Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
Aggregates documentation from multiple sources (llms.txt format or web scraping) and provides semantic search capabilities using vector embeddings and hybrid search for each documentation source.
Enables AI-powered querying and management of documentation through markdown file serving, keyword search, and OpenAI-based Q\&A capabilities. Supports document indexing, analysis, and agent handoffs with rate limiting protection.
Enables AI agents to ingest documentation from websites or local files and perform semantic search over the indexed content using pgvector-backed embeddings.
