578 lines
62 KiB
YAML
578 lines
62 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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- '96'
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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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- First document about cats.
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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: 6
|
|
total_tokens: 6
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '97'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- Second document about dogs.
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: 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
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 6
|
|
total_tokens: 6
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '97'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- Third document about birds.
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
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- embedding: 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
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 6
|
|
total_tokens: 6
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '7325'
|
|
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: How many documents are in the database?
|
|
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:
|
|
- '514'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Need to list docs.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
|
|
name: execute_code
|
|
id: call_stp0fimx
|
|
index: 0
|
|
type: function
|
|
created: 1771924497
|
|
id: chatcmpl-750
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 44
|
|
prompt_tokens: 1623
|
|
total_tokens: 1667
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '7759'
|
|
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: How many documents are in the database?
|
|
role: user
|
|
- content: null
|
|
reasoning: Need to list docs.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
|
|
name: execute_code
|
|
id: call_stp0fimx
|
|
type: function
|
|
- content: '{"code":"docs = await list_documents(limit=1000)\nprint(len(docs))","stdout":"3\n","stderr":"","success":true}'
|
|
role: tool
|
|
tool_call_id: call_stp0fimx
|
|
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:
|
|
- '416'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: stop
|
|
index: 0
|
|
message:
|
|
content: '{"answer":"There are 3 documents in the database.","program":"docs = await list_documents(limit=1000)\nprint(len(docs))"}'
|
|
role: assistant
|
|
created: 1771924498
|
|
id: chatcmpl-945
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 38
|
|
prompt_tokens: 1709
|
|
total_tokens: 1747
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
version: 1
|