Усильте агента
Собственная команда издателя. Не эндпоинт PlugMCP.
Host a copy with our keys and logs.
uvx haiku-rag==0.112.0У Claude Code нет ссылки для установки локальной команды. Скопируйте команду ниже и выполните её в терминале.
claude mcp add io.github.ggozad-haiku-rag -- uvx haiku-rag==0.112.0Выполните в терминале.
{ "mcpServers": { "io.github.ggozad-haiku-rag": { "command": "uvx", "args": [ "haiku-rag==0.112.0" ] } }}claude_desktop_config.json
Удалённый URL или команда на вашем компьютере? Чем различаются два способа установки
Запустить на 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.
ЗапускAgentic 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" ] } }}Инструменты
Списка инструментов пока нет. У удалённых серверов он появляется после первой проверки состояния.
FAQ
Скопируйте на этой странице конфигурацию stdio для Cursor, Claude Desktop, Claude Code или VS Code. Она использует установку издателя, а не прокси PlugMCP.
В этой записи не указано обязательных переменных окружения.
Haiku.rag работает локально через stdio.
Последняя проверка: 2026-11-15. Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval