How to Build a Production-Ready RAG Chatbot Using Pinecone, LangChain, and GPT-4

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A technical walkthrough published on DEV Community outlines how developers can build a retrieval-augmented generation (RAG) chatbot capable of answering questions from internal SOP documents. The system combines OpenAI's GPT-4, Pinecone's vector database, and LangChain for orchestration, all containerized within a Docker-compose stack. RAG works by first retrieving relevant document snippets and feeding them into the language model, reducing the risk of hallucinated responses. The full build is estimated to take six to eight hours for a developer familiar with Python and Docker, with a per-query cost of roughly $0.014 based on typical token usage. The guide targets teams seeking citation-ready, document-grounded answers from an internally hosted chatbot pipeline.

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