Summary
Fahd Mirza demonstrates how to combine three open-source tools — OpenClaw, TurboVec, and Ollama — into a fully local, privacy-preserving RAG agent. OpenClaw serves as the terminal-based AI agent interface, running a Qwen 3.6 (23 billion parameter) quantized model via Ollama. TurboVec, a pip-installable implementation of Google’s Turbo Cont algorithm, compresses the vector search index 8x with near-zero quality loss. Nomic Embed handles document embedding.
The architecture is concrete and reproducible: a FastAPI server on port 8811 loads a personal text document, converts it to a TurboVec-compressed vector index, and exposes a retrieval endpoint. OpenClaw is given a “skill” — a plain markdown file that tells the agent when to call the RAG server via curl and how to incorporate the returned context. When the agent receives a query about content in the document, it fetches relevant passages from TurboVec before passing the enriched prompt to the local Qwen model.
Mirza walks through the full conda environment setup, dependency installation, server configuration, and a live demo where the agent accurately answers questions from a custom personal information file. All components run locally with no external API calls, making this setup well-suited for sensitive data or air-gapped environments. Configuration files and the skill definition are shared in Mirza’s GitHub repository linked in the video description.
📺 Source: Fahd Mirza · Published April 22, 2026
🏷️ Format: Hands On Build
