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? I 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 - *Parsing*: Docling 2.13.0 (The first parser I've found that doesn't choke on nested tables in legacy PDFs). - *Storage*: ChromaDB (Lightweight, file-based, no Postgres/pgvector overhead needed for personal knowledge bases). - *Search*: Hybrid (Vector similarity + BM25 keyword search) fused using Reciprocal Rank Fusion (RRF) for better retrieval on specific legal jargon. - *Footprint*: Optimized to run under 2GB RAM (excluding the local LLM).
### The tricky part: Citation Preservation Getting 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 Last 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". It found the relevant paragraph in 3 seconds, cited the page/section, and provided the exact quote. Argument closed.
## Quick Start The repo includes a `docker-compose.yml` that spins up everything including the vector store:
```bash # 1. Start ClawRAG docker compose up -d
# 2. Add your documents curl -X POST http://localhost:8080/api/v1/rag/documents/upload \ -F "files=@my_lease.pdf" \ -F "collection_name=personal"
# 3. Connect to your agent openclaw mcp add --transport stdio clawrag npx -y @clawrag/mcp-server ```
## Community & Feedback Code 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 - *Privacy*: Zero external data leaks. Everything stays on your metal. - *LLM Agnostic*: Tested with Ollama (Llama 3.2) and Claude 3.5 via API. - *Context Management*: Explicit context window limiting to prevent GPU crashes on 8GB VRAM cards.