Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.
Provides semantic vector search over local codebases via MCP, enabling hybrid search (dense + sparse + RRF) for any MCP client like GitHub Copilot or Claude Desktop.
Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.
Enables AI-driven semantic code search via natural language queries, integrating with MCP clients like Claude Desktop to retrieve relevant code context from any codebase.
Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.