Developer Builds RAG App Using LangChain, OpenAI, and Pinecone to Query Private Docs

A developer has built a Retrieval-Augmented Generation (RAG) application that allows large language models to answer questions based on private documents they were not trained on. The app uses LangChain to orchestrate the pipeline, OpenAI to generate embeddings and answers, and Pinecone as a vector database to store and search document chunks by meaning. During ingestion, the source text is split into 31 chunks, converted into numerical vectors, and stored in a Pinecone index configured with 512 dimensions. At query time, the most relevant chunks are retrieved and passed to the language model, so it answers from document context rather than relying solely on memorized training data. The project is structured into two separate Python scripts — one for document ingestion and one for retrieval and answer generation.