MongoDB

AI System Design: From Idea to Production - Apoorva Joshi, MongoDB

Jun 28, 2026 · 28:53

Apoorva Joshi presents a structured framework for designing AI systems from idea to production, arguing that thorough product specification and evaluation are now more critical than coding itself. Using a health insurance claims review system for MDB Health as an example, she walks through four phases: defining business problems with measurable success metrics (e.g., reducing urgent claim processing from 2 days to 1 hour within 90 days), designing data strategy and architecture using patterns like RAG, control flow, and human-in-the-loop, establishing guardrails and evaluation metrics such as faithfulness and cost per recommendation, and optimizing for accuracy, cost, latency, and reliability before shipping. She emphasizes building evaluation in from the start and iterating from the simplest system.

RAG in 2025: State of the Art and the Road Forward — Tengyu Ma, MongoDB (acq. Voyage AI)

Jun 27, 2025 · 18:48

Tengyu Ma, Chief AI Scientist at MongoDB (acquired Voyage AI), argues that retrieval-augmented generation (RAG) will outlast fine-tuning and long-context models for enterprise AI because it mirrors how humans use libraries—retrieving only relevant information rather than memorizing or reprocessing entire corpora. He reports that Voyage's embedding models now achieve ~80% average accuracy across 100 datasets, with half exceeding 90%, and that 100x storage compression via matryoshka learning and quantization loses only 5–10% accuracy. Ma advocates hybrid search with rerankers and domain-specific embeddings (e.g., for code) to further improve retrieval. He predicts the model layer will absorb current 'tricks' like query decomposition and contextual chunking, simplifying RAG pipelines. New products like multimodal embeddings (accepting screenshots of PDFs, tables, videos) and auto-chunking embeddings that incorporate cross-chunk metadata are designed to shift complexity from users to model providers.

The State of AI Powered Search and Retrieval — Frank Liu, MongoDB (prev Voyage AI)

Jun 27, 2025 · 12:35

Frank Liu from Voyage AI (now part of MongoDB) presents the state of AI-powered search and retrieval, arguing that embedding quality is core but not sufficient—systems must integrate structured data filtering and agentic query decomposition for real-world RAG applications. He details three live use cases: chatting with codebases (where Continue.dev found Voyage Code 3 best, emphasizing domain-specific evaluation), legal document search requiring pre-filtering by metadata, and agentic retrieval where LLMs decompose queries into multiple sub-queries. Looking ahead, Liu predicts three trends: multimodal embeddings that combine text and images (e.g., Voyage Multimodal 3 embedding Claude 3.5 blog posts), instruction tuning to steer embeddings toward specific document aspects, and the rise of agent-native databases (like MongoDB integrating Voyage for embedding, reranking, and query augmentation within a single platform).

Architecting Agent Memory: Principles, Patterns, and Best Practices — Richmond Alake, MongoDB

Jun 27, 2025 · 17:37

Richmond Alake from MongoDB presents memory management as the key pillar for building believable, capable, and reliable AI agents, arguing that agentic systems require structured memory types—persona, toolbox, conversational, workflow, episodic, and entity—to achieve statefulness and reduce reliance on prompt engineering. He introduces MemoRiz, an open-source library implementing these memory design patterns, and positions MongoDB as a flexible memory provider with its document model and hybrid retrieval capabilities (vector, text, graph). Alake details practical patterns: storing tool schemas for scalable tool use, persisting conversation history with timestamps and recall signals, leveraging workflow memory to learn from failures, and using MongoDB's upcoming integration of Voyage AI embedding models to simplify chunking and retrieval. He connects these advances to neuroscience, citing how feline visual cortex research inspired CNNs and noting recent collaborations with neuroscientists to further agent memory research.

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.