150 lines
4.2 KiB
Markdown
150 lines
4.2 KiB
Markdown
## Agents
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Two agentic flows are provided by haiku.rag:
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- Simple QA Agent — a focused question answering agent
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- Research Multi‑Agent — a multi‑step, analyzable research workflow
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### Simple QA Agent
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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.
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Key points:
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- Uses a single `search_documents` tool to fetch relevant chunks
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- Can be run with or without inline citations in the prompt (citations prefer
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document titles when present, otherwise URIs)
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- Returns a plain string answer
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Python usage:
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```python
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from haiku.rag.client import HaikuRAG
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from haiku.rag.qa.agent import QuestionAnswerAgent
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client = HaikuRAG(path_to_db)
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# Choose a provider and model (see Configuration for env defaults)
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agent = QuestionAnswerAgent(
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client=client,
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provider="openai", # or "ollama", "vllm", etc.
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model="gpt-4o-mini",
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use_citations=False, # set True to bias prompt towards citing sources
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)
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answer = await agent.answer("What is climate change?")
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print(answer)
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```
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### Research Graph
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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.
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```mermaid
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---
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title: Research graph
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---
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stateDiagram-v2
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PlanNode --> SearchDispatchNode
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SearchDispatchNode --> EvaluateNode
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EvaluateNode --> SearchDispatchNode
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EvaluateNode --> SynthesizeNode
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SynthesizeNode --> [*]
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```
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Key nodes:
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- Plan: builds up to 3 standalone sub‑questions (uses an internal presearch tool)
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- Search (batched): answers sub‑questions using the KB with minimal, verbatim context
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- Evaluate: extracts insights, proposes new questions, and checks sufficiency/confidence
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- Synthesize: generates a final structured report
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Primary models:
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- `SearchAnswer` — one per sub‑question (query, answer, context, sources)
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- `EvaluationResult` — insights, new questions, sufficiency, confidence
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- `ResearchReport` — final report (title, executive summary, findings, conclusions, …)
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CLI usage:
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```bash
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haiku-rag research "How does haiku.rag organize and query documents?" \
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--max-iterations 2 \
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--confidence-threshold 0.8 \
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--max-concurrency 3 \
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--verbose
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```
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Python usage (blocking result):
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```python
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from haiku.rag.client import HaikuRAG
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from haiku.rag.research import (
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PlanNode,
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ResearchContext,
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ResearchDeps,
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ResearchState,
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build_research_graph,
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)
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async with HaikuRAG(path_to_db) as client:
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graph = build_research_graph()
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question = "What are the main drivers and trends of global temperature anomalies since 1990?"
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state = ResearchState(
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context=ResearchContext(original_question=question),
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max_iterations=2,
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confidence_threshold=0.8,
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max_concurrency=2,
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)
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deps = ResearchDeps(client=client)
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result = await graph.run(
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PlanNode(provider="openai", model="gpt-4o-mini"),
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state=state,
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deps=deps,
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)
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report = result.output
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print(report.title)
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print(report.executive_summary)
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```
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Python usage (streamed events):
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```python
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from haiku.rag.client import HaikuRAG
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from haiku.rag.research import (
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PlanNode,
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ResearchContext,
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ResearchDeps,
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ResearchState,
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build_research_graph,
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stream_research_graph,
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)
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async with HaikuRAG(path_to_db) as client:
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graph = build_research_graph()
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question = "What are the main drivers and trends of global temperature anomalies since 1990?"
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state = ResearchState(
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context=ResearchContext(original_question=question),
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max_iterations=2,
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confidence_threshold=0.8,
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max_concurrency=2,
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)
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deps = ResearchDeps(client=client)
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async for event in stream_research_graph(
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graph,
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PlanNode(provider="openai", model="gpt-4o-mini"),
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state,
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deps,
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):
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if event.type == "log":
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iteration = event.state.iterations if event.state else state.iterations
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print(f"[{iteration}] {event.message}")
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elif event.type == "report":
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print("\nResearch complete!\n")
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print(event.report.title)
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print(event.report.executive_summary)
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```
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