The MongoDB Chatbot Framework is a set of libraries that you can use to build full-stack intelligent chatbot applications using MongoDB and Atlas Vector Search.
You can use the MongoDB Chatbot Framework to create retrieval augmented generation (RAG) applications.
To learn how to use the MongoDB Chatbot Framework, refer to the documentation: https://mongodb.github.io/chatbot/.
This repo also contains the implementation of the MongoDB Docs Chatbot, which uses the MongoDB Chatbot Framework.
The MongoDB Docs Chatbot uses the MongoDB documentation and Developer Center as its sources of truth.
The chatbot builds on the following technologies:
- Atlas Vector Search: Indexes and queries content for use in project.
- MongoDB Atlas: Persists conversations and content.
- ChatGPT API: LLM to pre-process user queries and summarize responses to user queries.
- OpenAI Embeddings API: Create vector embeddings for user queries and content. Used by Atlas Vector Search.
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.
To learn how to get started contributing to the project, refer to the Contributor Guide.
This project is licensed under the Apache 2.0 License.