Jerry Liu
@jerryjliu0
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co-founder/CEO @llama_index Careers: https://t.co/EUnMNmbCtx Enterprise: https://t.co/Ht5jwxSrQB
Joined September 2011
RT @itsclelia: We surpassed 1000 stars on @github with NotebookLlaMa!.Thanks you all for your support and your interest, and stay tuned for….
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NotebookLlama is our open-source @streamlit clone of NotebookLM 🦙📓 - and we just launched a v2 that makes it a way better tool for document research!. 1️��� When you upload a knowledge base of files, you can now extract/download images/tables, and even visualize tabular data as a
🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed @NotebookLM alternative, we decided to give you more features!.In the latest release you can:.📊 Extract and download images and tables from your files, and interactively visualize all tabular data.✅ Chat with
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RT @itsclelia: 🦙📚 NotebookLlaMa just got upgraded!. Following the strongly positive feedback from our community, we decided to push some ne….
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RT @llama_index: 🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed @NotebookLM alternative, we decided to give you more f….
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RT @llama_index: Turn research hours into research breakthroughs with LeSearch - built using our ReActAgent framework 🚀. LeSearch tackles t….
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RT @llama_index: Ready to build production-grade data agents that work with real enterprise data? 🏗️. Join us and @Snowflake in Amsterdam o….
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Build legal AI agents from scratch 🧑⚖️. This is a great tutorial that shows you how to build a legal database as an MCP server (with a comprehensive set of endpoints to view/review cases, lawyers, and clients), and plug it into a @llama_index agent with a @Gradio frontend. Check
This comprehensive guide shows you how to create intelligent agents that can manage legal databases through natural language, combining MCP's standardized communication with our agent orchestration:. 🔧 Set up MCP servers with FastMCP to expose database operations as standardized
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RT @llama_index: Grok 4 is out and it claims to be the best model in the world!. You can use it right now in LlamaIndex in just 1 line of c….
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RT @llama_index: This comprehensive guide shows you how to create intelligent agents that can manage legal databases through natural langua….
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A lot of companies run on @Snowflake ❄️. A lot of companies also depend on massive collections of PDFs. 📑.For the first time, you can now combine the two with high accuracy!. This is a great tutorial by @_jreini highlighting how you can use LlamaParse to parse complex documents
Build a complete RAG pipeline using our LlamaParse document parser with @Snowflake Cortex for enterprise-grade document processing and search. This tutorial shows you how to combine LlamaParse's agentic parsing capabilities with Snowflake's powerful data platform:. 📄 Parse
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RT @llama_index: Build RAG applications with @googlecloud's Gemini models using our LlamaIndex integration 🚀. Google Cloud Platform has cre….
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RT @llama_index: Friend of LlamaIndex Yujian Tang has launched a LinkedIn learning course that's all about using LlamaIndex for RAG!. The c….
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RT @llama_index: Build a complete RAG pipeline using our LlamaParse document parser with @Snowflake Cortex for enterprise-grade document pr….
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Building the MCP Toolbox for Documents 🛠️📑. This is a fantastic tutorial from @tuanacelik showing you how to give your favorite AI agent frontend (in this case Claude Desktop) access to all the tools to process and manipulate document context, thanks to LlamaCloud. 1️⃣ Do
Want to see how you can use LlamaCloud tooling as MCP tools? Well, we have a few MCP server templates, and in this video @tuanacelik walks you through our LlamaCloud MCP server in Python 👇. · Use your extract agents as MCP tools.· Query any index you have in LlamaCloud as MCP
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RT @llama_index: Want to see how you can use LlamaCloud tooling as MCP tools? Well, we have a few MCP server templates, and in this video @….
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RT @tuanacelik: It's clear that many agentic applications are designed for tackling complex documents—think massive PDFs, slide decks, and….
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One of the biggest pain points in using LLMs for large-scale document extraction is actually coming up with the schema in the first place - it’s a long and tedious process ⏳. We built an two-stage, e2e agent workflow that does both schema generation and extraction:.1️⃣ It first
Build a complete structured data extraction workflow with human-in-the-loop validation for data extraction 📄🔄. This demo notebook shows how you can use LLMs to pre-process your data to create exactly the schema you need for bulk extraction work. 📋 Parse documents using
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RT @llama_index: Tomorrow! Our next office hours hangout will be all about MCP. Join the team in our Discord server to ask all of your Llam….
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RT @llama_index: Build a complete structured data extraction workflow with human-in-the-loop validation for data extraction 📄🔄. This demo n….
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RT @llama_index: Join us for another AI Hack Night at @github where you'll build cutting-edge applications with AI Agents, MCPs, RAG and mo….
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