--- title: "Vector Search" description: "13 posts about Vector Search, summarised, each linking to the original." --- # Vector Search > 13 posts about Vector Search, summarised, each linking to the original. ## Articles ### [Three layers, one repo: a tour of google/adk-recipes](https://yomu.fyi/post/three-layers-one-repo-a-tour-of-google-adk-recipes.md) - Company: [Google](https://yomu.fyi/company/google.md) - Author: Pier Paolo Ippolito, Shahin Saadati - Published: Oct 15, 2026 - Type: Explainer The google/adk-recipes repository serves as the single source of truth for ADK content across Google Cloud’s Agent Platform, including the Agents CLI, Agent Garden, and Gemini Enterprise app. It is organized into three top-level folders: core/ with reference patterns curated by the Agents CLI and DevRel teams, plugins/ with installable vertical skills packaged as Plugin Recipes conforming to the Agent Plugins v1.0.0 spec, and contrib/ with end-to-end deployable services in five languages. Every recipe ships a validated manifest.yaml and an AGENTS.md written primarily for AI coding assistants, and CI enforces the standards on every pull request. The core/ layer features Horizon, a reference harness for long-horizon memory agents that handles cross-session memory, per-user sandboxing, guardrails, and multi-step task orchestration. The retail Plugin Recipe includes product-search and virtual-tryon skills using Vector Search 2.0 and Gemini models, with deployable counterparts in contrib/ that can be surfaced on five deployment personas. ### [Enriching Product Catalogs For Agentic Commerce Discovery with MongoDB](https://yomu.fyi/post/enriching-product-catalogs-for-agentic-commerce-discovery-with-mongodb.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Oct 12, 2026 - Type: Problem & solution AI agents are transforming retail discovery, requiring product catalogs to be machine-readable and context-rich for systematic retrieval, with McKinsey estimating AI agents could orchestrate $3 trillion to $5 trillion globally by 2030. Retailers need a single enriched catalog that projects consistently to three surfaces: outbound product feeds, agentic commerce endpoints via REST APIs or MCP, and product detail pages with schema.org JSON-LD. MongoDB’s document model supports three tiers of enrichment—identity and transactability, filterable attributes, and agentic context such as use cases and semantic embeddings—to help AI agents reason over products. Change streams and aggregation pipelines keep projections current across surfaces, ensuring consistency that AI agents cross-check. MongoDB Search and Vector Search enable hybrid retrieval for intent-based queries, helping retailers improve discoverability and gain a competitive advantage in the agentic commerce era. ### [NEAREST BY Join: Scaling Vector Search in Databricks Runtime](https://yomu.fyi/post/nearest-by-join-scaling-vector-search-in-databricks-runtime.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Zero Qu (Zero), Alexis Schlomer, Akash Nayar, Yingyi Bu, Sergei Tsarev - Published: Oct 5, 2026 - Type: Explainer Batch vector search workloads, such as entity resolution and semantic tagging, require high throughput across millions of queries against billions of vectors, unlike real-time serving. Databricks built NEAREST BY, a first-class top-k ranking join syntax, directly into its runtime engine rather than federating to an external endpoint. The feature is implemented as a logical join that the optimizer lowers onto standard relational operators, enabling fault tolerance and elastic parallelism. A fused Photon operator collapses the plan’s middle section with a blocked GEMM kernel designed to approach peak arithmetic intensity for vector scoring. An optional IVF index, stored as an ordinary liquid-clustered Delta table, allows approximate queries to probe only relevant clusters while preserving the same primitives and correctness guarantees. ### [AI Search is now generally available](https://yomu.fyi/post/ai-search-is-now-generally-available.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Ashish Vinodkumar - Published: Oct 1, 2026 - Type: Announcement Cloudflare’s AI Search, a managed pipeline combining Workers AI, Vectorize, R2, and Browser Run, is now generally available after a year of preview. The update adds native image embeddings via the Qwen3-VL-Embedding model with Matryoshka Representation Learning, enabling visual search by texture and composition alongside caption-based text search. AI Search now supports files up to 10 MiB and optical character recognition for scanned PDFs; queries undergo optional rewriting, parallel vector and keyword search, fusion, and optional reranking. Previously reliant on caption generation only, image retrieval now embeds query images directly using multimodal models, while text-only models convert images to text via ToMarkdown. Billing starts November 1, 2026, with a free monthly allotment of 5 million ingestion tokens, 10 GB storage, 1,000 semantic queries, and 1,000 full-text queries, with no capacity units or monthly minimums. ### [Context Engineering Has a Retrieval Issue: Introducing rerank-3 and Native Reranking](https://yomu.fyi/post/context-engineering-has-a-retrieval-issue-introducing-rerank-3-and-nat.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Sep 30, 2026 - Type: Announcement AI agents require precise context retrieval, not larger windows, and MongoDB’s new Rerank-3 and Rerank-3-lite models outperform Cohere Rerank v4.0 Pro by 2.14% and Qwen3-Reranker-8B by 3.31% overall, with greatest improvements on long documents and code. The $rerank native aggregation stage rescores candidate sets index-free within MongoDB, while Automated Embedding generates and refreshes vectors near-real-time to prevent model version drift. This unified platform eliminates separate vendors and external API calls, supporting lexical, vector, and hybrid search directly against operational data without moving or syncing. The result is simpler retrieval architecture that improved Gravity’s accuracy and cut latency by roughly 50% by better discriminating between strong and weak matches. Retrieval quality forms one layer of MongoDB’s broader agent platform, which also includes performance scaling with Atlas Infinite and agent memory with Atlas Agent Engine. ### [Lakebase Search: State-of-the-art full text and vector search for Postgres](https://yomu.fyi/post/lakebase-search-state-of-the-art-full-text-and-vector-search-for-postg.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Zhou Sun, Jinjing Zhou, Keming Yang, Usamoi Cui, Pranav Aurora - Published: Sep 28, 2026 - Type: Announcement Traditional OLTP systems like Postgres face challenges meeting the low-latency, high-accuracy search demands of AI agents, often requiring standalone search engines with ETL pipelines. Lakebase introduces lakebase\_vector and lakebase\_text, two extensions that bring scalable approximate nearest neighbor search and BM25 full-text search directly into Lakebase Postgres. lakebase\_vector overcomes pgvector’s memory-bound HNSW index by using hierarchical IVF clustering and binary quantization, achieving 71-millisecond P99 latency at 97 percent recall on the 100-million-vector LAION benchmark. The extensions decouple storage from compute for pay-per-use scaling, enable fast parallel index builds, and support native hybrid search that fuses vector similarity with keyword relevance in a single SQL query. Early adopters like Conexiom reduced infrastructure costs by three times while achieving five times higher throughput, and the service is generally available on AWS and Azure. ### [Financial Crime Mitigation with MongoDB / Part III: Behavioral Risk Modeling for Event-Driven Fraud Detection](https://yomu.fyi/post/financial-crime-mitigation-with-mongodb-part-iii-behavioral-risk-model.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Sep 21, 2026 - Type: Explainer MongoDB’s document model and event-driven architecture support behavioral risk modeling for fraud detection in financial institutions. The platform combines rule-based policies with probabilistic reasoning, using change streams for real-time event processing and vector search for similarity assessment. MongoDB captures behavioral events like typing patterns and device IP addresses, aggregating them into dynamic customer profiles. Vector search compares incoming transactions against historical patterns, but it does not evaluate numerical data such as transaction amount, which requires traditional risk scoring. This approach reduces false positives and lowers total cost of ownership by minimizing manual intervention, while MongoDB’s flexible schema allows adding risk factors without downtime. ### [Beyond the Cloud: Building an Offline](https://yomu.fyi/post/beyond-the-cloud-building-an-offline.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 27, 2026 - Type: Explainer A digital-first economy assumes constant high-speed internet, leaving mobile wallet apps unusable when connectivity drops, such as on subways or in rural areas. A demo wallet solution combines MongoDB Atlas as the cloud source of truth with ObjectBox as an on-device operational store that mirrors the same schema. The ObjectBox Sync Server continuously bridges the two, enabling local reads and writes that sync automatically when connectivity returns. For offline sends, the wallet checks the requested amount against the last-synced balance to prevent overspending, ensuring safe local operations. The architecture treats connectivity as an enhancement rather than a requirement, keeping the wallet responsive in airplane mode with full data access. ### [The Cheapest Token You Never Send: Agentic AI Search on MongoDB + Voyage AI](https://yomu.fyi/post/the-cheapest-token-you-never-send-agentic-ai-search-on-mongodb-voyage.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 27, 2026 - Type: Problem & solution The post reframes cost, latency, and answer quality in agentic AI search as a single retrieval precision problem. Its pipeline embeds with flexible Voyage AI dimensionality and automatic quantization in MongoDB index, with binary reducing RAM by roughly 24x. Hybrid search via $rankFusion combines vector and full-text search with metadata filters in one server-side query, eliminating separate systems. A Voyage reranker then reorders the broad candidate set by true relevance, allowing the LLM to receive only the top few high-signal chunks and drastically shrinking input context. The agentic loop over consolidated Atlas where data, vectors, embeddings, and reranking coexist minimizes network hops per iteration, and an illustrative example shows an 80% reduction in retrieved-context input tokens. ### [Building E-commerce AI Agents on MongoDB with CrewAI](https://yomu.fyi/post/building-e-commerce-ai-agents-on-mongodb-with-crewai.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 19, 2026 - Type: Tutorial A 34% year-over-year increase in consumers using generative AI for online shopping, especially among Gen Z and Millennials, is motivating e-commerce teams to adopt AI shopping agents. This tutorial demonstrates building a chat-based shopping assistant on MongoDB Atlas with CrewAI that supports zero-click buying from product discovery to post-purchase follow-up. The agent interprets natural-language queries, retrieves semantically relevant products via MongoDB Vector Search with Voyage AI embeddings using price and inventory pre-filters before similarity ranking, and uses Change Streams for atomic inventory checkout. Atlas Triggers enable proactive order notifications, while Atlas Stream Processing detects delivery patterns by monitoring updates in two-minute tumbling windows, writing confirmed signals to agent\_triggers when at least 10 orders per carrier-region are delayed by 45 minutes. MongoDB consolidates operational data, semantic search, and event-driven capabilities into a single platform, reducing latency and complexity. ### [The Merchant Identity Gap: The Hidden Payment Data Problem](https://yomu.fyi/post/the-merchant-identity-gap-the-hidden-payment-data-problem.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 19, 2026 - Type: Problem & solution Banks face a merchant identity gap: transaction descriptors from acquirers (e.g., SBUX) often don’t match customer search terms (e.g., “Starbucks” or “星巴克”) due to script differences, colloquial aliases, and cross-subsidiary naming inconsistencies. This gap inflates support costs, degrades spend categorization, and undermines personalized financial management and AI advisors. The solution uses a semantic resolution architecture on MongoDB storing a master merchant catalog with canonical names, aliases, and vector embeddings. An escalating pipeline applies fuzzy matching, semantic vector search with Voyage AI embeddings and MongoDB Vector Search, and gen AI write-back to resolve unknown descriptors and enrich the catalog. With canonical identities indexed, customers search in their own language, and institutions power conversational AI that accurately answers queries like “How much did I spend at Starbucks last month?” across markets. ### [Closing the Gap Between Agentic Promise and Impact with Industry Leading Retrieval](https://yomu.fyi/post/closing-the-gap-between-agentic-promise-and-impact-with-industry-leadi.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Aug 13, 2026 - Type: Announcement McKinsey projects AI agents will generate $2.9 trillion annually; a WRITER survey finds 97% of executives have deployed agents, yet only 29% see significant ROI due to data accuracy issues. MongoDB announced capabilities embedding benchmark-leading retrieval directly into its operational database, closing the gap between agentic promise and impact. Automated Embedding in MongoDB Atlas powered by Voyage AI, now GA, uses the top-ranked Voyage 4 model family for accurate retrieval without separate pipelines, as demonstrated by MedINT. The Atlas Embedding and Reranking API and voyage-code-4 model, designed for agentic coding retrieval, extend accurate retrieval to any stack and coding agent workloads at low cost. $vectorSearch in Atlas Stream Processing enables agents to retrieve accurate data in motion, and a joint MongoDB-Temporal reference architecture provides a forkable starting point for durable execution on live data. ### [Go From 0 To 1 To Enterprise-Ready With MongoDB Atlas And LLMs](https://yomu.fyi/post/go-from-0-to-1-to-enterprise-ready-with-mongodb-atlas-and-llms.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Published: Jun 22, 2023 - Type: Explainer Generative AI applications require grounding in up-to-date operational data to avoid incorrect or uninformed answers, yet moving from prototype to enterprise-ready production is a significant challenge. MongoDB Atlas unifies operational, analytical, and vector search data services, enabling developers to provide long-term memory to large language models without costly fine-tuning. The post presents five resources for integrating MongoDB Atlas with LLMs, including tutorials for Atlas Vector Search with OpenAI, LlamaIndex, LangChain, MindsDB, and HuggingFace. These integrations support semantic search, chat applications with proprietary data, prediction generation, and automated reactions to data changes via serverless triggers. MongoDB Atlas offers a flexible document model, high performance, and scalability, validated by enterprises, to accelerate building production-ready generative AI applications.