Build a RAG System with Python: Expert Knowledge Worker for Insurance Tech Company | Code Infinity

CODE INFINITY · Intermediate ·🔍 RAG & Vector Search ·11mo ago

Skills: RAG Basics90%

Key Takeaways

Builds a RAG system with Python for an insurance tech company to answer employee questions accurately

Original Description

🚀 Learn how to build a powerful RAG (Retrieval Augmented Generation) system from scratch! In this comprehensive tutorial, I'll show you how to create an expert knowledge worker for Insurellm, an insurance technology company. This system can answer employee questions with high accuracy while keeping costs low. What You'll Learn: - Document Processing: Automatically load and chunk documents from multiple directories - Vector Database: Set up Chroma with OpenAI embeddings (1,536 dimensions) - Semantic Search: Implement similarity search across company knowledge base - Data Visualization: Create interactive 2D/3D visualizations with Plotly - Web Interface: Build a Gradio-based Q&A system - Real-world Application: Handle company, contracts, employees, and product data Technologies Used: - LangChain: Document processing and vector operations - OpenAI Embeddings: Semantic understanding - Chroma: Vector database for storage - Gradio: Web interface for user interaction - Plotly: Interactive visualizations - t-SNE: Dimensionality reduction for visualization Key Features: ✅ Multi-domain knowledge base (Company, Contracts, Employees, Products) ✅ Interactive 2D/3D visualizations with color coding ✅ Real-time semantic search ✅ Web-based Q&A interface ✅ Export capabilities for flagged data ✅ Configurable chunk sizes and search parameters 🎯 Perfect For: - Data Scientists building RAG systems - Software Engineers implementing knowledge management - AI/ML practitioners working with embeddings - Anyone interested in modern NLP applications 📁 GitHub Repository: 🔗 Full Code & Documentation:https://github.com/ankitmalik84/youtube/tree/main/embeddings 🔗 Additional Resources: - LangChain Documentation: https://python.langchain.com/ - OpenAI API: https://platform.openai.com/ - Chroma Vector Database: https://www.trychroma.com/ - Gradio: https://gradio.app/ #RAG #RetrievalAugmentedGeneration #Python #AI #MachineLearning #LangChain #OpenAI #Chroma #VectorDatabas

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