Wikipedia-Chatbot-with-RAG-System
This project is a Retrieval-Augmented Generation (RAG) system that allows users to chat with Wikipedia articles on specific topics. By leveraging FAISS for vector-based document retrieval and the Flan-T5 language model for natural language generation, this chatbot can retrieve, generate, and summarize information interactively. LangChain is used to manage conversation history with a summary memory, ensuring coherent dialogue flow.
Project Overview
- Vector Database: FAISS (Facebook AI Similarity Search) enables efficient similarity search and dense vector storage, helping to retrieve relevant Wikipedia snippets based on user queries.
- Language Model: Google’s Flan-T5 model is used for generating human-like responses based on retrieved information, enabling more natural and informative conversations.
- Chat Management: LangChain’s ConversationSummaryMemory stores and manages conversation history, creating a seamless chat experience where context is preserved across multiple turns.
Features
- Topic Selection: Choose a specific Wikipedia topic to explore.
- Conversational Chat: Ask questions and receive answers based on Wikipedia content, enhanced by the language model for readability and context.
- Conversation History: Maintains and summarizes conversation history using ConversationSummaryMemory from LangChain to ensure smooth, coherent dialogue.
