interactions:
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '96'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
encoding_format: base64
input:
- First document about cats.
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:
- 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:
- 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:
- '7686'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
messages:
- content: |-
You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### search(query, limit=10) -> list[dict]
Search the knowledge base using hybrid search (vector + full-text).
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
### list_documents(limit=10, offset=0) -> list[dict]
List available documents in the knowledge base.
Returns list of dicts with keys: id, title, uri, created_at
### get_document(id_or_title) -> str | None
Get the full text content of a document by ID, title, or URI.
Returns the document content as a string, or None if not found.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### ask(question) -> str
Ask a question using the QA agent with RAG. Returns the answer as a string.
Use this for semantic analysis that benefits from LLM reasoning.
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using ask() for semantic analysis
```python
# First search to find relevant content
results = search("machine learning approaches")
# Then use ask() to synthesize an answer
summary = ask("What are the main machine learning approaches discussed?")
print(summary)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: How many documents are in the database?
role: user
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
Structured result with success status, stdout, and stderr.
name: execute_code
parameters:
additionalProperties: false
properties:
code:
description: Python code to execute.
type: string
required:
- code
type: object
strict: true
type: function
- function:
description: Result from RLM agent execution.
name: final_result
parameters:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '519'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: tool_calls
index: 0
message:
content: ''
reasoning: We need to list documents.
role: assistant
tool_calls:
- function:
arguments: '{"code":"docs = list_documents(limit=1000)\nprint(len(docs))\n"}'
name: execute_code
id: call_hk3j646j
index: 0
type: function
created: 1769703338
id: chatcmpl-806
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 46
prompt_tokens: 1599
total_tokens: 1645
status:
code: 200
message: OK
- request:
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, zstd
connection:
- keep-alive
content-length:
- '8124'
content-type:
- application/json
host:
- localhost:11434
method: POST
parsed_body:
messages:
- content: |-
You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.
IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.
CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
- search("query") ✓ CORRECT
- from haiku.rag import search ✗ WRONG - will fail
You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):
## Available Functions
### search(query, limit=10) -> list[dict]
Search the knowledge base using hybrid search (vector + full-text).
Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings
### list_documents(limit=10, offset=0) -> list[dict]
List available documents in the knowledge base.
Returns list of dicts with keys: id, title, uri, created_at
### get_document(id_or_title) -> str | None
Get the full text content of a document by ID, title, or URI.
Returns the document content as a string, or None if not found.
### get_docling_document(id_or_title) -> DoclingDocument | None
Get the structured DoclingDocument object for advanced analysis.
Returns a DoclingDocument object, or None if not found.
See "DoclingDocument API" section below for how to use it.
### ask(question) -> str
Ask a question using the QA agent with RAG. Returns the answer as a string.
Use this for semantic analysis that benefits from LLM reasoning.
## Standard Library Modules
You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing
## Strategy Guide
1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.
## DoclingDocument API
When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.
### Properties
- `doc.texts` - List of all text items (paragraphs, headings, etc.)
- `doc.tables` - List of all tables
- `doc.pictures` - List of all pictures/figures
- `doc.name` - Document name
### Methods
- `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
Returns tuples of (item, level) where level is nesting depth
- `doc.export_to_markdown()` - Export entire document as markdown string
### Text Item Properties
- `item.text` - The text content
- `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
- `item.prov` - Provenance (page numbers, bounding boxes)
### Table Access
- `table.data.num_rows`, `table.data.num_cols` - Dimensions
- `table.data.table_cells` - List of TableCell objects
- `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`
### Example Usage
```python
doc = get_docling_document("My Document")
# Get all headings
headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]
# Iterate with structure
for item, level in doc.iterate_items():
print(" " * level + item.text[:50])
# Extract table data
for table in doc.tables:
for cell in table.data.table_cells:
print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
```
## Example Patterns
### Counting documents matching a condition
```python
docs = list_documents(limit=100)
count = 0
for doc in docs:
content = get_document(doc['id'])
if content and 'keyword' in content.lower():
count += 1
print(f"Found in: {doc['title']}")
print(f"Total: {count}")
```
### Aggregating data across documents
```python
import re
numbers = []
results = search("financial data", limit=20)
for r in results:
matches = re.findall(r'\$([\d,]+)', r['content'])
for m in matches:
numbers.append(int(m.replace(',', '')))
print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
```
### Using ask() for semantic analysis
```python
# First search to find relevant content
results = search("machine learning approaches")
# Then use ask() to synthesize an answer
summary = ask("What are the main machine learning approaches discussed?")
print(summary)
```
## Workflow
1. **ALWAYS start by using execute_code** to explore the knowledge base
2. Run multiple code blocks as needed to gather information
3. After collecting data, provide your final answer
## Output Format
After executing code and gathering information, provide:
1. A clear answer to the user's question
2. Key findings from your analysis
3. References to specific documents/chunks that informed your answer
CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
role: system
- content: How many documents are in the database?
role: user
- content: |-
We need to list documents.
role: assistant
tool_calls:
- function:
arguments: '{"code":"docs = list_documents(limit=1000)\nprint(len(docs))\n"}'
name: execute_code
id: call_hk3j646j
type: function
- content: '{"code":"docs = list_documents(limit=1000)\nprint(len(docs))\n","stdout":"3\n","stderr":"","success":true}'
role: tool
tool_call_id: call_hk3j646j
model: gpt-oss
reasoning_effort: low
stream: false
tool_choice: auto
tools:
- function:
description: |-
Execute Python code in the sandboxed environment.
The code has access to haiku.rag functions (search, list_documents,
get_document, get_docling_document, ask) and safe standard library
modules (json, re, collections, math, statistics, itertools,
functools, datetime, typing).
Use print() to output results. Variables persist between executions.
Structured result with success status, stdout, and stderr.
name: execute_code
parameters:
additionalProperties: false
properties:
code:
description: Python code to execute.
type: string
required:
- code
type: object
strict: true
type: function
- function:
description: Result from RLM agent execution.
name: final_result
parameters:
$defs:
CodeExecution:
additionalProperties: false
description: Result of executing a code block in the RLM sandbox.
properties:
code:
description: The Python code that was executed
type: string
stderr:
description: Standard error captured during execution
type: string
stdout:
description: Standard output captured during execution
type: string
success:
description: Whether execution completed without error
type: boolean
required:
- code
- stdout
- stderr
- success
type: object
additionalProperties: false
properties:
answer:
description: The answer to the user's question
type: string
code_executions:
description: History of code executions during the RLM session
items:
$ref: '#/$defs/CodeExecution'
type: array
required:
- answer
type: object
type: function
uri: http://localhost:11434/v1/chat/completions
response:
headers:
content-length:
- '706'
content-type:
- application/json
parsed_body:
choices:
- finish_reason: stop
index: 0
message:
content: '{"answer":"There are **3 documents** in the database.\n\nKey findings:\n- The `list_documents` function
returned a list of 3 entries.\n- No further pagination or filtering was required.\n\nReference: The result from
`list_documents(limit=1000)` showing 3 items.","code_executions":[{"code":"docs = list_documents(limit=1000)\nprint(len(docs))\n","stdout":"3\n","stderr":"","success":true}]}'
role: assistant
created: 1769703340
id: chatcmpl-326
model: gpt-oss
object: chat.completion
system_fingerprint: fp_ollama
usage:
completion_tokens: 117
prompt_tokens: 1694
total_tokens: 1811
status:
code: 200
message: OK
version: 1