w3-mcp-server-qdrant

# W3 MCP Qdrant Server Python MCP server for vector search using [Qdrant](https://qdrant.tech/) vector database and [Ollama](https://ollama.ai/) embeddings. **Status:** ✅ Working with Qdrant vector search and Ollama embeddings + Advanced query techniques ## Features - **qdrant_search** - Search for similar documents using text queries (auto-embedded via Ollama) - ✨ Query Expansion - Generate N query variations, search all, merge with RRF - ✨ HyDE - Hypothetical Document Embeddings for semantic enrichment - ✨ Reranking - Use LLM to reorder results by relevance - **qdrant_list_collections** - List and manage Qdrant collections Supports flexible output formats (Markdown or JSON) with configurable similarity thresholds and advanced search options. ## Quick Start ### 1. Prerequisites Setup #### Qdrant Server ```bash # Using Docker (Recommended) docker run -p 6333:6333 qdrant/qdrant:latest ``` Or install locally: [Qdrant Quick Start](https://qdrant.tech/documentation/quick-start/) #### Ollama Server ```bash # Install: https://ollama.ai ollama pull bge-m3 ollama pull mistral ollama serve ``` Available embedding models: - `bge-m3` (384 dims) - ⭐ **recommended** - best quality-speed balance - `nomic-embed-text` (768 dims) - balanced, good for general use - `mxbai-embed-large` (1024 dims) - highest quality - `all-minilm` (384 dims) - ultra-lightweight, good for mobile ### 2. Clean Setup (Important!) ```bash cd /path/to/w3-mcp-server-qdrant # Remove old lockfile and venv rm -rf uv.lock .venv venv # Unset old environment variable unset VIRTUAL_ENV ``` ### 3. Install Dependencies with uv ```bash # Install all Python dependencies using uv uv sync ``` That's it! `uv sync` installs all dependencies including MCP, pydantic, qdrant-client, and httpx. ### 4. Configure Environment Create a `.env` file from template: ```bash cp .env.example .env ``` Edit `.env`: ```bash # Qdrant Configuration QDRANT_URL=http://localhost:6333 QDRANT_API_KEY= # Optional i…