{"id":46847406,"title":"Show HN: Self-hosted RAG with MCP support for OpenClaw","points":2,"user":"2dogsanerd","time":1769964328,"time_ago":"8 hours ago","type":"link","content":"

I've been using OpenClaw to control my home server via WhatsApp, but it couldn't access my documents. Instead of uploading my private contracts to OpenAI, I built ClawRAG – a self-hosted RAG engine that connects to OpenClaw via MCP (Model Context Protocol). Now I can ask "What did the contract say about liability?" and get cited answers, not hallucinations.

Most RAG systems are either too complex for a solo dev's home setup or they rely on cloud-hosted vector stores. I needed something that runs in a single Docker container, understands messy PDFs (tables!), and integrates natively as a "tool" for agents rather than just another REST endpoint.

## Technical Deep Dive

### Why MCP instead of REST?\nI chose the Model Context Protocol (MCP) because it provides structured schemas that LLMs understand natively. The MCP server exposes `query_knowledge` as a tool, allowing the agent to decide exactly when to pull from the knowledge base vs. when to use its built-in memory. It prevents "tool-drift" and ensures type-safe responses.

### The Stack\n- *Parsing*: Docling 2.13.0 (The first parser I've found that doesn't choke on nested tables in legacy PDFs).\n- *Storage*: ChromaDB (Lightweight, file-based, no Postgres/pgvector overhead needed for personal knowledge bases).\n- *Search*: Hybrid (Vector similarity + BM25 keyword search) fused using Reciprocal Rank Fusion (RRF) for better retrieval on specific legal jargon.\n- *Footprint*: Optimized to run under 2GB RAM (excluding the local LLM).

### The tricky part: Citation Preservation\nGetting citations to work reliably over a WhatsApp round-trip was the biggest challenge. I had to ensure chunk IDs and source metadata survive the transformation from ChromaDB → LlamaIndex → LLM → OpenClaw → WhatsApp without getting "summarized away" or sanitized by the LLM's output formatting.

## Use Case\nLast week my landlord claimed I signed a clause about garden/snow maintenance. I pulled up my phone, wrote to my OpenClaw bot: "Search my lease for gardening obligations". \nIt found the relevant paragraph in 3 seconds, cited the page/section, and provided the exact quote. Argument closed.

## Quick Start\nThe repo includes a `docker-compose.yml` that spins up everything including the vector store:

```bash\n# 1. Start ClawRAG\ndocker compose up -d

# 2. Add your documents\ncurl -X POST http://localhost:8080/api/v1/rag/documents/upload \\\n -F "files=@my_lease.pdf" \\\n -F "collection_name=personal"

# 3. Connect to your agent\nopenclaw mcp add --transport stdio clawrag npx -y @clawrag/mcp-server\n```

## Community & Feedback\nCode is MIT licensed. I'd love feedback on the MCP implementation – specifically if you see better ways to handle tool schemas for multi-collection search.

*Ask me anything about the architecture or how I handled the citation logic!*

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### Hidden Technical Details \n- *Privacy*: Zero external data leaks. Everything stays on your metal.\n- *LLM Agnostic*: Tested with Ollama (Llama 3.2) and Claude 3.5 via API.\n- *Context Management*: Explicit context window limiting to prevent GPU crashes on 8GB VRAM cards.","url":"https://github.com/2dogsandanerd/ClawRag","domain":"github.com","comments":[],"comments_count":0}