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Ana Sayfaya Dön

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't... be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟show more

Clara Shih

27,298 subscribers

49,613 görüntüleme • 2 yıl önce •via X (Twitter)

Haberler & Politika Eğitim Bilim & Teknoloji

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Anshu Sharma 🌶 profil fotoğrafı

Anshu Sharma 🌶2 yıl önce

this is epic and awesome. congrats.

⟁ndrew V profil fotoğrafı

⟁ndrew V2 yıl önce

I'm a solopreneur with no capital or cashflow, but I know if I want to move forward I need Salesforce experience. What would it take to have the team provide me the tools to learn with Einstein?

Clara Shih profil fotoğrafı

Clara Shih2 yıl önce

Check out Trailhead, our free online learning and certification platform:

Willem Mulder profil fotoğrafı

Willem Mulder2 yıl önce

Good stuff! If we use just Service Cloud, would that work to do a Vector Search on our Knowledge Base? And does it support external retrievers through e.g. Apex to do a callout to our own server that then returns relevant info to be included in the prompt?

Clara Shih profil fotoğrafı

Clara Shih2 yıl önce

Yep - Vector Search works on knowledge articles, both Salesforce Knowledge and coming soon (currently in pilot) also external sources

Sudhir profil fotoğrafı

Sudhir2 yıl önce

That's a awesome, congrats!

Nicholas Read profil fotoğrafı

Nicholas Read2 yıl önce

This looks amazing will there be options to connect external data stores?

Mamoon profil fotoğrafı

Mamoon2 yıl önce

This is amazing!!

John Furrier profil fotoğrafı

John Furrier2 yıl önce

I've said on @theCUBE that SFDC has one of the best opportunities to bring together all the disparate systems of all the leaders out there including MSFT.

Rohit Kapoor profil fotoğrafı

Rohit Kapoor2 yıl önce

Go team!

Benzer Videolar

🚀 Introducing Salesforce's game-changer: Data Cloud Vector Database + Einstein Copilot Semantic Search! ✨ Harness both structured & unstructured data for smarter AI responses. No more costly training! Real-time data-driven insights with RAG, complete with citations to curb AI hallucinations. Trust in enterprise AI just got a major upgrade, thanks to Salesforce! ❤️

🚀 Introducing Salesforce's game-changer: Data Cloud Vector Database + Einstein Copilot Semantic Search! ✨ Harness both structured & unstructured data for smarter AI responses. No more costly training! Real-time data-driven insights with RAG, complete with citations to curb AI hallucinations. Trust in enterprise AI just got a major upgrade, thanks to Salesforce! ❤️

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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.

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.

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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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Salesforce Einstein Copilot=CRM+Data+AI+Trust: unlocking groundbreaking decisions & strategies in your enterprise. Experience how our unique data and metadata integration brings our models to life with grounding, actions, and plans. Einstein: delivering 1 Trillion AI transactions weekly.❤️

Salesforce Einstein Copilot=CRM+Data+AI+Trust: unlocking groundbreaking decisions & strategies in your enterprise. Experience how our unique data and metadata integration brings our models to life with grounding, actions, and plans. Einstein: delivering 1 Trillion AI transactions weekly.❤️

Marc Benioff

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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:

Andrew Ng

137,034 görüntüleme • 2 yıl önce

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,441 görüntüleme • 1 yıl önce

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 finding data across formats easier. Learn more in this blog post by Leonie, my all-time favorite:

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 finding data across formats easier. Learn more in this blog post by Leonie, my all-time favorite:

Femke Plantinga

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,314 görüntüleme • 11 ay önce

Introducing: Enterprise Search 🎉 Unlock the power of your connected data sources. 🔐 Search across your company’s connected apps and data for richer results, leading to smarter decisions and faster actions. Oh, and all without leaving Slack. 🙌

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Jason ✨👾SaaStr.Ai✨ Lemkin

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We are hiring for Harvey's Retrieval and Data team to expand Harvey to over 1,000 data sources in the 50+ countries we operate in. Our clients use Harvey to do research over legal, financial, tax, and regulatory data around the world. Thread on search + data at Harvey:

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Today, Box announced new AI Agents to work with enterprise content, powering Deep Research, Search, and enhanced Data Extraction. There’s a tremendous amount of value that’s trapped in unstructured data, from contracts to research data, that we can finally unlock with AI.

Today, Box announced new AI Agents to work with enterprise content, powering Deep Research, Search, and enhanced Data Extraction. There’s a tremendous amount of value that’s trapped in unstructured data, from contracts to research data, that we can finally unlock with AI.

Aaron Levie

60,262 görüntüleme • 1 yıl önce

Letting the copilot builder, model builder, and prompt builder into the wild. And the power of the new version of Data Cloud with Zero Copy silent integration into Snowflake, Big Query, and every major data source. The new Copilot family and Data Cloud fueling the power of artificial intelligence for every enterprise. Let’s make every Trailblazer a Datablazer! I can’t wait for tomorrow for #TDX24! ❤️

Letting the copilot builder, model builder, and prompt builder into the wild. And the power of the new version of Data Cloud with Zero Copy silent integration into Snowflake, Big Query, and every major data source. The new Copilot family and Data Cloud fueling the power of artificial intelligence for every enterprise. Let’s make every Trailblazer a Datablazer! I can’t wait for tomorrow for #TDX24! ❤️

Marc Benioff

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Vector databases are a key part of many LLM applications that need search or data retrieval, for example with Retrieval Augmented Generation (RAG). Learn how they work + how to use them in our new short course, taught by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿!

Vector databases are a key part of many LLM applications that need search or data retrieval, for example with Retrieval Augmented Generation (RAG). Learn how they work + how to use them in our new short course, taught by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿!

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Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

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🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

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Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:

Andrew Ng

71,672 görüntüleme • 1 yıl önce