Скачать или смотреть Build a RAG System in Python: Chat with ANY PDF Using LangChain & Vector Db | Complete Tutorial

🚀 Transform any PDF into an intelligent chatbot that you can have conversations with!

In this comprehensive tutorial, you'll learn how to build a complete RAG (Retrieval-Augmented Generation) system from scratch using Python, LangChain, and vector databases. Perfect for beginners and intermediate developers who want to understand how modern AI document processing works.

🎯 What You'll Learn:
✅ How to extract and process text from PDF documents
✅ Create embeddings using OpenAI's embedding models
✅ Set up and use Chroma vector database for semantic search
✅ Build a conversational AI system with memory
✅ Implement RAG (Retrieval-Augmented Generation) architecture
✅ Create an interactive chat interface for document Q&A
✅ Handle document chunking and text splitting strategies
✅ Best practices for production-ready RAG systems

🛠️ Technologies Covered:
Python - Core programming language
LangChain - AI application framework
OpenAI API - Embeddings and language models
Chroma - Vector database for semantic search
PyPDF - PDF document processing
RAG Architecture - Advanced AI retrieval patterns

📚 Example Use Case:
We'll use Shakespeare's "Julius Caesar" as our demo document, but this system works with ANY PDF - from research papers to technical manuals, legal documents to educational materials!


💡 Code Highlights:
Complete PDF processing pipeline
Vector database setup and querying
Conversational memory implementation
Interactive chat interface
Error handling and optimization

🎓 Perfect For:
AI/ML enthusiasts
Python developers
Data scientists
Anyone building document AI systems
Students learning about RAG architecture

🚀 Next Steps:
After this tutorial, you'll be ready to:
Build document Q&A systems for your business
Process multiple PDFs simultaneously
Integrate with web applications
Scale to production environments

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#RAG #LangChain #Python #AI #MachineLearning #VectorDatabase #OpenAI #DocumentAI