I Built a Simple RAG App with LangChain, OpenAI, and Pinecone

Short summary

A beginner-friendly walkthrough of building a Retrieval-Augmented Generation app using LangChain, OpenAI embeddings, and Pinecone as the vector database. The project reads a text document, chunks it, embeds it, stores vectors in Pinecone, and answers user questions by retrieving relevant chunks before passing them to the LLM. Includes practical code for ingestion and retrieval scripts, plus debugging tips with LangSmith.

• •RAG app reads documents, chunks them, embeds into Pinecone, and retrieves relevant sections before LLM answering
• •Covers embeddings, vector dimensions matching, chunk sizing, and API key setup
• •Includes working Python ingestion and query scripts with LangSmith debugging

Generated with AI, which can make mistakes.