This folder contains Jupyter Notebooks that demonstrate how to integrate various AI frameworks with MongoDB. These notebooks show you how to implement RAG and other features for your AI-powered and agentic applications by leveraging MongoDB as both a vector database and document database.
| Notebook | Description |
|---|---|
| langchain | Implement basic RAG with LangChain and MongoDB Vector Search |
| langchain-memory-semantic-cache | Implement RAG with memory with LangChain and MongoDB |
| langchain-hybrid-search | Combine vector search with full-text search using LangChain and MongoDB |
| langchain-parent-document-retrieval | Perform parent-document retrieval with LangChain and MongoDB |
| langchain-self-query-retrieval | Perform self-querying retrieval with LangChain and MongoDB |
| langchain-local-rag | Implement RAG with local models with LangChain and MongoDB |
| langchain-graphrag | Implement graph-based RAG with LangChain and MongoDB |
| langchain-natural-language | Perform natural language querying with LangChain and MongoDB |
| langgraph | Build an AI agent with LangGraph and MongoDB |
| llamaindex | Implement basic RAG with LlamaIndex and MongoDB |
| haystack | Implement basic RAG with Haystack and MongoDB |
| semantic-kernel | Implement basic RAG with Microsoft Semantic Kernel and MongoDB |