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.
Provides RAG search and ask capabilities over markdown and text files using Qdrant vector database, with tools for ingestion, retrieval, and source management.
Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).