---
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

### [Introducing Notion AI for Work](https://yomu.fyi/post/introducing-notion-ai-for-work.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: John Hurley
- Published: May 13, 2025
- Type: Announcement

Notion introduces Notion AI for Work, an all-in-one set of AI capabilities built into its workspace to address time spent searching for answers, writing updates, and following up after meetings. The release adds AI Meeting Notes, Enterprise Search across connected tools, Research Mode for drafting documents from workspace sources and the web, access to top AI models, and bundled pricing. AI Meeting Notes transcribes conversations, summarizes them, surfaces action items, and stores meetings in Notion so they can be searched and connected to projects; connectors support Slack, Microsoft, Jira, Google Workspace, and GitHub, with more planned. Research Mode can produce project updates, research reports, and internal best-practice documents from a single prompt, while the offering starts at $20 per user per month versus the stated $150–300-plus range for separate tools. Research Mode and AI Meeting Notes are available, while Enterprise Search and connectors are rolling out.


### [Tinder’s migration to Elasticsearch 8](https://yomu.fyi/post/tinder-s-migration-to-elasticsearch-8.md)
- Company: [Tinder](https://yomu.fyi/company/tinder.md)
- Author: Tinder
- Published: Apr 1, 2025
- Type: Problem & solution

Tinder modernized its recommendation system by migrating its core Elasticsearch infrastructure from an end-of-life Elasticsearch 6 deployment to Elasticsearch 8 on Kubernetes. The legacy setup relied on manual EC2 provisioning and custom scripts, preventing the engineering team from utilizing modern vector search capabilities while incurring substantial operational overhead. Tinder implemented Elastic Cloud on Kubernetes integrated with its in-house Infrastructure as Code framework, Scaffold, while refactoring custom Java ranking plugins to handle Elasticsearch 8 breaking changes like prohibited negative scoring. The migration was validated through offline benchmarking, online verification, and a three-wave A/B testing rollout. This transition completed with zero outages and under 0.2% data discrepancy, reducing p99 search latency by 12% to 56% and cutting annual infrastructure costs by over one million dollars.


### [Stripe’s new AI Assistant in VS Code](https://yomu.fyi/post/stripe-s-new-ai-assistant-in-vs-code.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Mathew Varughese
- Published: Mar 18, 2025
- Type: Announcement

Stripe has introduced an AI Assistant in its VS Code extension to answer developer questions using current Stripe knowledge rather than relying on LLM pre-training. The extension retrieves API reference entries, integration guides, code examples, and curated summaries of developer Discord threads, while inserting the user’s API key into generated snippets and supporting either GitHub Copilot’s @stripe agent or its own chat interface. Its retrieval pipeline classifies queries, combines BM25 keyword and k-nearest-neighbor embedding search, reranks results, and sends selected sources, code, user code, and the question to Claude Sonnet through a RAG prompt. A nightly Temporal workflow refreshes indexed content, while evaluation uses golden and synthetic datasets, Mean Reciprocal Rank, and human and automated review. On its synthetic test dataset, the system includes the best source about 91.11% of the time and reports an MRR of about 78%.


### [Real-time payment analytics: Building a data pipeline from Stripe to AWS](https://yomu.fyi/post/real-time-payment-analytics-building-a-data-pipeline-from-stripe-to-aw.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Mar 14, 2025
- Type: Tutorial

Payment processors such as Stripe emit numerous transaction events, while dashboard and API-based or batch approaches can limit historical analysis, real-time visibility, and correlation with business metrics as volumes grow. The proposed pipeline sends signed Stripe webhooks through API Gateway to Lambda for validation and enrichment, buffers them in Kinesis Data Streams, and uses a second Lambda to transform and index records in OpenSearch. OpenSearch Dashboards provides visualizations, while CloudWatch tracks pipeline health; Kinesis retention and replay support recovery and reprocessing. The post also describes a Kinesis Data Firehose route that can remove the consumer Lambda and reduce costs but may add batching latency. The resulting design is presented as scalable to millions of transactions per day, with sub-minute payment metrics visibility and flexible historical querying, while security guidance includes encryption, IAM configuration, and regular audits.


### [Boolean Query Operators Guide, With Examples](https://yomu.fyi/post/boolean-query-operators-guide-with-examples.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Mélanie Corolleur
- Published: Mar 4, 2025
- Type: Tutorial

Social listening queries filter relevant brand, product, industry, or consumer mentions from large volumes of online conversation, beginning with the research questions the analysis should answer. The guide explains Boolean operators including quotation marks for exact phrases, OR for alternatives, AND for context, NEAR/x for proximity, NOT for exclusions, wildcards for variations, and brackets for grouping. It recommends adding misspellings, hashtags, account handles, websites, and exclusions, then testing and adjusting the query while keeping its structure simple where possible. Brandwatch Consumer Research offers Boolean, non-Boolean, and blank query options, with editor validation and Iris AI suggestions for entities, keywords, hashtags, subreddits, and multilingual terms.


### [Image replacement in Canva designs using reverse image search](https://yomu.fyi/post/image-replacement-in-canva-designs-using-reverse-image-search.md)
- Company: [Canva](https://yomu.fyi/company/canva.md)
- Author: Sam Jacobs
- Published: Jan 28, 2025
- Type: Problem & solution

Canva needed an automated way to replace media in design templates, such as when third-party licensing partnerships expire across more than 150 million images. Existing recommendation engines, perceptual hashing, and text metadata searches failed to capture visual similarity hierarchies or ensure replacement relevance. To build a reverse image search system, engineers evaluated embedding models including CLIP, ViTMAE, DreamSim, CaiT, and DINOv2 alongside an external vector database supporting metadata filtering. Evaluation on sample datasets identified DINOv2 as the best model for preserving subjects, background context, and color tones in photos. Integrated into the Template Assistant as a human-in-the-loop tool, the automated suggestions increased image replacement speeds by 4.5 times during initial pilot testing.


### [LLM-assisted vector similarity search](https://yomu.fyi/post/llm-assisted-vector-similarity-search.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Md Riyadh
- Published: Oct 23, 2024
- Type: Benchmark

Vector similarity search can struggle with queries containing negation, constraints, or conceptual requirements, even though it efficiently retrieves semantically similar records. The proposed method uses FAISS and OpenAI text-embedding-ada-002 embeddings to shortlist 15 candidates, then asks GPT-4o to rank the candidates against the original query and return the top three. On two GPT-4o-curated synthetic datasets—100 food dishes and 100 Asian tourist spots—the assisted method matched raw search on straightforward queries but produced more relevant results for complex ones, including excluding fish or shrimp and identifying wildlife-related places. The authors note limits from the small datasets and query set, report similar relevance improvements in an internal deployment involving more than 4,500 relational-database rows, and identify shortlist size and added LLM latency as areas for further evaluation.


### [How Shopify improved consumer search intent with real-time ML](https://yomu.fyi/post/how-shopify-improved-consumer-search-intent-with-real-time-ml.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Oct 18, 2024
- Type: Explainer

Shopify describes how it built near-real-time text and image embedding pipelines to support semantic search, replacing keyword matching with representations that enable similarity-based retrieval. Using Google Cloud’s Dataflow and Apache Beam, the pipelines process roughly 2,500 embeddings per second, preprocess images, run GPU-backed inference, and write outputs to both a data warehouse and an event topic for downstream ingestion. The engineering discussion focuses on trade-offs among memory, throughput, latency, and cost: reducing harness threads cut memory use about 2.6x and allowed a return to cheaper workers, while model sharing reduced throughput. For batching, bursty input produced bundles of one, and stateful batching was rejected because its shuffle increased latency; Dataflow’s per-process model instances instead provided enough parallelism to saturate GPUs.


### [What is a Vector Database?](https://yomu.fyi/post/what-is-a-vector-database.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Oct 11, 2024
- Type: Explainer

Vector databases store and manage high-dimensional vector representations of data, grouping them by similarity rather than traditional row-and-column organization. They support semantic and similarity-based retrieval by converting text, images, audio, or video into embeddings, indexing them with methods such as product quantization and HNSW, and comparing query vectors with indexed vectors. These capabilities underpin use cases including RAG, hybrid search, recommendations, NLP, image and video recognition, and anomaly detection, while sharding, partitioning, caching, and replication support scale. The article distinguishes a vector database from a standalone index because it also provides storage, CRUD, metadata filtering, horizontal scaling, and data management functions, and describes Databricks AI Search as an integrated implementation with automatic scaling, optimization, security, and governance.


### [Enabling conversational data discovery with LLMs at Grab](https://yomu.fyi/post/enabling-conversational-data-discovery-with-llms-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Shreyas Parbat
- Published: Sep 26, 2024
- Type: Problem & solution

Grab faced major bottlenecks in dataset discovery across its 200,000 data lake tables due to low documentation coverage and vanilla Elasticsearch limitations in its internal tool, Hubble. To resolve this, engineers first tuned Elasticsearch by boosting frequently queried P80 tables and certified schemas while deboosting deprecated datasets. They then deployed a GPT-4 pipeline that generated documentation from table schemas and sample data, raising P80 documentation coverage from 20% to roughly 90%. Finally, the team developed HubbleIQ, an LLM-powered assistant built via Glean Apps and integrated into Slack and Hubble search to answer semantic queries. These initiatives increased search click-through rates to 94% and lifted the proportion of users finding discovery easy to 73%.


### [Improving Retrieval on Ramp with Transaction Embeddings](https://yomu.fyi/post/improving-retrieval-on-ramp-with-transaction-embeddings.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Calix Huang, Anton Biryukov
- Published: Aug 13, 2024
- Type: Problem & solution

Ramp developed transaction embeddings to help automate accounting coding, where employees choose general ledger (GL) categories for transactions. The model represents enriched transaction features—such as merchant, category, department, location, amount, memo, spend program, and trip—as stringified documents labeled with chart-of-accounts codes. Starting from a pre-trained encoder, it is fine-tuned with sentence-transformers and BatchSemiHardTripletLoss, using large batches and contextual sampling to learn embeddings whose similarity is measured with cosine similarity. Triplet loss pulls same-label transactions together and pushes different-label examples apart, while informed mining addresses subtle distinctions such as Travel: Sales versus Travel: Engineering. The embeddings support GL coding suggestions, high-confidence defaults, business-spend similarity analysis, and transaction context for LLM-enabled features, with the stated aim of keeping predictions personalized and data private.


### [eBay Introduces Intuitive Search Redesign to Elevate Shopper Experience](https://yomu.fyi/post/ebay-introduces-intuitive-search-redesign-to-elevate-shopper-experienc.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: Prerna Dubey, Avital Derazon, Harshal Parikh and Kimi Robertson
- Published: Aug 8, 2024
- Type: Announcement

eBay redesigned its search experience after 18 months of user research and testing, targeting a more seamless, intuitive, and visually rich shopping journey. The update uses larger high-resolution listing images, a modernized information layout, redesigned navigation menus, and streamlined filters, with particular attention to visual categories such as Clothing, Shoes & Accessories, Jewelry & Watches, and Toys & Hobbies. A new Shopping View replaces the previous gallery view with a full-width display without sidebar ads, while delivery options are consolidated and the price filter adds an interactive graph showing price distribution. eBay says it will continue using artificial intelligence and machine learning to refine relevance, listing benefits, and seller details, with the stated aim of improving efficiency and trust for buyers and sellers.


### [From Pre-trained to Fine-tuned: Nextdoor’s Path to Effective Embedding Applications](https://yomu.fyi/post/from-pre-trained-to-fine-tuned-nextdoor-s-path-to-effective-embedding.md)
- Company: [Nextdoor](https://yomu.fyi/company/nextdoor.md)
- Author: Karthik Jayasurya
- Published: Sep 7, 2023
- Type: Problem & solution

Nextdoor transitioned its ranking and recommendation pipelines from traditional continuous and discrete interaction features to transformer-based representation learning. The engineering team deployed pre-trained Sentence-BERT models to generate multilingual post and comment representations, which were aggregated daily by interaction type to form user embeddings. To improve search recall, the team fine-tuned sentence transformers on unlabeled query session logs using contrastive learning and integrated HNSWlib for approximate nearest neighbor retrieval. Subsequent iterations incorporated labeled feedback, BERTopic for coarse personalization, and experiments with CLIP image embeddings. Infrastructure scaling challenges were addressed by performing embedding transformations directly within FeatureStore and optimizing feature payload formats to minimize microservice network bandwidth.


### [pgvector: Fewer dimensions are better](https://yomu.fyi/post/pgvector-fewer-dimensions-are-better.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregnr,oli\_rice,egor\_romanov
- Published: Aug 3, 2023
- Type: Benchmark

Embeddings support semantic search, retrieval augmented generation, and clustering in pgvector, but high-dimensional vectors increase storage, memory, and distance-computation costs as datasets grow. The post explains how IVF indexes reduce the records examined and compares embedding models using Massive Text Embedding Benchmark results, noting little correlation between dimension count and performance. In a 224,482-vector Wikipedia dataset, all-MiniLM-L6-v2 with 384 dimensions delivered 78% higher queries per second than OpenAI text-embedding-ada-002 with 1536 dimensions at accuracy@10 of 0.99, while using 4GB rather than 7.5GB of RAM. The 384-dimensional gte-small model produced more than a 200% queries-per-second improvement over all-MiniLM-L6-v2 at the same accuracy, though model choice still depends on task performance, sequence length, language support, and operational costs.


### [pgvector 0.4.0 performance](https://yomu.fyi/post/pgvector-0-4-0-performance.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: egor\_romanov,pavel
- Published: Jul 13, 2023
- Type: Benchmark

The post benchmarks pgvector 0.4.0 for approximate nearest-neighbor search and assesses whether PostgreSQL can support production-scale vector workloads. Using the ANN Benchmarks methodology, pre-warmed RAM, the dbpedia-entities-openai-1M dataset of 1 million 1,536-dimensional OpenAI embeddings, and Supabase ARM instances, it varies probes, lists, concurrency, and compute size. On an 8-core, 32 GB instance, pgvector reached 0.91 accuracy@10 at 380 QPS with 10 probes and 0.98 accuracy at 140 QPS with 40 probes; a 64-core, 256 GB server reached about 1,800 QPS at 0.91 accuracy. The results indicate predictable scaling, while higher lists can improve query speed at the cost of index-build time, and the roadmap identifies HNSW, product quantization, and parallel index builds as planned improvements.


### [Supabase Vecs: a vector client for Postgres](https://yomu.fyi/post/supabase-vecs-a-vector-client-for-postgres.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: oli\_rice
- Published: May 29, 2023
- Type: Announcement

Vecs is a new Python library for managing embeddings in Postgres databases that use pgvector, offering a vector-store-like interface for table creation, indexing, querying, and metadata filtering. Collections are stored in a vecs schema with an id text primary key, a vector column, and JSONB metadata; queries support cosine distance, l2 distance, and maximum inner dot product, with optional filters and returned values. A short Python workflow can create a collection, upsert records, create an index after insertion, and search by vector; the collection can also be queried without an index. With Supabase, a collection can be exposed through a public-schema view and accessed from supabase-js, while proposed preprocessing transforms and an asynchronous client remain under debate.


### [ChatGPT plugins now support Postgres & Supabase](https://yomu.fyi/post/chatgpt-plugins-now-support-postgres-supabase.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: egor\_romanov,paul\_copplestone
- Published: May 25, 2023
- Type: Tutorial

Retrieval plugins let ChatGPT access relevant content from private datasets by chunking documents, generating embeddings with OpenAI’s text-embedding-ada-002, and querying a vector database. The article describes Postgres and Supabase datastore implementations using pgvector, with a shared documents schema and a match\_page\_sections function that filters by source, author, document, and date before ranking vectors with inner product distance. It then gives a local example: downloading the Postgres 15 documentation as a PDF, uploading it through the plugin’s /upsert-file endpoint, and configuring environment variables for Supabase or Postgres. The example produced 7,904 document records, and an optional IVFFlat index can trade exact nearest-neighbor search for approximate search; after registering the local plugin in ChatGPT, users can ask questions answered from the documentation.


### [Launch Week 7 Hackathon Winners](https://yomu.fyi/post/launch-week-7-hackathon-winners.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: tyler\_shukert
- Published: Apr 24, 2023
- Type: Announcement

Supabase Launch Week 7 announces the winners of its hackathon, which introduced an AI theme and recognized projects across five categories. Page Assist won Best Overall for an open-source Chrome extension that lets users chat with webpages through the ChatGPT API, while other winners included Rect, Supathreads, Replay.ai, Supa0SQL, and Generation: Hotdog. The projects apply AI to educational content, blog-to-thread conversion, browsing-history search with cosine similarity, natural-language chart creation, and hotdog-image generation using OpenAI and Stable Diffusion APIs; runner-ups included tools for video chat, drum patterns, tweet scheduling, and music guessing. Members of the Best Overall winning team receive a limited-edition Launch Week 7 Keyboard, while winners and runner-ups in other categories receive Launch Week 7 swag kits, alongside guides for getting started with Supabase resources.


### [dbdev: PostgreSQL Package Manager](https://yomu.fyi/post/dbdev-postgresql-package-manager.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: oli\_rice,daltjoh-aws
- Published: Apr 14, 2023
- Type: Announcement

database.dev is a public preview of dbdev, a PostgreSQL package manager whose registry is currently read-only and preloaded with packages demonstrating packaging and discovery possibilities. It aims to provide repeatable deployment of libraries and applications, with third-party publishing tools planned after community feedback, and uses organization- or user-namespaced package names to address name and typo squatting. The in-database client installs packages, which PostgreSQL then exposes as native extensions; dbdev adds major.minor.patch version constraints, while compatibility currently requires Supabase new projects or PostgreSQL with pg\_tle and pgsql-http. Examples include a hybrid document-search backend, an index advisor that reports a sample total-cost reduction from 25.88 to 6.40, and administration views for database diagnostics. The preview depends on Trusted Language Extensions, and the post invites reports, feature ideas, and contributions while noting that pl/rust had not yet reached Supabase.


### [Supabase Clippy: ChatGPT for Supabase Docs](https://yomu.fyi/post/supabase-clippy-chatgpt-for-supabase-docs.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: paul\_copplestone
- Published: Feb 7, 2023
- Type: Announcement

Supabase introduced “Supabase Clippy,” a ChatGPT-like interface trained on its documentation, as a hidden MVP in the Supabase docs. It responds to questions by retrieving relevant documentation and streaming GPT-3's answer in realtime. The implementation parses docs into sections, creates OpenAI embeddings, stores them in Postgres with pgvector, and injects matching documents as context for generation. The project was motivated by the growth of Supabase's product suite and documentation, plus the need to support developers unfamiliar with Postgres; the current version uses only Guides and is presented as a one-week MVP intended to gather feedback. Planned directions include adding reference docs and community discussions, caching repeated queries, thumbs-up relevance feedback, and interactive guidance.


[Newer posts](https://yomu.fyi/topic/search/page/7.md) · [Older posts](https://yomu.fyi/topic/search/page/9.md)