RAG - Enhanced Chatbot Application Using Langchain and Streamlit

In this project, I showcase the RAG-Enhanced Chatbot Application, an AI-powered chatbot built using LangChain, Streamlit, and ChromaDB with OpenAI's GPT models. This chatbot combines powerful language generation with Retrieval-Augmented Generation (RAG) to provide more accurate, document-backed responses.

Key Features:

Real-time Chat Interface: Interact with the chatbot using OpenAI’s GPT models.

Document Uploads for RAG: Enhance responses by uploading your own documents (PDF, TXT, DOCX, MD).

URL-based RAG: Pull information from websites and use it in your chatbot interactions.

Model Selection: Easily switch between OpenAI models such as GPT-4.

Logging: Track user interactions and system responses through automatic logging.

This project is ideal for customer service, research assistants, or knowledge-based applications that require accurate responses.

Watch the full demo to see how the chatbot processes user inputs, retrieves data from uploaded sources, and combines AI capabilities to provide contextually rich answers.

Tech Stack: LangChain OpenAI (GPT-4) Streamlit ChromaDB Docker