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New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with...

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Andrew Ng

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218,284 görüntüleme • 2 yıl önce •via X (Twitter)

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Shiya profil fotoğrafı
Shiya2 yıl önce

@seldo @llama_index This is an overlap I did not anticipate 🤩

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Olu💯2 yıl önce

@seldo @llama_index I'm new to AI, just wanna study from scratch. Any advice?

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Abhinav Elimineti 𝕏2 yıl önce

@seldo @llama_index Ranhhhhh haha bags

Kara 🦇 🔊/🔮 profil fotoğrafı
Kara 🦇 🔊/🔮2 yıl önce

@seldo @llama_index Right on time. Amazing knowledge share.

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Thinking Garden2 yıl önce

@seldo @llama_index awesome course.

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Himanshu Singh2 yıl önce

@seldo @llama_index One more use case of Javascript 😅

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AI Architect 🤖🔧2 yıl önce

@seldo @llama_index Nice course for newbies. But if someone wants to build production grade LLM apps, then bookmark the below thread🧵 : 👇

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Samarth🦇🔊🦄 | Full Stack Dev ⚛️🥑2 yıl önce

@seldo @llama_index Sounds amazing! Can't wait to dive in and start building with RAG and LlamaIndex. 🚀

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@seldo @llama_index Wow

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@seldo @llama_index Thanksgiving

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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:
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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!

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297,053 görüntüleme • 2 yıl önce

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!
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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!

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

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17:07

Let's build a dashboard to evaluate and monitor your Agentic and RAG apps! . . In this video, I'll guide you through creating an evaluation and observability pipeline for your AI apps using a 100% open-source tool! Tech Stack: - Comet's Opik to eval and monitor - LlamaIndex to build a RAG pipeline - Ragas for synthetic datagen You'll learn: - Setting up Opik - Building a RAG pipeline - Creating an eval dataset - Evaluating the RAG pipeline - Monitoring all activities during the process It's a hands on demo with code and step-by-step guide to do everything listed above. CometML's Opik is fully open-source, offering the most Pythonic and easiest way to monitor LLM apps. I have shared link to their repo in next tweet!

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