Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.
Enables file-based knowledge management with ranked keyword and semantic hybrid search, allowing AI agents to learn from documents and recall relevant knowledge as a persistent memory tool.
Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.
Provides semantic code search and retrieval capabilities for AI agents, enabling them to query codebases using natural language with automatic learning, hybrid search, and intelligent chunking of functions and classes.
Enables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.
Enables AI agents to ingest documentation from websites or local files and perform semantic search over the indexed content using pgvector-backed embeddings.