--- title: How to Build a RAG Chatbot for Your Docs with Node.js,... description: A step-by-step guide to building a RAG chatbot that answers questions about uploaded PDF documents. The stack uses Node.js with Express, Google Gemini for... canonical: https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How to Build a RAG Chatbot for Your Docs with Node.js, Google Gemini, and pgvector | daily.dev og:description: A step-by-step guide to building a RAG chatbot that answers questions about uploaded PDF documents. The stack uses Node.js with Express, Google Gemini for... og:url: https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm og:image: https://api.daily.dev/og/posts/Za3dFNltm.png og:image:alt: How to Build a RAG Chatbot for Your Docs with Node.js, Google Gemini, and pgvector og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How to Build a RAG Chatbot for Your Docs with Node.js, Google Gemini, and pgvector **[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 20 min read · 3 upvotes · 0 comments ## Summary A step-by-step guide to building a RAG chatbot that answers questions about uploaded PDF documents. The stack uses Node.js with Express, Google Gemini for generating embeddings, Groq (LLaMA 3.1) for text generation, and PostgreSQL with pgvector for vector storage and cosine similarity search — all running locally with Docker, entirely free. Covers the full pipeline: PDF parsing, text chunking with overlap, embedding storage, semantic retrieval, and LLM-based answer generation. Includes detailed troubleshooting for common errors (port conflicts, dimension mismatches, regional API quota limits), an OpenAI swap-in guide, and suggestions for production improvements like streaming, metadata filtering, and re-ranking. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How to Build an AI-Powered RAG Search Application with Next.js, Supabase, and OpenAI](https://daily.dev/posts/how-to-build-an-ai-powered-rag-search-application-with-next-js-supabase-and-openai-twprkjxz1) · freeCodeCamp · 3 upvotes · 0 comments --- Tags: [#nodejs](https://daily.dev/tags/nodejs), [#postgresql](https://daily.dev/tags/postgresql), [#rag](https://daily.dev/tags/rag), [#google-gemini](https://daily.dev/tags/google-gemini), [#pgvector](https://daily.dev/tags/pgvector) [View this post on daily.dev](https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"How to Build a RAG Chatbot for Your Docs with Node.js, Google Gemini, and pgvector","url":"https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm"},"datePublished":"2026-07-15T18:41:55.909Z","dateModified":"2026-07-15T18:49:26.119Z","description":"A step-by-step guide to building a RAG chatbot that answers questions about uploaded PDF documents. The stack uses Node.js with Express, Google Gemini for...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/5f8fd9e2796a1b4ab346a90086d64e30?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/5f8fd9e2796a1b4ab346a90086d64e30?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"freeCodeCamp","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"freeCodeCamp","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1628412854/logos/freecodecamp","url":"https://daily.dev/sources/freecodecamp"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-to-build-a-rag-chatbot-for-your-docs-with-node-js-google-gemini-and-pgvector-za3dfnltm","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"nodejs,postgresql,rag,google-gemini,pgvector","timeRequired":"PT20M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"freeCodeCamp","item":"https://daily.dev/sources/freecodecamp"},{"@type":"ListItem","position":3,"name":"How to Build a RAG Chatbot for Your Docs with Node.js, Google Gemini, and pgvector"}]} ```