MongoDB RAG Framework
+Build full stack retrieval augmented generation (RAG) applications using MongoDB +and Atlas Vector Search.
+This framework is used to build the MongoDB Docs Chatbot, a RAG chatbot that answers questions about the MongoDB documentation. You can try it out on mongodb.com/docs.
+How It Works
+The MongoDB RAG Framework has the following core components:
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- MongoDB Atlas: Database for the application that stores content and conversation. +Indexes content using Atlas Vector Search. +
- Ingest CLI: Configurable CLI application that you can use to ingest content into a MongoDB collection for use with Atlas Vector Search. +
- Chat Server: Express.js server routes that you can use to build a chatbot application. +
- Chat UI: React.js UI components that you can use to build a chatbot application. +
Quick Start
+To get started using the MongoDB RAG framework, refer to the Quick Start guide.
+Design Principles
+The MongoDB RAG Framework is designed around the following principles:
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- Composability: You can use components of the RAG framework independently of each other. +For example, we have some users who are using only our ingestion CLI to ingest content into MongoDB Atlas, but use other tools to build their chatbot and UI. +
- Pluggability: You can plug in your own implementations of components.
+For example, you can plug in your own implementations of the
DataSourceinterface +to ingest content from different data sources.
+ - Inversion of Control: The RAG framework makes decisions about boilerplate aspects +of RAG systems so that you can focus on building logic unique to your application. +
Architecture
+Here's a reference architecture for how the MongoDB RAG system works for the MongoDB Docs Chatbot.
+Data ingestion:
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Chat Server:
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How We Built It
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- To learn more about how we built the chatbot, check out the MongoDB Developer Center blog post Taking RAG to Production with the MongoDB Documentation AI Chatbot. +