LlamaIndex

At the KPMG US Tech and Innovation Symposium, Swami Chandrasekaran and Jerry Liu demonstrated the future of rapid app development - vibe coding! In a live session, they transformed a simple sketch into a functioning app. Google Stitch handled design conversion, Cursor generated the codebase, and LlamaIndex’s LlamaCloud managed the data processing. This showcases a fundamental shift in development. The ability to move from concept to near production-ready appls with AI signals the next evolution in software development. #KPMGTechandInnovation25 #KPMGEnterpriseInnvoation

It was an honor to share the stage with Swami Chandrasekaran this past week at the KPMG Tech and Innovation Symposium to present a concept that I’ve been excited about for a while now: vibe-coding agentic automations. The promise of agentic automation is that any user can describe the task they want to solve in natural language and get back an application that can fulfill that end-to-end task repeatably. Building these automations is not limited to AI engineers. Our demo showed that anyone can build a deeply custom, advanced AI agent application - we built an invoice processing application in < 10-20 mins, provided that you have the right building blocks around data and compute. We’ve primarily focused on infrastructure for developers - LlamaCloud gives developers core document processing/indexing capabilities for their AI agents - but with vibe-coding that gap is closing rapidly. With vibe coding, we become building blocks that anyone can use to automate knowledge work. We'll be putting out more content in this direction soon, stay tuned.

Whether you want to chat with your terminal or add a voice assistant to your web-app, we got you covered with our Gemini Live integration, now available in TypeScript! 👇 Check out the demo below, where @itsclelia shows you how to set up and run a simple terminal chat - but if you're very eager to try it you can just run npx @cle-does-things/live-chat 🏃 📚 Learn about LlamaIndex TS: https://ts.llamaindex.ai ⭐ Star the demo code on GitHub: https://lnkd.in/egcUkETw ⭐

Build robust LLM applications with private data using LlamaIndex and Novita AI's powerful model inference capabilities. 🔄 Connect diverse data sources through our comprehensive connector ecosystem - from PDFs and databases to APIs and documents 🧠 Transform your data into searchable vector embeddings with flexible indexing strategies including tree, list, and keyword indexes 💬 Enable natural language querying over your private knowledge base for chatbots, semantic search, and intelligent agents Read the step-by-step integration guide: https://lnkd.in/eX-bUHKv

LlamaIndex is proud to partner with KPMG US and AI Leaders like Swami Chandrasekaran. Spreading AI literacy across some of the largest enterprises doesn't have to be intimidating or scary, we love to make the process fun through dedicated workshops like live 'vibe coding' sessions. This room was high on aura 🔥 Vibe check: ✅

Calling all robots! Want to know what LlamaIndex is about? We've got your LLMs.txt right here! https://lnkd.in/emjjDcHz (Hey humans, you can use it too! Plug LLMs.txt into your favorite IDE or vibe coding platform to teach it the latest LlamaIndex syntax)

LexiconTrail shows how to build 10x faster agentic AI systems using @nvidia Small Language Models with our advanced indexing capabilities. This open-source project demonstrates a production-ready architecture that combines specialized SLMs with LlamaIndex's semantic search, knowledge graphs, and multi-modal indexing: 🚀 Multi-agent orchestration with intelligent routing between specialized SLMs for different tasks ⚡ 90% reduction in computational resources while achieving superior accuracy across document QA and reasoning tasks 🏗️ Complete integration with our VectorStoreIndex, KnowledgeGraphIndex, and custom QueryEngine implementations 📊 Real benchmarks showing 240ms response times vs 2400ms for traditional LLM approaches The system leverages our TreeIndex for structured navigation, DocumentSummaryIndex for hierarchical processing, and ResponseSynthesizer with custom prompts - proving that smaller, specialized models can outperform larger ones when properly orchestrated. Built by The AI Cowboys! Explore the repository: https://lnkd.in/eeesHE7W

Build a production-ready, managed retrieval pipeline in < 2 mins, with 0 lines of code 🤖⚡️ We've created a true "point-and-click" experience in LlamaCloud where you can just upload a ton of docs, not configure anything, and have an e2e advanced retrieval pipeline working. Our managed embeddings simplifies another step that you'd otherwise have to configure! With our MCP server support (mcp.llamaindex.ai), this also means you can plug this into any of your favorite AI tools to give them access to massive volumes of document context. Come check it out: https://lnkd.in/ddvUHeda

Build an automated asset manager fund analysis 📊 This comprehensive notebook shows how to process complex financial documents and extract actionable insights for investment analysis: · LlamaParse converts PDF → structured markdown · Custom document splitter uses LLM to identify fund boundaries across pages · LlamaExtract runs parallel extraction per fund using our own Pydantic schemas ·Results aggregate into analyzable DataFrame for comparative analysis Check out the complete implementation with code examples and step-by-step guidance: https://lnkd.in/emVmT_SX

🚨 New LlamaCloud Feature 🚨 Managed embeddings are live! Since we launched LlamaCloud Indexes, a minor point of friction has been that you had to bring your own API key to embed your content. No more! Now in addition to hosting your vectors, we will embed your vectors for you with our managed embeddings. It's one less thing to think about, making LlamaCloud even easier to use! Try out LlamaCloud Indexes today with 10,000 free credits: https://lnkd.in/eTFTB_wT