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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:...

124,090 次观看 • 2 年前 •via X (Twitter)

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

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 次观看 • 4 个月前