MongoDB Atlas

RAG and the MongoDB Document Model: Ben Flast

Feb 8, 2025 · 13:13

Ben Flast, Director of Product at MongoDB, explains how the MongoDB document model and Atlas Vector Search enable more efficient and scalable Retrieval Augmented Generation (RAG). He contrasts MongoDB's JSON-based documents with relational databases, highlighting that documents store application objects directly without stitching tables. Flast details MongoDB's HNSW-based vector search, which stores embeddings alongside transactional data in the same document, supporting up to 4,096 dimensions. He introduces Search Nodes, which decouple vector search infrastructure from transactional databases for independent scaling. Flast covers AI integrations with LangChain, LlamaIndex, and others, enabling features like semantic caching and chat history memory within a single database. He cites the startup 4149, which uses MongoDB to store user data, meeting notes, and vector embeddings for an AI teammate that assists with tasks by combining transactional and semantic search.

Build an AI Research Agent: Apoorva Joshi

Oct 25, 2024 · 27:33

In this workshop, Apoorva Joshi, an AI Developer Advocate at MongoDB, teaches how to build an AI research agent using MongoDB as the memory provider and knowledge store, open-source LLMs from Fireworks AI (Fire Function V1) as the agent’s brain, and LangChain to orchestrate the workflow. The agent searches for research papers, summarizes them, and answers questions based on past research, using tools like ArXivLoader and a MongoDB vector store. Joshi explains key agent concepts—planning with chain of thought and react patterns, short-term and long-term memory, and tool creation—then guides attendees through hands-on sections to build the agent step by step. Attendees learn to create agent tools, implement reasoning with react, and add short-term memory persisted to MongoDB. The workshop emphasizes that agents enable complex, multi-step tasks through iterative reasoning, tool use, and memory, trading higher cost and latency for improved accuracy.