正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Introducing our new agentic RAG framework. A collab with Google Cloud, our multi-agent workflow goes beyond standard RAG by breaking down complex enterprise queries & iteratively searching for sufficient context before generating dependable responses. 📜→

Google Research

99,902 subscribers

504,393 次观看 • 14 天前 •via X (Twitter)

新闻政治科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

Introducing `Personalized Agentic RAG` - where the LLM remembers key details about the user and automatically chooses the tool for RAG. We'll build: 👏 ChatGPT like memory ⚙️ Function calling 🧙 Multi-Agent orchestration code:

Ashpreet Bedi

45,035 次观看 • 2 年前

We're introducing a new set of primitives in the Responses API for long-running agentic work on computers. Server-side compaction • Enable multi-hour agent runs without hitting context limits. Containers with networking • Give OpenAI-hosted containers controlled internet access to install libraries and run scripts. Skills in the API • Native support for the Agent Skills standard and our first pre-built spreadsheets skill.

We're introducing a new set of primitives in the Responses API for long-running agentic work on computers. Server-side compaction • Enable multi-hour agent runs without hitting context limits. Containers with networking • Give OpenAI-hosted containers controlled internet access to install libraries and run scripts. Skills in the API • Native support for the Agent Skills standard and our first pre-built spreadsheets skill.

OpenAI Developers

322,333 次观看 • 4 个月前

I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

Erika Shorten

61,317 次观看 • 1 年前

The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case

The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case

Dhravya Shah

88,105 次观看 • 24 天前

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.

Shubham Saboo

124,490 次观看 • 1 年前

I’m excited to announce a new course with DeepLearning.AI - Building Agentic RAG 💫 In this course, you’ll learn how to build a research assistant that can reason over multiple documents and answer complex questions. You’ll also learn how to step through the execution of the agent and steer it with human feedback. This represents a big step beyond any standard RAG pipeline, which is mostly good for simple questions over a small set of documents. Learn the layers first and then put them together: ✅ Routing ✅ Tool Use ✅ Multi-step reasoning with Memory ✅ Tool retrieval ✅ Debugging + user input Check it out!

I’m excited to announce a new course with DeepLearning.AI - Building Agentic RAG 💫 In this course, you’ll learn how to build a research assistant that can reason over multiple documents and answer complex questions. You’ll also learn how to step through the execution of the agent and steer it with human feedback. This represents a big step beyond any standard RAG pipeline, which is mostly good for simple questions over a small set of documents. Learn the layers first and then put them together: ✅ Routing ✅ Tool Use ✅ Multi-step reasoning with Memory ✅ Tool retrieval ✅ Debugging + user input Check it out!

Jerry Liu

76,280 次观看 • 2 年前

Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

Warp

1,186,356 次观看 • 1 个月前

🚀 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! ❤️

Marc Benioff

113,094 次观看 • 2 年前

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

PlanetScale

114,704 次观看 • 5 个月前

Your AI agents are giving you generic outputs because they have zero context... I just built a RAG workflow that ingests your data and creates a searchable database that your AI can reference. Now your workflows will give precise, contextual responses instead of generic slop. Follow + comment "RAG" and I'll send you the complete n8n workflow.

Your AI agents are giving you generic outputs because they have zero context... I just built a RAG workflow that ingests your data and creates a searchable database that your AI can reference. Now your workflows will give precise, contextual responses instead of generic slop. Follow + comment "RAG" and I'll send you the complete n8n workflow.

Tom

51,466 次观看 • 1 年前

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

Victor

260,273 次观看 • 1 年前

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

LlamaIndex 🦙

475,732 次观看 • 2 年前

Great to see our Google DeepMind Google AI AI co-scientist innovations (multi-agent idea generation tournament) ship fast with Google Cloud Agentspace! Incredible shipping momentum at Google! 🚢 Agentspace - AI co-scientist -

Great to see our Google DeepMind Google AI AI co-scientist innovations (multi-agent idea generation tournament) ship fast with Google Cloud Agentspace! Incredible shipping momentum at Google! 🚢 Agentspace - AI co-scientist -

Vivek Natarajan

16,020 次观看 • 1 年前

Introducing Gemini Enterprise Agent Platform—our new platform to build, scale, govern, and optimize agents.

Introducing Gemini Enterprise Agent Platform—our new platform to build, scale, govern, and optimize agents.

Google Cloud

67,611,444 次观看 • 1 个月前

Finally! A Text-to-SQL tool that actually works! Vanna is an open-source RAG framework for complex Text-to-SQL generation. It manages dynamic data and allows custom RAG model training for greater accuracy. 100% open-source.

Finally! A Text-to-SQL tool that actually works! Vanna is an open-source RAG framework for complex Text-to-SQL generation. It manages dynamic data and allows custom RAG model training for greater accuracy. 100% open-source.

Akshay 🚀

168,586 次观看 • 1 年前

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,053 次观看 • 2 年前

BREAKING 🚨: Google is working on an Agent import feature for Gemini Enterprise, to let users connect external agents into their own multi-agent workflows. Also, Agent evaluation tool, scheduled execution, new Connectors and more!

BREAKING 🚨: Google is working on an Agent import feature for Gemini Enterprise, to let users connect external agents into their own multi-agent workflows. Also, Agent evaluation tool, scheduled execution, new Connectors and more!

TestingCatalog News 🗞

26,260 次观看 • 7 个月前

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

Y Combinator

23,251 次观看 • 1 年前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,257 次观看 • 2 年前