haiku.rag/docs/agents/rlm.md

RLM Agent (Recursive Language Model)

The RLM agent enables complex analytical tasks by writing and executing Python code in a sandboxed environment. It solves problems that traditional RAG struggles with:

• Aggregation: "How many documents mention security vulnerabilities?"
• Computation: "What's the average revenue across all quarterly reports?"
• Multi-document analysis: "Compare the key findings between Report A and Report B"
• Structured data extraction: "Extract all dollar amounts and compute totals"

How It Works

1. The agent receives a question
2. It writes Python code to explore the knowledge base
3. Code executes in a sandboxed Python interpreter with access to haiku.rag functions
4. The agent iterates: run code, examine results, refine approach
5. Final answer is synthesized from the gathered data

CLI Usage

# Basic usage
haiku-rag rlm "How many documents are in the database?"

# With document filter (restricts what the agent can access)
haiku-rag rlm "Summarize the key points" --filter "uri LIKE '%report%'"

# Pre-load specific documents
haiku-rag rlm "Compare these two reports" --document "Q1 Report" --document "Q2 Report"

Python Usage

from haiku.rag.client import HaikuRAG

async with HaikuRAG(path_to_db) as client:
    # Basic question
    result = await client.rlm("How many documents mention 'security'?")
    print(result.answer)    # The answer
    print(result.program)   # The final consolidated program

    # With filter (agent can only see filtered documents)
    result = await client.rlm(
        "What is the total revenue?",
        filter="title LIKE '%Financial%'"
    )

    # Pre-load specific documents
    result = await client.rlm(
        "Compare the conclusions",
        documents=["Report A", "Report B"]
    )

Available Functions

Inside the sandbox, these functions are available (no imports needed):

search(query, limit=10)

Search the knowledge base using hybrid search (vector + full-text).

results = search("climate change impacts", limit=20)
for r in results:
    print(r['document_title'], r['score'])
    print(r['content'][:200])

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 available documents in the knowledge base.

docs = list_documents(limit=100)
for doc in docs:
    print(doc['id'], doc['title'])

Returns list of dicts with keys: id, title, uri, created_at

get_document(id_or_title)

Get the full text content of a document by ID, title, or URI.

content = get_document("Q1 Report")
if content:
    print(len(content), "characters")

Returns the document content as a string, or None if not found.

get_chunk(chunk_id)

Get a specific chunk by its ID (from search results). Use this to retrieve full chunk details and metadata for citations.

results = search("safety requirements", limit=5)
for r in results:
    chunk = get_chunk(r['chunk_id'])
    print(f"From '{chunk['document_title']}', page {chunk['page_numbers']}: {chunk['content'][:100]}")

Returns dict with keys: chunk_id, content, document_id, document_title, headings, page_numbers, labels

llm(prompt)

Call an LLM directly for classification, summarization, or extraction tasks.

content = get_document("Q1 Report")
sentiment = llm(f"Classify the sentiment as positive, negative, or mixed: {content}")
print(sentiment)

Use this when you have content and need LLM reasoning without RAG search.

Pre-loaded Documents

When documents are pre-loaded via the documents parameter, they're available as a documents variable:

# Available when documents are pre-loaded
for doc in documents:
    print(doc['title'], len(doc['content']))

Each document dict has keys: id, title, uri, content

Python Features

The sandbox uses pydantic-monty, a minimal secure Python interpreter written in Rust. It supports a subset of Python:

Supported: variables, arithmetic, strings, f-strings, lists, dicts, tuples, sets, loops, conditionals, comprehensions, functions, 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:

# Extract data with llm() instead of regex
numbers = []
results = search("financial data", limit=20)
for r in results:
    extracted = llm(f"Extract all dollar amounts 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)}")

Sandboxed Execution

Code executes in an isolated interpreter with:

• No filesystem access: Code cannot read or write files
• No network access: Code cannot make HTTP requests or open sockets
• No imports: Only the json module is available
• Execution timeout: Code times out after configurable limit (default 60s)
• Output truncation: Large outputs are truncated to prevent memory issues

Context Filter

The filter parameter restricts what documents the agent can access. Unlike tool parameters, the filter is applied automatically and cannot be bypassed by the LLM:

# Agent can only see documents with "confidential" in the URI
result = await client.rlm(
    "Summarize all findings",
    filter="uri LIKE '%confidential%'"
)

This is useful for:

• Scoping to specific document sets
• Enforcing access control
• Limiting context for focused analysis

Configuration

RLM settings can be configured in haiku.rag.yaml:

rlm:
  model:
    provider: anthropic
    name: claude-sonnet-4-20250514
  code_timeout: 60.0      # Max seconds for code execution
  max_output_chars: 50000 # Truncate output after this many chars