Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud
haiku.rag
Overview
Description from the maker (original English)
Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiEarly↓ 1.3K/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"haiku-rag": {
"command": "uvx",
"args": [
"haiku-rag"
]
}
}
}Run this in your terminal
claude mcp add haiku-rag -- uvx haiku-ragOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"haiku-rag": {
"command": "uvx",
"args": [
"haiku-rag"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"haiku-rag": {
"type": "stdio",
"command": "uvx",
"args": [
"haiku-rag"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account ggozad, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5716 servers in this category
- Published to the registry more recently than 94% of the other 5716 servers in this category
- Basic checks equal to or better than 100% of the other 5716 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is haiku.rag safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.109.0) reached the registry 2 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud✓ exa.ai
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
haiku.rag
Agentic 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.
Features
- Ingest PDFs, office documents, HTML, Markdown and images with Docling, in-process or on docling-serve. The stored DoclingDocument keeps headings, tables, pictures and page provenance.
- Search with hybrid vector and full-text retrieval, optional reranking (cross-encoders, Jina, Cohere, Zero Entropy, vLLM, OpenRouter), section-aware context expansion, and image search with a multimodal embedder (vLLM, OpenRouter, VoyageAI, Cohere). Across several named databases at once.
- Answer with the RAG capability: it searches, runs sandboxed Python over the documents for counting and aggregation, and cites page numbers and headings. Vision models receive the figures. Optional capabilities compact earlier evidence in long conversations and require every answer to declare its grounding.
- Check a citation by drawing its chunk on the page image, from the CLI, the chat TUI or Python.
- Integrate through the Python API, native Pydantic AI capabilities, plugin hooks that observe document writes and transform searches, an MCP server for Claude Code, Codex and Claude Desktop, and a reference web app.
- Operate with the
haiku-ingesterservice (filesystem, HTTP, S3 and WebDAV sources, a SQLite or Postgres job queue with retries, a control plane and dashboard), tags and rollback, vacuum, andhaiku-rag doctorhealth checks.
Installation
Python 3.12 or newer.
pip install haiku.rag # Docling, the VoyageAI and Cohere embedders, every reranker, the TUIpip 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.
Quick start
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 databasehaiku-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 servePython API
from haiku.rag.client import HaikuRAGasync 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.
MCP server
haiku-rag mcp --stdioThe 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.ragclaude plugin install haiku-rag
Codex and Claude Desktop setup is in the MCP docs.
Examples
- Docker setup: docling-serve, the ingester and the MCP server
- Web application: chat, ingestion, inspector and doctor in the browser
Documentation
- Quickstart: install, index, chat
- Installation: packages and extras
- Architecture: how a document becomes a cited answer
- CLI and Chat and inspector
- Capabilities: native Pydantic AI capabilities
- Configuration: every setting, and tuning
- Ingester, MCP and remote processing
- Python API and custom pipelines
- Benchmarks and the changelog
License
MIT.
mcp-name: io.github.ggozad/haiku-rag