Power your agent
Perintah milik penerbit sendiri. Bukan endpoint PlugMCP.
Host a copy with our keys and logs.
uvx haiku-rag==0.112.0Claude Code tidak punya tautan instal untuk perintah lokal. Salin perintah di bawah dan jalankan di terminal.
claude mcp add io.github.ggozad-haiku-rag -- uvx haiku-rag==0.112.0Jalankan di terminal.
{ "mcpServers": { "io.github.ggozad-haiku-rag": { "command": "uvx", "args": [ "haiku-rag==0.112.0" ] } }}claude_desktop_config.json
URL remote atau perintah di mesin Anda? Perbedaan kedua cara instal
Jalankan di PlugMCP
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.
BukaAgentic 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" ] } }}Tool
Belum ada daftar tool. Server remote mendapatkannya setelah pemeriksaan kesehatan pertama.
FAQ
Salin konfigurasi stdio untuk Cursor, Claude Desktop, Claude Code, atau VS Code di halaman ini. Konfigurasi ini memakai instalasi dari penerbit, bukan proxy PlugMCP.
Tidak ada variabel lingkungan wajib yang tercantum di catatan ini.
Haiku.rag berjalan secara lokal melalui stdio.
Terakhir diperiksa 2026-11-15. Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval