interactions:
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '116'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
encoding_format: base64
input:
- The company was founded in 1985 by Jane Smith.
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: 15
total_tokens: 15
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '127'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
encoding_format: base64
input:
- Our mission is to make technology accessible to everyone.
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: 11
total_tokens: 11
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '8159'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
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: Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
index: 0
type: function
created: 1769707352
id: chatcmpl-5
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 65
prompt_tokens: 1719
total_tokens: 1784
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '9143'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '680'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
index: 0
type: function
created: 1769707353
id: chatcmpl-973
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 86
prompt_tokens: 1971
total_tokens: 2057
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '10213'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '626'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: 'We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing
variables via __dict__? Let''s attempt: "print(__dict__.keys())".'
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
index: 0
type: function
created: 1769707355
id: chatcmpl-404
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 65
prompt_tokens: 2247
total_tokens: 2312
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '11089'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '593'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
index: 0
type: function
created: 1769707356
id: chatcmpl-596
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 54
prompt_tokens: 2464
total_tokens: 2518
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '93'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
encoding_format: base64
input:
- company founded mission
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:
- '11590'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '637'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
index: 0
type: function
created: 1769707358
id: chatcmpl-564
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 77
prompt_tokens: 2557
total_tokens: 2634
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '93'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
encoding_format: base64
input:
- company founded mission
model: qwen3-embedding:4b
uri: http://localhost:11434/v1/embeddings
response:
headers:
content-type:
- application/json
transfer-encoding:
- chunked
parsed_body:
data:
- embedding: TIsSuYSE3Txwv7E5gk7LOnJb6bkfxII9DnGXPaEaVDwJIiI8Q3rgO57vd7zAgOE4Uz7YOkYSsLzvB3O7oSxCvFncmLxBWja9n0CpO0disbtFwUq8fYwoPaScqD2YG2s9oPXsO4fAlryXtcG8Ek0bvYHKErxBF748P38GOlmJHr2b5808dyvXvA+7IzvubjC8aSQzvNCDArxMUDK68EUHPZGC4jy/LS29D615PJDd3DwAHr+8IvlEPKMr8TqddkM9L7fkO7JJqrzsBBg8ZqI2u7cW0Dw7HN28uZYwvJ45ETy9r3Y8ec8du8KLOzx+xK484r7nOvYbdrxA3Gm8VyQUvVBJz7rTUOa8/ynHOrLYCr2w8Eo7lYY1vLirqLy5ibk7omPqO8HiIjxieH+8usALvWgU7rvcrGU8+/guvGoPhjsgYAK8wH2KuxXiSDxscDg7QuQ4PGVPfbxmo2C8X0WNOxnszrzcTbG7v/PNO0BTXTuVQ5U6tZqEPL24YTv1NBU89UQNvFxaVrzkXeS8ynJNuoqAhrxjDcw7PbKFPPMiprzH8Xe8qo4AvcyTB716KA+8f/BaPIYSdru0d+87k07wuyS8NDz7mQU8wqLGvNt9Zrzax9q8BW8KPQBWcju4Iv48QUV6vLvgmjwPuzQ8xQKkO7MKRbyR77I7oa0au6nskryb2008fDnkPO7iJzzrUAq9hflcO/LJk7wSn3C8XwBKPC5AET2AP5C8aKwgvSSmkzxmMZO8xDStPK+mBjwV5D48OLs+u3bKBb2fR2G7pd+UPLLIoDykjvS6lTZPPKzLMby6XqE6QMhyPKir8rtqOgQ8UD9VvHmVvDyO0bc82D3oPA2kxDwei4A8bcWWuxWNYTycqAg7pPwIPS34BbzdW5A8iSzxuyVVNL0fqcA8IP4jvBMdurxYL8q8n8aHvEzcXDwzWpO841UbvQt8qLsc0GA7NycCPByAv7so77I8wFJ0O1chuDwJUqk7bDIivGtkfjxKvpo6lGTQueCPE7o+OQy9GkkUOz/tkTzzU768BwQrvBmQDLzxOvG8QOoXPKWxmTxvekQ8MJEAvMkQnTvvsXM8NJg1vHtu2Dod8Sy8Qpt0uxrzprzEhZu7gigMPJWAcrzU3La7maO7vHNZYruEuH08tegBvO04i7zzr2881GAJPHO8rDzFqRW7LgGCuqcYIzxeG2m8LCi4O0/9VDx9sUk7lsiQu7sXOTuY1fQ8KMTVPA2gMLzq5xS8k4XMuwyaqzsce+q8TpeavAktMTxuc+q7ls7qu/DwWLzej1U8hPOnO97MYbxGusu89NDpOuqEQbzY9Lc6yUTxu05cQLxncFS7J1QSO9xm0rxtbwE9mRZsvEA2krw7+hM5tYOLO9fL3Dy0ml64WoCkvIZHlLxO6oS8VVUwPJV/nTxg1ic88MUlPOLQijti8SA8kUqlPELJS7wbNQ+5cTVJuyBjkbwGdZa8gCEUO/FSlTtkshK6lwW9PFMQ3rxJD1M83TWxPBMqq7vMGnC8ln6EvCVaEz3s8h27YPK0vMKXjzxHOw678GmKuwHNAT0NmZu75EAvPeosLrxyWiI7Wx31uiR/KbxC9Ke7UyqkuyCPIDwYYmC8D7vau3AI+bmqtAu7aRBvvLHZF7s1SiW8XztAvVYxP7ulKiy8aW8cO5V97TvxtRi9BYH7vOqGl7tMTmW8PgrXvHgHsjrdqQg72GyKvddDhrw1Fa+8LkZ6vLwlPDwrnkg8E14JvJs+4TtGqxe9DDJ1uj51dDt1B+a86Areu/FRlDsEdcu7Uik+vDSRqzz+/Q08M2oIvFBwlbpWxKC8U/CWvDmsK7xWSGm8xdYhvD9azrzPR1E8WFaTu5mdD733G0w8A5mavLXbnTrvlUE6wSzBu/x83TzSqQy9mvSXO6q/NjxsfEU8BkEtvAVJDTynEkQ8Yh8iO4IS9jxu7oG8nVzBvKPMFz3qUB05By4GvFcbSTzMELS8SV6DvFin2rzDaP+8CyywvDxyUby6GCy8vkxwPKrTj7z2ObY8K2xtvHD6Db3/kjY8PC7zu4pxBz0GI1q7HJHJu5MOnDw8SUy72PicvP+CDTwX0lQ8FlkqvL9XNr0MMTS7XlqyO/TyhjyVf8e79/e2vI2T0DuYRVS8702DvEGRpDzLP568O0EmvdI1ZjyMPT89oT/tPAbJHbxAY6q878/IPKhsH7wkFGS9Or35PPcXqjwHfQi8D7vdvGAzYTx9bPo7KXe3vKBzQLwLIkY6wkM0PBUvuLwbSR48yL59PAqH0DxTuI+8e1pWvNktjbyVL088sJJ5PBoRCz3V6LI7fkjXOsEXiry5Nve7BQwVPaq+GbtCiR47nImfPER93jxWuZk8yLIKvFAQJjt9m0Y8zW8xPQ+5MDyo76q8QNCMuzgUMz3hX5U8rIHNO9AvOrobRtE6qrYdO9IqiDwbuCS9yXFAOydpcb1L49O7DFn1PFhPDb0n0Eq7RlFpvC5RaLxkqrS7He+XvJ7eZTz2NwG93vdcvNFBEr2nsgg9iqzGPI+IvzzwjHu8losKvedQLzxni1A7PglzuhpvXzthAhQ9TOIPvHVis7z5lMo8uVa6PLx9sTwsDCs922e3u3dCj7nNRVw9YIjNvNpX2rydeKI74b+wu30XkTxr9KO8G005PDpmFLz5Bcw89AnEvDZS5DwqU/k7JZEnvd4bo7qMd4g8MeAHPGSHsDoa0cA8FktDPDUWtDw8GhO9ZdKvvKhOtzxrbN47iGtzvMr/PzyfjsG7uFRLvBT9+bnPJrk8pfytPKu+jb1Wmh68RTToPH0k2zwWf9O7kOgDve8gTDpSmf28ISRDPItwozy2HKo8nlotvURfRTxdG2Q8h9hAPX+T9bxpl4G7a2x3vL/WcLydXFg8fP1jvASJiTwNp847oocAPIw2BzyLLSi9pVgJPeKDgzq3lsW8cJKtPK4LDrzHk227s7m+O+O7WTzLjpS8EThjuRxYnzupuHw8V37iu/p+QDyMF86774jOPL8z47t6z1A8oW3EvDog7boks/q8w5mWO+ydFzwho5u6IvFXPEEqhbsZTyW9KL+uvDS0GzyRtLK8BqJTvNvujbsCAXk83iHJO+pZAz3aLbY8eHOZOt98ALpkYPs7Wxs2PEReQDy9lTy9tUvPOwiMVbrXZ/S7cTifOwtY3rz3uU074qGwOrUZGb0d5tG8Z7AKPRnXFb06vPu8k4pMPcBA0ro/uSm9IjKkPIqCXbsiYdK8DdFkPBIuirt9iMe736UEvWuyjjzMn5s87/IuvHS9AL183+G8qghAvOkJoboW6jE738YeO8llhTwFWA28tlwJvNudLL1Etaa6Hs8UvMMRDzo6ncY8JJQQPFzwlbpsCGY8NZCDvFxM4DzkF6e7SCcgvbtvTLwEcl8727qPO97LgbpMpNC8v411Omn6l7z+ixQ8AosAvUR4Cr0qjeK8S2/JvGjvITutMm+8lA7XPFNiUbsqNzA7912CPEFfFjt+T2u8lHG4vBv6szzf8Pk6zLSMvBYAP7xxm4Q7GHDmukbb67ur1sG8/Z9GvLAE9buOGF89J5zdvIRH9rxFWwM99BkCvED/wrzmuu48BG4IvSkJijzEX5U8sKsePbknEL0vSKm879QOPJOvbzzMOs+7MRv8u5JttjwLhwu9+5UYvKs5+Luph5Y8io9VO+snX7yJfVo9r2FqPJBlCr1uKB085DOevEMQ5TwhTlG7Xk8IvaxsI7zRBgY9iHkGPKIJWbyLAEo7FfQHPDZnPTuInKM8h8I3PUF+Hj0IRQS8JiRAvUGJ77yt2VS8OBxlPJIqJL3l+768i4v3u6MlqDxEeR07TlGVOyA3HL3HIja9ji/+vPaS4bw7Xrk8MjqfvCD3DLsF1Kw82rQ4PYyryzs2+g07HIj9vKxm6TwyGwK8Q4cAPb4lqLxX3Se6df6YOovGwbzxPSk9tkzOOiUe1TsgjAq7rIkjvem6OjyYF5K8OXcLuzvEAjzgqzU8Nk9IPOnXzjyrCh082im/vLdVW7xTIT483DfHPGWPobv7VAa8XOmOvLL1Dby+ps+8/1s/vNgPJrx1yHi85nJMvN3blDzOvzY8/fP1vN6IIT1dqAW9kBgbPW5h0bsvNOM8nJCfOyaA7LzT2aK8NSBavLl/HT0UsvO8vAR9vKThQL0sxdg7WjUpPBxmqLzrrJI80I5nvMXn27xgWgC7hsV/u3b7Bz2l+Wm8vbIHvH/hdLqXyoc8NQ5LO6ADhLszp8C83bDIvMOZR7yiEpy7NgnjvGIDALwxVv08EksCPM4/ZTzznKA89gZZvAWNujwXne45U8rhOsWSCr0m/wG9fTETPGN7AL3sRVU8X+lCvJqf4rvvZsO73N6bPM6q+bvGMY48iYlmvBBYyjv1WrM8yqINPCdaVT2037Y7QNXnOLFI4jzxLMa8si46OIUfSju4h7Q8PrwRvMsYbLxwqwO99RAOvQX3tLzy8p08hLUxPM51VDwsQRo921oGvRMeAjzo14S8m1WgvFPRITsI3je9AUdfu5VEUj2vcBE99BYvO2rvwjxBY967TVKYvKdohDziVGg8961/vCdQC70q6iw8fraEvW7giru3OxC8/bppvA/iobvZt8Q85nFZvALw4rsMruy8anO+O4k5jTgvRXs8oaNCu+5BEz1ySqM8iiKjuyRiFrzpd8O7lskQu0Wpz7nXiey898D1PMltXLx3P6S8vZGqPE74G7tMbDS8TjGmvDg91rvDhC08R7pkPNFTxTzfQoo8fKD0OhVPdLv9B588seGVu4sGHz1D6pS89HVvvOnFK7sp53Q8/rc/PPmKBD3k9qc7NfHbO77UJrxTxZe7npoIvP6bJDw1I1i890m/u7ZYPDyAPoe8naSfum0MhzxWuPY7E3kAvKu6H7yF3v88tNplPCgcE716WRE8qhmwPJc0GLzgqVy8rQkGPErr6rzqeYy8ftuVvHJCDD1VklK94xvrvE0qFLzXeOg8KmUJvRVcPTzheQM8OifSPCULFL0JlUE9u8IkPGVHLT0BcIC8vEKXO8yUgLujKr08x2/dvB5BRT1WphA9edqAvDSBlbz6A8G7LjFJvK+sf7rnV5s70SJJu5ix87pbR5u7RmqbvMaoAr28HHA85FrgPGGHb7svP3S8v1ELPBxVfLoAMjG9/z4zO1UVxLth0pC6WF+LvIAdQLyCTKa8yK44Ow+w5jvpPwg9TRCZPOQTWLwsSPi8mlO4O39JDLvKs4e8RvpgPNecGrwlFog8FL0Su5w9XDxVxf+7Y3OmvHvBz7sCtmy7nLIOPVQ2krw7hAM9dAIVO76LazzS6b07isn1O0nQgLzqNNs7yaWePPCEz7wKU/W8OHG1vJb70jr7skK8SulePD/lxLxRYd67DPzyu7rguDuFUAO8j14sPBY/6TyTKmU71G/APEjfj7wDIrE8lyWHuzs3Ijz3xe07tfbpPBNiQz1N6QO8Hhovu/WXarsSjYI7yV6+vChGyLp5P8M71w8ePJWsKzvVyJC81JrgPG0MAjoka368nPvLPNQxdDtUQdu8Z+u7vHABqrxxLQG9cojRvCALF718I0s9XP9BO4LdjjzZ08g6vJ4TPKcGGzy0Ahs7RqI1vKzys7vrgNc6LTIKvHI/bTwxAFY73CHaPPvPvzu1vIE8nxUAuS1jhTv9FPy8h6PUuxt2kzyG5TE8AVC6vMPQhrvn+Fa9a2NRvL5dAb3CtV88YLNYPEPt8jtdvF28oRN8vENHartJDvS8LenaugfFOTz5U5S8MXl/vNdrfLwb8xG9alHUPMnLsjyj0qe8kJE5O9SvxTyTeWq6PCUdvIxNhjwA3Ek6Mw+HvBhyMDzsMSg9Kuvbu+NFojw5lwO6nGeBPBkDyju0iiM8m+HJusGdKrsM+OW8zjUiPSGDAr0LUhU8AhkQOqNLFzxnBAC9ZeHVO+6INbv7Rki8CJMVvDUhIjor8Zs8H62XvLg1FTxnBsA4R3OHvMl0GT2CY+48RHnTPDB59zw2ViM8CwMXPNROlTyzmrq7LS2LPIWrTjt+PYE8X/0pPVv1Vj06SDo8GIeJvOSm/Txx+JM881c6vGreUDt6D2E8es5/O29GprtWTAc98rR2O29jOj3pb6G7wiiWPKXnLrz9FxQ8mY49vGLVZ7wbXyy8pyXFPGUoWTu+A+A884BDvW8U17zTtZk7luKDPDwuuTunMxM93xETvQgY2zxrPWS8mrSMu7VAv7yxia27MVH6OvsqybywQJc85iyeO+9rDbzLw/M7/MGjPNvE4jydFxC8AjTPujtJPTw3Okq8bz+bPHDNJr1CZzQ80fJyPJu5pbrOPo46Y7L7u6HFjrzp6MA83B/WOz64Rjy4hwU8FKwrvIQKNL3cMJE8XMSVO1KPH72AGb+8AFHBur5jwryLKY07SVFUPHZKybwnSPE7MKmJvGE/UjuOGqk7Asj+PDBnZjxq36Y8Lapvuj02iTv/2ZM89ooDPdEsFDxfDf68wFSmPJgmtrzUNJW8RhKBPLx7OLzfEHm84GinO0CKWryRG4m8xiMPPaT1zjwCQCK7oRYXO/nKHruzvlg937QfO8Zdo7zonq873cSwu4W3RLw/0FE8rZnBPJU3xTzeu+w7aisdPfjSqDz0b0w9+uUHPdMbybpG4Gs8ZswKOoMxXrzyhi88yORovEe0Szu1Hfe8sh1pO8D8JDy+Bgw8da0ZvWX4Lbs3aBc9DWnUvMabWrxM/Iq6kPugvPBj/DwymEm77IkHPX8F37s2Rs27kSzgPKpsBD2w78Y7+iGvutpDCj1C1sE6OB+VvCDB5LykeCW7/4TYu3kanTwwbkm7BnWWPOYn3TtPhEI8k7MmPK/qEL3jeBS9i4j+OmAhl7zD06e84tyAPPMZET1pTY+7UA5nvI6TMTuRPRI71fgFPNYsort/uSU8dLZjOi7ZNbvaV9s7ZMa4PBiICLwZaYi7SRT4vLnr9TupRrc8/eCTuyYQCr1Wo3o8deiAvA8Dnrx3IKk8zyZEvPVjuTp+4p68nrKou7vx4rwQDJg6nJe+vAssBLitNCQ7jkXkOw7E6TymKcI6StsNvLmf9Lxh2VU8g0O6vG11oTyYsQ08/1i8vDXzrztItpG7caTrO/P2ND2LYlY8SAHYPAPw17wTGi+96dEIOw+jxbz+gT07f4M/O+hAJLxMhhW9Tnu0PKauN7sHcZO89zFPu1e9vLx4sbE8ec48vBjuJrxn9K+8IdmUO7jgJLo2NKK8cDqqPGO1orwdZce8HXhqu3V40bw7xue7z0eVOnpjDj3Wu4k7/hkMvXsbGD1Pxqa8t6LlPIspqrxAAfo892L1OxnPKjw+iho8gPPcPKMjAT2RMmQ8Ba88PEW4m7zpZ0o8ZCOHuxmZ8TzpFAK6wPD5vP+hKz1s40I8cnCVPFo9bryx5Xa8SMGlO8QpFbx7wNM8FVUSvUd4Bj1E9oo8Jqehu4IesDybz9082CGPPLY+xjt5oa+8IV6quzf5qbplSh49ImyFvL5b8rvwW6a8+O/NvA2P1zwMnTe86MnsvIrGzDsgbpy7v+2guyI2Er1qcmA8i9wZvShivzne1Ew8qt30OeMv4ruw8168SW3YvEVJOT3ISaK807dWvH7aYDylbUW7jvz3vG4oIbxb7My62J2ZPGU2/bpRcBI8Ob+tPP0qET0Tkz679ACLvC1kj7z8vFk8pATnu+IEmDw+bkI8csH3Ox0PgrxmdsC8qoh6uo0Xrrx8kFw86jV4PD8n1TwzD9w8Uz1dvHfQDDyM/x07Hg4jPMsrfTvDTDU8jYUlPQj6vrw4x3M8QJKfPHdN5bzRiAa9mI+auj6MVjzTQ9q8dAXavIqHBjzVL+g78O8ivBOPmrxm0fw8UWMUPf5HD70lZB08v/FOu2Oq1TslCgU916QePALPfTz+bWu7yB+xuzbpTrztvzm8BnqYPETywLyeI9088RrOO0fVzDwoZXG8LrfMPBVznLsmAK+7EBaSu3xS9zrnD/+8rrS8uzZabbwwzQE9QCcCvLeuvDsmLOu7bMcwPXOEmTvk3+o8eufhuoY7BTrX5r88EDoCPKfkJzwNA1694QQNPUVqPDy2HuS7tOr3vKmCoDzQ/FE83juzvGnXfjw+wv+8rZ++PHMDu7zuBPU8DLaxO+Aibzz31GK6XXQgvfZw/buvjqo8WkinPHg6FL3GZl46u6zyvJLi/7y8RdS8qRUwPAYANLzZdZq89GufvAKkurtkw0A8CFwAvfvC0TpRjNS8kHlRPEiOiTwGgr07yO1IPO22U7xKsbE8mZwAvCnLC7uH8Qc6SrUFPZhqIb2kAJ0727kAvGwbqjynhyk8iLpAOqYdL73+3rS82FFgvHCM4Tw2TAG9GsdPOyU5D70ylLG7hn5tPEZMTzx/2p+8FC3aO4MXgTz1Ejg7c+o5u8JFRzzbaYA875pJvOjFJTz3XL06lYHcO/SQzbuS9VO5zTpKuZtV5TwDPKq87hbvvBBMgTwtS9w89ueGu40k9DuUHuG8wrKUPFcKC7ywMrs8ami8vP2KfLyBAKK8x/LQuOemxzr6qx69bcjAOyelhjxq8Q+91VkSvLJUvzzW9hW8pc62vDWXnbs+w1i7BQazOiJT+bwSfAy8KuM3PYifdTuxIeg5lAEwPb4mpzu1EJ27pwSnvFWu3jxXXMQ8tib9vOdFWboml+I8l4XVu+XPqjyCnc287JW6vLAKBzwzOPq8YnhKvP4CKLs4Hb88XrjlOmyW07tGytU7GkyFvI44KDwZNyQ8eDVDPPR6sLwpvoY85VqnvH9mQ7yIdGQ8BfyoO+jZLj2+LKi7J6gVvKBV/LtLlOo61wORvMd5ITzfUdc80uu+PDtH3zsOMM+7EfBZvOVHhTzpEAK9nPeRPLVntjuiabG8hnmjvFMuvjsxKCK7YYglveoQIj1LGVM9RnJevHJlnDsQvF876H7EPOq98bwaK+67Q0qUvDa3wbxz5yU7ftl9vK+OiLwAbdi8YruYPJKsyTzApEG83hgHPSW1/Lv1jre7tUtGPNxbf7wbTZI7fyEuPG4ptTwKcwq9bQOwvAJzozwn4Ua8jmb/uyU217uyFeI7Wu8NvEM+JD3mpzu93s6jvBYQrrxCHrW7YtNLOwPkaDvgOSs8TfoDPTHOrjs47I67Ux92u7+HpzzNNjs8au8QO8FXvjvh3dM8azYbvJtnKz32Gw88PwGcO0g05Lpsp6U86FKvu5n0JDyVII+8uNuqPC2NGzxgXOq8rMqbvFc3RbteSaY76JaOOzfVgrzr5Ai8zHO6vPV2ozxe+DS8Ska+PIIvILySsEa8cj3CvOD4GzxUgwA9uqIEvKjB07pds2M8+LBKvJrSEzveSTG8EIzGPIP30DtDIQw9Bjx1PN1dsDxxQW+7NJPBvBL3kztFL5o6OpcvO07r5LzWtNm6bsvZvMts1zuZM4o7cPGBvDd+3Tx1A3Q7On0gPXwKLTy06548mSBlvOg6SjzvPEs8l1qzvEph2btBwkw8MOnrvC1gLbzfch49bZNfu+L+QDx+oF+8qrOHPPTJpjy9tsk8rB0SPLRysLpOeU+8F4T4unTm87ttjy668uIfPbmZEb24N+M8qacaO9j6KL1cil88WvBcvJJt/zrd7UK89jMGvR592jzk56U8H62aPBaOTT1Rfrs81JhCPDMteTwhHQa8N4qcPKmvCL2JNq08dycFvQ7QhLxoFry8zZslvMwY3bwAf0Y8drq4urHuBr1QxCY8nOfDu4WUaTyH4WA6lf0+vBdDODohq5e8PT33vLGjqjns7ku7TyoGPVErsLw9sve8JBROvQEo/Dt3BVI73ZWzvK7my7r7QMa8WBdvvMovc7yE59W8v/BnvEU64zypXWg8qvANvb6B0ToeUtI8JWGmvCE/yzvVl7e74ieSPJBoubzIe6+8LxHNvFcJlzynlLw88/KuPLRvQbrtM8m8LWo+vMZxozrNIRe9G35BPO/H0jxV77Q7/hX4ueP2hTu8M1K7v/4VPUMi1jxwUxu6/CeAPIWjL7yfsZ489kb5PHwktDwWTW68bYBwPKn6UTzMQN885gwNPd4njTxdmw29w/P/OqnQ/zzXAsQ8mRfiu5JiJ7znRxG9YzBLvGL66jyXspm7/GRDOyiv1rxE+cQ6u4uuvA+ygTwN+0s8nGJTPM0oLzwHbNa8Q/aCumfKEjzCqbe8f6G8vNyIvTrgeYa8TOOSu8r5CD15yKY8g3+cPKzgrrvvuew7BSCvvL4PLD1iojQ9lA4cvMgZtjuP7Jc7KLzZO6BHirygc247CZgvPDRzgjrBt5W8svcYPD3HgLzRjZ08q2dKPPRNVjpFVsS7HY71PNIbgTzf6xi9Ua36O5Y6rbrGqfg8ex0YPcPwr7sPATo8b4uHPBdCLz0QUea7oKmLPKvHNryqvck7gvkXvJdZOzySnJa8xwkPPei69LyZj2q7An4EPYwinrpzbdc7KdwsvXAW3LyqFHW8GsfAu3zt4buDJua83ZEjPVsD4LxpSeg4ipiDPCXNBL1Cpkg7UgA0PBFDZjuEXAW6bDeyuwAo7bzHpVC9PzGHPKNYAr25pBW8qx4iPIOHNryLxgq9ZqkVPS/cETyaTwc7O9jsPDFws7nm06S8R6wivZ+9rzyH7SI9V5IbO7iGmTvXeAs9wJaHO7uKVTu5qWy9DrIUO94GYzzy3IQ8jhv5u5lTEDjF2ee8m99YvLtdgLz6RuC8C0oTuXGMJr0mvq88Df+0u4oATDsi+Pe7a2y8PIvdqTuQHXu7dyY8vNUeCrywEwy8hQsBvGzigruo8Zy8LkDYvMuxozwRKa28B+8QvHd3tzwNuiK8/llhulNIjDuYRec8SN3BO4arF70NFDK9/aUovSTNID3agYM8aMlmOOav0zxMmKo8pv96PK03xLwdv9O6r+zNvAJS/rvvNGe7B3DrPPll5btpRxk8ZN62vPsIgjsgeR+7cBf1vInmDT2ee+m6sV6NvKYFjrtbgje95fWoPGX8hzuBNAK9Qc5VO5S7lrvfm6a8Zi85PN4MObxUKpw7J12Zu+2pozz0Z+a8HrFwvK5pNTzOI5C8cCcEvQ/1HT0cqxM8VJ6Pu9HgPLz4J968wIc6vI1k4jt9SY68J+MXvZmdbby7Xeo7GUfzO5C4ULy9MJS8iKgcOm2R0zx0QQA7nYnyOo7wiDz8XCs89zqpO3qcl7wHcOu8Ob6mO7EQCT0H79c7UEDTO2cucDxAGJo8Mg/iPHWh0zvGwfa6sOeYvH0WFb1KpRK9HMc6PZztDLwqrdU7/QstPJB6Hrz8aIi8u+MOvHGh3zyo2g89y6z/PGdp7rvFYb48xa+rOxeMmbwNO9q5j0+IPKsGnry4oSC7QAW0vJjWSbxfz6o88G/KO5fCiTz8e5W8cma9PD4fbTwr3tW8Fi5EPOlmoTukmeq8ZTcBPcOSxbrBWLm65H+oO8SoCzxzrJc5kucIPSJpFj3nZfO7DJURPKDLhrwvsN277RP/O41R27wuVws8GfTYOY8BxLs3Uoi7vQqKPJvZtbvejBE8cN76vM66Izxdpl27bIMJvO1XzbxOiT47ELwVvBabwbzmXBm8U6khPTKkWbyDJZI8NcLKvM1OpbsFhOY7vq+CurkkjDuOdTS81MauPJtdnbzs0So8uLIMPDO+t7pgMsq8eoqcO1eokjwElgM8DLh0PC4martCFd0843NzO/7BXrwhaKk8Ue5APFTy5LzRWli9gS8xvNj4NjupHFg8fBAEPP17dDzFoXc7C4kKvQ1ww7w4BAS9/nkGPHWSODy3tMO8PqCHvOJKY7wT4AE9cTMNvTV5hjquHdk7FZaevIzCtbtOodW7Gi6Lu8mGuzsIkQE8q67oOpgIjzx9uy48bDSSu3+ONjqwi5684gD+OabwDLyFY3i67e8mPfHQYzsIiss8loyaOrO/mzvhaow8X1OwO2SexzxzFQ27bX1kvKavlrwFK/u8HMeNPBQ6Bz3I/MA6Dg2svMuss7slOxe9gA2CvEnQVbxzQXk7GknrumfQBbuOzY684SiDvF38X7sIarg5TbwrvC8iJjxi8pY7zB+EPIIYfzxRJ4G87K3XuI9RGjuFffa88czZvJ3IBLx41Y67Fhatuwb6lLxwrwo9icfEO7PwsjzGGNG8uYYdO720YrsVNKy8KoJ0PEArPLwQ4Q490M8UvWo1fLxCZUW8DsONO73mTLo+hOs7JiXbPF+sqrtGwJ884h6BvOru1Tt4vfQ7M0VNPG+C7Tx/gkY8ihQNPFs77jwTlx28rF1HvC1gXzy+tSo8SygivDSYobz13SS71l0uvCNaYrzOgdA89IkQPB01pDzulw883Z+FPDut5rwfE748a4CqvMD+vTwaIyY8SgP6vEHdu7uP1og8rKgoPQDslzyy2iY89pQIPKez07uAepI8Eh+JOnbIFrwlIBO7HeTUvJ3m+Lvjr6e883oJPAgfCryT/kW8ZFgIvS4Y37sECQ49+KgDPfac3blGvRy8oYGmvL5VULoTp5e8kgWJu9qpLTzQgM+8nDQ1O26tKryPgeG825efPKk3tbxtWAw8630OvFNi2Dw/J5u62WEjvM9px7w0fBu9EeGsuyvRvroNj848ln84PGGDpDwzloC7HrWqvH6OMb2xQ1Q6ehX0uyWRtbrx9sC8SZHQPMYZdTy2MFS66um+PIkz2bzkagm7L4JGPWoAirxeOHs8UMdSvHBPdjvqLhk8VOEOvZW307r5Xe47s6t7vKmpcDzuI0O73nPuvAAKmrwnZM88d/WVPFjdlbsK7R09T9v9u39sILu1JpK8AsnhPHEbHzuSAJG7R66Zu6weQbteCbK8WwQwvDj1zboDekE8M5kVvdJ3RD3Bk4i8ye59vN7H67ueuum8ky2wvLfOODsawto7/faPvFkIHrzbcw+7So4IPNP4Jzz3GrQ8rqKCuyAxb7yB8VK86b3MO3fJhzy+dze9SRHFuhp73rtWOgI8ww+tPBHgRTybgZO8PZp3PHZSJzvFQoq8iKriPPUvpjuxqho59tbDPH44N7y1BQC5MWRPPImO2Lsef+i614aXPMw6qTyOJ8U7DgiXPD6DwTw+YOM81ZrJPO+5wjqHCAC9nnO/vGDJIjxaNjo8wMykO33zerzMsB48KtaUPJwvozt9f5G8hEnQOWeXHL26hWw8SPAOPMF0zTys6OW7A+ecvBqzFTz/Ykm9fQJoOnjtgzwaXfY689HKvNq7Y7zXr6G8TTINPVdYzrxvsL08AbN8vKcilbv1qD27BYmivMtx2rydeRe8R5Q9PD6BP7sGi0o7tXg5O5BurrsujG08kTnFPLOSJDp8wYO8G1wsPOiUNbxyxxi8dqwKvEx79TqZMcG8pmqRvIGljDucTiE8sHBuPNMzp7rCa6E7+1ytO6T5Lbv3k2Y8ntqzuz+Akzzs54w8bAULvW6+ILwEUB28zv3QvIgfhLsBroE79hfauuKmXzz+XZm8clxWO4Iot7vO9Me86qzQPNxCybtpvR+8son3PG5gmLylAr489yf6u8hVs7suei89WYuLOxG9QDyMhBW9IAyDupe4KTwy0Q+8cmh8PG8PujvQ1A88pZAfvGHEwbtGYz06fgWjPOW6hLu5X/E8GLVSOgX4CLswtw48N0qKvFYmW7wvVsc7UXFquw==
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:
- '12462'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '872'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: stop
index: 0
message:
content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
created: 1769707361
id: chatcmpl-700
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 118
prompt_tokens: 2766
total_tokens: 2884
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '13928'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
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 the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '833'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
index: 0
type: function
created: 1769707364
id: chatcmpl-261
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 110
prompt_tokens: 3076
total_tokens: 3186
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '15192'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
type: function
- content: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology
accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))","stdout":"{\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hx2g2fr3
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '507'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: stop
index: 0
message:
content: |-
```json
{
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
```
role: assistant
created: 1769707366
id: chatcmpl-781
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 56
prompt_tokens: 3346
total_tokens: 3402
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '15914'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
type: function
- content: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology
accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))","stdout":"{\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hx2g2fr3
- content: |-
```json
{
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "missing",
"loc": [
"answer"
],
"msg": "Field required",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
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 the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '861'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: 'We need to wrap within {"answer": {...}}.'
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\":
\"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output,
indent=2))"}'
name: execute_code
id: call_docxc0ic
index: 0
type: function
created: 1769707370
id: chatcmpl-624
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 115
prompt_tokens: 3509
total_tokens: 3624
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '17302'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
type: function
- content: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology
accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))","stdout":"{\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hx2g2fr3
- content: |-
```json
{
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "missing",
"loc": [
"answer"
],
"msg": "Field required",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
Fix the errors and try again.
role: user
- content: |-
We need to wrap within {"answer": {...}}.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\":
\"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output,
indent=2))"}'
name: execute_code
id: call_docxc0ic
type: function
- content: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\": \"Our
mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company
History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output, indent=2))","stdout":"{\n \"answer\":
{\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology accessible to everyone.\",\n \"Sources\":
{\n \"Founded\": \"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_docxc0ic
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '543'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: stop
index: 0
message:
content: |
```json
{
"answer": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
```
role: assistant
created: 1769707372
id: chatcmpl-185
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 64
prompt_tokens: 3802
total_tokens: 3866
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '18080'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
type: function
- content: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology
accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))","stdout":"{\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hx2g2fr3
- content: |-
```json
{
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "missing",
"loc": [
"answer"
],
"msg": "Field required",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
Fix the errors and try again.
role: user
- content: |-
We need to wrap within {"answer": {...}}.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\":
\"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output,
indent=2))"}'
name: execute_code
id: call_docxc0ic
type: function
- content: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\": \"Our
mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company
History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output, indent=2))","stdout":"{\n \"answer\":
{\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology accessible to everyone.\",\n \"Sources\":
{\n \"Founded\": \"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_docxc0ic
- content: |
```json
{
"answer": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "string_type",
"loc": [
"answer"
],
"msg": "Input should be a valid string",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
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 the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '729'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: Need answer field be string. So output should be {"answer":"..."} where string contains info. Let's create
string summary.
role: assistant
tool_calls:
- function:
arguments: '{"code":"answer_text = \"Founded: 1985; Mission: Our mission is to make technology accessible to
everyone.\";\nprint(json.dumps({\"answer\": answer_text}, indent=2))"}'
name: execute_code
id: call_uk6y6wn8
index: 0
type: function
created: 1769707375
id: chatcmpl-533
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 86
prompt_tokens: 3978
total_tokens: 4064
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '18952'
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.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### 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
### 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
### 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.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### 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: `if 'documents' in dir(): ...`
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use llm().
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using llm() for classification
```python
# Get document content
content = get_document("Q1 Report")
# Use llm() to classify sentiment
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: Using the pre-loaded documents variable, tell me when was the company founded and what is their mission?
role: user
- content: |-
Check documents variable.
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')"}'
name: execute_code
id: call_1uonnu1t
type: function
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''No
preloaded documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''dir'' is not defined\n","success":false}'
role: tool
tool_call_id: call_1uonnu1t
- content: |-
dir not defined; we can use globals(). Let's check if 'documents' in globals().
role: assistant
tool_calls:
- function:
arguments: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in
documents:\n print(doc[''title''])\nelse:\n print(''no documents'')"}'
name: execute_code
id: call_40ur54cl
type: function
- content: '{"code":"if ''documents'' in globals():\n print(''preloaded'', len(documents))\n for doc in documents:\n print(doc[''title''])\nelse:\n print(''no
documents'')","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''globals'' is not defined\n","success":false}'
role: tool
tool_call_id: call_40ur54cl
- content: |-
We cannot use dir or globals. Maybe the execution environment disallows these. We can try accessing variables via __dict__? Let's attempt: "print(__dict__.keys())".
role: assistant
tool_calls:
- function:
arguments: '{"code":"print(__dict__.keys())"}'
name: execute_code
id: call_xwupy2ag
type: function
- content: '{"code":"print(__dict__.keys())","stdout":"","stderr":"Traceback (most recent call last):\n File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
line 340, in _execute_sync\n exec(code, exec_globals, self.locals)\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File
\"\", line 1, in \nNameError: name ''__dict__'' is not defined\n","success":false}'
role: tool
tool_call_id: call_xwupy2ag
- content: |-
The sandbox likely restricts introspection. Maybe the preloaded documents are not available. We can search.
role: assistant
tool_calls:
- function:
arguments: '{"code":"search(\"company founded mission\", limit=5)"}'
name: execute_code
id: call_5dmqpjra
type: function
- content: '{"code":"search(\"company founded mission\", limit=5)","stdout":"","stderr":"","success":true}'
role: tool
tool_call_id: call_5dmqpjra
- content: |-
We need to print results.
role: assistant
tool_calls:
- function:
arguments: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')"}'
name: execute_code
id: call_tjt6miwo
type: function
- content: '{"code":"results = search(\"company founded mission\", limit=5)\nfor r in results:\n print(r[''document_title''],
r[''score''])\n print(r[''content''][:200])\n print(''---'')","stdout":"Mission Statement 0.032522473484277725\nOur
mission is to make technology accessible to everyone.\n---\nCompany History 0.032522473484277725\nThe company was
founded in 1985 by Jane Smith.\n---\n","stderr":"","success":true}'
role: tool
tool_call_id: call_tjt6miwo
- content: |-
**Answer**
| Information | Source |
|-------------|--------|
| **Founded:** 1985 | Search result “Company History” |
| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |
**Key Findings**
1. The company was founded in **1985** (document “Company History”).
2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).
These details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches.
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "json_invalid",
"loc": [],
"msg": "Invalid JSON: expected value at line 1 column 1",
"input": "**Answer**\n\n| Information | Source |\n|-------------|--------|\n| **Founded:** 1985 | Search result “Company History” |\n| **Mission:** “Our mission is to make technology accessible to everyone.” | Search result “Mission Statement” |\n\n**Key Findings**\n\n1. The company was founded in **1985** (document “Company History”).\n2. The mission statement is “to make technology accessible to everyone” (document “Mission Statement”).\n\nThese details were retrieved by searching the knowledge base for “company founded mission” and examining the top matches."
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need to return JSON, not markdown. Let's output JSON array with fields.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make
technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))"}'
name: execute_code
id: call_hx2g2fr3
type: function
- content: '{"code":"import json\nanswer = {\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology
accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\nprint(json.dumps(answer, indent=2))","stdout":"{\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company History\",\n \"Mission\":
\"Mission Statement\"\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hx2g2fr3
- content: |-
```json
{
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "missing",
"loc": [
"answer"
],
"msg": "Field required",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
Fix the errors and try again.
role: user
- content: |-
We need to wrap within {"answer": {...}}.
role: assistant
tool_calls:
- function:
arguments: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\":
\"Our mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\":
\"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output,
indent=2))"}'
name: execute_code
id: call_docxc0ic
type: function
- content: '{"code":"import json\noutput = {\n \"answer\": {\n \"Founded\": 1985,\n \"Mission\": \"Our
mission is to make technology accessible to everyone.\",\n \"Sources\": {\n \"Founded\": \"Company
History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\nprint(json.dumps(output, indent=2))","stdout":"{\n \"answer\":
{\n \"Founded\": 1985,\n \"Mission\": \"Our mission is to make technology accessible to everyone.\",\n \"Sources\":
{\n \"Founded\": \"Company History\",\n \"Mission\": \"Mission Statement\"\n }\n }\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_docxc0ic
- content: |
```json
{
"answer": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
```
role: assistant
- content: |-
1 validation error:
```json
[
{
"type": "string_type",
"loc": [
"answer"
],
"msg": "Input should be a valid string",
"input": {
"Founded": 1985,
"Mission": "Our mission is to make technology accessible to everyone.",
"Sources": {
"Founded": "Company History",
"Mission": "Mission Statement"
}
}
}
]
```
Fix the errors and try again.
role: user
- content: |-
Need answer field be string. So output should be {"answer":"..."} where string contains info. Let's create string summary.
role: assistant
tool_calls:
- function:
arguments: '{"code":"answer_text = \"Founded: 1985; Mission: Our mission is to make technology accessible to everyone.\";\nprint(json.dumps({\"answer\":
answer_text}, indent=2))"}'
name: execute_code
id: call_uk6y6wn8
type: function
- content: '{"code":"answer_text = \"Founded: 1985; Mission: Our mission is to make technology accessible to everyone.\";\nprint(json.dumps({\"answer\":
answer_text}, indent=2))","stdout":"{\n \"answer\": \"Founded: 1985; Mission: Our mission is to make technology
accessible to everyone.\"\n}\n","stderr":"","success":true}'
role: tool
tool_call_id: call_uk6y6wn8
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
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:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '407'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: stop
index: 0
message:
content: |
```json
{
"answer": "Founded: 1985; Mission: Our mission is to make technology accessible to everyone."
}
```
role: assistant
created: 1769707377
id: chatcmpl-312
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 34
prompt_tokens: 4161
total_tokens: 4195
status:
code: 200
message: OK
version: 1