A demo notebook showcasing how to eliminate hallucinations due to out-of-distribution queries on modern LLMs, using Pinecone's own release notes!
Key Features
• End-to-End RAG Pipeline: Complete implementation from data ingestion through retrieval and generation, using Pinecone's release notes as demonstration data • LangChain Integration: Demonstrates professional orchestration patterns for AI applications using industry-standard frameworks • Hallucination Mitigation: Direct comparison showing improved accuracy when LLMs are augmented with real-time data retrieval • Production-Ready Architecture: Includes proper text splitting, embedding generation, and vector storage strategies for scalable deployment
Technical Implementation
The notebook implements a complete RAG workflow using LangChain's text splitters for document preprocessing, Pinecone for vector storage and similarity search, and OpenAI for both embedding generation and final response synthesis. The architecture demonstrates how to handle post-training data updates by retrieving relevant context before generation, addressing the common enterprise challenge of keeping AI systems current with evolving documentation and release information.