How to Build LLM Applications With pgvector Vector Store in LangChain

This tutorial introduces building LLM applications with the LangChain framework in Python using PostgreSQL and pgvector as a vector database for OpenAI embeddings data. It covers creating embeddings from your data, splitting text into smaller chunks while preserving associated metadata, inserting OpenAI embeddings into PostgreSQL and pgvector, performing similarity searches to fetch relevant documents, and tying everything together with Retrieval Augmented Generation (RAG) using LangChain's LLMs and a vector store-backed retriever. The tutorial also includes a bonus section on citing sources used in the RAG process.