RAG with MongoDB / Conclusion
RAG with MongoDB
Excellent work! You learned how to:
Learning Objectives
Identify the Architecture of a RAG Application: Describe the components of a RAG application and understand how they interact with each other to make the most of your application.
Describe Chunking Strategies: Results from your RAG application are only as good as the data you give it, learn how to chunk data and prepare it for use in a RAG system.
Create a RAG Application: Build a RAG application with LangChain and MongoDB that can answer questions based on your own data.
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Resources
Use the following resources to learn more about RAG with MongoDB:
- Docs: Atlas Vector Search Quickstart
- GitHub: Using Atlas Vector Search for RAG code
- What is retrieval-augmented generation (RAG)?
- Docs: Retrieval-Augmented Generation (RAG) with Atlas Vector Search
- Docs: Integrate Vector Search with AI Technologies
- Docs: Build a Local RAG Implementation with Atlas Vector Search
- RAG with Atlas Vector Search, LangChain, and OpenAI
- Docs: Get Started with the LangChain Integration