2339 lines
139 KiB
YAML
2339 lines
139 KiB
YAML
interactions:
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- request:
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headers:
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accept:
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- application/json
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accept-encoding:
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- gzip, deflate, zstd
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connection:
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- keep-alive
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content-length:
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- '116'
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content-type:
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- application/json
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host:
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- localhost:11434
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method: POST
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parsed_body:
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encoding_format: base64
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input:
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- The company was founded in 1985 by Jane Smith.
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model: qwen3-embedding:4b
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uri: http://localhost:11434/v1/embeddings
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response:
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headers:
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content-type:
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- application/json
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transfer-encoding:
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- chunked
|
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parsed_body:
|
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data:
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- 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:
|
|
- '7390'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '610'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
index: 0
|
|
type: function
|
|
created: 1771924591
|
|
id: chatcmpl-850
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 68
|
|
prompt_tokens: 1635
|
|
total_tokens: 1703
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '8037'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '524'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
index: 0
|
|
type: function
|
|
created: 1771924592
|
|
id: chatcmpl-798
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 43
|
|
prompt_tokens: 1777
|
|
total_tokens: 1820
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '8486'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '581'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
index: 0
|
|
type: function
|
|
created: 1771924593
|
|
id: chatcmpl-566
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 51
|
|
prompt_tokens: 1862
|
|
total_tokens: 1913
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '8970'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
- content: null
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'search'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_ee8bn4w7
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '550'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: The tool is execute_code. Use that.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n"}'
|
|
name: execute_code
|
|
id: call_o1c87rz5
|
|
index: 0
|
|
type: function
|
|
created: 1771924595
|
|
id: chatcmpl-37
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 51
|
|
prompt_tokens: 1948
|
|
total_tokens: 1999
|
|
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:
|
|
- founded mission company
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: 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
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 5
|
|
total_tokens: 5
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '10069'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
- content: null
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'search'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_ee8bn4w7
|
|
- content: null
|
|
reasoning: The tool is execute_code. Use that.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n"}'
|
|
name: execute_code
|
|
id: call_o1c87rz5
|
|
type: function
|
|
- content: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n","stdout":"[{''chunk_id'':
|
|
''d43b4445-8425-4434-bc36-e28e6f7e573e'', ''content'': ''Our mission is to make technology accessible to everyone.'',
|
|
''document_id'': ''bb1e588e-8014-4607-bf9b-a077c16d1cdf'', ''document_title'': ''Mission Statement'', ''document_uri'':
|
|
None, ''score'': 0.032522473484277725, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''6d291645-590e-498c-a22c-0883e7b1582b'',
|
|
''content'': ''The company was founded in 1985 by Jane Smith.'', ''document_id'': ''a2e1e13c-e316-4198-a5a8-49353eaa7337'',
|
|
''document_title'': ''Company History'', ''document_uri'': None, ''score'': 0.032522473484277725, ''page_numbers'':
|
|
[], ''headings'': None}]\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_o1c87rz5
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '509'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Got info. Need citation get_chunk.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_id":"6d291645-590e-498c-a22c-0883e7b1582b"}'
|
|
name: get_chunk
|
|
id: call_b0i0vdgg
|
|
index: 0
|
|
type: function
|
|
created: 1771924596
|
|
id: chatcmpl-840
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 54
|
|
prompt_tokens: 2265
|
|
total_tokens: 2319
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '10484'
|
|
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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
- content: null
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'search'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_ee8bn4w7
|
|
- content: null
|
|
reasoning: The tool is execute_code. Use that.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n"}'
|
|
name: execute_code
|
|
id: call_o1c87rz5
|
|
type: function
|
|
- content: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n","stdout":"[{''chunk_id'':
|
|
''d43b4445-8425-4434-bc36-e28e6f7e573e'', ''content'': ''Our mission is to make technology accessible to everyone.'',
|
|
''document_id'': ''bb1e588e-8014-4607-bf9b-a077c16d1cdf'', ''document_title'': ''Mission Statement'', ''document_uri'':
|
|
None, ''score'': 0.032522473484277725, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''6d291645-590e-498c-a22c-0883e7b1582b'',
|
|
''content'': ''The company was founded in 1985 by Jane Smith.'', ''document_id'': ''a2e1e13c-e316-4198-a5a8-49353eaa7337'',
|
|
''document_title'': ''Company History'', ''document_uri'': None, ''score'': 0.032522473484277725, ''page_numbers'':
|
|
[], ''headings'': None}]\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_o1c87rz5
|
|
- content: null
|
|
reasoning: Got info. Need citation get_chunk.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_id":"6d291645-590e-498c-a22c-0883e7b1582b"}'
|
|
name: get_chunk
|
|
id: call_b0i0vdgg
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'get_chunk'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_b0i0vdgg
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
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content-length:
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- '573'
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content-type:
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- application/json
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parsed_body:
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choices:
|
|
- finish_reason: tool_calls
|
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index: 0
|
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message:
|
|
content: ''
|
|
reasoning: We need to call get_chunk via execute_code.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"chunk = await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\nprint(chunk[''content''])\n"}'
|
|
name: execute_code
|
|
id: call_1o9o5lvl
|
|
index: 0
|
|
type: function
|
|
created: 1771924598
|
|
id: chatcmpl-401
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 71
|
|
prompt_tokens: 2356
|
|
total_tokens: 2427
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
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accept-encoding:
|
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- gzip, deflate, zstd
|
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connection:
|
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- keep-alive
|
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content-length:
|
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- '11056'
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content-type:
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- application/json
|
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host:
|
|
- localhost:11434
|
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method: POST
|
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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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
- content: null
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'search'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_ee8bn4w7
|
|
- content: null
|
|
reasoning: The tool is execute_code. Use that.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n"}'
|
|
name: execute_code
|
|
id: call_o1c87rz5
|
|
type: function
|
|
- content: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n","stdout":"[{''chunk_id'':
|
|
''d43b4445-8425-4434-bc36-e28e6f7e573e'', ''content'': ''Our mission is to make technology accessible to everyone.'',
|
|
''document_id'': ''bb1e588e-8014-4607-bf9b-a077c16d1cdf'', ''document_title'': ''Mission Statement'', ''document_uri'':
|
|
None, ''score'': 0.032522473484277725, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''6d291645-590e-498c-a22c-0883e7b1582b'',
|
|
''content'': ''The company was founded in 1985 by Jane Smith.'', ''document_id'': ''a2e1e13c-e316-4198-a5a8-49353eaa7337'',
|
|
''document_title'': ''Company History'', ''document_uri'': None, ''score'': 0.032522473484277725, ''page_numbers'':
|
|
[], ''headings'': None}]\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_o1c87rz5
|
|
- content: null
|
|
reasoning: Got info. Need citation get_chunk.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_id":"6d291645-590e-498c-a22c-0883e7b1582b"}'
|
|
name: get_chunk
|
|
id: call_b0i0vdgg
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'get_chunk'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_b0i0vdgg
|
|
- content: null
|
|
reasoning: We need to call get_chunk via execute_code.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"chunk = await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\nprint(chunk[''content''])\n"}'
|
|
name: execute_code
|
|
id: call_1o9o5lvl
|
|
type: function
|
|
- content: '{"code":"chunk = await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\nprint(chunk[''content''])\n","stdout":"The
|
|
company was founded in 1985 by Jane Smith.\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_1o9o5lvl
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '550'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Similarly mission.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"chunk2 = await get_chunk(''d43b4445-8425-4434-bc36-e28e6f7e573e'')\nprint(chunk2[''content''])\n"}'
|
|
name: execute_code
|
|
id: call_dn2a823n
|
|
index: 0
|
|
type: function
|
|
created: 1771924600
|
|
id: chatcmpl-408
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 67
|
|
prompt_tokens: 2502
|
|
total_tokens: 2569
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '11618'
|
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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 call them with `await`:
|
|
- results = await search("query") ✓ CORRECT
|
|
- from haiku.rag import search ✗ WRONG - will fail
|
|
- results = search("query") ✗ WRONG - must use await
|
|
|
|
You have access to a sandboxed Python interpreter with these haiku.rag functions (use them directly with `await`, no imports needed):
|
|
|
|
## Available Functions
|
|
|
|
### await search(query, limit=10) -> list[dict]
|
|
Search the knowledge base using hybrid search (vector + full-text).
|
|
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
|
|
|
|
### await list_documents(limit=10, offset=0) -> list[dict]
|
|
List available documents in the knowledge base.
|
|
Returns list of dicts with keys: id, title, uri, created_at
|
|
|
|
### await get_document(id_or_title) -> str | None
|
|
Get the full text content of a document by ID, title, or URI.
|
|
Returns the document content as a string, or None if not found.
|
|
|
|
### await get_chunk(chunk_id) -> dict | None
|
|
Get a specific chunk by its ID (from search results).
|
|
Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels
|
|
Use this to retrieve full chunk details and metadata for citation.
|
|
|
|
### await 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(): ...`
|
|
|
|
## Available Python Features
|
|
|
|
The interpreter supports: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, async/await, `map()`, `sorted()`/`.sort(key=...)`, try/except, and the `json` module.
|
|
|
|
Not supported: imports (other than `json`), class definitions, generators/yield, match statements, decorators, `with` statements.
|
|
|
|
For pattern matching or text extraction, use string methods (`str.split`, `str.find`, `str.startswith`, `in` operator) or the `llm()` function.
|
|
|
|
## Strategy Guide
|
|
|
|
1. **Explore First**: Start by listing documents or searching to understand what's available. Document 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 `await list_documents()` to see actual document titles, or `await 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 sandbox 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 data structures.
|
|
6. **Use llm() for Classification/Extraction**: When you need to classify, summarize, or extract structured data from content you already have, use `await llm()`.
|
|
7. **Cite Your Sources**: Use get_chunk() to retrieve chunk metadata for citations. Track which documents/chunks informed your answer.
|
|
|
|
## Example Patterns
|
|
|
|
### Counting documents matching a condition
|
|
```python
|
|
docs = await list_documents(limit=100)
|
|
count = 0
|
|
for doc in docs:
|
|
content = await get_document(doc['id'])
|
|
if content and 'keyword' in content.lower():
|
|
count += 1
|
|
print(f"Found in: {doc['title']}")
|
|
print(f"Total: {count}")
|
|
```
|
|
|
|
### Extracting data with llm()
|
|
```python
|
|
numbers = []
|
|
results = await search("financial data", limit=20)
|
|
for r in results:
|
|
extracted = await llm(f"Extract all dollar amounts from this text as a comma-separated list of numbers (no $ signs): {r['content']}")
|
|
for part in extracted.split(','):
|
|
part = part.strip().replace(',', '')
|
|
if part.isdigit():
|
|
numbers.append(int(part))
|
|
if numbers:
|
|
print(f"Average: {sum(numbers) / len(numbers)}")
|
|
```
|
|
|
|
### Using search results with get_chunk for citations
|
|
```python
|
|
results = await search("safety requirements", limit=5)
|
|
for r in results:
|
|
chunk = await get_chunk(r['chunk_id'])
|
|
print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")
|
|
```
|
|
|
|
### Using llm() for classification
|
|
```python
|
|
content = await get_document("Q1 Report")
|
|
sentiment = await 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
|
|
|
|
CRITICAL: Your final response MUST be valid JSON matching this exact schema:
|
|
```json
|
|
{"answer": "Your complete answer here as a string", "program": "Your final consolidated program here as a string"}
|
|
```
|
|
|
|
- `answer`: A clear answer to the user's question with key findings and references to specific documents/chunks.
|
|
- `program`: A single, self-contained Python program that produces the answer. Consolidate your exploratory code executions into one clean script.
|
|
|
|
Do NOT return arbitrary JSON structures. Always use the exact format: {"answer": "...", "program": "..."}
|
|
|
|
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: null
|
|
reasoning: Need 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
|
|
documents'')\n"}'
|
|
name: execute_code
|
|
id: call_2ao52bvz
|
|
type: function
|
|
- content: '{"code":"if ''documents'' in dir():\n for doc in documents:\n print(doc[''title''], len(doc[''content'']))\nelse:\n print(''no
|
|
documents'')\n","stdout":"","stderr":"NameError: name ''dir'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_2ao52bvz
|
|
- content: null
|
|
reasoning: The environment may not provide dir(); use globals(). Check.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"print(globals().keys())\n"}'
|
|
name: execute_code
|
|
id: call_rjj1hq9z
|
|
type: function
|
|
- content: '{"code":"print(globals().keys())\n","stdout":"","stderr":"NameError: name ''globals'' is not defined","success":false}'
|
|
role: tool
|
|
tool_call_id: call_rjj1hq9z
|
|
- content: null
|
|
reasoning: Probably no python environment? Might need simple print? Might fail. Probably cannot see documents. Could
|
|
search.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"founded mission company","limit":5}'
|
|
name: search
|
|
id: call_ee8bn4w7
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'search'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_ee8bn4w7
|
|
- content: null
|
|
reasoning: The tool is execute_code. Use that.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n"}'
|
|
name: execute_code
|
|
id: call_o1c87rz5
|
|
type: function
|
|
- content: '{"code":"results = await search(''founded mission company'', limit=5)\nprint(results)\n","stdout":"[{''chunk_id'':
|
|
''d43b4445-8425-4434-bc36-e28e6f7e573e'', ''content'': ''Our mission is to make technology accessible to everyone.'',
|
|
''document_id'': ''bb1e588e-8014-4607-bf9b-a077c16d1cdf'', ''document_title'': ''Mission Statement'', ''document_uri'':
|
|
None, ''score'': 0.032522473484277725, ''page_numbers'': [], ''headings'': None}, {''chunk_id'': ''6d291645-590e-498c-a22c-0883e7b1582b'',
|
|
''content'': ''The company was founded in 1985 by Jane Smith.'', ''document_id'': ''a2e1e13c-e316-4198-a5a8-49353eaa7337'',
|
|
''document_title'': ''Company History'', ''document_uri'': None, ''score'': 0.032522473484277725, ''page_numbers'':
|
|
[], ''headings'': None}]\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_o1c87rz5
|
|
- content: null
|
|
reasoning: Got info. Need citation get_chunk.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_id":"6d291645-590e-498c-a22c-0883e7b1582b"}'
|
|
name: get_chunk
|
|
id: call_b0i0vdgg
|
|
type: function
|
|
- content: |-
|
|
Unknown tool name: 'get_chunk'. Available tools: 'final_result', 'execute_code'
|
|
|
|
Fix the errors and try again.
|
|
role: tool
|
|
tool_call_id: call_b0i0vdgg
|
|
- content: null
|
|
reasoning: We need to call get_chunk via execute_code.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"chunk = await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\nprint(chunk[''content''])\n"}'
|
|
name: execute_code
|
|
id: call_1o9o5lvl
|
|
type: function
|
|
- content: '{"code":"chunk = await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\nprint(chunk[''content''])\n","stdout":"The
|
|
company was founded in 1985 by Jane Smith.\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_1o9o5lvl
|
|
- content: null
|
|
reasoning: Similarly mission.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"chunk2 = await get_chunk(''d43b4445-8425-4434-bc36-e28e6f7e573e'')\nprint(chunk2[''content''])\n"}'
|
|
name: execute_code
|
|
id: call_dn2a823n
|
|
type: function
|
|
- content: '{"code":"chunk2 = await get_chunk(''d43b4445-8425-4434-bc36-e28e6f7e573e'')\nprint(chunk2[''content''])\n","stdout":"Our
|
|
mission is to make technology accessible to everyone.\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_dn2a823n
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: |-
|
|
<summary>Execute Python code in a sandboxed interpreter.
|
|
|
|
The code has access to haiku.rag functions (search, list_documents,
|
|
get_document, get_chunk, llm).
|
|
|
|
Use print() to output results.</summary>
|
|
<returns>
|
|
<description>Structured result with success status, stdout, and stderr.</description>
|
|
</returns>
|
|
name: execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
description: Python code to execute.
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
- function:
|
|
description: Result from RLM agent execution.
|
|
name: final_result
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
answer:
|
|
description: The answer to the user's question
|
|
type: string
|
|
program:
|
|
description: The final consolidated program
|
|
type: string
|
|
required:
|
|
- answer
|
|
- program
|
|
type: object
|
|
strict: true
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '1388'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: stop
|
|
index: 0
|
|
message:
|
|
content: '{"answer":"The company was founded in 1985 by Jane Smith. Its mission is to make technology accessible
|
|
to everyone.","program":"# Consolidated program to extract founding year and mission\nimport json\n\nasync def
|
|
main():\n # Search for founding year and mission statements\n results = await search(''founded mission company'',
|
|
limit=10)\n founded = None\n mission = None\n for r in results:\n if ''founded'' in r[''content''].lower()
|
|
and not founded:\n founded = r[''content'']\n if ''mission'' in r[''content''].lower() and not
|
|
mission:\n mission = r[''content'']\n # Fallback to chunk extraction if needed\n if not founded:\n chunk
|
|
= await get_chunk(''6d291645-590e-498c-a22c-0883e7b1582b'')\n founded = chunk[''content'']\n if not
|
|
mission:\n chunk2 = await get_chunk(''d43b4445-8425-4434-bc36-e28e6f7e573e'')\n mission = chunk2[''content'']\n #
|
|
Print results\n print(''Founded:'', founded)\n print(''Mission:'', mission)\n\n# Run the async main function\nimport
|
|
asyncio\nasyncio.run(main())"}'
|
|
role: assistant
|
|
created: 1771924607
|
|
id: chatcmpl-899
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 296
|
|
prompt_tokens: 2645
|
|
total_tokens: 2941
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
version: 1
|