haiku.rag/docs/agents.md

Agents

Two agentic flows are provided by haiku.rag:

• Simple QA Agent — a focused question answering agent
• Research Multi‑Agent — a multi‑step, analyzable research workflow

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 (citations prefer document titles when present, otherwise URIs)
• Returns a plain string answer

Python usage:

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

client = HaikuRAG(path_to_db)

# Choose a provider and model (see Configuration for env defaults)
agent = QuestionAnswerAgent(
    client=client,
    provider="openai",  # or "ollama", "vllm", etc.
    model="gpt-4o-mini",
    use_citations=False,  # set True to bias prompt towards citing sources
)

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

Research Graph

The research workflow is implemented as a typed pydantic‑graph. It plans, searches (in parallel batches), evaluates, and synthesizes into a final report — with clear stop conditions and shared state.

---
title: Research graph
---
stateDiagram-v2
  PlanNode --> SearchDispatchNode
  SearchDispatchNode --> EvaluateNode
  EvaluateNode --> SearchDispatchNode
  EvaluateNode --> SynthesizeNode
  SynthesizeNode --> [*]

Key nodes:

• Plan: builds up to 3 standalone sub‑questions (uses an internal presearch tool)
• Search (batched): answers sub‑questions using the KB with minimal, verbatim context
• Evaluate: extracts insights, proposes new questions, and checks sufficiency/confidence
• Synthesize: generates a final structured report

Primary models:

• SearchAnswer — one per sub‑question (query, answer, context, sources)
• EvaluationResult — insights, new questions, sufficiency, confidence
• ResearchReport — final report (title, executive summary, findings, conclusions, …)

CLI usage:

haiku-rag research "How does haiku.rag organize and query documents?" \
  --max-iterations 2 \
  --confidence-threshold 0.8 \
  --max-concurrency 3 \
  --verbose

Python usage:

from haiku.rag.client import HaikuRAG
from haiku.rag.research import (
    ResearchContext,
    ResearchDeps,
    ResearchState,
    build_research_graph,
    PlanNode,
)

async with HaikuRAG(path_to_db) as client:
    graph = build_research_graph()
    state = ResearchState(
        question="What are the main drivers and trends of global temperature anomalies since 1990?",
        context=ResearchContext(original_question=... ),
        max_iterations=2,
        confidence_threshold=0.8,
        max_concurrency=3,
    )
    deps = ResearchDeps(client=client)
    result = await graph.run(PlanNode(provider=None, model=None), state=state, deps=deps)
    report = result.output
    print(report.title)
    print(report.executive_summary)