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 and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.
A uniform API for agents to read, write, and search across papers, documents, code, and personal state, using PostgreSQL with pgvector for hybrid retrieval.