Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
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 analyze codebases through semantic search, call graph generation, and function metadata extraction. Provides real-time code analysis with persistent vector storage for understanding complex code structures and relationships.
Transforms local Git repositories into queryable, context-rich knowledge bases via AST-aware chunking and Git metadata, enabling AI assistants to search and understand codebases with semantic precision.
Enables context-aware semantic search across codebases using Qdrant vector database with intelligent GitHub issue resolution, Projects V2 management, and progressive context retrieval for 95%+ token reduction in AI-assisted development.
Enables AI agents to semantically search and navigate code repositories using natural language, with support for multiple repos, incremental indexing, and no local install needed.