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We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata...

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i can now search my entire ad library by concept. "ugc testimonials with urgency hooks" and it just finds them. here's how i built it: searchable ad swipe file > drag in creatives > AI analyzes them > search by concept > find anything in seconds -> uses google's new multimodal embedding model. images, video, and text all searchable together. -> type "ugc testimonials with urgency hooks" and it finds them. no manual tags needed. -> every ad gets auto-analyzed by gemini on import. hook type, structure, audience, CTA. all searchable. -> dual-vector scoring. visual embedding + text embedding. takes the stronger signal. full build breakdown in video + notion guide in the comments
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i can now search my entire ad library by concept. "ugc testimonials with urgency hooks" and it just finds them. here's how i built it: searchable ad swipe file > drag in creatives > AI analyzes them > search by concept > find anything in seconds -> uses google's new multimodal embedding model. images, video, and text all searchable together. -> type "ugc testimonials with urgency hooks" and it finds them. no manual tags needed. -> every ad gets auto-analyzed by gemini on import. hook type, structure, audience, CTA. all searchable. -> dual-vector scoring. visual embedding + text embedding. takes the stronger signal. full build breakdown in video + notion guide in the comments

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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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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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Search Live is now global 🌍 Interactive, multimodal conversations in AI Mode are now available in over 200 countries & territories. This update is powered by Gemini 3.1 Flash Live, our highest quality audio and voice model yet. This model is also inherently multilingual, so people around the world can speak with Search in their preferred language.

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12,279 Aufrufe • vor 5 Monaten

We’re excited to share the beta release of our tool use API! Now developers can easily equip any LLM with hosted open-source tools for web search, web crawling, and maps data (+ more coming soon). Under the hood, it's powered by MCP — but that's just an implementation detail ↓
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We’re excited to share the beta release of our tool use API! Now developers can easily equip any LLM with hosted open-source tools for web search, web crawling, and maps data (+ more coming soon). Under the hood, it's powered by MCP — but that's just an implementation detail ↓

OpenTools

125,965 Aufrufe • vor 1 Jahr

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:
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Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Andrew Ng

74,060 Aufrufe • vor 2 Jahren

Google just announced the biggest upgrade to Search in over 25 years 🚨 - Gemini 3.5 Flash is now the default model powering AI Mode - New search box designed for longer, natural language queries - Upload documents, photos, videos and even Chrome tabs directly into Search - Smart suggestions help you build more complex questions - AI Overviews and AI Mode now work together more seamlessly - New agents can monitor topics and bring you updates later - Search can book services and even call businesses on your behalf - Custom widgets, tools and mini apps are coming to Search - More interactive AI-powered experiences are coming directly to results This is CRAZY!
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Google just announced the biggest upgrade to Search in over 25 years 🚨 - Gemini 3.5 Flash is now the default model powering AI Mode - New search box designed for longer, natural language queries - Upload documents, photos, videos and even Chrome tabs directly into Search - Smart suggestions help you build more complex questions - AI Overviews and AI Mode now work together more seamlessly - New agents can monitor topics and bring you updates later - Search can book services and even call businesses on your behalf - Custom widgets, tools and mini apps are coming to Search - More interactive AI-powered experiences are coming directly to results This is CRAZY!

Shishir

76,750 Aufrufe • vor 3 Monaten

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs
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⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,874 Aufrufe • vor 2 Jahren

Last week, we introduced Personal Intelligence in the Google Gemini. Today, we’re bringing that same helpful experience to AI Mode in Google Search 🔍 Now, you can securely connect Search to Google apps like Gmail and Google Photos to get responses that are uniquely relevant to you. This experimental feature is built with Gemini 3, our most intelligent model, and is strictly opt-in — meaning you choose if and when you want to connect these apps. Here’s what that looks like in Search 🧵(1/7) ↓
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Last week, we introduced Personal Intelligence in the Google Gemini. Today, we’re bringing that same helpful experience to AI Mode in Google Search 🔍 Now, you can securely connect Search to Google apps like Gmail and Google Photos to get responses that are uniquely relevant to you. This experimental feature is built with Gemini 3, our most intelligent model, and is strictly opt-in — meaning you choose if and when you want to connect these apps. Here’s what that looks like in Search 🧵(1/7) ↓

Google

1,102,328 Aufrufe • vor 7 Monaten

Gemini 3 Pro in Google Search can help you quickly visualize complex topics, right in AI Mode. When you select “Thinking with 3 Pro” from the model drop-down menu in AI Mode, Gemini 3 will build a custom response to your question with visual elements so you can quickly get a richer and more helpful understanding of any topic. See it in action ⬇️
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Gemini 3 Pro in Google Search can help you quickly visualize complex topics, right in AI Mode. When you select “Thinking with 3 Pro” from the model drop-down menu in AI Mode, Gemini 3 will build a custom response to your question with visual elements so you can quickly get a richer and more helpful understanding of any topic. See it in action ⬇️

Google

184,111 Aufrufe • vor 7 Monaten

Gemini 3 Flash is starting to roll out as the default model for AI Mode in Search globally. AI Mode can now tackle your most complicated questions with greater precision — without compromising speed. ⚡ With this upgrade, AI Mode becomes an all-around more powerful tool, better at understanding your needs. You can ask more nuanced questions and it will consider each aspect to provide a thoughtful response. And as always, you’ll have access to real-time information and useful links from across the web.
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Gemini 3 Flash is starting to roll out as the default model for AI Mode in Search globally. AI Mode can now tackle your most complicated questions with greater precision — without compromising speed. ⚡ With this upgrade, AI Mode becomes an all-around more powerful tool, better at understanding your needs. You can ask more nuanced questions and it will consider each aspect to provide a thoughtful response. And as always, you’ll have access to real-time information and useful links from across the web.

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67,545 Aufrufe • vor 8 Monaten

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓
0:37

File systems are quickly becoming a core abstraction for AI agents for knowledge work. By having access to a file system, agents can effectively manage context, process any amount of information, and create new files and data. The challenge is that local file systems are inherently limited in size, lack governance capabilities, and are inherently personal -not collaborative- in nature. Now, with the Box API and LangChain's deepagent SDK, you can bring the full power of a secure, collaborative cloud file system to your AI agents. Repo ↓

Box

227,348 Aufrufe • vor 6 Monaten

The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.
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The upgraded Google app for desktop is now available for Windows users globally in English. 💻✨ Use a simple keyboard shortcut (Alt + Space) to instantly find what you need—information from the web, your computer files, installed apps, and Google Drive files—all from the Search box. With AI Mode in Search and Lens built right in, you can ask whatever’s on your mind or search what’s on your screen to get helpful AI-powered responses with links to the web.

Rajan Patel

114,317 Aufrufe • vor 4 Monaten

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:
0:44

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

140,618 Aufrufe • vor 2 Jahren