Design and Implementation of a Library AI Assistant: A RAG-Based Chatbot Leveraging Large Language Models (LLMs).
Retrieval-Augmented Generation (RAG) AI Agent with Chat UI Integration
- Built a full-stack RAG AI Agent that answers user queries from documents (PDF, Excel, Markdown, TXT).
- Backend designed using Langflow and n8n to orchestrate the RAG flow: file ingestion → text splitting → OpenAI embeddings → vector store → retriever → LLM.
- Used Supabase (Postgres + pgvector) for vector storage and retrieval.
- Powered by OpenAI/Gemini LLMs and deployed locally with Docker.