The RAG-based Intelligent Conversational AI Agent combines a vector store-backed retrieval layer with Google’s Gemini LLM via LangChain to power context-rich, conversational knowledge extraction. Users ingest and index documents—PDFs, web pages, or databases—into a vector database. When a query is posed, the agent retrieves top relevant passages, feeds them into a prompt template, and generates concise, accurate answers. Modular components allow customization of data sources, vector stores, prompt engineering, and LLM backends. This open-source framework simplifies the development of domain-specific Q&A bots, knowledge explorers, and research assistants, delivering scalable, real-time insights from large document collections.

SvectorDB is a comprehensive serverless vector database designed to simplify the management and querying of vectorized data. Built to be highly scalable and cost-effective, it supports high-dimensional vectors and is optimized for performance. The platform is ideal for applications that necessitate efficient vector handling, such as image search, natural language processing, and machine learning. With easy integration and robust APIs, SvectorDB ensures a seamless experience for developers and data scientists alike. The free tier allows users to experiment and prototype without upfront costs, making it an attractive option for both startups and enterprises.