agent-rag-mcp

# Agent_rag Agent_rag is a RAG (Retrieval-Augmented Generation) MCP (Model Context Protocol) Server. It uses ChromaDB for vector storage and provides flexible embedding options—from lightweight Ollama integration to local ONNX models—with zero external dependencies. ## Available Tools This server exposes several MCP tools for the orchestrator (`Agent_head`) or any other MCP client: - **`rag_ingest`** — Ingest documents, directories, or raw text into a collection - **`rag_search`** — Perform semantic search against your knowledge base - **`rag_list_collections`** — List all active collections - **`rag_delete_collection`** — Delete a specific collection ## Features - **Flexible Embedding Providers** — Choose your embedding backend: - **Ollama** (~50 MB): Lightweight, runs with Ollama service—94% smaller than torch-based setup - **Local ONNX** (~300 MB): Fully offline with CPU/GPU support—no external services needed - **GPU-Accelerated** (~1.2 GB): PyTorch + sentence-transformers for maximum performance - **ChromaDB Integration**: Persistent vector database for efficient semantic search - **FastMCP Built-in**: Asynchronous, thread-safe tool execution - **Easy Configuration**: Flexible `config.yaml` for chunk size, collections, embedding models, and provider selection - **Zero External Dependencies**: No API keys required—everything runs locally ## Quick Start For a complete installation guide with size comparisons, troubleshooting, and provider-specific setup: 👉 **See [INSTALLATION.md](INSTALLATION.md)** ## Installation & Usage ### Interactive Setup (Recommended) ```bash cd Agent_rag python setup_agent_rag.py ``` This interactive script guides you through provider selection and runs the appropriate installation command. ### Direct Installation **For Ollama** (lightweight, ~50 MB): ```bash uv install .[ollama] python server.py ``` **For Local Offline** (ONNX, ~300 MB): ```bash uv install .[local] python server.py ``` **For GPU** (PyTorch accel…