Four-layer hybrid search and knowledge graph for AI coding assistants: BM25 + vector embeddings + RAPTOR directory summaries + graph expansion fused into a single MCP tool.
Enables AI assistants to index and search codebases using semantic search powered by multiple embedding providers (OpenAI, VoyageAI, Gemini, Ollama) and vector database storage.
Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
A semantic code retrieval engine for AI agents that enables hybrid search, graph expansion, and token-aware context packing, integrating with MCP to provide precise code context to LLMs.
A smart code retrieval tool based on Model Context Protocol that provides efficient and accurate code repository search capabilities for large language models.