AI Chatbots & RAG Systems – Intelligent Assistants

About the project

Designed and developed Retrieval-Augmented Generation (RAG) systems that enable AI assistants to answer questions using specific organizational documents and knowledge sources instead of relying solely on a language model’s general knowledge. Documents are converted into embeddings and indexed within Pinecone, allowing relevant passages to be retrieved through semantic search for each user query.LangChain orchestrates retrieval, prompt construction, and conversational context, while MongoDB maintains conversation history and application data independently from the vector index. The AI agents are exposed through FastAPI REST services, enabling straightforward integration with web applications and other systems. This architecture produces context-aware, document-grounded responses with traceable source information.