In this video, let's talk about AI agents that do specific tasks for us over documents. Imagine you have a réé book where it's not just one ré, but you have backto-back résumés and you want to be able to extract structured information, specific information from each of those résumés. Ideally, we need to have an agent that understands that first it has to be able to split that résé book into individual résumés and then for each of those résumés extract some sort of structured information.On top of that, our ...

Hi and welcome to Llama Cloud. I'm Tuana and in this video I'm going to show you how to get started with the platform in just 60 seconds. Llama Cloud simplifies building end-to-end document agents.We provide powerful tooling for agentic document processing plus Llama agents, a low code but fully customizable agent builder that makes developing and deploying agents super easy. It's all built on our trusted open- source framework used by millions of developers to build agents and it all starts with highqualit ...

Hi there, Clea from Lamindex here and today we're going to v code a lama index workflow using cloud code cloud. mmd and cloud skills. And once we coded the llama index workflow, we're going to package it as a llama agent and deploy it to the cloud.So let's start by creating a u directory called financial documents classification. And once we created this directory, we're going to initialize it as a UV project. And we're going to remove the main.py file since we do not need it. And we're going to add uh VBLA ...

Today we're opening Llama agents to early access. And in this video I'm going to walk you through how you can get started. Um at the end of the video, what I want to achieve is an agent that's been deployed to our Llama Cloud project.Um and to start off with that, what we first need to install is Llama Control. So you can start with UV install Llama control. I already have it.And once you have that installed, you can say llamactl in it, which will spin up a CLI that allows us to decide what kind of llama ag ...

Product Overview - Llama Cloud 的 Agents 产品旨在帮助客户快速构建、部署和扩展文档工作流程 [1] - 该产品旨在加速原型到生产的转换，并支持多种部署类型 [1] - 提供多种 Agent 模板，方便用户快速开始特定 Agent 的使用 [2] Deployment and Workflow - 用户可以通过 Llama Control 下载并初始化模板，根据需求进行编辑和配置 [2][4] - 可以将代码存储库部署到 GitHub，并通过 Llama Control 进行部署创建 [4][5][6] - 部署过程需要 API 密钥，用户可以在 Llama Cloud 中创建 [6] - 部署完成后，应用可以在 Llama Cloud 的 Agents 界面中访问，并可在 staging 或 production 环境中运行 [9][10] Functionality and Access - UI 界面可用于提取和修正信息 [11] - 提供 headless 部署选项，允许用户通过 API 访问工作流程，并构建自定义 UI [11]

Llama Index Overview - Llama Index has observed the maturation of generative AI applications over the past 2 years [2] - Llama Index provides tools from basic RAG to multi-agent frameworks, supporting millions of production workflows [2] - Llama Index has reached over 4 million downloads per month [2] Llama Cloud Platform - Llama Cloud is positioned as a complete Agentic document automation platform, integrating parsing, extraction, and indexing [5] - Llama Cloud experienced over 700% growth in self-served revenue within one year [5] - Llama Cloud enables parsing complex documents into markdown, extracting information into normalized schemas, and indexing document repositories [6] - Llama Cloud facilitates the creation of end-to-end agentic workflows for research and business process automation [7] Use Cases and Applications - Llama Cloud is used to build research co-pilots that can access and extract insights from enterprise knowledge bases, reducing compilation time from weeks to a shorter timeframe [8] - Llama Cloud enables high-accuracy automated workflows such as invoice processing and Excel transformation [9] Call to Action - Llama Index invites new users to join the millions already building with the platform [10] - Llama Index offers 10,000 in credits for new users to sign up and provide feedback [10]

Llama Cloud Features & Functionality - Llama Cloud introduces open-source nodes for N8 workflows, integrating Llama Extract, Parse, and Cloud Indexes [1] - Llama Cloud offers three main products: Llama Parse, Llama Extract, and Llama Cloud Indexes [3] - Llama Extract uses large language models to extract information based on predefined schemas like invoice details (invoice number, amount, total amount, merchant) [4] - Llama Cloud nodes include Llama Parse for parsing PDF files and Llama Cloud for connecting to any given index name [10] Workflow Automation - N8 workflows can be triggered by changes in a specific folder, such as adding a new file to an invoices folder [2][6] - The extracted information can be used to automate tasks like sending emails with invoice details and labeling those emails in Gmail [3][8][9] - The Llama Cloud node can be used as a retriever and connected to other nodes such as an OpenAI message [11] Customization & Open Source - Users can create their own extraction agents or use pre-made schemas for research papers, filing reports, technical résumés, etc [4][5] - The N8 Llama Cloud integration is open source, allowing users to add extra functionality [11]

Hello everyone. This is Tuana from Llama Index and in this video I'm going to quickly walk you through how you can use Llama Cloud nodes in NAT workflows. These are open-source nodes that we've just created and it brings Llama Extract P and Llama Cloud indexes to NAN workflows.In this video, we're going to be looking at a very simple example. As you can see, I've already run this workflow. And what it does is once there is a change in a specific folder, specifically in my case the invoices folder on my mach ...

Automation Solution - Lana Index introduces an open-source application, Auto RFP, designed to automate the request for proposal (RFP) process [2] - The automation aims to reduce the time required to build an RFP from hours to minutes [1] - The application allows users to create multiple organizations and projects [2][3] - Users can integrate their own LlamaCloud API key and index to run their RFP process [11] Key Features - The application uses pre-written questions about documents to generate answers for RFPs [5] - It can generate answers in a simple, one-shot manner using OpenAI, citing sources and page numbers [6][7] - Reasoning mode offers a multi-step approach to answering questions, including analyzing requirements, searching documents, extracting key facts, creating an RFP structure, and validating the response [8][9] - The generated answers can be exported as a CSV file, providing a structured RFP with sections, questions, and detailed answers in markdown [10][11]