LlamaIndex

Learn to build advanced AI agents and multi-agent systems with LlamaIndex in this comprehensive workshop by Laurie Voss! 🚀 Master AgentWorkflow for high-level agent construction 🔧 Dive into Workflows, the building blocks of multi-agent systems 🛠️ Create a multi-agent system from scratch Originally presented at @[Open Data Science Conference (ODSC)](urn:li:organization:9358547)'s AI Builders Summit, this step-by-step guide takes you from basics to advanced concepts in LlamaIndex agent development: https://lnkd.in/gen7XjCS

Excited to share a sneak peak into what our team has been cooking up 🔥 - LlamaReport, a report generation agent 📝🤖 to help you create complex, multi-section reports that adhere to any template you specify given massive amounts of unstructured context! In this video I dump 4 full-length 10Ks on Apple into LlamaReport and ask it to compare gross margins across years. Note: you can’t do this on existing chat UIs and get back this level of detail. This tool is available both as a UI and also accessible (soon) via an API. If you have a report generation use case come let us know 💪 LlamaReport: https://lnkd.in/gyungdQ7 Contact us: https://lnkd.in/gE9aSVV7

Build a multimodal financial report generator with LlamaIndex and OpenAI 📊💼 Learn how to create dynamic reports that combine text and visuals from PDF documents: 🔍 Extract text and images from PDFs using LlamaParse 📊 Generate structured outputs with text summaries and visual elements 🔬 Set up observability for complex pipelines with Arize Phoenix Read the full tutorial: https://lnkd.in/gehWRcQp

The highest value agent use cases in the enterprise are around creating automated workflows (that still enable human review). In insurance, a key use case is around automated claims analysis. By using agents to validate against exclusions and automatically retrieve similar cases, you not only drive efficiency, but can increase accuracy and reduce mistakes through human error. We're excited to feature our joint case study below with Scaleport AI which used LlamaCloud and LlamaIndex workflows to build a case analysis agent. ✅ LlamaParse helps with handwritten notes/PDFs/diagrams ✅ Data standardization through extraction ✅ Index similar cases in a knowledge base Case study link: https://lnkd.in/guUV6rey Want a template to get started building these agentic document workflows for insurance? Check out our diagram and notebook example below: https://lnkd.in/gkpTrESC Signup to LlamaCloud: https://lnkd.in/grgiv-vB

Agentic web scraping with LlamaIndex ScrapeGraphAI 🤖💰 A frequent task of AI agents is extracting unstructured information from regular websites, aka web scraping. ScrapeGraphAI's integration with LlamaIndex lets you do that quickly and efficiently, and you can build it straight into an agent! Key benefits: 🚀 Automate complex scraping tasks with minimal setup 🔄 Handle dynamic content changes traditional scrapers can't 📊 Process large volumes of data for market research and pricing strategies See how it works: https://lnkd.in/dGYcrdMK

SOFTIQ's new SaaS app, Przetargi.io, uses LlamaIndex Workflows to transform how construction companies handle public sector tenders: ➡️ Slash analysis time from hours/days to under 10 minutes per tender ➡️ Boost tender selection accuracy, reducing wasted effort on irrelevant bids ➡️ Generate comprehensive 20-30 page reports with executive summaries and risk assessments Learn how SOFTIQ used LlamaIndex to create this solution, saving an estimated 2 months of development time and enabling processing of thousands of complex, multimodal documents in our case study: https://lnkd.in/gsCei4PQ

If you are ready to take a break from the DeepSeek AI R1 news (and market selloff) for just a minute: we just released vectara-ingest with support for llama_parse from LlamaIndex. Repo: https://lnkd.in/gRDnAEHu The Vectara "table understanding" blog post: https://lnkd.in/gDEWXfVT that describes how we integrate tables into the RAG query flow.

Learn how SCALEPORT AI partnered with a leading travel insurance provider and used LlamaIndex to automate claim estimation from complex medical reports. 🔍 Advanced OCR extracts data from diverse document formats 🤖 AI-driven analysis for exclusion checking and adequacy assessment 💰 Accurate price estimation using historical data and vector search Results: ➡️ 50-75% reduction in claim processing time ➡️ Doubled claim processing capacity without compromising accuracy Read the full case study: https://lnkd.in/gFGxRS77

RAG is just a (limited) technique, but a huge use case that users care about is multi-step document research. Marcus Schiesser has built a fully open-source full-stack template to help you build a document research agent, inspired by Google Deep Research. Given an input task, the agent will break it down into smaller research tasks, step through each task and stream the results back to the user, and generate a final result. Powered by create-llama, check out the video below 👇 create-llama: https://lnkd.in/gPTYt6um Building a document research agent requires a production-level knowledge management layer. Check out LlamaCloud: https://lnkd.in/gi8dxGnt

We’re excited to feature Presenter 🎨 🖼️: a multi-agent workflow that can generate visually rich presentations. It’s a perfect reference repo if you’re looking to build a report generation agent, and contains a ton of features:  ✅ generate Mermaid diagrams  ✅ generates scripts with narration through ElevenLabs 🎙️ ✅ Render the presentation as HTML (reveal.js) or video with ffmpeg Huge shoutout to RS Rohan. Check out the project: https://lnkd.in/gDGQipJN Powered by LlamaIndex workflows. Get started here: https://lnkd.in/gvB764h2