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 RAGApp 💫 A no-code interface to configure a RAG chatbot, as dead-simple as GPTs by OpenAI. It’s a docker container that’s easily deployable in any cloud infrastructure. Best of all, it’s fully open-source 🔥 1️⃣ Setup the LLM: Configure the model provider (OpenAI, Gemini) 2️⃣ Setup the data:... show more

LlamaIndex 🦙

116,177 subscribers

123,977 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

7 条评论

Kent Langley 的头像

Kent Langley2 年前

@OpenAI that may be the easiest and fastest I have ever been able to create anything that useful in this category. very nice work.

Alignment Lab AI 的头像

Alignment Lab AI2 年前

@OpenAI srsly give it a shot vectors are for data science, databases are for databasing

David Alexander 的头像

David Alexander2 年前

A solution like this is great, but like the other OS solutions that offer a prebuilt chat interface, like Streamlit, LangChain, Flowise, the interface is well, not the most pretty. It’s great for a quick test and prototype, but I couldn’t imagine anyone putting it into production. Open Chat UI, is an example of something you could actually put into production. IMHO

Alex Avery 的头像

Alex Avery2 年前

@OpenAI is there a limit to the size of the knowledge base?

Artur Formella 的头像

Artur Formella2 年前

@OpenAI Finally! :)

Houdini 的头像

Houdini2 年前

@OpenAI For extended functionality. How about if I want to get the LLM to gather and digest data for me from a site that has an API access for example a blockchain site. How do I go about that?

Bryce Amacker 的头像

Bryce Amacker2 年前

@OpenAI @memdotai mem it

相关视频

Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to Perplexity! Now possible in one-line of code through create-llama. Amazing work by Marcus Schiesser 💫 Check it out:

Build a UX for your LLM chatbot/agent that not only includes streaming, but also showcases sources as expandable UI elements 📖🔎, similar to Perplexity! Now possible in one-line of code through create-llama. Amazing work by Marcus Schiesser 💫 Check it out:

LlamaIndex 🦙

54,461 次观看 • 2 年前

Today we’re excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application 🎨🤖 without writing a single line of code 💫 Add any number of agents that you wish, and assign each agent a role, system prompt, and a set of tools. In this example, use a researcher, analyst, and report generation agent to write a news article. This directly generates a full chat interface where you can ask questions and get back answers with full streaming and sources. Huge shoutout to Marcus Schiesser for working on this! RAGApp: create-llama: If you want finer control, you can define your own agentic workflows through code:

Today we’re excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application 🎨🤖 without writing a single line of code 💫 Add any number of agents that you wish, and assign each agent a role, system prompt, and a set of tools. In this example, use a researcher, analyst, and report generation agent to write a news article. This directly generates a full chat interface where you can ask questions and get back answers with full streaming and sources. Huge shoutout to Marcus Schiesser for working on this! RAGApp: create-llama: If you want finer control, you can define your own agentic workflows through code:

LlamaIndex 🦙

93,015 次观看 • 1 年前

Want to build an OpenAI chatbot? Use Chatbot UI! It’s a simple, fully-functional chatbot starter kit using Next.js, TypeScript, and Tailwind CSS. Clone it and start building instantly. GitHub:

Want to build an OpenAI chatbot? Use Chatbot UI! It’s a simple, fully-functional chatbot starter kit using Next.js, TypeScript, and Tailwind CSS. Clone it and start building instantly. GitHub:

Mckay Wrigley

98,113 次观看 • 3 年前

With Chatbot UI you can use 100+ models in the same chat experience. Watch me use Mistral 8x7b via Groq and then switch to the new Claude 3 Opus. Any model. One interface.

With Chatbot UI you can use 100+ models in the same chat experience. Watch me use Mistral 8x7b via Groq and then switch to the new Claude 3 Opus. Any model. One interface.

Mckay Wrigley

104,921 次观看 • 2 年前

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

NVIDIA AI Developer

140,699 次观看 • 1 年前

✨Introducing Civitai models support. You can now Install ANY models from Civitai. Open and run comfy UI in the cloud. Run via simple API. This is Comfy Deploy. Join discord for the latest updates!

✨Introducing Civitai models support. You can now Install ANY models from Civitai. Open and run comfy UI in the cloud. Run via simple API. This is Comfy Deploy. Join discord for the latest updates!

BennyKok

24,443 次观看 • 2 年前

🤖 Introducing AI Agent Builder—create, configure and deploy conversational AI Agents powered by Google Gemini via Firebase, OpenAI, and Anthropic into your app. Your app just got a lot smarter.

🤖 Introducing AI Agent Builder—create, configure and deploy conversational AI Agents powered by Google Gemini via Firebase, OpenAI, and Anthropic into your app. Your app just got a lot smarter.

FlutterFlow

13,778 次观看 • 1 年前

Newest features on Clawdbot VM in Intelligence Cubed ( Introducing Brave + Clawdbot VM Terminal)! 🚀🚀🚀 We’ll go from connecting your wallet and logging in with Gmail, to creating a deployment with: 1⃣OpenAI as the model provider (paste your OpenAI API key) 2⃣A Telegram bot token (create a bot using BotFather) 3⃣Your Telegram user ID (get it quickly using “Get My ID” bot) 4⃣A Brave API key (so Clawdbot can search the internet) After the deployment finishes spinning up, I’ll show you how to test it in the Open Web UI and through Telegram, and how to use the terminal with: openclaw tui so you can chat directly, check which model is running, and see token usage. If you want a simple setup that lets you chat on the web, in Telegram, and in the terminal, this is a super clean workflow. #AI #TelegramBot #OpenAI #WebUI #DePIN #Clawdbot #IntelligenceCubed

Newest features on Clawdbot VM in Intelligence Cubed ( Introducing Brave + Clawdbot VM Terminal)! 🚀🚀🚀 We’ll go from connecting your wallet and logging in with Gmail, to creating a deployment with: 1⃣OpenAI as the model provider (paste your OpenAI API key) 2⃣A Telegram bot token (create a bot using BotFather) 3⃣Your Telegram user ID (get it quickly using “Get My ID” bot) 4⃣A Brave API key (so Clawdbot can search the internet) After the deployment finishes spinning up, I’ll show you how to test it in the Open Web UI and through Telegram, and how to use the terminal with: openclaw tui so you can chat directly, check which model is running, and see token usage. If you want a simple setup that lets you chat on the web, in Telegram, and in the terminal, this is a super clean workflow. #AI #TelegramBot #OpenAI #WebUI #DePIN #Clawdbot #IntelligenceCubed

Intelligence Cubed i³

31,191 次观看 • 4 个月前

I spent the last 48 hours building on top of the new ChatGPT API. You can now create a custom ChatGPT from the content of your website using You can then take that chatbot and add it to any website as a chat bubble. Pinecone OpenAI

I spent the last 48 hours building on top of the new ChatGPT API. You can now create a custom ChatGPT from the content of your website using You can then take that chatbot and add it to any website as a chat bubble. Pinecone OpenAI

Yasser

929,920 次观看 • 3 年前

Engineering isn’t just about faster typing; it’s about better thinking. Our team is excited to showcase Gemini 3.1 Pro, our best model for systemic reasoning. Watch as the model handles a complex "Legacy Sync" migration, not by just writing functions, but by identifying architectural risks. That’s the difference between a chatbot and an engineering partner.

Engineering isn’t just about faster typing; it’s about better thinking. Our team is excited to showcase Gemini 3.1 Pro, our best model for systemic reasoning. Watch as the model handles a complex "Legacy Sync" migration, not by just writing functions, but by identifying architectural risks. That’s the difference between a chatbot and an engineering partner.

Google Antigravity

124,023 次观看 • 4 个月前

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Akshay 🚀

66,067 次观看 • 8 个月前

Google release its Data science Agent The Google Data Science Agent in Colab (powered by Gemini) revolutionizes data analysis by allowing users to easily formulate their analysis goals in natural language. This automatically generates fully functional Colab notebooks, which drastically reduces tedious routine work such as code creation, data import and setup. Particularly noteworthy are the flexibility of the generated notebooks, the ease of collaboration and the fast, accurate results thanks to Gemini 2.0.

Google release its Data science Agent The Google Data Science Agent in Colab (powered by Gemini) revolutionizes data analysis by allowing users to easily formulate their analysis goals in natural language. This automatically generates fully functional Colab notebooks, which drastically reduces tedious routine work such as code creation, data import and setup. Particularly noteworthy are the flexibility of the generated notebooks, the ease of collaboration and the fast, accurate results thanks to Gemini 2.0.

Chubby♨️

111,412 次观看 • 1 年前

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

LlamaIndex 🦙

124,174 次观看 • 2 年前

Introducing Build That Idea A platform that lets anyone launch their own AI agent in 60 seconds - Define your Agent - Choose a base LLM (OpenAI, Claude, DeepSeek, etc.) - Upload knowledge base - Set pricing and start making money Join the waitlist below

Introducing Build That Idea A platform that lets anyone launch their own AI agent in 60 seconds - Define your Agent - Choose a base LLM (OpenAI, Claude, DeepSeek, etc.) - Upload knowledge base - Set pricing and start making money Join the waitlist below

Okara

149,613 次观看 • 1 年前

I’ve created a full-stack financial analysis bot that can query both text + embedded tables across multiple SEC 10Ks 📊🤖 It’s made possible with `create-llama` scaffolding + LlamaIndex 🦙 advanced RAG, and I’m sharing the full template below for you to clone! It’s more sophisticated than a basic RAG setup: 🦾 Unstructured to parse embedded tables into a node graph 🦾 Recursive retriever to retrieve/query embedded tables + text 🦾 An agent to do chain of thought + document comparisons 🦾 Custom callback to stream intermediate function calls to the UI Our goal is to make building full-stack advanced RAG as easy as possible. Created a repo to host this template + future templates here: Want to submit a project? We’d love contributions! 🙌 One caveat: - Right now the index is lazily built/cached during the first query, we’re working to decouple the ingestion process - Check out `create-llama` if you haven’t already:

I’ve created a full-stack financial analysis bot that can query both text + embedded tables across multiple SEC 10Ks 📊🤖 It’s made possible with `create-llama` scaffolding + LlamaIndex 🦙 advanced RAG, and I’m sharing the full template below for you to clone! It’s more sophisticated than a basic RAG setup: 🦾 Unstructured to parse embedded tables into a node graph 🦾 Recursive retriever to retrieve/query embedded tables + text 🦾 An agent to do chain of thought + document comparisons 🦾 Custom callback to stream intermediate function calls to the UI Our goal is to make building full-stack advanced RAG as easy as possible. Created a repo to host this template + future templates here: Want to submit a project? We’d love contributions! 🙌 One caveat: - Right now the index is lazily built/cached during the first query, we’re working to decouple the ingestion process - Check out `create-llama` if you haven’t already:

Jerry Liu

131,437 次观看 • 2 年前

The LM Arena source and the “anonymous-chatbot-0717” tag reads o3 alpha responses 2025 07 17. O3 Alpha is live in stealth. I have a suspicion that it’s the same model that snatched second in the coding comp, It’s one-shotting every task. Doubt it is the incoming ChatGPT agent; if it were, I feel as if OpenAI would be parading full coding benchmark flexes by now. Just look at this thing one shot a Minecraft replica 👇

The LM Arena source and the “anonymous-chatbot-0717” tag reads o3 alpha responses 2025 07 17. O3 Alpha is live in stealth. I have a suspicion that it’s the same model that snatched second in the coding comp, It’s one-shotting every task. Doubt it is the incoming ChatGPT agent; if it were, I feel as if OpenAI would be parading full coding benchmark flexes by now. Just look at this thing one shot a Minecraft replica 👇

Chris

22,234 次观看 • 11 个月前

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

Pratham

33,920 次观看 • 4 个月前

OpenAI just dropped the Apps SDK, built on MCP. This is the future of how users will interact with your apps. You won’t need to build a chatbot anymore. OpenAI is the chatbot. You just expose your tools through MCP. Exciting progress toward Jarvis!

OpenAI just dropped the Apps SDK, built on MCP. This is the future of how users will interact with your apps. You won’t need to build a chatbot anymore. OpenAI is the chatbot. You just expose your tools through MCP. Exciting progress toward Jarvis!

Kent C. Dodds 🏹

152,179 次观看 • 8 个月前

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