---
title: "Search"
description: "185 posts about Search, summarised, each linking to the original."
---

# Search
> 185 posts about Search, summarised, each linking to the original.

## Articles

### [Understanding Etsy’s Vast Inventory with LLMs](https://yomu.fyi/post/understanding-etsy-s-vast-inventory-with-llms.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Vipul Setty
- Published: Oct 13, 2025
- Type: Problem & solution

Etsy’s seller-created inventory spans more than 100 million items and thousands of categories, making it difficult to capture attributes such as color, material, or niche properties without global SKUs. Because sellers often provide descriptions, titles, and photos instead of complete structured fields, Etsy uses LLMs to distill unstructured listing data into attributes for search filters, comparisons, and other experiences. The pipeline combines seller data, expert-selected few-shot examples, taxonomy logic, and category rules; parallel prompts are routed through LiteLLM, then responses are validated with Pydantic dataclasses and monitored with tracing, metrics, alerts, and performance checks. Human-reviewed silver labels support evaluation, while target categories increased complete attribute coverage from 31% to 91%, and inferred attributes improved search-filter engagement and overall post-click conversion.


### [Cars24 Improves Search For 300 Million Users With MongoDB Atlas](https://yomu.fyi/post/cars24-improves-search-for-300-million-users-with-mongodb-atlas.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Nick Bell
- Published: Oct 12, 2025
- Type: Problem & solution

Online car marketplace Cars24 previously operated a dual architecture where relational databases like Postgres synchronized data into separate search engines like Elasticsearch. As service offerings expanded globally for 300 million users, multiple engineering teams piping data into a single search index caused severe synchronization challenges, operational overhead, and developer friction. To simplify this infrastructure, Cars24 migrated to MongoDB Atlas hosted on Google Cloud, utilizing integrated Apache Lucene-powered MongoDB Atlas Search directly within the database layer. This architectural shift eliminated synchronization pipelines while accelerating search feature delivery through a unified API. Cars24 also replaced its ArangoDB geospatial search deployment with MongoDB Atlas, achieving robust multi-document ACID transactions across shards and cutting operational costs by 50%.


### [How AI Agents are Evolving Shopify's Product Taxonomy at Scale (2025) - Shopify](https://yomu.fyi/post/how-ai-agents-are-evolving-shopify-s-product-taxonomy-at-scale-2025-sh.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Oct 9, 2025
- Type: Explainer

Shopify’s product classification system processes over tens of millions of predictions daily, while its taxonomy contains more than 10,000 categories and 2,000 attributes that must adapt as commerce changes. Manual curation created bottlenecks, expertise gaps, and inconsistencies that could reduce discoverability, search quality, filtering, and classification quality. The proposed AI multi-agent system combines structural analysis with product-driven analysis grounded in merchant titles, descriptions, and categories, then uses intelligent synthesis, equivalence detection, and quality assurance to recommend taxonomy improvements. Agents can search and validate hierarchical relationships, while human experts retain oversight; Shopify tested the method in Electronics > Communications > Telephony against its previous manual expansion approach and presents it as a proactive, scalable way to improve taxonomy consistency before gaps affect merchants and customers.


### [Smarter AI Search, Powered by MongoDB Atlas and Pureinsights](https://yomu.fyi/post/smarter-ai-search-powered-by-mongodb-atlas-and-pureinsights.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Kamran Khan (CEO, Pureinsights), Prasad Pashte
- Published: Oct 1, 2025
- Type: Announcement

MongoDB Atlas has announced the general availability of its integration with the Pureinsights Discovery Platform to deliver a unified keyword, vector, and generative search experience. The combined solution pairs MongoDB Atlas Search for standard text matching with MongoDB Atlas Vector Search and Voyage AI embeddings to interpret ambiguous or multilingual queries. Pureinsights provides the orchestration layer that ingests content, coordinates retrieval, and integrates large language models such as GPT-4 to generate cited responses. Users can customize generated outputs according to preferred technical depth, length, language, and role-specific requirements. The architecture enables enterprises to apply retrieval-augmented generation and semantic search across technical documentation, community forums, and internal knowledge bases.


### [Introducing RTEB: A New Standard for Retrieval Evaluation](https://yomu.fyi/post/introducing-rteb-a-new-standard-for-retrieval-evaluation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Frank Liu, Kenneth Enevoldsen, Solomatin Roman, Isaac Chung, Tom Aarsen, Fődi, Zoltán
- Published: Oct 1, 2025
- Type: Announcement

Public embedding benchmarks often fail to measure true generalization because training sets overlap with public evaluation data, rewarding memorization over general search quality. The Retrieval Embedding Benchmark (RTEB) beta introduces a standardized evaluation framework designed for real-world enterprise retrieval applications. To prevent overfitting while preserving transparency, RTEB combines fully public datasets with private evaluation sets managed exclusively by MTEB maintainers. The suite spans 20 languages across domains like healthcare, law, finance, and code, measuring ranked retrieval quality using NDCG@10 on datasets of at least 1,000 documents. While currently limited to text-only retrieval and containing repurposed question-answering datasets, the benchmark provides clear signals when models degrade on unseen data.


### [Top Considerations When Choosing a Hybrid Search Solution](https://yomu.fyi/post/top-considerations-when-choosing-a-hybrid-search-solution.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Jose Parra
- Published: Sep 30, 2025
- Type: Explainer

Natural language queries and generative AI workflows like retrieval-augmented generation have exposed the limitations of standalone vector search, particularly for tokens outside an embedding model's training corpus. Hybrid search addresses these gaps by merging traditional lexical precision with dense vector context. Platforms combine these modalities using techniques such as reciprocal rank fusion and relative score fusion, often complemented by downstream cross-encoders or learning-to-rank models. Architecture choices center on the trade-offs between lexical-first systems using inverted indexes and vector-first platforms employing sparse vectors. MongoDB has integrated vector search indexes alongside its existing lexical indexes, releasing native hybrid search functions across Atlas, Community Edition, and Enterprise Server to unify operational data and search retrieval.


### [Carrying Complexity, Delivering Agility](https://yomu.fyi/post/carrying-complexity-delivering-agility.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Akshat Vig, Ashish Kumar
- Published: Sep 25, 2025
- Type: Explainer

MongoDB centers its engineering architecture around resilience, intelligence, and simplicity to minimize developer cognitive and operational burdens when building distributed applications. Security is enforced through dedicated clusters in isolated virtual private networks alongside Queryable Encryption, which allows equality and range queries on ciphertext without decryption keys ever leaving the client. High availability is built on replica sets across independent availability zones and multi-cloud topologies, utilizing consensus mechanisms that commit writes only after majority acknowledgment in the active term. Operational friction in artificial intelligence workloads is addressed by integrating vector search directly into the core query engine, eliminating brittle extract-transform-load pipelines and separate vector databases.


### [From Niche NoSQL to Enterprise Powerhouse: The Story of MongoDB's Evolution](https://yomu.fyi/post/from-niche-nosql-to-enterprise-powerhouse-the-story-of-mongodb-s-evolu.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Ashish Kumar
- Published: Sep 25, 2025
- Type: Explainer

Early perceptions categorized MongoDB alongside eventually consistent NoSQL databases, creating doubts regarding its suitability for mission-critical enterprise workloads. Over fifteen years of development, the platform addressed enterprise requirements by introducing replica sets with Raft-style consensus, native horizontal sharding, tunable read and write concerns, and multi-document ACID transactions across sharded clusters. Security capabilities were expanded through Role-Based Access Control, Field-Level Encryption, and Queryable Encryption supporting prefix, suffix, and substring searches. Current initiatives focus on decoupling compute and storage scaling, developing AI-driven tooling within the Application Modernization Platform to translate legacy relational applications to microservices, and optimizing query execution for non-denormalized data models. MongoDB Atlas now incorporates Atlas Search, Atlas Vector Search, native Stream Processing, and embedding generation powered by Voyage AI models.


### [MongoDB.local NYC 2025：定义 AI 时代的理想数据库](https://yomu.fyi/post/mongodb-local-nyc-2025-ai-cn.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 18, 2025
- Type: Announcement

The page associated with the title “MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era” is rendered as a MongoDB Resources page rather than an event article. Its metadata describes a collection of webinars, video presentations, white papers, datasheets, and other resources, with controls for content type and language. The visible resource content includes “MongoDB Search Fundamentals: Powering Modern Search Experiences,” which presents MongoDB Search as an integrated full-text search capability and covers lexical search, search indexes, autocomplete, fuzzy queries, scoring, filters, and facets. The provided text does not state details about the NYC event or establish a conclusion about an ideal database for AI.


### [MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era](https://yomu.fyi/post/mongodb-local-nyc-2025-defining-the-ideal-database-for-the-ai-era.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

At MongoDB.local NYC, MongoDB introduced new database capabilities, AI integrations, and modernization tools designed for agentic and generative AI workloads. The release of MongoDB 8.2 arrives alongside public previews of Search and Vector Search for both MongoDB Community Edition and Enterprise Server. MongoDB also highlighted Voyage AI embedding models and rerankers to improve retrieval accuracy across raw data, metadata, and embeddings. To address the costs and constraints of legacy infrastructure, the new MongoDB Application Modernization Platform combines AI-assisted tooling and specialized expertise to migrate legacy systems. According to MongoDB, early migrations using this platform run two to three times faster, while code rewriting tasks accelerate by an order of magnitude.


### [MongoDB.local NYC 2025: Definiendo la base de datos ideal para la era de la IA](https://yomu.fyi/post/mongodb-local-nyc-2025-definiendo-la-base-de-datos-ideal-para-la-era-d.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

At MongoDB.local NYC 2025, MongoDB introduced new capabilities and product updates tailored for artificial intelligence workloads. MongoDB 8.2 was unveiled alongside public previews of full-text and vector search for both MongoDB Community Edition and Enterprise Server. The company also detailed its integration with Voyage AI embedding and reranking models to deliver precision retrieval across raw data, metadata, and vectors. To address legacy infrastructure migration hurdles, MongoDB launched the Application Modernization Platform, which combines AI-assisted tooling and specialized workflows to accelerate legacy application refactoring two to three times faster. These architectural updates aim to position the JSON document model as the persistent memory and context store required by autonomous, agentic AI workflows.


### [MongoDB.local NYC 2025 : définir la base de données idéale à l'ère de l'IA](https://yomu.fyi/post/mongodb-local-nyc-2025-de-finir-la-base-de-donne-es-ide-ale-a-l-e-re-d.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

At the MongoDB.local NYC 2025 event, MongoDB unveiled several platform updates aimed at modernizing enterprise database architectures and supporting emerging agentic artificial intelligence workloads. The newly released MongoDB 8.2 version provides enhanced performance alongside expanded developer features across the broader document database ecosystem. The company also integrated Voyage AI embedding and reranking models to improve context retrieval precision and efficiency when building production-grade AI systems. Furthermore, dedicated Search and Vector Search capabilities have reached general availability across both MongoDB Community Edition and Enterprise Server environments. To streamline infrastructure transitions, the newly introduced Application Modernization Platform leverages AI-powered tooling and specialized techniques to migrate legacy database systems two to three times faster.


### [MongoDB.local NYC 2025: Definindo o Banco de Dados Ideal para a Era da IA](https://yomu.fyi/post/mongodb-local-nyc-2025-definindo-o-banco-de-dados-ideal-para-a-era-da.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

At MongoDB.local NYC, new product capabilities were unveiled to support modern enterprise workloads and artificial intelligence applications. The release of MongoDB 8.2 delivers improved performance alongside embedding and reranking models from Voyage AI for AI application development. Additionally, search and vector search capabilities have entered public preview for both MongoDB Community Edition and Enterprise Server across customer runtime environments. To address the high operational costs of legacy database systems, MongoDB introduced the Application Modernization Platform to accelerate migration and automated code rewriting using AI-driven tooling. These capabilities position the JSON-based document database as a foundational data layer providing persistent state, contextual retrieval, and memory for agentic AI systems.


### [MongoDB.local NYC 2025: AI 시대를 위한 이상적인 데이터베이스 정의](https://yomu.fyi/post/mongodb-local-nyc-2025-ai.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

MongoDB presented multiple updates at MongoDB.local NYC 2025 aimed at supporting modern AI workloads and enterprise modernization. The event detailed MongoDB 8.2 alongside Voyage AI embedding models and rerankers designed to enhance accuracy and efficiency in AI applications. Search and vector search capabilities have expanded into public preview for MongoDB Community Edition and Enterprise Server. Furthermore, the company launched the MongoDB Application Modernization Platform, which combines AI-driven tools and expert workflows to migrate legacy systems to MongoDB two to three times faster. MongoDB positions its JSON document model as a foundation providing memory, facts, and state continuity for agentic AI applications.


### [MongoDB.local NYC 2025: Definition der idealen Datenbank für das KI-Zeitalter](https://yomu.fyi/post/mongodb-local-nyc-2025-definition-der-idealen-datenbank-fu-r-das-ki-ze.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

MongoDB unveiled several core database updates aimed at supporting artificial intelligence workloads during MongoDB.local NYC 2025. The release of MongoDB 8.2 introduces performance enhancements alongside Voyage AI embedding models and rerankers for AI applications. Furthermore, Search and Vector Search are now available in public preview for both MongoDB Community Edition and MongoDB Enterprise Server. To address the costs and operational friction of legacy systems, MongoDB launched the Application Modernization Platform (AMP), which combines AI-powered tools with specialized talent to accelerate database migrations. These capabilities position the JSON-based document model to supply the persistent state, memory, and retrieval mechanisms required by emerging agentic AI architectures.


### [MongoDB.local NYC 2025: Definire il database ideale per l'era dell'AI](https://yomu.fyi/post/mongodb-local-nyc-2025-definire-il-database-ideale-per-l-era-dell-ai.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dev Ittycheria, President and CEO, MongoDB
- Published: Sep 18, 2025
- Type: Announcement

MongoDB announced several product releases and platform updates aimed at supporting artificial intelligence and agentic workflows at MongoDB.local NYC. The release of MongoDB 8.2 arrives alongside integrations with Voyage AI embedding and reranker models designed to enhance data retrieval precision. In addition, MongoDB launched Search and Vector Search in public preview for both Community Edition and Enterprise Server deployments, bringing vector capabilities to self-managed environments. To assist organizations transitioning away from rigid legacy database systems, MongoDB also introduced the Application Modernization Platform, which combines AI-driven tooling and specialized migration expertise. Early benchmarks from the modernization platform demonstrate legacy migrations running two to three times faster while accelerating code rewriting tasks by an order of magnitude.


### [mmBERT: ModernBERT goes Multilingual](https://yomu.fyi/post/mmbert-modernbert-goes-multilingual.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Marc Marone, Orion Weller, William Fleshman, Eugene Yang, Dawn Lawrie, Ben Van Durme
- Published: Sep 9, 2025
- Type: Announcement

mmBERT is a massively multilingual encoder model trained on more than 3T tokens across over 1,800 languages to improve upon existing multilingual architectures like XLM-R. Built upon ModernBERT with a Gemma 2 tokenizer, mmBERT employs a three-phase training curriculum consisting of pre-training on 60 languages, mid-training on 110 languages, and a final decay phase covering 1,833 languages. The training pipeline integrates an inverse mask ratio schedule, dynamic language temperature annealing, and TIES merging across three decay variants. Benchmark evaluations demonstrate strong natural language understanding on English GLUE and multilingual XTREME, as well as competitive retrieval performance on MTEB v2 and CoIR. The release includes standard base and small models alongside open training data and checkpoints.


### [Welcome EmbeddingGemma, Google's new efficient embedding model](https://yomu.fyi/post/welcome-embeddinggemma-google-s-new-efficient-embedding-model.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen, Joshua, Alvaro Bartolome, Aritra Roy Gosthipaty, Pedro Cuenca, Sergio Paniego
- Published: Sep 4, 2025
- Type: Announcement

Google DeepMind released EmbeddingGemma, a multilingual embedding model with 308 million parameters and a 2048-token context window designed for on-device applications. Based on the Gemma3 transformer backbone, the architecture replaces causal attention with bidirectional attention to function as an encoder, followed by mean pooling and two dense layers producing 768-dimensional vectors. The model incorporates Matryoshka Representation Learning, allowing outputs to be truncated down to 512, 256, or 128 dimensions for reduced memory and storage footprints. Trained on approximately 320 billion multilingual tokens across more than 100 languages, the quantized model operates under 200 MB of RAM. In domain-specific evaluations on the MIRIAD dataset, fine-tuning increased NDCG@10 from 0.8340 to 0.8862, outperforming larger baselines.


### [Leveraging multimodal LLMs for Shopify’s global catalogue: Recap of expo talk at ICLR 2025](https://yomu.fyi/post/leveraging-multimodal-llms-for-shopify-s-global-catalogue-recap-of-exp.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jul 16, 2025
- Type: Explainer

Shopify’s Global Catalogue addresses fragmented, multilingual, multimodal product data that makes semantic search, faceting, matching, and AI-driven discovery difficult across millions of merchants. The system combines product data, understanding, matching, and reconciliation layers to standardize metadata, identify duplicate listings, and create canonical records with Universal Product IDs. Shopify fine-tunes vision large language models for multiple tasks per catalogue entity, while streaming infrastructure processes more than 10 million product updates daily and serves 40 million multimodal LLM-powered inferences. The resulting data supports merchant-admin suggestions, search, recommendations, embeddings, and conversational commerce, although scalability, accuracy, latency, model consolidation, graph reasoning, and continuous retraining remain active challenges.


### [Augmented commerce: Machine learning at Shopify](https://yomu.fyi/post/augmented-commerce-machine-learning-at-shopify.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jul 4, 2025
- Type: Explainer

Shopify frames commerce as a complex, continuous optimization problem in which merchants have different definitions of success and need support across the business lifecycle. Its machine-learning work uses a historical dataset covering millions of businesses to optimize product metadata, transaction fraud assessment, merchant assistance, product discovery, capital decisions, behavioral modeling, and search query rewriting. The systems include fine-tuned Qwen multimodal models, LLaMa models and MCPs for Sidekick, Nomic embeddings for billions of products, a tabular transformer for forecasting merchant GMV, and experiments with HSTU sequence models. To support this work, Shopify partners with GCP, Nebius, CentML, and Toloka, while providing access to GPUs, data services, frontier models, and internal tooling; the post also presents these efforts as part of its hiring pitch.


[Newer posts](https://yomu.fyi/topic/search/page/6.md) · [Older posts](https://yomu.fyi/topic/search/page/8.md)