에이전트 강화
게시자의 자체 명령입니다. PlugMCP 엔드포인트가 아닙니다.
Host a copy with our keys and logs.
uvx haiku-rag==0.112.0Claude Code에는 로컬 명령용 설치 링크가 없습니다. 아래 명령을 복사해 터미널에서 실행합니다.
claude mcp add io.github.ggozad-haiku-rag -- uvx haiku-rag==0.112.0터미널에서 실행합니다.
{ "mcpServers": { "io.github.ggozad-haiku-rag": { "command": "uvx", "args": [ "haiku-rag==0.112.0" ] } }}claude_desktop_config.json
원격 URL인가요, 내 컴퓨터에서 실행하는 명령인가요? 두 설치 방식의 차이
PlugMCP에서 실행
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
실행Agentic RAG that answers questions about your own documents with citations to page numbers and section headings. It runs on an embedded LanceDB database with open models through Ollama by default, so no server or API key is needed. Any provider Pydantic AI supports works in their place, and the same database can live on S3, GCS, Azure or LanceDB Cloud.
Built on LanceDB, Pydantic AI and Docling. Documentation: ggozad.github.io/haiku.rag.
haiku-ingester service (filesystem, HTTP, S3 and WebDAV sources, a SQLite or Postgres job queue with retries, a control plane and dashboard), tags and rollback, vacuum, and haiku-rag doctor health checks.Python 3.12 or newer.
pip install haiku.rag # Docling, the VoyageAI and Cohere embedders, every reranker, the TUI
pip install haiku.rag-slim # the core, with extras chosen by you
The ingester, S3 access and model providers other than Ollama and OpenAI-compatible endpoints are extras. See Installation.
The default configuration uses Ollama for embeddings and answers. The quickstart covers the models to pull and using OpenAI instead.
haiku-rag init # create the database
haiku-rag add-src paper.pdf # index a file, URL or directory
haiku-rag search "attention mechanism"
haiku-rag ask "What datasets were used for evaluation?"
haiku-rag ask "How many documents mention transformers?"
haiku-rag ask "Does this figure match the spec?" --image figure.png
haiku-rag chat # multi-turn chat in the terminal
Continuous ingestion from configured sources runs as a separate service, with the ingester extra (pip install 'haiku.rag[ingester]'):
haiku-ingester serve
from haiku.rag.client import HaikuRAG
async with HaikuRAG("knowledge.lancedb", create=True) as rag:
await rag.create_document_from_source("paper.pdf")
await rag.create_document_from_source("https://arxiv.org/pdf/1706.03762")
results = await rag.search("self-attention")
for result in results:
print(f"{result.score:.2f} | p.{result.page_numbers} | {result.content[:100]}")
answer, citations = await rag.ask("What is the complexity of self-attention?")
print(answer)
for cite in citations:
print(f" [{cite.chunk_id}] p.{cite.page_numbers}: {cite.content[:80]}")
To compose your own agent, see Capabilities.
haiku-rag mcp --stdio
The server gives an assistant search, document reading and a Python sandbox over the documents. In Claude Code, the plugin registers it with a skill:
claude plugin marketplace add ggozad/haiku.rag
claude plugin install haiku-rag
Codex and Claude Desktop setup is in the MCP docs.
MIT.
mcp-name: io.github.ggozad/haiku-rag
Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval
{ "mcpServers": { "io.github.ggozad-haiku-rag": { "command": "uvx", "args": [ "haiku-rag==0.112.0" ] } }}도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
이 레코드에는 필수 환경 변수가 등록되어 있지 않습니다.
Haiku.rag은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-11-15. Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval