryoipY7OcCIOlVpmDwye3S7rIVnu9Ffs7xAuAe7mB0DOGhPIzzLPYo8uF20O9vpZbxwqmO8OFTvvNbgLz2/ehw97j9QPcTnhjwBt2c5oPoyO/kYXD2HjC69eJPAu14eg7oY9bc8a3GZPPGpTTswk8k89SrPvGdn1bzmXOq6UpEIPPuObz2SwM07VDOBvMMQvLvEJJ283m+rOpYPtjzIjBw83xbtvMIyzLteMLq846DXPJkPpzwR3gG78POkOsvlm7yla0I8SRFjPBsogTx3JDq8hHZtvAhK2Tt7p7O8CJ+TvIXZoDzjbYa7SQSLPORplDzAU0k7870xO4LhAjxp6X+87989PIEaVDxfNZG8um62PAlS4bsq4PM7WgIivPaUwjpvJyS71whZu6NI8zsAy4G87tyuPGWQRjziUGQ8jFTnPJQiJT0wgdC8G8e9vK+mE7sydQc9P1U7vDy+OjzE2je4Fo2Ou9vN4btaKLI7zXaHPB6xrDsLRAK8FqBtvGnVYr0Yhio8hPv/uz7dN7wCDOm8+yvDPClHPTxNvrG8P9aFvEeTKzzOF4M8BEWkuxJobDwQRtk8oI7GPDyyLDxHyU48Fr/ou2AWjLsgrUw8Kyq8u0oAbLwoojo8wCV7vNkjGbyAO528iVVOPZFYOD0+MbW84wJUvAgCwLvHvWO7NW5fvGjaiTyRtcG7yjC3vMOvdDyu0208o5bXuxmxKzyHE/i8JXhvuzX8QLwtbq28680cPIAgnjv/k8u8msjlPOGGMjxwvYa8vj6WvOhBajvnJMi8B3voO5PKWDx3a988Uh1oO3pukjyIV2k8QWwjPch//7pmd1W8mDlqPHMIgTtpBNS85+mJPJwK1jvXukQ8qsaPPHZehLrD/wW7vIBnPIr4zLz35J882/z0uKv2njwDPNG8JuWSuhuRs7w7TOo7E8pTvEbPnDpNODc8zaSHvLejvjy/B0M8/qnvO23eHjwZ0Jw87LXBPOkD3Twznpc7A45UvBFuwDyW00o8QhecvNjyyzyodNS8zgI8vPHNTjvZP5a86ASxu4KlfbziTvi7Mw1Du0NN5DwRkTQ8UqKgu7hlrTvZLtm8aaXaPIG7JD1cvhc84lOKu60Z6zxmuO83Zhg5PI7QGL0xvxI5NkqdPM4f1TtJAV27m+DBvJ9ZEzzj1Ky8g44WvDnc1LoPaRK7J4LbvEcXDD2Tpc280F9bPOEcA7woIzW8j4djPIQ2TTzOY8k6poYTO9m5tjyi7n+8v2iDvAB98DuQqjW9SbbMvLCl1DvX3xS9W8FtvOrmbrpJo267qzDjPPMYlbk77fu8dndkPF3muTwmW+S8o12rO2nSn7tTioW8J679uwzpiTsn77U7T+lxO+GXH7wJq1W6Lb3RvH5/qry7Hki8UlmXu62Wnjx6+s48PVkQuIQb0Tw5lKM8lzCTuqE/KT2shw89YjE5O9k4cjqXZ1+8iZfbPIukGrrifOg8DCASvauk5Ty1GaE6ohKOvOrMZLz+s0y8KYK0vKdi6Lun48c76OEBOy55SLuqztY7YRWYvEZpmjszVAy9c1hjuoSpKLviLdk7RvGEu+g+gbxuPME8JVTRPA6EAz3Otqi8ThY+vOV7vrlJ5yW9IttAPKX0hLwZ2gi8TW8IvSEPjrysBiU8VJT5vJ6Fb7xJugG93e/WO9UqLrxinZk53U2YPLpjy7pWMji8d15zvJobx7yYuBO6H/UxPEI0W7vmfW284AeCvASE5zr6T++6tzyIPFMUMzsj9AK8RWoDvHDQGr11VuC6sZh+PREd0TyP5vM8qButPGGekDsRmFO7TSAsOZX4Izwx4z67j8J7O93Mf7zl9DQ9Dl8iPUYsvTuc3T69U9vdPEJ/DjznARI8+9w3vNP+BLxIE4Q7o7Afuy0AaLuumJm8uYarPBMsfbsB0gq8kdOTvFzeULonDKi8CMoZPIfFiDtFJRe9JGfuu8coPz2Z0eW4pFM0OmjC2zymNXK80VLvugHXDDywkZU8K34/PDqyKT2te7S8pEHXvLnfDD2jL4a7BJ6BvBSFKTzj1Zk8RHw6POwN/7wmPAK6N2PyOv/YJrxKQhq95XBavL07hzpkzJG8AE0tPFAzc7sdSww97S7BPOpQXrxOb4K8Q6gTPV5yubyApLs8xWjGO3LLb7zlqiY8WxnaO/+MXzpUqwE8tx+wO+0+E73qSq27EJNyO7aLx7uQKOA8L1qAvJ8vizuu9zk8wOfaO+4aAD1zoj68AdqzvNBYDL3VCai75RgGvNlFfz33Z+S8mN0BPHo8gTxgV2i8ZLBovKLVZLymYUK8NECZu7vUvjxb6YC6VhpDvAGcijwo4xO9pGQxPFBdvLqg0XO5uZLtPP8+Cj29Jaa8iNQdPQAoL73HNIo8h15qPfhsbLxjz128PGqTPDa44LyLwtw86xNovFxntDxUIi89XfYXvOYaTjvLT1O8XXICO6/mGD0yYcC83OeRvGWoA71P5gQ8EGmbO6Fpsrw5VRO8ylbCPCASIr1kMl88I5tWO/1y57yCYVI7MLs4PeOtKzxkx4+8JOjLu3pbLL0c3YC7nBz4PGV8KbmK0LG8E8Lsu+piPjzEuwo9XNKhvLUJTbwCc/q7vd0CPKx02DyS/q87X3moPLAi1bx2N9C71NHSu/GcEzwIVAG9Uo9gOynnTTxwqgQ91FtYvJhRqLviA7a8Pt7nvDBh/rsr9Cc8g41oOwjdYrzzKo48LazcO7MNoLx4Rbm8XGAAOyRQ0zxjWia7C/6iu0qYtDyxDzC7uOSmOigYBT0+i8e8uncFPKeckrxzv/+8f0A9vPWgQTz6/am8D8yivAnlGbxEsjY8QI41PSaCr7su3tk821eFuxbLCD39aCS9ob2aPCuSprv8OhI8NnydvDWofDyEoFo8RhSfvFlK+7txUVA8xlhYun02pLwziTw7p30cPSJ6vrzRLxw8yAawPBT2fTuStqU7mi+au2vy5bwO7EG81QUBPFMxYjxdDLq85gBgvDd4njwkyYm8SeHru4+OpDxPg3y8jDL5vGWzYzyZMje9kDw0vDxuI7slQsO7HS0evHIcnruiCf47NKUGO99AmrxG7+S7pHcLvWenG72YQdk8ZYSaPJu+eDyjFTY8uXwIvJATm7z3KTk9NwdDvKWdALw9s0484Sx1vA01TbyMH4W86P/wvAsV/rx7GBI8WVuaunowwTyg9BC8SVlyuq8oGT0zwwU8KwaHuijbpzz6gpq8MWhUPegLFrz1y4E8awfhPK7ZK7xZkY88VvNbuiwEsrvcVi08j757O9rUuDpripk8USTyO5AGSDz6TXE8IosxvNMR9zwrDym9rNVVO6+yCr1K7668c60Rut476bxalJm8UC3EvAfFDLrdLpe790wpvSJwRzw3r4C8tjmhPJgRlzwJI+g8uWKqO7feXL3Svzw9IIksvYZNKLyQvUw8mIyKO6Oprzz5LwI9pYa+Om5Qp7tkwxQ7pf2DveaoTzstnHQ8leo2PemjpDzZ69W8/YdTOi3i8rs7Qyg8so6lvKkfYjsLa4C81i+SvMwCnroJSAS9NuTHu1MY/LpLAhe9er0MvKP71bzGMbs847ANvIMn3zyQcMI89QBbvKZqmbvD6iq8BVM7OzU0Lz37ucM82680OxZ6mjzeTO48IUwCPf1EwTtT15g7LJY5vHCnRTrrmD+95BIdOzz/0LxoDkE9kDpOuzOrojzzcos75yR8vG3JsjwUzy26O8m7vPO7wTtiq7M8Bcn6O2pD17zVSEm8TUi5PIEPEby5RIM8KkqGvCLanbwaFoq7nREBvf42erzI1P87hU7au9qMF7wIWnY6pONSPJ7ZPjsuhUM8/BWkvPOB3jxBlBe9yyZQvMvZQrrnhrs8uPtzvHOUFLwck0G9txRLus8WTrz2y548WvvfPLXJTDxfACO9BtDWvC2vYbt2wTs7y/1oO501njhWdKY8RX5zO4GewTuJ96S8wufruxmAED248ag8aQQTPSOevrz0vxg80+02PBJZoDtLWtw87AfFu3MmCL1FUSu8HNXJPChPDby2cwQ8l7jMO21Tibv5L5W8XpnMPMLvdTyhW8I8mH8EvR6vP7wPn5s806LMO/UWtTkiTKE83FXrvCu+CDsALwg80e+/u0hHcDrAYgM9Ir4ZPHkNsbtrDL87W5sivOuTzDxVEQW8dDRMvQ/psjmiZ4k8OOeOOhemhTzVW668di6OvG5XbLvDMRC5i1HpvGAgO7y0Goc7OMLjOsAmLj3QkvE8L1WoPA9yyTvObHe8D8gBvQQZF7zG1Yg8TJZAPJoeLb1p0r28mCE0vEGWpLs3hk48UaaRPP+O7jwPJJA7LWIyvb36SDw2uiG8RY7HOz9zFL1Kc+68n78JvKhY6bo1JRM9lZrdPEcrtDudDxu9uVbkPHlHZbtuhi48+k60PGzbiDz1mv67loS4PH9k47rhQjC8XdHhvPXAZTwuF3g8zwveu/G8Lbtzgi48YWh+PJH/zTymd467ZwChvLzRRzwGHyi7IhebPBqwwzulApe8lv5gPc83jbypsfM8iNdFPLTo3DsVk1g43D4wO0+4orwE5qq8ZEtIO21DLLsV1TU8fFHQusiIQ7xyOPc7SSuvvIM40Two8Rc8RqccPOol2juPWqe71AVIvHlvizxSZsK8Bo/6O/2rhTzNNoG8fHjuO+agbbwWHew7BVK1OzubwDyzO1M9unqxvOC0brvVbZI7v2N+vFqgjLwZ4y28sAW/vLw6BL3dlZk8LFQFvcc1pTzZBKq6IrThOP2ZbLtQLyI7M8EjPEj4mzyVGR48VDS1PMSZwTsB62K8GplIPS8NXjxt6bI7IUgTuuNvgTwB2jQ8EplsuwYiCT0PROU7wUqNO8TEFTzJ+lo65+r3vFiKZjy6MoG7WS6IuodIJ7w0Mtw8k6qAPK1rHjzB9Dc89yiBPEgA5ToudTs7jGQOPacO1bzdJR+9BDKyu2/ybjyXGcK8SJO/PBSokbwUcSm8wp6hPIeEazz1mGi81EzSPAeGyTweJmu8tgC3u4g26TkpIhQ7Ff2SvNhYaLv5+n67YgUIvWOkMbylEIA90u4yPS41DTwDmlC8qz66u+NDzzwOfFW8BpkEPOIQWbwasi87phaXvEp85TyoDrm7NCeUPNDXMTz8MBe8YD8PvWyO0bwYPLq8juvPOyrUnDyyHFC7t5Q5vCIWjTzCWoO8eaQIvPdqJz0Zuse8rkgQu0L9NbxAkGu8t+/aO59KpLzMBUY8Z80BvFt4FL3id887jRfnO3Sszrv0CNw82hP2Oyl93TpBGPy7iausvDZ7zbzzdUU73RoDPBRbtTzY7i88JtSnOxsUkLveJyG86CchvFNhaLzuNnA7/pEIO5vtGjuuRg684sEzvVlViLyKTts7Q00fPCTVo7z4mDg80TEZvChwMDtB/NO8jPyqOw==
- index: 0
- object: embedding
- model: qwen3-embedding:4b
- object: list
- usage:
- prompt_tokens: 11
- total_tokens: 11
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '3501'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a search and question-answering specialist.
-
- Process:
- 1. Call search_and_answer with relevant keywords from the question.
- 2. Review the results ordered by relevance.
- 3. If needed, perform follow-up searches with different keywords (max 3 total).
- 4. Provide a concise answer based strictly on the retrieved content.
-
- The search tool returns results like:
- [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
- Source: "Document Title" > Section > Subsection
- Type: paragraph
- Content:
- The actual text content here...
-
- [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
- Source: "Another Document"
- Type: table
- Content:
- | Column 1 | Column 2 |
- ...
-
- Each result includes:
- - chunk_id in brackets and rank position (rank 1 = most relevant)
- - Source: document title and section hierarchy (when available)
- - Type: content type like paragraph, table, code, list_item (when available)
- - Content: the actual text
-
- Output format:
- - query: Echo the question you are answering
- - answer: Your concise answer based on the retrieved content
- - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
- - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
- IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
- Guidelines:
- - Base answers strictly on retrieved content - do not use external knowledge.
- - Use the Source and Type metadata to understand context.
- - If multiple results are relevant, synthesize them coherently.
- - If information is insufficient, say so clearly.
- - Be concise and direct; avoid meta commentary about the process.
- - Results are ordered by relevance, with rank 1 being most relevant.
- role: system
- - content: What are the major taxonomic classifications of animals?
- role: user
- - content: |-
-
- Need search.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"major taxonomic classifications of animals taxonomy major groups animals"}'
- name: search_and_answer
- id: call_1i5os0e1
- type: function
- - content: |-
- [9ff0d394-1227-4e47-9c52-445c227a10e4] [rank 1 of 2]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
-
- [6143420a-0443-4c95-844d-a006e88751ba] [rank 2 of 2]
- Source: "Dog Facts"
- Type: text
- Content:
- Document about dogs: Dogs are loyal companions that bark.
- role: tool
- tool_call_id: call_1i5os0e1
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
- description: Answer to a search query with chunk references.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the question
- type: string
- cited_chunks:
- description: IDs of chunks used to form the answer
- items:
- type: string
- type: array
- confidence:
- default: 1.0
- description: Confidence score for this answer (0-1)
- maximum: 1.0
- minimum: 0.0
- type: number
- query:
- description: The question that was answered
- type: string
- required:
- - query
- - answer
- type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '600'
- content-type:
- - application/json
- parsed_body:
- choices:
- - finish_reason: tool_calls
- index: 0
- message:
- content: ''
- reasoning: Search returns irrelevant. Might need broader query. Let's try "animal taxonomy major classifications".
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"animal taxonomy major classifications\""}'
- name: search_and_answer
- id: call_6weik2xe
- index: 0
- type: function
- created: 1769799588
- id: chatcmpl-495
- model: gpt-oss
- object: chat.completion
- system_fingerprint: fp_ollama
- usage:
- completion_tokens: 50
- prompt_tokens: 796
- total_tokens: 846
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '109'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- encoding_format: base64
- input:
- - animal taxonomy major classifications"
- model: qwen3-embedding:4b
- uri: http://localhost:11434/v1/embeddings
- response:
- headers:
- content-type:
- - application/json
- transfer-encoding:
- - chunked
- parsed_body:
- data:
- - embedding: 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
- index: 0
- object: embedding
- model: qwen3-embedding:4b
- object: list
- usage:
- prompt_tokens: 6
- total_tokens: 6
- status:
- code: 200
- message: OK
-- request:
- headers:
- accept:
- - application/json
- accept-encoding:
- - gzip, deflate, zstd
- connection:
- - keep-alive
- content-length:
- - '4218'
- content-type:
- - application/json
- host:
- - localhost:11434
- method: POST
- parsed_body:
- messages:
- - content: |-
- You are a search and question-answering specialist.
-
- Process:
- 1. Call search_and_answer with relevant keywords from the question.
- 2. Review the results ordered by relevance.
- 3. If needed, perform follow-up searches with different keywords (max 3 total).
- 4. Provide a concise answer based strictly on the retrieved content.
-
- The search tool returns results like:
- [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
- Source: "Document Title" > Section > Subsection
- Type: paragraph
- Content:
- The actual text content here...
-
- [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
- Source: "Another Document"
- Type: table
- Content:
- | Column 1 | Column 2 |
- ...
-
- Each result includes:
- - chunk_id in brackets and rank position (rank 1 = most relevant)
- - Source: document title and section hierarchy (when available)
- - Type: content type like paragraph, table, code, list_item (when available)
- - Content: the actual text
-
- Output format:
- - query: Echo the question you are answering
- - answer: Your concise answer based on the retrieved content
- - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
- - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
- IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
- Guidelines:
- - Base answers strictly on retrieved content - do not use external knowledge.
- - Use the Source and Type metadata to understand context.
- - If multiple results are relevant, synthesize them coherently.
- - If information is insufficient, say so clearly.
- - Be concise and direct; avoid meta commentary about the process.
- - Results are ordered by relevance, with rank 1 being most relevant.
- role: system
- - content: What are the major taxonomic classifications of animals?
- role: user
- - content: |-
-
- Need search.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"major taxonomic classifications of animals taxonomy major groups animals"}'
- name: search_and_answer
- id: call_1i5os0e1
- type: function
- - content: |-
- [9ff0d394-1227-4e47-9c52-445c227a10e4] [rank 1 of 2]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
-
- [6143420a-0443-4c95-844d-a006e88751ba] [rank 2 of 2]
- Source: "Dog Facts"
- Type: text
- Content:
- Document about dogs: Dogs are loyal companions that bark.
- role: tool
- tool_call_id: call_1i5os0e1
- - content: |-
-
- Search returns irrelevant. Might need broader query. Let's try "animal taxonomy major classifications".
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"animal taxonomy major classifications\""}'
- name: search_and_answer
- id: call_6weik2xe
- type: function
- - content: |-
- [9ff0d394-1227-4e47-9c52-445c227a10e4] [rank 1 of 2]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
-
- [6143420a-0443-4c95-844d-a006e88751ba] [rank 2 of 2]
- Source: "Dog Facts"
- Type: text
- Content:
- Document about dogs: Dogs are loyal companions that bark.
- role: tool
- tool_call_id: call_6weik2xe
- model: gpt-oss
- reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
- description: Answer to a search query with chunk references.
- name: final_result
- parameters:
- additionalProperties: false
- properties:
- answer:
- description: The answer to the question
- type: string
- cited_chunks:
- description: IDs of chunks used to form the answer
- items:
- type: string
- type: array
- confidence:
- default: 1.0
- description: Confidence score for this answer (0-1)
- maximum: 1.0
- minimum: 0.0
- type: number
- query:
- description: The question that was answered
- type: string
- required:
- - query
- - answer
- type: object
- type: function
- uri: http://localhost:11434/v1/chat/completions
- response:
- headers:
- content-length:
- - '367'
+ - '931'
content-type:
- application/json
parsed_body:
@@ -768,17 +164,23 @@ interactions:
- finish_reason: stop
index: 0
message:
- content: I’m sorry, but I couldn’t find relevant information in the available documents.
+ content: |-
+ {
+ "is_complete": false,
+ "reasoning": "The user’s initial query is very broad and could refer to any number of aspects—taxonomy, behavior, conservation, ecological roles, etc. To start a focused investigation, we first need to determine the specific topic of interest. A useful first question is to clarify the user’s intent regarding ‘animals’.",
+ "next_question": "What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation status, habitats, or something else)?"
+ }
+ reasoning: 'Need to propose first question. Ask about scope: species, habitats, etc. Provide sub-question.'
role: assistant
- created: 1769799589
- id: chatcmpl-163
+ created: 1772626928
+ id: chatcmpl-983
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 20
- prompt_tokens: 976
- total_tokens: 996
+ completion_tokens: 112
+ prompt_tokens: 285
+ total_tokens: 397
status:
code: 200
message: OK
@@ -791,7 +193,7 @@ interactions:
connection:
- keep-alive
content-length:
- - '4463'
+ - '2967'
content-type:
- application/json
host:
@@ -844,92 +246,16 @@ interactions:
- Be concise and direct; avoid meta commentary about the process.
- Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: What are the major taxonomic classifications of animals?
- role: user
- - content: |-
-
- Need search.
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"major taxonomic classifications of animals taxonomy major groups animals"}'
- name: search_and_answer
- id: call_1i5os0e1
- type: function
- - content: |-
- [9ff0d394-1227-4e47-9c52-445c227a10e4] [rank 1 of 2]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
-
- [6143420a-0443-4c95-844d-a006e88751ba] [rank 2 of 2]
- Source: "Dog Facts"
- Type: text
- Content:
- Document about dogs: Dogs are loyal companions that bark.
- role: tool
- tool_call_id: call_1i5os0e1
- - content: |-
-
- Search returns irrelevant. Might need broader query. Let's try "animal taxonomy major classifications".
-
- role: assistant
- tool_calls:
- - function:
- arguments: '{"limit":5,"query":"animal taxonomy major classifications\""}'
- name: search_and_answer
- id: call_6weik2xe
- type: function
- - content: |-
- [9ff0d394-1227-4e47-9c52-445c227a10e4] [rank 1 of 2]
- Source: "Cat Facts"
- Type: text
- Content:
- Document about cats: Cats are small furry mammals that purr.
-
- [6143420a-0443-4c95-844d-a006e88751ba] [rank 2 of 2]
- Source: "Dog Facts"
- Type: text
- Content:
- Document about dogs: Dogs are loyal companions that bark.
- role: tool
- tool_call_id: call_6weik2xe
- - content: I’m sorry, but I couldn’t find relevant information in the available documents.
- role: assistant
- - content: |-
- Validation feedback:
- Please include your response in a tool call.
-
- Fix the errors and try again.
+ - content: What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation status,
+ habitats, or something else)?
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
- description: Search the knowledge base for relevant documents.
- name: search_and_answer
- parameters:
- additionalProperties: false
- properties:
- limit:
- anyOf:
- - type: integer
- - type: 'null'
- default: null
- query:
- type: string
- required:
- - query
- type: object
- type: function
- - function:
+ response_format:
+ json_schema:
description: Answer to a search query with chunk references.
- name: final_result
- parameters:
+ name: RawSearchAnswer
+ schema:
additionalProperties: false
properties:
answer:
@@ -953,12 +279,33 @@ interactions:
- query
- answer
type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '673'
+ - '560'
content-type:
- application/json
parsed_body:
@@ -967,25 +314,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: Need to return via functions.final_result.
+ reasoning: We need to search.
role: assistant
tool_calls:
- function:
- arguments: '{"query":"What are the major taxonomic classifications of animals?","answer":"I’m sorry, but I couldn’t
- find relevant information in the available documents.","cited_chunks":[],"confidence":0}'
- name: final_result
- id: call_sxru3fr1
+ arguments: '{"limit":5,"query":"specific aspect of animals to learn about taxonomy behavior conservation status
+ habitats"}'
+ name: search_and_answer
+ id: call_w8oaem61
index: 0
type: function
- created: 1769799592
- id: chatcmpl-505
+ created: 1772626929
+ id: chatcmpl-906
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 70
- prompt_tokens: 1021
- total_tokens: 1091
+ completion_tokens: 45
+ prompt_tokens: 563
+ total_tokens: 608
status:
code: 200
message: OK
@@ -998,7 +345,47 @@ interactions:
connection:
- keep-alive
content-length:
- - '2114'
+ - '158'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - specific aspect of animals to learn about taxonomy behavior conservation status habitats
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: aUCTuQEHOzzIqTa9K1scvZ1wrbo4/CY9Qi2DPZieubxG0m08LAcUOhLyvTy8JyM8jG9GuuoBEbxBevU8jwsHu5SGhj3Sj4i5PGQ0vUks/btFC4i8dDmxO7tlEbzFam49BZ9cOwT6XL14kJW8PUFDvINKKT1/2ba8UMlMPOlH4Lxvt1s9JzdsvDz8+Dvv5fi7U1nfu9yhGLyrRle8EctLvFtuRjwchlC9LgH8POZAqDxOAZi8TPRGvITJb7qtrSY9Cu+cvDnzgLxoQ047Ss/uO3Rwq7rcMZq8RgzPPN/N8rwYdgQ9p3F5vLAUYTwN1bI85EkKuzI/lLwn+zW8yb7hvOCIsrsKN8S8ccGivPVbRL0GtRI8R3VHPCy9J7wEgAk9MIbgvMSjRbsfTYQ6IpHBvEblULlAJQI9d/hCvFuqnDxPFzW6/cpyPHTEHTxR7S89a1rvPEPxRz1LRx48BHB6O7N4ZL2IyBy8bVWJPMGYgjvpRS273sdOOhgeoLtjcgs8pUMxOwdHfLzj9He77bCiPIA+B7xR5w08FGk/PZyH7btrM4s8NpEIvWOfYrxV6Wu6isvYOkkRDLz9nDi6kmaIO6rZ1zrHNBu96NaCu406C7ux/qk8nbKZPAgemTrg/eE8tu7Iu6UD0Ts5fgA8KE70O6Otqjzw4BW8A0zJuiDB9rpN1M28LECHPB8B8TwpOS29csugPLisJ7wF8zu7kYnsurSw5zl/udu5/mk6uqbKbzyXm3W75iclvIrgM7x1rS09khyOO6hqgbz5rsg6QwXxO6EVBT1o3hO8SittPATkILyJQzq8ALarPN726DuuclY8A43MvHHIibwBE0Q8XVSau3QmyTsVKmQ8J+2avNC277vgOFg5sqV4PFqMADzbb268nGfqO/SSiTtweys7wXuwuzKxYjx3REM7c7JxvDwPlDsQI1q8HH/VPHpPobsxTYs7XFJKvK5YmzzeLuw7IWIaO6STqTsMsQY7zspNvMkPlzzkJqk7BUOvu4OG5bxoleK8P4qfuxHe+rvRweq6cUrpvIbFCbyd3V478eCEulyRKD2tmRE7EkCnuqlygLoikJS7qja+u6KL1LtC5iQ8v2zYvIl72DsHR5G8AJ0gOwd3OzyQokw7kuAdvOYE8Tq9NUu65eL1vBgkwrxtw+c8CUEJPbYlM7p0VHk80yFtvAU6sDvAQMy8ETqgPI1hCTwImBg83wEnvE+u4juM5ya9PrnKO0C9Vrz0r5W8oZbCOqoooLtve5O8NhxTvM/PcjzTIaW8q64OvZWQ87wRVrK8Eu3KO2T337tkpUS8rfizu/yO47w5gk085VP3u/7SGjyzjh48woOCPG0JvLxkO0+83/ohu5mZ2LxtDpK8Dj9+vKq4Tzw2qdK6ZP5RvKuYdjuFwt67sntLPNjNlriuobK86nSDvCCSJT2rcna8pkWCPdKxCzv72RU8/2KaOzfxSTwjn2G8PWOIO5RIoLtB1m484PdQPZsTPbwAJoC8AdVFPKMCCj0kN2a8iE6GPCp92rwO+Bc8U33dvCBJ0zwvDLE8biKkPPhRFDxJYUW84TnrvIPmZbxpYtI7BphfvHeP5ru6l6S8ZaNBO6n+CbxZick747HpPAPgODxucZk8+m3+utAg4jvBUoa91MAKPO5UG7weWlq82dQiPC+8TTz3CTW8NTAQvbSJDbzhaI48Kd2jvNwxMbssRHk7JaGlO1WG7jsIEK26l1azPHbHlTwkg3g8sDvwPO4HJLvCpAY97z20u6D0pTya2K28t9XKvE+udjut0ly8IZgEveeTiTxPGp88FADRO1PAYDrBCC68dbZzPPjyFb3QINu7ilq5uihUYrrMSiO8p5LDvIw+oLwmBOi7edzsu9RYk7wr9Ig6f2OiOuah+DyDzb6770s9PIyUQDzrmly9Fmj0vNc7FzwNujy8862PPDGeHb0Vsso7mYx8vFCiEj1XnxG8jkQiPONpEzsXXBS9h7wkPZH0nztVXJe8KKCfvLdxSzxqRia9Xxs/vNhCyLtFmNW6GzgZPY3Pp7zcznE8egJSu+KrAj1OB2C7oxdxvMPKTLz4sZU5+tDwOveatzxlRJ07P1nVPN0frbl5pps6duc0PK2+KzyDz4s8/p44vM10C7w46E68EH7yvHJZtbzJhhk8D1E1vVq8tzryc3o9RDqgPFY7mzzhk4i8oPTgPO+Vf7z6HH68HjMwO9kewruF0HQ82syHvNjBnbwxOY67jJkDPIJD5Tq7y/E6KWMVPDNGlTwpI8m6RmhHvCOYGbznCj08ihqMvCfiyDu2A4M8PkcXPeO83zyE9oS73b+2u3YYzrwGMQe8T0NtvKnM5LsGODU8m8n0O/8ZszxgaBs8kBjDvG48mjubZKe6wwUJvRnSzDuyC+K8JCeXO7yFhbj4KQQ8eiMqPKo4r7x2OoQ8gyaUO6GBr7z+wce88dr1u+iT0b0Vjy08h+bgPMtxxLza3bu8GwZ2uw07q7xyhuu7kmNDPIz1Qrt2NVO8AvnfvKd147wV3c26a8OROypwkjtx/Fo8EYYqvOBe4jtiTgU8RoQGPMiB5Dywjok8Qf11PMXdCrvuBg09CuqRO1Cm2Ty2KcC8Q5/Qu8ux5Twd+0S8ukArPKznp7r7G/Q820u3OyhAezzR0x+9dJyOPLy5XDvU4mC76exEPYoPgzwntgm9OdzjvHs6kTzM3U48ERmCvI2dqrsMV7k89OGWPOBhijydjyy7QReMvPVhBz3Hh4C6O/ovvKP5GTuG5Mu7K34VPdqX+bzI19o6PZJQu8TUHrwVX3a8sh2OPPAxkjzOS0K9aqEevHOb9LmrOeO7+dZaPN7q4LssvaQ87APVvMSBgzwhEZY8gRiMPS16ojz7BPE74IGEPL9xBL23SOU3+JioPOzOBj3dpvy8afOCPOpYA7zcUmW9YHtDPGiYpDzoBw68hNyduy6c4rxis5C8wDEQPYHvNDzrsw89mRgku4Dp4TwPS8u8YjflPGS/c7yaIai8rsFXuiHENb36ckq6ozo8vMRiorwekdk7xKPju/xo4DxzcKC7q+zFOyPx5ryF+xe8TepDPBJc0DyayKC9f+SZvOJlpjs4rPa7G4mwO6QKCL3HaWk8G3RwPG8XaTz2hcY8upifPOD1RbyodKq8tqGvu9w6y7xTccW8Nfx7PKcuTLzbeFO8NsROO7YD97xPZoo8HKyNO9mWz7ymZam8m5WiPBNDALumFM08K63bO4Li6TxHNtq8BQMfO0nzhru1rDe9iGYGvXevzTxdmsg7l6uyO5JatLvzwAe8AcT+vEyREL0yFC+9kxlkPF50V7xbgyI8YXhuPBAjdL1rIhY7J7fOPBU29rxnvaa8O9wYvGhFCDyMgAA9Efv5u6uUSDyibYu8h9wSvWlcwzxRWoO7OvETvUF1ADxkF4g7NYyFPK9GWbtMhzA9HzvovELzp7tXWlu71QQxPL9pvzxt96C7Rj1KuwKLrryTNcw6TU+rOboJsTxGH1s85gwyObUixLt2iUi7RcehvAgwUrxTbSi8WAcQOxuBpTzJXTa8H22IvVaQ5bzpdwg9nAurvGTa2rsur+K8h4WKvKJVSzuySxC7tZbDvBW15DtF+BQ8CJCcvMn5ALx38a28nvr0uz9fezxWmxS9OL8GPOQasjzvMw67Qf5kPCm9rjzXQdw8QLCNPPMembzIS928tSCWPAuhPLufPP87E2POvD1C2Tv1Q8a8ckF1vIjwSzthiK07wftuO4FiR7wy5he8Y+Q2PbygzzzI95s80WgoPByeKz1QC4e8Zz5FvT3bR72gAOk656Ppu6Bph7yDRte8nAyXO3DjM7w2HJK8EI+1PNXLk7tBahW8PCW4vDMEDLytB6G894oTvPVxPbrPeL88nkPJPE6nED2xFF67phlkOzkSCj3PexG9qtXZuv+vcrsr3yk8BaoCPLjgerwkvSe8A0PTvAwFPbzZujk9gBsKvYeaAzzok2K9BXynvMfLGbz93ME8cDq8O4Lmujxj/7+8PusbvbxhHLxwc1I8GQLSOph3QbyGGtE8SvZzvIDnkLzkM467OUTvPEPllbySC867q/FrOyqyyTz0c+k6XB0bvVnZTjxuj8O8ZAaTPNNuELvSB0E8g34SPUiQMrxCvN+8EbXfvB24uzsN5dq89FMEvUkunryuYTY8TjcoOwZ3mrzy+X87IKFnvADjFr32bc47+aNNvQuWX7xvnyw9NdtYPIeTL73/vLs7fNAfvFo9pjssR9m8MP0PvZD8ezwzhNy7bLY2vK1cRTvFedE80SraOyoyET2IhEK8s3HHPLF2Ib20TK48edUiO81eAzwjx+y8X1g5PIXRrDyuagK8IoHBOxkpkr1fFLE6yoP4O6nEnTyFYw49PHDLO5KKDLtHd8G8PwI2PCQQkzwMlJW8lFK5u5iBDD0RJqc8+TckvObzgjx8agm72d+BvC5lYbzn2mM7vF7bvE93u7xFgkm8DXLsOmeyvTxYFo887wrNvL0WmLsUzy68Sv//O0K2MDve+rI8fVluPI8sKTwzEU0908OLPNeuUTtXJg49nNoeOqQfiTsEVRG8IzGauzDr+bsfWAQ90JqfvJcVK7gHbGC9l+XKPMdbZzxhcUy8SPYdPBqCurydcKs6mAw1PBu9bzsF4C898C9kvErFHjwUMgw8VlhRPHdZtbwiU7o728VpvMRveDwPKoG8014cPS9TwzzUno28RgZxPJRgRDzOnyq79fYGvADYILxciZg6ODzWPKWbC73hG948pcCRO0DkSrtDYHe7/JKtvIkVDD1jsSK9U/lkOo7v+juJVQ67bwvyuzZaxTwVAB07wR2ivBN3HrzPul28xqxsO9uYNrzSzz2919ddPPhuhbxbFyy9ci8WPShO5zoiUaM8Au4kvJQ5hbxy3Ho8+o8+vPhfNL1lRD+9/KCaPfyAr7w0BVe8NkwmvINGN72esiU83arwvAdp0LuoTf+7flWtumO2zbth8O87iZmlO8+4uztBgx09XGxSvJGZK72+wM67hKWQu6tL5zz0elw8cXKTPMeCYTzpvi876GTuO+4T4Dz1LBc8f3oNPDcCVLyGw028JHfhPKClALwcyga96fggPBBGrrzc/aA8nbUBPUrvxzvr28s7uum0Os/8WTsnUZs66pPjPOuTMrywZga8+WIavIrK87sgYMG8GDNavF+uoDtPZK07ZhE2PABxlbseyA89YbKWPGoEsjyKowW9KLqTOx55zzyete27Pd6eOu2u37xBXqY7MjmEvCqpSD3UC3k8PlsPO7B7BrxKyoE8Y8VRPAN38Tx9xDA79mkMvZ4tD7wbeoA7JANDvOWx87yom9Q8eXWUPIlU9jtrgI47EicivSBSIz0H+Jm8A2iHPES/obwlVH+8HN4gPOQy/rudbzi7maWCvAXqnruj2i+8FeadvP78g7zQNys8MA6EvcG0VLxgiJI7E6KkOcYt6TzF8RG9ISslPJMaDjyF1QG9UqEqvDFR/Tx67om7j4INvCaM4DpEHfI7Spcjvdft27teoxU7kpj5uyTioryJSiW8hOOKutlVYjqMzIO8321XvC0s+TsHYF88NqIhPLnKqTxJc1E7AR8KOy38Wjv/HAi7jWt/O9vJJDpgyKY8UKy0OgB78TsjWCE9KAUhPH7RhDr1HiW8JSe4POG4x7wZzRg7z8FWu+KIh7wFQFu83hITvOa9ljyvna28Fvy+PCYjf7zTWQM8I/dku+tNM72w7+I7ILndvEpa7TuMBeW8raWOvE36qjyRqsO8tTBgvIk7ODxc3z47Eqe1OxWSBjy+ChI91VarPIFmljyKZeG89qSKO+ihSTzczCs9F/BGvIeIFbvi19g8ApptOypM2bseGRM8dsgdvMwKiDzQto28Q/Gqu6i3VrxE+hy9KHilPH2opjvnRAk9xcwnvMfLnzyRtAa8fhmAPOm9U7t6KaU85eeAu1RaFDxTzgW8VeXpvDetWDt1dKM7IoGgvFcYqjwalRm8S4GVO9GlPDwsp7y8E7elvATLgDucNEM6JwIcPdUrVTtScrC8FqqHPWLkurt5v1u8cPomuy1VHTyIxgs8dH3COrG5jDyRkia8ZHGHu0+6kbyQ00+85nKhPBpJ+zwd9Lu7RdR0PK4veLyQxCO8kCpBPHgeDTwxSyI89N+fu5aAdzszpKc7mQNHuhO5mDw787i8leVguwjaQztLQQE9MWqkvLjsmzzX6cW7Z5TpPNpwQrz0kx28pP/MO90+/btWYos8PqZpvPsIHTtrBoE83tESPWU01LzLQAC8K5I/vfxQDDz1vdi6yGTbPEKruDuKj/68nzWpO1FkyDz0gJU7L2uYvBO2jjsMB3Q8huQSu0esUDxDnPq7QuZbPPe1bbxCvku6184GvQ3FjbsTcNm83TdSPDPfQb12ADi8evYHPI0HHjvtq9O74Wq4t7yR1DzeqJk65k9KPRwov7x3s7w8pbJyOxv89zxcLJq8j7edPBphU7xwQxW8Fvk5O3KkjbxUzYO7/+uePBNXD7vji1W8kjTLuvxCaTzAVou8ANLYPL3cOLtLBeg6Wt/0u07eOTxWaA08H3Q9PeEv+7wGWCY7I3FrvMhAUr1+tMw83J7uPGq+nTv6Qtc4l71eO9x9Dz3YZ2s981P5OzCzGLu7VIs74pSVPH97kTvXXbu8pIEvOzk6k7w9tIc8XZmWu0IdFDv3hyI9EB7ku7g22LysG1w8nZiCuyAPF7wniJK8JVtMPDj5RTyB7Lk6bQvqO9ll5jnCuFU83o9KPAncBD2WLQk9xUULvD5c1zo88Kg8ejluuxxS8rtzrPy8E/shu21Rh7uWiMU8B2e5PIuhyzttGbo7TFOLvJADB7zHdaY7gkJnPYRhgLyTmia9QTSWPDZ6jDsPhPG8/z8ou9f+vruktOq8/9/Guel1LLxlRpi7xUb+OwO/drxswIm8JvIEPOVhvztLGxS67c7YvCuAnztVW5s8O1WAvLHG47zRPOs77iOZvDgPlLuKq1G8pWHFPJKOSzu6re27aG3ovAde5Ts5AHE7oojovD/8JbypJqk8BVZZOy/HPDzUoK68GNp1uzaWqrsatiG8BqIpOXdQBbpye888yHt/vIzeCz3dMVQ8c0lXPF9gVj1HpOM8iEIaPSLRlLylqaK8ZRckvPuTKrwOPyW8I4PJuwv8s7zKae+8tIOEPFv8yzvynSS9v0wAO5M/2jmkeIE8CoiCPN/HNzvVE8W76u5LPBavjTteSZY8tmQqPP3vFjxS2JC8ennrOlZTg7wcgCu7kn2dO9Oc/zv9VBA85YlxPDRQIT0KCd87cYVQOiTfXLvAkDa8I6niPIDUPzyquxu8OBg2Omd70rrL5hG8ALckPDhgCbw877E8DgeDvOhNAD11sIs8utQKvUJLnDvcb608rDQbPYs0obw1jne8XGjOuo3zLzzRbAO8eXOQvOt39TxY3QK73OMhPE9QDD3QNvK5ZosrvE1ZHzz1v8o8yPJyvHlzI7wt6jY8cBgTPM0YrbyIENK8OpHmO0bUAj1qxpC6EwEEvBXH07y+Ga28REhXPC+mzrxI0bW7VPPWO18IBj2b1Se7vuuwvFXDCLsHeIa86y5wvBbRbzsdhRy7UCzlvM4k3bxlMWa8N7U0vXenbbsyR3o8zoBCvBQzHTz6Mts84lZ2PB6/ujwUnNM4WkANvfyXhLzEzOs8Ak1dvFj7Ezo5aJy8gyO4O0Ciurwvkmw8ttM8vASZKL2Edom6g9wxPWSYArsbryI8BjUmO0sKu7rlVVg81latu25WtDtOEi88kSbiOqy3djwu7zI8MvHxPBK0VryOoC284Qabu88I/btNV1G9RCkAu5c90LycP6M8Cq6dvALAnLwEVRg9cCKKPKA9kbv8Nm48iGEfvH/51rjCFEQ9fqrSPKW/IDwWgYi9w7MXvOs/Gb1+AmO6+QirPPbJoDz72Sk8hwwevfBdLbtjxIK8KEibPOptf7z3Joe8b5z5Okdks7o/85a86raRvF/KE7uQEQi9A4FmPJ20tLvhEd68plIhPH+ZY7y+xBA8FDVbPDeVVDz9zVa7azUqPGVWCzyY+EC7rlEkPUHfmTw+XUU8fWqRujVKUrw/JaK8pxjLvFjPrbw/iR86u1L8uvfipTwctP46qn2Rum5cibsYGJg8N6DEutYIkjkk2pA8mG+xvAm8grx74HS8J6UkvFoiRju90qS8N7AovMtFZ7uoMtk6KLOOPKKlBTzIiyI747idu+GtOLxq9Qs8aVM3PCzKIzzKQpQ5qknBO7Or6jvAbDG87JBRvRNPBD0wc6s8xDoWPXiW+bsch9o7ccO+uyPVzjx2mTq9V1gDPCVPsLy9XZC8WIJ5PF7D8zxuabO77RWbvEqsSr3Glke6FIgIPAyoNz2HcLq8sbmQvGQqdrsh0p87CxyoOlqHkzxYbQA81fTNOjxMfbxkdIs7+kcaPXETSTnXiso8ndOtulY3GzwEcQO9AGkDO3rSEDzj+627CJZivN+gibtw3dW8etzSvGwKpzwCq9O6Lzg1PXomZry/5Ce9O+2Ju/biwTtK9EC8AJ1MO7v9Vjyr5768SgT2PFcdg7xDnum77BlpPOAJsTzWtXG7dS+6Ol3LILxwHlu8+4E9PAfdODx9/8c8YvlbPdLquTxXmWy8w5fpu1lkzDzOZL48Tw/YvF+WTTy9Hpy839w5PPuw9DuN/xY8gDMUPH9DpLsoGaG8FgbbvKfZ17wQAj494nwqvEo4Dr3lxWq7+iYrO5gE/TxvgjC9Vw08PMUHrjv+XLk7ECcYu5P8QTzh0Rg8S/DFO0hxiTxtVZM83+veuCxNrTzuFIQ8qDoOPIBoaDs7lds7er8VNgbtrLxn/gi9nCgRPb33DTwOykO75j6GvJDdbLwgZ5M6mrcMvQ+RzDyHoUQ7aNSAu4ctijwQSgw9znsIvc9geLxXLjW93gEOOwZX9ryRyNi7PKcyuyze0LzoyNK8x9OtPA/yb7t1rP282856vD+noTz+mnc7Nf44PAwqUzw5vdc7cU+rvCaeuzxVMd+60dmlPNQlJbzwdl28PC3NO8IRqDuEUOS88ZnTO+6Y+Lp/gMY8ZHCrPOA2mjsft827LgzuuwriSbwM6Kg8gAf9Ob6LozzDvbA7qNFUO2y8mLt5JzY8aMiYO7h8DzvY2GE85INkO6lAcDyHc0E8kMTLvGkqgTxJurg7hzMcPRSCwTwtweW6WjByPLuoGj1lGsA7urBpur9bzDxzvr28J85CvB6libwgoTy7mJdsvILCGLx0v5q8keXdvGifWDzZp7O7vnsYvN/jlTtKT4C8v1QDPQXHnTz+ogM84Zdcu4GEHzwLfKA7GwwIPHi4Cr0ii5o8fzvwPFPwpDt/Ijy7/ugMO7bmGbwQpg08lQZEvKKEWzxMBdq83FVsvGGiYDv+G/u8LdORO7wGKLvDaWi8iGhvO8z8szzCQqo8t19vOxHw3DzPDy68NMTOvAhw/DxCiDa9EQg5O8hlabyiQ368S0QZvC5xW7x43Xy7Yz9hOxwQLjzm6Zi8C8d3PGfanrvkHSy9OTKJuzt8tTxPD5a8A6ssOvmPHT2sQxu7Uy/rPBxs0DsN1wc6aNjiu44XkbypIN07sra5Oj01bD0s32A8txpPvGm9NzyBcC08Fz5APLxqAj0ye9c8NFpTvFXXgLtbnam7qYkvPFVZw7xtj807Y0UBvQyRqjxO99Q7gVYPvRAbG7z+VBS95JmpvEsDFL2TKx68FYZzvKQTirykhNI7oqydumnAV7ycphG9xJgbPDscozzmakw8x/yaO12ZNbzP8sc8tNE3OQKJ2zwgej68mNLFufDqUjuJ6Bm9cQMuuRfz5bylwbs59NSbvCSHBz0Quii7dJpfOmneebx7nQ+9eiwxvGToybsrnAO8L/HsPCFutDr3YHi8Cjn2u4qIJDyiK4U8jeCrPFPsaTvWICE8iraWPAmLa7zoV6O8zIFcPFHWRLyFg7y71MHQvBSG1bzVMBI7uA44PZ0n77v677U8QSfcPG8W3TuKOrM8pYsUPDYhEDye6NI8j4XzuIkfC7kQ3Ys8BBdCPXkigbwxBga9PWoqO/ruWD1ushY8MKrrO9O+sbucEBI6iF6fPNN+BLtC2++70BoCPbOb0DegfUI5+gHZvP5q5Tr4wI88WolgvGgmMTsrar28mXBivI4gaT04sxk7BAXNO4c09zv4SNy7mjS5O/zNDz1fquE8RqGrvNAyljz+SJm8UNAuu6zcczzey0E8E9WGvJGv5zviZcQ8Hm2bOvCax7yWl7w5qaCFvED4GDy+K0697PVwvF/4lzuEiXW7tLITOojmo7sn0k88uKdNPE3w/jvGMFq7baEYPUk1kLwgHxg91jiGPNvqo7uHDry7rSVVPDup8zv+hEA8GUveuOyrNL0uCAC9Ifzmu9hv7bzakoE80EIou2xSI7khs7I8Dkrmu5VFAj3lbs86wXbCvMdwvbweNoG82Hr3OsqRfj1V99O8/vqdPGuJYLwLgIi8ZFL0u84i1Ls/7ae72jJ0vBmVIjwV1Wc75iAGvBCYLTwFxze9XqqGPAWIc7yHxdS8UpaXOYgglTxcZje8ey/aPJOtWbx4pwa6lcOFPc3QULpVpJi8JPKGvBcpj7v2GgY9x5YRvIQo8zxMqcw8MnnQOynVUby93Ye87duROxx48jzpxgC83t6kvMFoqrzMNlG8gIcEvEcM9LzftF68kS6GPBCZFL0/PYq8ABiDu+Nl4LxRsmI6ff2HPB248buyS+W8cjKkvO9Id7wJxss7youJPLwmr7zCRso7eGeKvGQfpzzeAKk78H/TO4nZVbxPrRa8MywFPNIb7zt9Hc07cmXjPJirFb0PJp+8DoqkvExELbzzhHq8kcSqvBf0ETxpy088M5nFPPJ7ijvaTuG8autRPBzlVrxIJSe7eQNZO4fv8LtAnjI8SFtQPMXUsTx6tN68O3/EO3ihVTyZ2Na8sGicPMw6IT25bFK9s0P4u8EF5DxoNii9tr6VOlgiTLvM5LG8JrOcu3t8Az0ebYk7k4nZvIo2BbsnwPu53HGruQUEW7tGr2M8Yr1kPLc9sTxoZ128pRGoO6LXdryF+ay7j4UcvMu/5zxMTQ68UUs3O7B4XrzA1RU9M1qlOkbvurz2mHG89UNCPImeuLwUa/I72ViBPKC+mTpD1NE7+te0ur+TTb37Kfe8M06duqvOC7y7m0I8kDo2PNwoNDyL2g07j3hHPDxaozyqwT+8nTucuy7JULwczB299v85PNkLbbwYXhe81F6/O5Sb/byt30s40O21Oyye5zufwZc7swKKvJa9srxxwK08x2ohPFFCqzlnjsI8wFPnOzMbkrwhVQI9ctDavIrhUbxZMRY8m+qHvK4e+jw/bdI73Pbluw5RKLyDIhS8XHMjPAOmbTzPvOi8618pPD9WPz3VDdK7XGkLPPtv6TxQfJ68zqR7PBHuzTyZoSU8ivAEPZbl57sh9JA842ZVuj6+0rvuySE73YlAvCi3V7wxckk8/lSEPAQcrDyDrQM7qqQXvCiXTjy0+E69O8jDOOBd0LwtKx47U1MJvZAeCryGPs67n4GvvP55Fzy+1Rq8a9yYvIrP0bxpWdW82mDOObWI1jvAeFI87aMHvbPQj71kowU9JdA+vIeW8jtCdYM7/agZvOL7hDsAdwM9AKcWPLikM7w6v2e6m0DAvG5wB73Tylc864K0PAfIlDx96/S8ZPIvvKigFTnp1r47FJZwvJa2sjvWEwO89naFvBrnDDzuA828A4M6PA6r+bsQpnC8Zqc5PC5jp7w7JjQ9IH2FvCvgzzvPCqw844WMvBzcUzzNXwC8t2uVO17g/zzQEdW7z9fIvEuBzzv1rRg8fz0VPSwoQDyywxW8VFN6vKfimby9Ppa7ouxHPFjAZbz+Xlc9g8rhO76TwTuTJiS8fIRJvHcDDTuEUrE7ZZ3NvMAxfzyxfqi6fiViPK6qKrzARhO8NCY7PayhHjyYvvQ7vHz8uyF5p7xrF8e8LdExvd65Gzudhjq8B1DZPGJUFLz/UZM8i/HEO2uaMDvdoQo8nb/NuwilMTxCZhu7DTGeuyxoyjog5gm9cKgBva1wULshohS9tNuotr9DAL2n/GY8yuiou+bearstNKC89GalvAg617w/ZHI81JBmPM8x4zu5rgk7TOwyPFJAKjxoUr688F6YvASr0jwDf6k6bqnePPykX7ycPCE9O9HHPBPRBTz6w/M7duEXPIjAVLzufP47+IizPA2wmTyFbBe8okq/u5273zyoebO7kecyPK1t7jzQXYS7PLgMvZOtBbzvgI88OUoMu6qPYTuV1s88dfQqvEC9cLynQCQ8o/D6vOgFIDpH1Nk8S3h6O21WgrzIoPc8szolvJD6ODxehi68bE+yu+eNujz0eUw6TakKOwiABDtw3/C7jwo6O5SEnzwraD27FLi6vGG2i7zxdsQ8b04UO9VlUDx2Tts8LP9FPKvT0Lrug4O8AVxyvH1dC7zW4pI8yXpwPIi4tbwmElK9ysOHuyhhXjyBwS08SBH2PBGp8zxLuim8mlcKvbREGDsIESS9rZ1PPPxoVLyIVw69RoAtvYNf0rt7WLA8CDZMu6KdyDxASGK8PFUcPEjUibvAquo7rKZ+PB8bRjzBIyq8/rP8PD6IJrn8cc26KlLovI5wlDzkAhQ7X9RjPGZKojz7Qao8+fcJPEwSBj0BwA68qLa3vBzarzyDPVE8cIcWPZhLYjt2yHg7ni4aPZgWqbzofsY8xVi9PMShtjw6Qa48eajUPMgw8Lox4vq7kklNPOwoWjulED486dviu/t/oLxsyuQ7Yko6vLp3QTzXgFE8loeqO0H0SDwxa0g7rEvdu/MpojzVZge94+qVO6ji3DvuhbI85Ja4O6zMEb3SdZ28XgHNvKrMWDxaI1c93V0+vKx+WrxEjoU8/CSdvBvH9rzjiJu7toosvJY/Cjt0cBc8p9CMuksv1js2Keq7VEQKuuD7K7zEBJw6d2QbPKvv1TxE52o8yCWaPFnxNLto6gG7B9n7PNclqDyaZRc70E9nu2Otojzf9UQ7+TmcvE5sXjwrZjG7ACO/PLDBXjzI3Fa8U9rAvHJsBTwxK687de0DPL07sLwN4qU8O5m1OmgzYTwfPwC79BKhPMOY37yB3wA8AJmDuxOb8rwQgIS8I0wDvKtCdDtClgK9BfsXPLnbTbvaraw67ceMu1GhDj31Lb260KRBPevJ5DyF+F+8hm45OymbRLx7eZg7k6Mavb3OBzyuUA28XxpKvUWWOTt++kc9F2LXPHzoJzvNsbk7r550O5twCTyA/Ma8dxicPMcSmLx5UmC8Ofw7vLO0uTyX5pm7rjWGPGq1obsHx6C7HmOpO6ho4zp+iy678crROYLXGTyiHi06j831vJOWeDwUAWo8mim3vK+FlLpImoK8BQEOuIw2n7xt/KW8SRYYO4EbRrzpunE8XexCPF9fpbyFRdc7kvVJvDC8mDy1iLE7rR8dvBc/B7wKJl88J7KkvJQCuLynpzA8kXATvL6wdDuUX5G8brsYvBAEX7y+mAK9SPJlO8WeIDqJ5g48QD37u5uk27xhJog8bgDWvIRAJrsBMYI8CB5FPJhZPLwVrDw8lEk/vFe6tTr9omy88q+FvA==
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 13
+ total_tokens: 13
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '3642'
content-type:
- application/json
host:
@@ -1007,77 +394,134 @@ interactions:
parsed_body:
messages:
- content: |-
- You are the research orchestrator evaluating gathered evidence.
+ You are a search and question-answering specialist.
- You have access to context that may include:
- - : Domain context for the conversation
- - : Previous Q&A pairs with confidence scores
+ Process:
+ 1. Call search_and_answer with relevant keywords from the question.
+ 2. Review the results ordered by relevance.
+ 3. If needed, perform follow-up searches with different keywords (max 3 total).
+ 4. Provide a concise answer based strictly on the retrieved content.
- Your task:
- 1. Review the provided evidence carefully
- 2. Assess whether it sufficiently answers the original question
- 3. Decide whether to continue research or synthesize
+ The search tool returns results like:
+ [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+ Source: "Document Title" > Section > Subsection
+ Type: paragraph
+ Content:
+ The actual text content here...
- Decision criteria:
- - Set is_complete=True if the evidence adequately answers the question
- - Set is_complete=False with a next_question if important gaps remain
+ [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+ Source: "Another Document"
+ Type: table
+ Content:
+ | Column 1 | Column 2 |
+ ...
- If not complete, propose exactly ONE high-value follow-up question in next_question:
- - Focus on the most critical gap not covered by prior_answers
- - The question must be standalone and self-contained
- - Avoid repeating questions that have already been answered
- - Include concrete entities, scope, and any qualifiers
+ Each result includes:
+ - chunk_id in brackets and rank position (rank 1 = most relevant)
+ - Source: document title and section hierarchy (when available)
+ - Type: content type like paragraph, table, code, list_item (when available)
+ - Content: the actual text
- Provide brief reasoning explaining your decision.
+ Output format:
+ - query: Echo the question you are answering
+ - answer: Your concise answer based on the retrieved content
+ - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+ - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+ IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+ Guidelines:
+ - Base answers strictly on retrieved content - do not use external knowledge.
+ - Use the Source and Type metadata to understand context.
+ - If multiple results are relevant, synthesize them coherently.
+ - If information is insufficient, say so clearly.
+ - Be concise and direct; avoid meta commentary about the process.
+ - Results are ordered by relevance, with rank 1 being most relevant.
role: system
- - content: |-
- Review the gathered evidence and decide whether to continue or synthesize.
-
-
- Tell me about animals
-
-
- What are the major taxonomic classifications of animals?
- I’m sorry, but I couldn’t find relevant information in the available documents.
- 0.0
- null
-
-
-
+ - content: What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation status,
+ habitats, or something else)?
role: user
+ - content: null
+ reasoning: We need to search.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"limit":5,"query":"specific aspect of animals to learn about taxonomy behavior conservation status
+ habitats"}'
+ name: search_and_answer
+ id: call_w8oaem61
+ type: function
+ - content: |-
+ [4962b4eb-c501-4351-a95b-2a63839a76de] [rank 1 of 2]
+ Source: "Cat Facts"
+ Type: text
+ Content:
+ Document about cats: Cats are small furry mammals that purr.
+
+ [1d66aecd-6092-4210-a43f-ab8c746ebe7c] [rank 2 of 2]
+ Source: "Dog Facts"
+ Type: text
+ Content:
+ Document about dogs: Dogs are loyal companions that bark.
+ role: tool
+ tool_call_id: call_w8oaem61
model: gpt-oss
reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Answer to a search query with chunk references.
+ name: RawSearchAnswer
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the question
+ type: string
+ cited_chunks:
+ description: IDs of chunks used to form the answer
+ items:
+ type: string
+ type: array
+ confidence:
+ default: 1.0
+ description: Confidence score for this answer (0-1)
+ maximum: 1.0
+ minimum: 0.0
+ type: number
+ query:
+ description: The question that was answered
+ type: string
+ required:
+ - query
+ - answer
+ type: object
+ strict: false
+ type: json_schema
stream: false
tool_choice: auto
tools:
- function:
- description: Output from iterative planning step.
- name: final_result
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
parameters:
additionalProperties: false
properties:
- is_complete:
- description: Whether research is complete and can be synthesized
- type: boolean
- next_question:
+ limit:
anyOf:
- - type: string
+ - type: integer
- type: 'null'
default: null
- description: Next question to investigate, if not complete
- reasoning:
- description: Brief explanation of the decision
+ query:
type: string
required:
- - is_complete
- - reasoning
+ - query
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- - '1196'
+ - '569'
content-type:
- application/json
parsed_body:
@@ -1086,30 +530,25 @@ interactions:
index: 0
message:
content: ''
- reasoning: 'We need to evaluate. Original question: "Tell me about animals". Prior answer didn''t answer. Need to
- decide if we have enough evidence. We have none. So need to continue. Provide next question: probably "What are
- the major taxonomic classifications of animals?" or maybe "Can you provide an overview of animal taxonomy, including
- Kingdom, Phyla, etc." That covers gap.'
+ reasoning: No relevant. Need different query.
role: assistant
tool_calls:
- function:
- arguments: '{"is_complete":false,"next_question":"What are the major taxonomic classifications of animals, including
- kingdom, phylum, class, order, family, genus, and species?","reasoning":"The prior answer did not provide
- any information. We lack any evidence covering animal taxonomy. To fill this gap, a direct question requesting
- the major taxonomic classifications is the most valuable next step."}'
- name: final_result
- id: call_j90gc9sq
+ arguments: '{"limit":5,"query":"animals taxonomy practice question what specific aspect would like to learn
+ about"}'
+ name: search_and_answer
+ id: call_k99kr7g3
index: 0
type: function
- created: 1769799599
- id: chatcmpl-73
+ created: 1772626931
+ id: chatcmpl-652
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 175
- prompt_tokens: 441
- total_tokens: 616
+ completion_tokens: 47
+ prompt_tokens: 730
+ total_tokens: 777
status:
code: 200
message: OK
@@ -1122,7 +561,240 @@ interactions:
connection:
- keep-alive
content-length:
- - '3213'
+ - '151'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ encoding_format: base64
+ input:
+ - animals taxonomy practice question what specific aspect would like to learn about
+ model: qwen3-embedding:4b
+ uri: http://localhost:11434/v1/embeddings
+ response:
+ headers:
+ content-type:
+ - application/json
+ transfer-encoding:
+ - chunked
+ parsed_body:
+ data:
+ - embedding: 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
+ index: 0
+ object: embedding
+ model: qwen3-embedding:4b
+ object: list
+ usage:
+ prompt_tokens: 13
+ total_tokens: 13
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '4326'
+ content-type:
+ - application/json
+ host:
+ - localhost:11434
+ method: POST
+ parsed_body:
+ messages:
+ - content: |-
+ You are a search and question-answering specialist.
+
+ Process:
+ 1. Call search_and_answer with relevant keywords from the question.
+ 2. Review the results ordered by relevance.
+ 3. If needed, perform follow-up searches with different keywords (max 3 total).
+ 4. Provide a concise answer based strictly on the retrieved content.
+
+ The search tool returns results like:
+ [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+ Source: "Document Title" > Section > Subsection
+ Type: paragraph
+ Content:
+ The actual text content here...
+
+ [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+ Source: "Another Document"
+ Type: table
+ Content:
+ | Column 1 | Column 2 |
+ ...
+
+ Each result includes:
+ - chunk_id in brackets and rank position (rank 1 = most relevant)
+ - Source: document title and section hierarchy (when available)
+ - Type: content type like paragraph, table, code, list_item (when available)
+ - Content: the actual text
+
+ Output format:
+ - query: Echo the question you are answering
+ - answer: Your concise answer based on the retrieved content
+ - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+ - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+ IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+ Guidelines:
+ - Base answers strictly on retrieved content - do not use external knowledge.
+ - Use the Source and Type metadata to understand context.
+ - If multiple results are relevant, synthesize them coherently.
+ - If information is insufficient, say so clearly.
+ - Be concise and direct; avoid meta commentary about the process.
+ - Results are ordered by relevance, with rank 1 being most relevant.
+ role: system
+ - content: What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation status,
+ habitats, or something else)?
+ role: user
+ - content: null
+ reasoning: We need to search.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"limit":5,"query":"specific aspect of animals to learn about taxonomy behavior conservation status
+ habitats"}'
+ name: search_and_answer
+ id: call_w8oaem61
+ type: function
+ - content: |-
+ [4962b4eb-c501-4351-a95b-2a63839a76de] [rank 1 of 2]
+ Source: "Cat Facts"
+ Type: text
+ Content:
+ Document about cats: Cats are small furry mammals that purr.
+
+ [1d66aecd-6092-4210-a43f-ab8c746ebe7c] [rank 2 of 2]
+ Source: "Dog Facts"
+ Type: text
+ Content:
+ Document about dogs: Dogs are loyal companions that bark.
+ role: tool
+ tool_call_id: call_w8oaem61
+ - content: null
+ reasoning: No relevant. Need different query.
+ role: assistant
+ tool_calls:
+ - function:
+ arguments: '{"limit":5,"query":"animals taxonomy practice question what specific aspect would like to learn about"}'
+ name: search_and_answer
+ id: call_k99kr7g3
+ type: function
+ - content: |-
+ [4962b4eb-c501-4351-a95b-2a63839a76de] [rank 1 of 2]
+ Source: "Cat Facts"
+ Type: text
+ Content:
+ Document about cats: Cats are small furry mammals that purr.
+
+ [1d66aecd-6092-4210-a43f-ab8c746ebe7c] [rank 2 of 2]
+ Source: "Dog Facts"
+ Type: text
+ Content:
+ Document about dogs: Dogs are loyal companions that bark.
+ role: tool
+ tool_call_id: call_k99kr7g3
+ model: gpt-oss
+ reasoning_effort: low
+ response_format:
+ json_schema:
+ description: Answer to a search query with chunk references.
+ name: RawSearchAnswer
+ schema:
+ additionalProperties: false
+ properties:
+ answer:
+ description: The answer to the question
+ type: string
+ cited_chunks:
+ description: IDs of chunks used to form the answer
+ items:
+ type: string
+ type: array
+ confidence:
+ default: 1.0
+ description: Confidence score for this answer (0-1)
+ maximum: 1.0
+ minimum: 0.0
+ type: number
+ query:
+ description: The question that was answered
+ type: string
+ required:
+ - query
+ - answer
+ type: object
+ strict: false
+ type: json_schema
+ stream: false
+ tool_choice: auto
+ tools:
+ - function:
+ description: Search the knowledge base for relevant documents.
+ name: search_and_answer
+ parameters:
+ additionalProperties: false
+ properties:
+ limit:
+ anyOf:
+ - type: integer
+ - type: 'null'
+ default: null
+ query:
+ type: string
+ required:
+ - query
+ type: object
+ type: function
+ uri: http://localhost:11434/v1/chat/completions
+ response:
+ headers:
+ content-length:
+ - '613'
+ content-type:
+ - application/json
+ parsed_body:
+ choices:
+ - finish_reason: stop
+ index: 0
+ message:
+ content: '{ "query":"What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation
+ status, habitats, or something else)?", "answer":"I don’t have any relevant information about that question in
+ the retrieved documents.", "cited_chunks":[] }'
+ reasoning: No data. Probably cannot answer.
+ role: assistant
+ created: 1772626933
+ id: chatcmpl-360
+ model: gpt-oss
+ object: chat.completion
+ system_fingerprint: fp_ollama
+ usage:
+ completion_tokens: 58
+ prompt_tokens: 913
+ total_tokens: 971
+ status:
+ code: 200
+ message: OK
+- request:
+ headers:
+ accept:
+ - application/json
+ accept-encoding:
+ - gzip, deflate, zstd
+ connection:
+ - keep-alive
+ content-length:
+ - '3306'
content-type:
- application/json
host:
@@ -1167,9 +839,9 @@ interactions:
Tell me about animals
- What are the major taxonomic classifications of animals?
- I’m sorry, but I couldn’t find relevant information in the available documents.
- 0.0
+ What specific aspect of animals would you like to learn about (e.g., taxonomy, behavior, conservation status, habitats, or something else)?
+ I don’t have any relevant information about that question in the retrieved documents.
+ 1.0null
@@ -1179,13 +851,11 @@ interactions:
role: user
model: gpt-oss
reasoning_effort: low
- stream: false
- tool_choice: auto
- tools:
- - function:
+ response_format:
+ json_schema:
description: Final research report structure.
- name: final_result
- parameters:
+ name: ResearchReport
+ schema:
additionalProperties: false
properties:
conclusions:
@@ -1226,60 +896,47 @@ interactions:
- conclusions
- sources_summary
type: object
- type: function
+ strict: false
+ type: json_schema
+ stream: false
uri: http://localhost:11434/v1/chat/completions
response:
headers:
+ content-length:
+ - '1119'
content-type:
- application/json
- transfer-encoding:
- - chunked
parsed_body:
choices:
- - finish_reason: tool_calls
+ - finish_reason: stop
index: 0
message:
- content: ''
- reasoning: We need final report structure. Use functions.
+ content: |-
+ {
+ "title": "Lack of Documented Information on Animals",
+ "executive_summary": "No retrieved documents contain information about animals. Therefore, no specific details can be provided regarding taxonomy, behavior, conservation status, habitats, or any other aspect of animals.",
+ "main_findings": [
+ "All accessed documents indicated no relevant information about animals.",
+ "The prior assistant response confirms an absence of data.",
+ "No citations or sources reference animal-related content."
+ ],
+ "conclusions": [
+ "There is insufficient evidence to discuss animals.",
+ "Further research or additional source material is required to provide any insights."
+ ],
+ "sources_summary": "None"
+ }
+ reasoning: We need to produce report but have no evidence. So say no info.
role: assistant
- tool_calls:
- - function:
- arguments: '{"title":"Overview of Animal Diversity and Taxonomy","executive_summary":"Animals encompass a vast
- array of life forms classified into major taxonomic groups such as Chordata, Arthropoda, Mollusca, and several
- others. Within these kingdoms, species are further organized by class, order, family, genus, and species,
- reflecting evolutionary relationships. This structure facilitates understanding of animal physiology, behavior,
- and ecological roles.","main_findings":["The kingdom Animalia is divided into multiple phyla, including Chordata,
- Arthropoda, Mollusca, Annelida, Echinodermata, and Cnidaria.","Classifications progress from phylum to class,
- order, family, genus, and species, providing a hierarchical framework.","Chordata contains vertebrates like
- mammals, birds, reptiles, amphibians, and fish, as well as some invertebrate subphylum, such as Cephalochordata
- and Urochordata.","Arthropoda, the largest phylum, includes insects, arachnids, crustaceans, and myriapods,
- characterized by exoskeletons and segmented bodies.","Mollusca comprises soft-bodied organisms with shells,
- such as snails, clams, and octopuses.","Animal classification systems rely on morphological, genetic, and
- ecological data to determine relatedness among species.","Ecological roles vary widely, from predators and
- herbivores to decomposers and symbionts, impacting ecosystem functioning.","Phylogenetic analyses using DNA
- sequencing have refined taxonomic relationships within major animal groups."],"conclusions":["Animal classification
- is hierarchical and based on shared traits and genetic data.","Phylum-level distinctions capture major evolutionary
- divergences among animals.","Within each phylum, diverse classes reflect specialized adaptations.","Taxonomy
- aids in ecological and conservation research."],"recommendations":["Use molecular phylogenetics to resolve
- ambiguities in lower-level taxonomic groups.","Standardize morphological descriptors to improve cross-study
- comparability.","Maintain updated databases to reflect changes in taxonomic consensus."],"limitations":["Sources
- primarily describe taxonomic frameworks; specific species-level data were limited.","The report relies on
- secondary summaries rather than primary research articles.","Rapid taxonomic revisions may outpace the current
- synthesis."],"sources_summary":"Primary source: \"The Animal Kingdom: A Comprehensive Guide\" (pp. 42-85).
- Secondary guide: \"Molecular Taxonomy in Vertebrates\" (pp. 10-27)."}'
- name: final_result
- id: call_i523e9k6
- index: 0
- type: function
- created: 1769799618
- id: chatcmpl-384
+ created: 1772626938
+ id: chatcmpl-126
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
- completion_tokens: 521
- prompt_tokens: 623
- total_tokens: 1144
+ completion_tokens: 142
+ prompt_tokens: 523
+ total_tokens: 665
status:
code: 200
message: OK
diff --git a/tests/test_utils.py b/tests/test_utils.py
index fd3ad83d..4c2951bf 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -139,6 +139,38 @@ Emoji test: 🚀 ✅ 📝"""
assert "🚀" in result_markdown
+def test_structured_output_type_native():
+ from pydantic_ai.output import NativeOutput
+
+ from haiku.rag.utils import structured_output_type
+
+ model = get_model(ModelConfig(provider="openai", name="gpt-4o"))
+ result = structured_output_type(str, model)
+ assert isinstance(result, NativeOutput)
+ assert result.outputs is str
+
+
+def test_structured_output_type_tool_fallback():
+ from pydantic_ai.output import ToolOutput
+
+ from haiku.rag.utils import structured_output_type
+
+ model = get_model(ModelConfig(provider="ollama", name="qwen3"))
+ result = structured_output_type(str, model)
+ assert isinstance(result, ToolOutput)
+ assert result.output is str
+
+
+def test_structured_output_type_string_model():
+ from pydantic_ai.output import ToolOutput
+
+ from haiku.rag.utils import structured_output_type
+
+ result = structured_output_type(str, "unknown:model")
+ assert isinstance(result, ToolOutput)
+ assert result.output is str
+
+
def test_get_model_ollama():
"""Test get_model returns OpenAIChatModel for Ollama."""
model_config = ModelConfig(provider="ollama", name="llama3")