2dogsandanerd

2dogsandanerd / ClawRag

Public

RAG system combining Docling document processing with ChromaDB vector storage to power openclaw

102
20
100% credibility
Found Feb 08, 2026 at 16 stars 6x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ClawRAG is a self-hosted web app that lets you upload documents to create AI-powered searchable knowledge bases for personal or agent use.

How It Works

1
🖥️ Discover ClawRAG

You find this free tool that turns your personal files into a smart, searchable helper.

2
📁 Gather your files

Put all your important documents like PDFs or notes into one simple folder on your computer.

3
🚀 Wake it up

Run one easy start command and watch your AI knowledge base come to life on your machine.

4
🌐 Open your dashboard

Click to your web page and see a friendly interface ready to make your files intelligent.

5
📤 Feed in your files

Drag your folder or upload documents – it reads and organizes everything automatically.

6
❓ Ask your questions

Type anything about your files and get clear, helpful answers right away.

✅ Your smart search

Now you have a private brain for all your documents, answering questions instantly whenever you need.

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Star Growth

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AI-Generated Review

What is ClawRag?

ClawRag is a self-hosted RAG system in Python that ingests documents via Docling processing and stores them in ChromaDB for fast retrieval, powering agents like OpenClaw with long-term memory. Developers get a Docker-ready setup with FastAPI backend, web UI for uploads and queries, and API endpoints for collections, document management, and hybrid search blending vector similarity with keyword matching. Configure LLMs like Ollama for offline use or cloud options, all via a simple .env file.

Why is it gaining traction?

This rag github open source project stands out with one-command docker-compose deployment—no infra headaches—and supports rag system python workflows out of the box, combining Docling parsing with ChromaDB for robust rag github example setups. Features like folder ingestion, real-time progress, and multi-LLM switching (Ollama to OpenAI) make it a practical alternative to heavier rag github langchain stacks, especially for clawrag ptt or rag system ai prototypes needing privacy.

Who should use this?

AI agent builders integrating knowledge bases into tools like OpenClaw, indie devs prototyping rag github copilot-style assistants, or teams evaluating rag github repos for on-prem rag system architecture without cloud bills. Ideal for Python shops handling PDFs/docs who want rag system einfach erklärt simplicity over enterprise complexity.

Verdict

Grab it for quick self-hosted RAG experiments—excellent README and API docs lower the entry barrier despite 17 stars and 1.0% credibility score signaling early maturity. Production? Add tests first, but it's a strong rag github project foundation.

(198 words)

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