Ever wondered how to automatically pull structured data out of messy PDFs, receipts, invoices, or text files? In this video, we'll walk you step-by-step through building your very own VLM-powered document parsing and information extraction agent. Notebook from this webinar: https://tinyurl.com/58xsy96n LlamaParse signup: https://www.llamaindex.ai/signup ...

Product Launch - Llama Cloud launches Llama Sheets API, enabling conversion of spreadsheets into structured parquet files for AI agent processing [1] - Llama Sheets is positioned as a generic document processing tool within Llama Cloud's existing suite of tools like Llama Parse, Llama Extract, and Llama Classify [2] - The API allows processing spreadsheets and using them directly in document workflows [3] - The API extracts each table as an individual object, along with a title and description [5][6] Technical Details - Parquet files are described as a structured format, similar to dataframes, that AI agents and coding agents can readily interact with [1][10] - The API can be integrated with coding agents like Cloud Code or frameworks like Llama Index [7] - The API includes examples and documentation for using it with coding agents [7] - The extracted data can be downloaded as a parquet file [10] Use Case & Feedback - The API can be used to analyze spreadsheets and automate workflows [14] - User feedback on the API's inputs, outputs, and features is encouraged via a feedback button in the UI [14]

Discover how to build a scalable, real-time document processing pipeline that transforms PDFs, reports, and contracts into searchable, enriched data. Learn how LlamaParse powers intelligent parsing and chunking, while MongoDB enables flexible storage, indexing, and vector search. ...

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 ...

Hi everyone, Cleia here from Llama Index and today I'm going to walk you through building a Llama agent from the grounds up. So first of all, what is a llama agent. A llama agent is basically uh our brand new product which is currently in private alpha that lets you build and deploy workflows both as a backend and as a fullstack service on our cloud platform Lama cloud.more of this later when we will actually see a llama agent in action. So first of all u let's understand what we want to build here. So we w ...

AI Development Focus - Carlile Group focuses on investment research agents that scan analyst notes and company data [1] - The company also develops AI assistants that answer ad-hoc queries over structured and unstructured data [1] - The company emphasizes the importance of measuring and scoping AI agents for effectiveness [1] AI in Investment - AI streamlines routine workflows for investment and support teams, boosting efficiency [2] - AI frees up people's time to focus on higher value analysis [2] - The industry should pick the right framework that keeps pace with the fast-moving AI and focuses on business logic [2]

Architecture and Workflow - Llama Index introduces a microservices architecture demo for e-commerce, breaking down a monolith into containerized services for specific jobs like front end, authentication, payments, orders, and stock management [1][2] - The demo uses Docker Compose for easy setup, requiring cloning the repository and running `docker compose app -d` [3] - The application structure includes front end, authentication, payment processing, orders, stock management, PostgreSQL for database, Kafka for event-driven communication, and Zookeeper for Kafka management [3] - Workflows are used for payments, orders, and stock management, triggered by events in Kafka, starting with data processing and syncing [3] - The workflow extracts structured data (order, payment, stock, item) from raw JSON data and builds a query to update the database [3] - The workflow ends by restituting the operation status (success or failure) and sending details back to Kafka [4] Data Flow and Communication - The front end sends orders as JSON data to a Kafka topic, which triggers workflows for payment, order creation, and stock update [4] - Each service (payments, orders, stock) subscribes to the orders topic and publishes operation statuses to separate partitions in the Kafka topic [4] - Payments write to partition zero, orders to partition one, and stock to partition two [5] - Operation statuses are streamed back to the front end, indicating success or failure of each operation [5] Demonstration and Potential Issues - The demo can be run locally by registering a user and placing an order, which triggers the described pipeline [7][8] - Failures in updating the order, placing the order, payment, and updating the stock may occur due to disturbances in communication [10]

Key Functionality & Customization - Llama Index provides a customizable extraction review template for specific data like receipts and invoices [1] - The template allows users to focus on schema development rather than building scaffolding [2] - Users can update front-end schemas to reflect changes in the data extraction process [3] - The system flags fields with low confidence, enabling efficient review and validation [4] - Users can deploy custom extraction agents with validation on Llama Cloud [6] Deployment & Data Handling - The deployed app can have a distinct production dataset compared to the local environment [5] - Users can re-upload data to improve extraction results and achieve valid states [4][6] Application & Validation - The system is designed for messy and hard-to-read documents with multiple line items and taxes [1] - The custom extraction agent includes validation to ensure accuracy [6]

Product Overview - Llama Index introduces a customizable extraction review template for specific data types, focusing on receipts and invoices [1] - The template aims to streamline the extraction process from messy and varied line items, including taxes [1] - The solution includes a validation feature to ensure data accuracy [6] - The custom extraction agent can be deployed on Llama Cloud [6] Schema Development & Customization - Schema development is a key aspect, and the template helps users focus on this task [2] - The system allows for updating front-end schemas and re-uploading data to reflect changes [3][4] - Users can build custom UI around receipts data for better context [5] Deployment & Validation - The deployed app allows for distinct production data sets [5] - The system highlights fields with low confidence, enabling efficient review [4] - Validation ensures the extracted data is accurate [6]

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 ...