Build a knowledge base chatbot with OpenAI, RAG and MongoDB vector embeddings

Who is this for?

This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering.

What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI.

What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs.

Automatically splits large documents into chunks for efficient searching.

Generates vector embeddings for each chunk using OpenAI embeddings.

Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search.

Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook).