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. The MongoDB Chatbot Framework includes first class support for retrieval-augmented generation (RAG).
The framework can take your chatbot application from prototype to production.
You can quickly get an AI chatbot enhanced with your data up and running using the framework's built-in data ingest process, chatbot server, and web UI. As you refine your application and scale to more users, you can modify the chatbot's behavior to meet your needs.
The framework is flexible and customizable. It supports multiple AI models and complex prompting strategies. It also includes tools for programmatic evaluation of your chatbot's AI components.
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.