Download entire documentation websites for offline access and RAG indexing. Supports configurable depth and concurrency settings for efficient website retrieval.
Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.
Extract and analyze web page content to answer specific questions using RAG (Retrieval Augmented Generation). Provide AI-generated responses based on relevant page sections for accurate insights.
Enables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.
Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
Integrates the LINE Messaging API with AI agents via the Model Context Protocol, supporting both stdio and SSE transport protocols. It allows agents to send messages, manage rich menus, and retrieve user profile information for LINE Official Accounts.