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How do computers understand data? With semantic search! Instead of just matching keywords, it understands context using vector embeddings. Here’s how: 1) Convert data (text, images, etc.) into vectors (embeddings) 2) Store these vectors in a vector database 3) Search by meaning, not just the keywords Semantic search makes... show more

Femke Plantinga

12,901 subscribers

23,911 Aufrufe • vor 1 Jahr •via X (Twitter)

Anya Rossi• Live Now

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8 Kommentare

Profilbild von shyamik 📊♻️

shyamik 📊♻️vor 1 Jahr

Great work 👏

Profilbild von Femke Plantinga

Femke Plantingavor 1 Jahr

Thanks! 😄

Profilbild von Uche

Uchevor 1 Jahr

Great presentation. I enjoyed it

Profilbild von Sdal

Sdalvor 1 Jahr

Understand data? Really. Or pattern matching?

Profilbild von Aklının yönetim kurulu başkanı

Aklının yönetim kurulu başkanıvor 1 Jahr

@femke_plantinga you are so beautiful. I am afraid of being in love with you. 🙈

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mariodeleonvor 1 Jahr

@memdotai mem it #Ai

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Davvor 1 Jahr

No entendí nada

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bruno maggivor 1 Jahr

👏👏👏

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Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

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New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

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To prove how scalable Upstash Vector is, we indexed the entire Wikipedia in 11 languages (144m vectors) in a single DB. ◆ Over 700GB of data ◆ Fast semantic search ◆ Chat with Wikipedia We got your back for a fast app that scales🫡 Quick demo 👇

To prove how scalable Upstash Vector is, we indexed the entire Wikipedia in 11 languages (144m vectors) in a single DB. ◆ Over 700GB of data ◆ Fast semantic search ◆ Chat with Wikipedia We got your back for a fast app that scales🫡 Quick demo 👇

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This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

Santiago

109,517 Aufrufe • vor 1 Jahr

I don’t think people realize the scale of data behind the JBP Search Engine. Every single episode since Episode 1 has been transcribed and converted into embeddings for semantic search. This isn’t keyword matching. It understands the context of your query and returns the exact moment you’re looking for…not just a list of episodes where something was mentioned. 1,415 episodes. ~3 hours each. Over 600GB of locally stored audio + additional metadata powering search quality. Could you do it? Maybe, but just know, I’ve personally spent thousands of dollars in compute to make this possible. There’s no way around it when dealing with this much data.

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burner account

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Supabase can be used as a vector database! This means that you can perform a semantic search against Supabase! This allows you to create RAG apps or content recommendation engines on top of Supabase! Learn what embeddings are, and how you can use them 👇

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New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:

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40,441 Aufrufe • vor 1 Jahr

What to expect this October? We’re building OptimAI Search Engine - the next leap in our network. Not just search. Not just keywords. But contextual search & analysis across the open web and social platforms (X, LinkedIn, Telegram, and more). Powered by the OptimAI community-driven Data Network, it cuts through expensive APIs and unlocks: ✨ Access to the freshest, high-quality, real-time data ✨ Context-aware insights that adapt to your needs ✨ Affordable, open infrastructure for everyone - not just the few This is how we build the foundation for the Agentic AI Systems of tomorrow: intelligent, accessible, and powered by people, not corporations. The future of search is decentralized. And it’s coming soon, powered OptimAI Network. Together we make impact.

What to expect this October? We’re building OptimAI Search Engine - the next leap in our network. Not just search. Not just keywords. But contextual search & analysis across the open web and social platforms (X, LinkedIn, Telegram, and more). Powered by the OptimAI community-driven Data Network, it cuts through expensive APIs and unlocks: ✨ Access to the freshest, high-quality, real-time data ✨ Context-aware insights that adapt to your needs ✨ Affordable, open infrastructure for everyone - not just the few This is how we build the foundation for the Agentic AI Systems of tomorrow: intelligent, accessible, and powered by people, not corporations. The future of search is decentralized. And it’s coming soon, powered OptimAI Network. Together we make impact.

OptimAI Network

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Tokenization -- turning text into a sequence of integers -- is a key part of generative AI, and most API providers charge per million tokens. How does tokenization work? Learn the details of tokenization and RAG optimization in Retrieval Optimization: From Tokenization to Vector Quantization, created in collaboration with Qdrant and taught by its Developer Relations Lead, Kacper Łukawski. This course focuses on Retrieval augmented generation (RAG), which has two steps: First, a retriever finds relevant information; then, the generator uses what’s retrieved as context to produce a response. You’ll learn to optimize the first step (the retriever) by understanding how tokenization works and how it impacts the relevance of your search. In addition, you will also learn to measure and improve retrieval quality, speed, and memory. In detail, you’ll: - Learn about the internal workings of the embedding models and how your text turns into vectors. - Understand how several tokenizers, such as Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece work. - Explore common challenges with tokenizers, such as unknown tokens, domain-specific identifiers, and numerical values, that can negatively affect your vector search. - Understand how to measure the quality of your search across relevance, ranking, and score-related metrics. - Understand how the main parameters in "HNSW", a graph-based algorithm, affect the relevance and speed of vector search, and how to tune its parameters. - Experiment with the three major quantization methods – product, scalar, and binary – and learn how they impact memory requirements, search quality, and speed. By the end of this course, you’ll have a solid understanding of how tokenization functions and how to optimize vector search in your RAG systems. Please sign up here!

Tokenization -- turning text into a sequence of integers -- is a key part of generative AI, and most API providers charge per million tokens. How does tokenization work? Learn the details of tokenization and RAG optimization in Retrieval Optimization: From Tokenization to Vector Quantization, created in collaboration with Qdrant and taught by its Developer Relations Lead, Kacper Łukawski. This course focuses on Retrieval augmented generation (RAG), which has two steps: First, a retriever finds relevant information; then, the generator uses what’s retrieved as context to produce a response. You’ll learn to optimize the first step (the retriever) by understanding how tokenization works and how it impacts the relevance of your search. In addition, you will also learn to measure and improve retrieval quality, speed, and memory. In detail, you’ll: - Learn about the internal workings of the embedding models and how your text turns into vectors. - Understand how several tokenizers, such as Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece work. - Explore common challenges with tokenizers, such as unknown tokens, domain-specific identifiers, and numerical values, that can negatively affect your vector search. - Understand how to measure the quality of your search across relevance, ranking, and score-related metrics. - Understand how the main parameters in "HNSW", a graph-based algorithm, affect the relevance and speed of vector search, and how to tune its parameters. - Experiment with the three major quantization methods – product, scalar, and binary – and learn how they impact memory requirements, search quality, and speed. By the end of this course, you’ll have a solid understanding of how tokenization functions and how to optimize vector search in your RAG systems. Please sign up here!

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we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

Big Pharmai

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AI-ready data, all under one roof. 🏠☁️💰 Introducing preview of Amazon S3 Vectors—the first cloud object store with built-in vector support. Store vectors at scale with up to 90% total savings and query with ease, all in one place. #AWS #AWSSummit 👉

AI-ready data, all under one roof. 🏠☁️💰 Introducing preview of Amazon S3 Vectors—the first cloud object store with built-in vector support. Store vectors at scale with up to 90% total savings and query with ease, all in one place. #AWS #AWSSummit 👉

Amazon Web Services

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New Salesforce Vector Database for Data Cloud integrates structured data with unstructured data—case files, call transcripts, Slack, emails, PDFs, audio, and video. This synergy powers the most accurate AI results, advanced automations, and profound customer insights, enabling seamless collaboration between AI and human workers to supercharge your sales, service, marketing, and commerce teams. New Salesforce Vector Database for Data Cloud, transforming how businesses leverage unstructured data integrating structured business entities & keyword search. Key highlights: 1. Ground all enterprise knowledge with copilots & digital agents. 2. Launch new Lightning apps like package search & customer intelligence. 3. Automate case routing & SDR via textual data analysis. 4. Use Tableau to analyze structured & unstructured data.

Marc Benioff

57,894 Aufrufe • vor 1 Jahr

ReZero: Enhancing LLM search ability by trying one-more-time! ReZero trains a model to improve search behaviors instead of memorizing data. It interacts with synthetic search engines using various retrieval methods, refining queries for exact answers🔥🔥

ReZero: Enhancing LLM search ability by trying one-more-time! ReZero trains a model to improve search behaviors instead of memorizing data. It interacts with synthetic search engines using various retrieval methods, refining queries for exact answers🔥🔥

Gradio

17,733 Aufrufe • vor 1 Jahr

🚨🚨🚨 We have released a database with the most searched keywords on the App Store, free for everyone! Just yesterday, we sent a message to all Astro ASO Tool users informing them that we had completed the integration of a database with one million keywords into our backend. This database allows us to access increasingly reliable data on popularity Today, we want to take a further step forward by making this database with 1 million keywords with a popularity value > 5 publicly available to everyone on our website! In this video, Alice Ercolani explains how to use this tool to discover new keywords to track in Astro! Link to the tool in the next post!

🚨🚨🚨 We have released a database with the most searched keywords on the App Store, free for everyone! Just yesterday, we sent a message to all Astro ASO Tool users informing them that we had completed the integration of a database with one million keywords into our backend. This database allows us to access increasingly reliable data on popularity Today, we want to take a further step forward by making this database with 1 million keywords with a popularity value > 5 publicly available to everyone on our website! In this video, Alice Ercolani explains how to use this tool to discover new keywords to track in Astro! Link to the tool in the next post!

Matteo Spada

53,256 Aufrufe • vor 8 Monaten

Filesystems vs Vector search is the new MCP vs CLI. Claude uses agentic search. And Dens Sumesh at mintlify like filesystems too. but Retrieval is still used by Notion, Cursor, and others. We hit the cafes of SF to see what people want. - The debate is hot. Filesystems won by 1 point - filesystems feel very simple and intuitive. - RAG requires embedding, vector search and other stuff filesystems won. Introducing SMFS - Supermemory Filesystem we brought the best of these worlds into one single product - it's a filesystem, but agent can also do semantic search using grep. live today. Try it! Works with any sandbox, you mount and sync with cloud, it has a sync engine built in, all filetypes supported - even images and videos can be grepped. So, what do you choose - Filesystems, or vector search? we have both!

Filesystems vs Vector search is the new MCP vs CLI. Claude uses agentic search. And Dens Sumesh at mintlify like filesystems too. but Retrieval is still used by Notion, Cursor, and others. We hit the cafes of SF to see what people want. - The debate is hot. Filesystems won by 1 point - filesystems feel very simple and intuitive. - RAG requires embedding, vector search and other stuff filesystems won. Introducing SMFS - Supermemory Filesystem we brought the best of these worlds into one single product - it's a filesystem, but agent can also do semantic search using grep. live today. Try it! Works with any sandbox, you mount and sync with cloud, it has a sync engine built in, all filetypes supported - even images and videos can be grepped. So, what do you choose - Filesystems, or vector search? we have both!

Dhravya Shah

153,119 Aufrufe • vor 1 Monat