LlamaIndex 🦙
@llama_index • 119,096 subscribers
The most accurate agentic OCR platform for production AI. LlamaParse: https://t.co/yQGTiRSNvj Docs: https://t.co/us6GCS1Clb
Videos
We've spent years building LlamaParse into the most accurate document parser for production AI. Along the way, we learned a lot about what fast, lightweight parsing actually looks like under the hood. Today, we're open-sourcing a light-weight core of that tech as LiteParse 🦙 It's a CLI + TS-native library for layout-aware text parsing from PDFs, Office docs, and images. Local, zero Python dependencies, and built specifically for agents and LLM pipelines. Think of it as our way of giving the community a solid starting point for document parsing: npm i -g @llamaindex/liteparse lit parse anything.pdf - preserves spatial layout (columns, tables, alignment) - built-in local OCR, or bring your own server - screenshots for multimodal LLMs - handles PDFs, office docs, images Blog: Repo:
LlamaIndex 🦙581,748 次观看 • 5 个月前
Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:
LlamaIndex 🦙475,732 次观看 • 2 年前
LiteParse hit 4.3K+ GitHub stars in a few weeks. Today it officially joins the LlamaIndex ecosystem, with its own page at ~500 pages in 2 sec. 50+ formats. Zero cloud dependency. Already powering agents in Claude Code, Cursor, and production pipelines. In a few days out head of OSS, Logan Markewich, is hosting a live workshop: build a fintech due diligence agent with LiteParse →
LlamaIndex 🦙85,279 次观看 • 4 个月前
Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level navigation working together in a single agent reasoning loop. 🦙🌤️ Grep a file, list what's in an index, read past a chunk boundary, run hybrid search with reranking — all as native agent tools. Now in beta across all paid tiers. Full breakdown in the blog 👇 Learn More:
LlamaIndex 🦙29,181 次观看 • 2 个月前
Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without having use CLIs or open the browser? We built exactly that. Using Tauria Penny ps, with a Rust backend powered by LiteParse and a React frontend, we created a cross-platform desktop app that lets you: • Drag and drop PDFs • Convert them into clean Markdown • Preview page screenshots with extracted bounding boxes overlaid for easy inspection It's a simple way to understand your document's structure and verify the parsing results before handing them off to an LLM. Check out the demo below! 👇 GitHub: Get started with LiteParse:
LlamaIndex 🦙21,173 次观看 • 1 个月前
LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo 📖 Read the docs: 🚀 Get started with LlamaCloud:
LlamaIndex 🦙51,845 次观看 • 5 个月前
Parsing a document accurately is one thing. Proving where every value came from is another. When a compliance team reviews an AI extraction, or an auditor needs to sign off on a figure pulled from a financial filing, "it came from this document" isn't enough. They need to see exactly where. The specific cell in the table, the exact line on the page, the precise word the agent used. Most parsers can get you to a paragraph or a table block. That's where the trail ends. Today we're shipping Granular Bounding Boxes in LlamaParse — word, line, and cell level coordinates for every value in your document. The result is a complete, verifiable trail from every extracted value back to its exact source in the document. Built for audit workflows, compliance review, and any pipeline where verification isn't optional. Read the full announcement →
LlamaIndex 🦙29,090 次观看 • 2 个月前
Check out the form-filling agent that automates PDF forms using AI by Jerry Liu 📄🤖 Use any fillable PDF with an agent that fills it out based on your prompts and context files. Our new experiment creates a multi-turn chat experience for form completion. 🔍 Upload fillable PDFs and automatically detect form fields using PyMuPDF 📝 Add custom prompts and context files (parsed via LlamaParse) to guide the AI 🤖 Multi-turn conversations let you refine and correct form entries after initial completion 💾 Download your completed forms when done The agent uses simple tools to list, set, get, and validate form fields. You can chat with it to make corrections and adjustments until your form is perfect. Check out the code on GitHub: Or the deployed app here:
LlamaIndex 🦙55,131 次观看 • 7 个月前
Built a vibe-coded presentation app generator that turns natural language into polished slides ✨ This project by Jerry Liu combines the Claude Agent SDK with LlamaParse to create an AI-powered presentation tool that handles everything from content creation to PDF export: 🎯 Chat-based slide creation - just describe what you want and watch slides appear ✏️ Real-time editing through natural conversation - refine slides by chatting with the AI 📄 Smart document parsing with LlamaParse for incorporating reference materials 📊 Full export functionality to PowerPoint and PDF formats Perfect example of how LlamaParse makes document processing seamless while Claude's conversational abilities create an intuitive slide editing experience. Check out the full project:
LlamaIndex 🦙50,184 次观看 • 7 个月前
We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:
LlamaIndex 🦙143,178 次观看 • 2 年前
Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:
LlamaIndex 🦙141,258 次观看 • 2 年前
Introducing RAGApp 💫 A no-code interface to configure a RAG chatbot, as dead-simple as GPTs by OpenAI. It’s a docker container that’s easily deployable in any cloud infrastructure. Best of all, it’s fully open-source 🔥 1️⃣ Setup the LLM: Configure the model provider (OpenAI, Gemini) 2️⃣ Setup the data: Define the system prompt and upload your knowledge base. 3️⃣ Launch the chatbot both via the UI or API 4️⃣ If via the UI, stream intermediate events and also sources! This is fantastic work by Marcus Schiesser and is built upon the same DNA as our create-llama project. Check out RAGApp today:
LlamaIndex 🦙123,977 次观看 • 2 年前
🚀 The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: 💻 GitHub: ⚡ Try LlamaParse:
LlamaIndex 🦙34,945 次观看 • 5 个月前
🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
LlamaIndex 🦙21,975 次观看 • 3 个月前
We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:
LlamaIndex 🦙124,202 次观看 • 2 年前
Today we’re excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application 🎨🤖 without writing a single line of code 💫 Add any number of agents that you wish, and assign each agent a role, system prompt, and a set of tools. In this example, use a researcher, analyst, and report generation agent to write a news article. This directly generates a full chat interface where you can ask questions and get back answers with full streaming and sources. Huge shoutout to Marcus Schiesser for working on this! RAGApp: create-llama: If you want finer control, you can define your own agentic workflows through code:
LlamaIndex 🦙93,022 次观看 • 1 年前
Let's talk parsing tables. Two days ago we launched ParseBench,the first document OCR benchmark built for AI agents. This deep dive breaks down TableRecordMatch (GTRM), our metric for evaluating complex tables the way your pipeline actually consumes them: as records keyed by column headers.
LlamaIndex 🦙25,999 次观看 • 4 个月前
We're listening 👂LlamaSheets is in beta and we want your feedback Spreadsheets in the wild are messy—merged cells, broken layouts, headers spanning multiple rows. LlamaSheets (now in beta) extracts regions and tables from these files and outputs clean Parquet files you can actually use. What it does: · Identifies and isolates regions in your spreadsheet · Extracts them as Parquet files (load directly into pandas/polars/DuckDB) · Generates cell-level metadata (40+ features: formatting, position, data types) · Creates titles and descriptions for sheets and regions Built for the spreadsheets nobody wants to deal with manually. We need your feedback. While in beta and actively improving based on real-world use cases. Try it out and let us know what works, what doesn't, and what you need. Get started here:
LlamaIndex 🦙35,693 次观看 • 8 个月前
Parsing documents with AI agents just got a lot more seamless🚀 We've rebuilt the LlamaParse MCP server to handle your document processing workflows, and you can connect it today to any MCP-compatible client at 🌐 Once connected, you'll be able to: 📁 Parse documents into clean markdown 🔍 Classify files against your own categories ✂️ Split long documents into labelled sections ⬆️ Upload files via URL or a browser-based upload flow Building a production MCP server surfaced some non-obvious challenges: getting auth to align with an existing platform identity system using WorkOS, working around MCP's lack of built-in file upload support, and making deployments, rate limiting and observability feel native with Vercel and . We wrote up all of it, from the OAuth flow, to the token-based upload design, to the tradeoffs we hit along the way📝 📚 Read the full blog: 👩💻 GitHub repository:
LlamaIndex 🦙19,906 次观看 • 4 个月前
Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:
LlamaIndex 🦙76,371 次观看 • 2 年前