Troubleshooting Chatbot (RAG)

Project Overview

This project explores Retrieval-Augmented Generation (RAG) to create an intelligent troubleshooting chatbot. We'll learn how to combine LLMs with custom knowledge bases to provide accurate, context-aware responses.

Key Learning Objectives

• Understanding RAG architecture
• Vector databases and embeddings
• Knowledge base creation and management
• Semantic search implementation
• Response generation and validation

Technologies Used

• Python
• LangChain
• Pinecone or Weaviate
• OpenAI API
• FAISS for vector search

Additional Resources

For detailed implementation guide, code examples, and step-by-step instructions, please visit the GitHub Project Repository.