LlamaIndex ๐ฆ
@llama_index โข 119,096 subscribers
The most accurate agentic OCR platform for production AI. LlamaParse: https://t.co/yQGTiRSNvj Docs: https://t.co/us6GCS1Clb
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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 ๐ฆ 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 ๐ฆ581,748 Aufrufe โข vor 7 Monaten
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 ๐ฅ 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: ๐ Home Page : Have a โbuilder agentโ build your RAG agent through natural language (you specify the data). โ๏ธ RAG Config: Look at configured parameters ๐ค Use your RAG agent! Check out details below ๐ Blog: Repo:
LlamaIndex ๐ฆ475,732 Aufrufe โข vor 2 Jahren
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 โ
LlamaIndex ๐ฆ85,279 Aufrufe โข vor 6 Monaten
Introducing ๐๐ ๐๐ฟ๐ฎ๐ฐ๐๐๐ฒ๐ป๐ฐ๐ต: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested: 14 systems โ frontier VLMs, coding agents, extraction APIs โ on 370 enterprise docs, 4,869 pages, 67 doc types. Zero LLM judges, fully deterministic. Biggest finding: past 50 pages, commercial VLMs collapse below 35% recall. Precision stays high, but they silently drop most of the table rows. What is your extraction agent missing? Run ExtractBench to see today. Blog: GitHub: HuggingFace:
LlamaIndex ๐ฆ32,699 Aufrufe โข vor 2 Monaten
Last week we brought LlamaParse to Claude. Now itโs available in ChatGPT, too ๐ฆ ChatGPT can already read PDFs. But document work gets harder when the information you need is buried in messy scans, dense tables, spreadsheets, and charts. Strike the right balance between burning tokens and accuracy, with a specialized API for document understanding. Turn complex documents into clean, structured context and let Astra run its magic. Use our connector to: โ Parse documents into clean Markdown, JSON, or HTML. โ Extract specific fields into a schema you define. โ Search across document collections to find the information you need. โ Classify files and split documents into logical sections. Live in the ChatGPT plugin directory now! โ
LlamaIndex ๐ฆ17,601 Aufrufe โข vor 1 Monat
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. ๐ Watch the demo ๐ Read the docs: ๐ Get started with LlamaCloud:
LlamaIndex ๐ฆ51,845 Aufrufe โข vor 7 Monaten
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. ๐ฆ๐ค๏ธ Grep a file, list what's in an index, read past a chunk boundary, run hybrid search with reranking โ all as native agent tools. Now in beta across all paid tiers. Full breakdown in the blog ๐ Learn More:
LlamaIndex ๐ฆ29,181 Aufrufe โข vor 3 Monaten
Weโre excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts ๐๐ 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 ๐ฅ We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now itโs better than ever. LlamaParse contains the following killer features: โ SOTA table/chart extraction โ Seamless integration with LlamaIndex ๐ฆ advanced RAG/agents โ โจ Natural language Parsing Instructions โ โจJSON mode and image extraction โ โจSupport 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 ๐ฆโ๏ธ: Come talk to us:
LlamaIndex ๐ฆ143,198 Aufrufe โข vor 2 Jahren
Check out the form-filling agent that automates PDF forms using AI by Jerry Liu ๐๐ค 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. ๐ Upload fillable PDFs and automatically detect form fields using PyMuPDF ๐ Add custom prompts and context files (parsed via LlamaParse) to guide the AI ๐ค Multi-turn conversations let you refine and correct form entries after initial completion ๐พ 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 ๐ฆ55,131 Aufrufe โข vor 9 Monaten
Introducing LlamaCloud ๐ฆ๐ค๏ธ Today weโre 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: 1๏ธโฃ LlamaParse ๐: 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 arenโt possible with the naive stack. Available publicly day 1 ๐ฅ 2๏ธโฃ Managed Ingestion/Retrieval API โ๏ธ: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. Weโre excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex ๐ฆ 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 ๐:
LlamaIndex ๐ฆ141,289 Aufrufe โข vor 2 Jahren
LlamaParse is officially a verified connector on Claude! ๐ฆ Claude is perfectly fine for the occasional text-only PDF. But most of the context your agent needs lives in messy PDFs, spreadsheets, scanned forms, dense tables, and charts. Feeding those docs straight to the model burns through tokens and can lead to hallucinations when layouts get scrambled, chart values are missed, or numbers are pulled from the wrong column. LlamaParse converts those documents into clean, structured context Claude can work with. With the connector you can โ ๏ธ Parse documents into clean Markdown, JSON, or HTML โ ๏ธ Extract specific fields directly into your schema โ ๏ธ Search across document collections with filesystem-style tools โ ๏ธ Classify documents or split them into predefined sections Live in the Claude Connectors Directory now !
LlamaIndex ๐ฆ13,467 Aufrufe โข vor 1 Monat
Built a vibe-coded presentation app generator that turns natural language into polished slides โจ 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: ๐ฏ Chat-based slide creation - just describe what you want and watch slides appear โ๏ธ Real-time editing through natural conversation - refine slides by chatting with the AI ๐ Smart document parsing with LlamaParse for incorporating reference materials ๐ 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 ๐ฆ50,184 Aufrufe โข vor 8 Monaten
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 โ 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 โ
LlamaIndex ๐ฆ29,090 Aufrufe โข vor 4 Monaten
Introducing RAGApp ๐ซ A no-code interface to configure a RAG chatbot, as dead-simple as GPTs by OpenAI. Itโs a docker container thatโs easily deployable in any cloud infrastructure. Best of all, itโs fully open-source ๐ฅ 1๏ธโฃ Setup the LLM: Configure the model provider (OpenAI, Gemini) 2๏ธโฃ Setup the data: Define the system prompt and upload your knowledge base. 3๏ธโฃ Launch the chatbot both via the UI or API 4๏ธโฃ 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 ๐ฆ123,977 Aufrufe โข vor 2 Jahren
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: โข Drag and drop PDFs โข Convert them into clean Markdown โข 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! ๐ GitHub: Get started with LiteParse:
LlamaIndex ๐ฆ21,173 Aufrufe โข vor 3 Monaten
Weโre excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language ๐ฌ A huge upgrade vs. the initial launch: ๐ซ Easily create multiple RAG pipelines and save them ๐ซ Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) ๐ซ Delete unused RAG pipelines ๐ซ (dev quality) added much-needed linting/CI Check out the video ๐ฅ for details. Itโs super easy to setup and use. Some additional features: ๐ง Supports a lot of LLMs both for building RAG and within each RAG pipeline ๐ Supports loading load files or web pages. Check out our repo here:
LlamaIndex ๐ฆ124,202 Aufrufe โข vor 2 Jahren
Today weโre excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application ๐จ๐ค without writing a single line of code ๐ซ 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 ๐ฆ93,022 Aufrufe โข vor 2 Jahren
๐ The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. ๐ฉโ๐ป We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: ๐ฎ๐๐ฑ๐ถ๐ผ-๐ธ๐ฏ, a knowledge base for your audio notes. With audio-kb, you can: ๐น Upload an MP3 or record directly from your terminal ๐น LlamaParse extracts the transcript from the audio ๐น Gemini Embedding 2 generates embeddings ๐น Metadata + vectors are stored in SurrealDB and indexed with HNSW ๐ Once ingested, you can search all your audio notes directly from the terminal. ๐๏ธ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. ๐ Full blog: ๐ป GitHub: โก Try LlamaParse:
LlamaIndex ๐ฆ34,945 Aufrufe โข vor 7 Monaten
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 ๐ฆ25,999 Aufrufe โข vor 6 Monaten
We're listening ๐LlamaSheets is in beta and we want your feedback Spreadsheets in the wild are messyโmerged 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: ยท Identifies and isolates regions in your spreadsheet ยท Extracts them as Parquet files (load directly into pandas/polars/DuckDB) ยท Generates cell-level metadata (40+ features: formatting, position, data types) ยท 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 ๐ฆ35,693 Aufrufe โข vor 9 Monaten