Enables AI assistants to semantically search and understand code repositories using PostgreSQL with pgvector embeddings. Provides repository indexing, natural language code search, and development task management with git integration.
Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.
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 analyze and optimize PostgreSQL database performance by identifying missing indexes and suggesting query rewrites. It allows users to retrieve database projects and implement performance fixes directly through natural language in their code editor.
