Enables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.
Enables semantic search through markdown documentation in code repositories using AI embeddings. Provides intelligent document chunking and similarity-based search to help users find relevant documentation based on meaning rather than just keywords.
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 semantically search through indexed documentation websites and local code repositories using OpenAI embeddings and ChromaDB vector storage.
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.