haiku.rag/docs/agents.md

Agents

Four agentic flows are provided by haiku.rag:

• Simple QA Agent — a focused question answering agent
• Chat Agent — multi-turn conversational RAG with session memory
• Research Graph — a multi-step research workflow with question decomposition
• RLM Agent — complex analytical tasks via sandboxed Python code execution (see RLM Agent)

See QA and Research Configuration for configuring model, iterations, concurrency, and other settings.

Simple QA Agent

The simple QA agent answers a single question using the knowledge base. It retrieves relevant chunks, optionally expands context around them, and asks the model to answer strictly based on that context.

Key points:

• Uses a single search_documents tool to fetch relevant chunks
• Can be run with or without inline citations in the prompt
• Returns a plain string answer

CLI usage:

haiku-rag ask "What is climate change?"

# With citations
haiku-rag ask "What is climate change?" --cite

# Deep mode (uses research graph with optimized settings)
haiku-rag ask "What are the main features of haiku.rag?" --deep

Python usage:

from haiku.rag.client import HaikuRAG
from haiku.rag.agents.qa.agent import QuestionAnswerAgent

async with HaikuRAG(path_to_db) as client:
    agent = QuestionAnswerAgent(
        client=client,
        provider="openai",
        model="gpt-4o-mini",
        use_citations=False,
    )

    answer = await agent.answer("What is climate change?")
    print(answer)

Chat Agent

The chat agent enables multi-turn conversational RAG. It maintains session state including Q/A history and uses that context to improve follow-up answers.

Key features:

• Composable toolsets: Built from reusable FunctionToolset factories in haiku.rag.tools
• Semantic prior answer recall: The ask tool embeds each question and matches it against conversation history — relevant prior answers are passed to the research planner, which can skip searching when they suffice
• Background summarization: After each ask call, an async background task summarizes the QA history into a compact session context, cached server-side for the next request
• Session context injection: The session context summary flows into the research planner as <background> XML, letting it resolve ambiguous references ("How does it handle X?")
• Document filtering: Natural language document filtering ("search in document X about...")

Tools

The chat agent composes five tools from haiku.rag.tools:

• list_documents — Browse available documents in the knowledge base
• summarize_document — Generate a summary of a specific document
• get_document — Retrieve a specific document by title or URI
• search — Hybrid search with query expansion and optional document filter
• ask — Answer questions using the conversational research graph

CLI Usage

haiku-rag chat
haiku-rag chat --db /path/to/database.lancedb

See Applications for the full TUI interface guide.

Python Usage

from haiku.rag.client import HaikuRAG
from haiku.rag.agents.chat import create_chat_agent, ChatDeps
from haiku.rag.tools import ToolContext

async with HaikuRAG(path_to_db) as client:
    # Create agent with composed toolsets
    context = ToolContext()
    agent = create_chat_agent(config, client, context)
    deps = ChatDeps(config=config, tool_context=context)

    # First question
    result = await agent.run("What is haiku.rag?", deps=deps)
    print(result.output)

    # Follow-up (uses session context)
    result = await agent.run("How does it handle PDFs?", deps=deps)
    print(result.output)

Session State

The ChatSessionState maintains:

• session_id — Unique identifier for the session
• qa_history — List of previous Q/A pairs (FIFO, max 50)
• session_context — Automatically maintained session context summary
• document_filter — List of document titles/URIs to restrict searches
• citation_registry — Stable mapping of chunk IDs to citation indices

Citation Registry: Citation indices persist across tool calls within a session. The same chunk_id always returns the same citation index (first-occurrence-wins). This ensures consistent citation numbering in multi-turn conversations — [1] always refers to the same source.

# Example: citation indices are stable across calls
state = ChatSessionState()

# First call returns citations [1], [2], [3]
# Second call reuses [1] if same chunk, assigns [4], [5] for new chunks
# User can reference [1] in follow-up and it still refers to original source

Conversational Memory

The chat agent maintains two layers of conversational memory that work together:

1. Semantic prior answer recall

When the ask tool receives a question, it:

1. Embeds the new question
2. Computes cosine similarity against all cached qa_history embeddings
3. Selects prior answers above a 0.7 similarity threshold
4. Passes them as prior_answers to the research graph's ResearchContext

The research planner sees these as <prior_answers> in its prompt. If the prior answers already cover the question, the planner marks research as complete and skips directly to synthesis — no new searches needed.

Question embeddings are cached per-session to avoid re-embedding on every turn. Uncached embeddings are batch-computed.

2. Background session summarization

After each ask call completes, trigger_background_summarization() spawns an async task that:

1. Formats the full qa_history (questions, answers, confidence, sources) as markdown
2. Sends it to an LLM with the current session context (if any) as input
3. Produces a compact summary capturing key facts, entities, and document references
4. Caches the result server-side under the session_id

If a new ask fires before the previous summarization finishes, the old task is cancelled. On the next request, the cached summary is picked up and injected as session_context into the research planner's <background> XML.

This means follow-up questions like "Tell me more about the authentication part" resolve correctly even though the planner never saw the original conversation — it has the summary.

AG-UI Integration

When using the chat agent with AG-UI streaming, state is emitted under a namespaced key to avoid conflicts with other agents:

from haiku.rag.agents.chat import AGUI_STATE_KEY, ChatDeps
from haiku.rag.tools import ToolContext

# AGUI_STATE_KEY = "haiku.rag.chat"

context = ToolContext()
agent = create_chat_agent(config, client, context)
deps = ChatDeps(
    config=config,
    tool_context=context,
    state_key=AGUI_STATE_KEY,  # Enables namespaced state emission
)

The emitted state structure:

{
  "haiku.rag.chat": {
    "session_id": "",
    "citations": [...],
    "qa_history": [...],
    "document_filter": [...],
    "citation_registry": {"chunk-id-1": 1, "chunk-id-2": 2}
  }
}

Frontend clients should extract state from under this key. See the Web Application for a complete implementation example.

Research Graph

The research workflow is implemented as a typed pydantic-graph. It uses an iterative feedback loop where the planner proposes one question at a time, sees the answer, then decides whether to continue or synthesize.

---
title: Research graph
---
stateDiagram-v2
  state plan_next_decision <<choice>>
  [*] --> plan_next
  plan_next --> plan_next_decision
  plan_next_decision --> search_one: Has next question
  plan_next_decision --> synthesize: Complete or max iterations
  search_one --> plan_next: Answer added to context
  synthesize --> [*]

  note right of plan_next
    Receives session_context as background
    and prior_answers from conversation history.
    Uses a different prompt when prior answers exist.
  end note

The graph receives a ResearchContext containing:

• original_question — the user's question
• session_context — summary of conversation history (injected as <background> XML)
• qa_responses — prior answers from semantic matching or previous iterations (injected as <prior_answers> XML)

When prior answers are provided, the planner uses a context-aware prompt that evaluates whether existing evidence is sufficient. If it is, the planner marks is_complete=True and the graph skips directly to synthesis without any searches.

Key nodes:

• plan_next: Evaluates gathered evidence and either proposes the next question to investigate or marks research as complete. Uses a context-aware prompt when prior answers exist, allowing it to skip research entirely.
• search_one: Answers a single question using the knowledge base (up to 3 search calls per question). Each answer is added to ResearchContext.qa_responses for the next planning iteration.
• synthesize: Generates the final output from all gathered evidence.

Output modes:

The graph supports two output modes via build_research_graph(output_mode=...):

Mode	Output type	Used by
"report"	ResearchReport (title, executive summary, findings, conclusions, recommendations)	CLI haiku-rag research, Python API
"conversational"	ConversationalAnswer (answer, citations, confidence)	Chat agent's ask tool

Iterative flow:

• Each iteration: planner evaluates context → proposes one question → search answers it → loop back
• Planner can decompose complex questions (e.g., "benefits and drawbacks" → start with "benefits")
• Session context resolves ambiguous references and informs planning
• Prior answers let the planner skip redundant searches
• Loop terminates when planner marks is_complete=True or max_iterations is reached

CLI Usage

# Basic usage
haiku-rag research "How does haiku.rag organize and query documents?"

# With document filter
haiku-rag research "What are the key findings?" --filter "uri LIKE '%report%'"

Python Usage

Basic example:

from haiku.rag.client import HaikuRAG
from haiku.rag.config import Config
from haiku.rag.agents.research.dependencies import ResearchContext
from haiku.rag.agents.research.graph import build_research_graph
from haiku.rag.agents.research.state import ResearchDeps, ResearchState

async with HaikuRAG(path_to_db) as client:
    graph = build_research_graph(config=Config)
    context = ResearchContext(original_question="What are the main features?")
    state = ResearchState.from_config(context=context, config=Config)
    deps = ResearchDeps(client=client)

    report = await graph.run(state=state, deps=deps)

    print(report.title)
    print(report.executive_summary)

With custom config:

from haiku.rag.client import HaikuRAG
from haiku.rag.config.models import AppConfig, ResearchConfig
from haiku.rag.agents.research.dependencies import ResearchContext
from haiku.rag.agents.research.graph import build_research_graph
from haiku.rag.agents.research.state import ResearchDeps, ResearchState

custom_config = AppConfig(
    research=ResearchConfig(
        provider="openai",
        model="gpt-4o-mini",
        max_iterations=5,
        max_concurrency=3,
    )
)

async with HaikuRAG(path_to_db) as client:
    graph = build_research_graph(config=custom_config)
    context = ResearchContext(original_question="What are the main features?")
    state = ResearchState.from_config(context=context, config=custom_config)
    deps = ResearchDeps(client=client)

    report = await graph.run(state=state, deps=deps)

Conversational mode with prior answers:

from haiku.rag.agents.research.dependencies import ResearchContext
from haiku.rag.agents.research.graph import build_research_graph
from haiku.rag.agents.research.models import SearchAnswer
from haiku.rag.agents.research.state import ResearchDeps, ResearchState

# Conversational mode returns ConversationalAnswer instead of ResearchReport
graph = build_research_graph(config=Config, output_mode="conversational")

# Pass session context and prior answers from conversation history
context = ResearchContext(
    original_question="How does it handle authentication?",
    session_context="User is building a Python web app with FastAPI.",
    qa_responses=[
        SearchAnswer(
            query="What authentication methods are supported?",
            answer="JWT and OAuth2 are supported.",
            confidence=0.95,
            cited_chunks=["chunk-1"],
        )
    ],
)
state = ResearchState.from_config(context=context, config=Config)
deps = ResearchDeps(client=client)

result = await graph.run(state=state, deps=deps)
print(result.answer)       # Direct conversational answer
print(result.confidence)   # 0.0-1.0
print(result.citations)    # Deduplicated citations from all searches

Filtering Documents

Restrict searches to specific documents via the search_filter parameter:

# Set filter before running the graph
state = ResearchState.from_config(context=context, config=Config)
state.search_filter = "id IN ('doc-123', 'doc-456')"

report = await graph.run(state=state, deps=deps)

The filter applies to all search operations in the graph. See Filtering Search Results for available filter columns and syntax.