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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...

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LlamaIndex 🦙

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143,136 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 10

Фото профиля Manish Sinha
Manish Sinha2 лет назад

Charts ( bar graph) and images ( of math formulae’s) don’t work still. Text and tables work well ( especially tables accuracy and markdown format). Will test more elaborately later.

Фото профиля DataStax
DataStax2 лет назад

Congrats @llama_index team! LlamaParse is 🔥🔥🔥

Фото профиля matthew muller
matthew muller2 лет назад

Is there a data privacy statement for llamacloud? I cant seem to find that... thx!

Фото профиля Duc Nguyen Huu
Duc Nguyen Huu2 лет назад

I have a question on information security when using llama-parse. For example, I have pdf files and I want to use llama-parse for parsing. My concern is whether utilizing llama-parse through API access poses any potential risks of information leakage.

Фото профиля Andrew Batutin ☸️/acc
Andrew Batutin ☸️/acc2 лет назад

Is this price on top of Claude 3 pricing? Or it’s for all including llm calls?

Фото профиля LlamaIndex 🦙
LlamaIndex 🦙2 лет назад

That's the full price!

Фото профиля nohrt
nohrt2 лет назад

Great stuff!! good job :) Now if you could fix the issue of auto rotating pages i would be able to use llama parse :P

Фото профиля RM
RM2 лет назад

Here's my article. Please upvote!

Фото профиля Art Intelligence
Art Intelligence2 лет назад

It doesn’t seem to work well with chinese documents and papers. It is also lacking the feature of translating text.

Фото профиля LLL
LLL2 лет назад

mathpix is excellent at parsing academic pdfs, did you have a comparison with them when you mentioned "better virtually every other parsers"?

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An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!
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An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 просмотров • 1 год назад

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:
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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 просмотров • 3 месяцев назад

Our default document parsing mode is now able to parse a complex research report with multiple embedded charts on a single page. This is the cheapest document OCR model out there that can turn complex visual documents into LLM-ready markdown. This is our agentic mode in LlamaParse. It starts at ~1c per page; if you’re looking to scale consumption we offer volume discounts. Come check it out! Sign up:
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Our default document parsing mode is now able to parse a complex research report with multiple embedded charts on a single page. This is the cheapest document OCR model out there that can turn complex visual documents into LLM-ready markdown. This is our agentic mode in LlamaParse. It starts at ~1c per page; if you’re looking to scale consumption we offer volume discounts. Come check it out! Sign up:

Jerry Liu

15,479 просмотров • 5 месяцев назад

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 POC 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 🌐:
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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 POC 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 🦙

141,236 просмотров • 2 лет назад

LiteParse Samples 📄🧑‍🏫 LiteParse is our free and fast document parser that can parse text with bounding boxes from any document. I’ve created a new repository that contains some demos on how you can make use of its outputs! ✅ Comparison against PyPDF and PyMuPDF ✅ Visual Citations: search for any keyword and see joined bounding boxes pop up in the source images It should be a default tool to help AI agents OCR any document type (supports PDF, Word, Powerpoint + dozens of other formats), before heavier-weight VLM-based document parsing solutions (like our own LlamaParse). LiteParse Samples repo: LiteParse:
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LiteParse Samples 📄🧑‍🏫 LiteParse is our free and fast document parser that can parse text with bounding boxes from any document. I’ve created a new repository that contains some demos on how you can make use of its outputs! ✅ Comparison against PyPDF and PyMuPDF ✅ Visual Citations: search for any keyword and see joined bounding boxes pop up in the source images It should be a default tool to help AI agents OCR any document type (supports PDF, Word, Powerpoint + dozens of other formats), before heavier-weight VLM-based document parsing solutions (like our own LlamaParse). LiteParse Samples repo: LiteParse:

Jerry Liu

13,132 просмотров • 3 месяцев назад

Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:
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Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Jerry Liu

256,399 просмотров • 3 месяцев назад

We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!
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We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!

Jerry Liu

20,857 просмотров • 4 месяцев назад

OpenRead is now open for PDF uploading and parsing, check it out! PDF upload parsing is one of the most desired features of our users, and now we can finally bring this futuristic technology to life. We have adopted the most advanced #NLP, #CV, #OCR technology, which can extract the text, formula, table, and figure in PDF to the greatest extent, and re-typeset these elements.
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OpenRead is now open for PDF uploading and parsing, check it out! PDF upload parsing is one of the most desired features of our users, and now we can finally bring this futuristic technology to life. We have adopted the most advanced #NLP, #CV, #OCR technology, which can extract the text, formula, table, and figure in PDF to the greatest extent, and re-typeset these elements.

OpenRead

26,656 просмотров • 3 лет назад

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:
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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 просмотров • 5 месяцев назад

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:
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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 🦙 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 🦙

581,217 просмотров • 3 месяцев назад

Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher accuracy than NotebookLM since it’s using LlamaCloud under the hood for high-quality parsing/extraction over complex docs. ✅ Generate summaries and knowledge graph mind-maps 🤯 ✅ Generate podcasts thanks to ElevenLabs 🗣️ ✅ Agentic chat with docs and view metrics with OpenTelemetry This all lives within an open-source repo so you can clone/modify at will to swap in your own components! Huge shoutout to Clelia Bertelli (🦙/acc) for leading this. Repo: LlamaCloud helps power the parsing/ingestion. You can always use your own stuff too, but in the meantime you can check out LlamaCloud here:
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Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher accuracy than NotebookLM since it’s using LlamaCloud under the hood for high-quality parsing/extraction over complex docs. ✅ Generate summaries and knowledge graph mind-maps 🤯 ✅ Generate podcasts thanks to ElevenLabs 🗣️ ✅ Agentic chat with docs and view metrics with OpenTelemetry This all lives within an open-source repo so you can clone/modify at will to swap in your own components! Huge shoutout to Clelia Bertelli (🦙/acc) for leading this. Repo: LlamaCloud helps power the parsing/ingestion. You can always use your own stuff too, but in the meantime you can check out LlamaCloud here:

Jerry Liu

137,768 просмотров • 1 год назад

As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It is extremely easy for agents to cite an entire document or document page, but much harder for them to trace back to the exact numbers/words/figures within a page. Today we've launched granular bounding boxes within LlamaParse, which allows you to obtain visual citations of every single word in the document. This allows human users to audit exact words and figures - not just general document regions or entire pages! Come check it out:
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As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It is extremely easy for agents to cite an entire document or document page, but much harder for them to trace back to the exact numbers/words/figures within a page. Today we've launched granular bounding boxes within LlamaParse, which allows you to obtain visual citations of every single word in the document. This allows human users to audit exact words and figures - not just general document regions or entire pages! Come check it out:

Jerry Liu

23,446 просмотров • 1 месяц назад

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark that measures parsing quality specifically for agent knowledge work: ✅ It optimizes for semantic correctness (instead of exact similarity) ✅ It has the most comprehensive distribution of real-world enterprise documents It contains ~2,000 human-verified enterprise document pages with 167,000+ test rules across five dimensions that matter most: tables, charts, content faithfulness, semantic formatting, and visual grounding. We benchmarked 14 known document parsers on ParseBench, from frontier/OSS VLMs to specialized parsers to LlamaParse. Here are some of our findings: 💡 Increasing compute budget yields diminishing returns - Gemini/gpt-5-mini/haiku gain 3-5 points from minimal to high thinking, at 4x the cost. 💡 Charts are the most polarizing dimension for evaluation. Most specialized parsers score below 6%, while some VLM-based parsers do a bit better. 💡 VLMs are great at visual understanding but terrible at layout extraction. GPT-5-mini/haiku score below 10% on our visual grounding task, all specialized parsers do much better. 💡 No method crushes all 5 dimensions at once, but LlamaParse achieves the highest overall score at 84.9%, and is the leader in 4 out of the 5 dimensions. This is by far the deepest technical work that we’ve published as a company. I would encourage you to start with our blog and explore our links to Hugging Face to GitHub. All the details are in our full 35-page (!!) ArXiv whitepaper. 🌐: Blog: 📄 Paper: 💻 Code: 📊 Dataset: 🎥 YouTube:
1:08

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark that measures parsing quality specifically for agent knowledge work: ✅ It optimizes for semantic correctness (instead of exact similarity) ✅ It has the most comprehensive distribution of real-world enterprise documents It contains ~2,000 human-verified enterprise document pages with 167,000+ test rules across five dimensions that matter most: tables, charts, content faithfulness, semantic formatting, and visual grounding. We benchmarked 14 known document parsers on ParseBench, from frontier/OSS VLMs to specialized parsers to LlamaParse. Here are some of our findings: 💡 Increasing compute budget yields diminishing returns - Gemini/gpt-5-mini/haiku gain 3-5 points from minimal to high thinking, at 4x the cost. 💡 Charts are the most polarizing dimension for evaluation. Most specialized parsers score below 6%, while some VLM-based parsers do a bit better. 💡 VLMs are great at visual understanding but terrible at layout extraction. GPT-5-mini/haiku score below 10% on our visual grounding task, all specialized parsers do much better. 💡 No method crushes all 5 dimensions at once, but LlamaParse achieves the highest overall score at 84.9%, and is the leader in 4 out of the 5 dimensions. This is by far the deepest technical work that we’ve published as a company. I would encourage you to start with our blog and explore our links to Hugging Face to GitHub. All the details are in our full 35-page (!!) ArXiv whitepaper. 🌐: Blog: 📄 Paper: 💻 Code: 📊 Dataset: 🎥 YouTube:

Jerry Liu

107,866 просмотров • 3 месяцев назад

I’ve excited to announce a brand-new website and documentation hub 💫 that solidifies our evolution towards automating knowledge work over your documents. You might’ve followed us since the “RAG framework” days. Even then, the biggest challenge users faced was figuring out how to actually ingest an entire collection of unstructured docs (.pdf, .pptx, .docx, and more) for chatbot/agentic workflow use cases. Over the past year we’ve progressively built up incredibly deep tech around document parsing, extraction, and indexing - while teaching developers how to build various workflows on top. We’re now going all in on documents, and we’re the only company that has both 1) SOTA document processing and file management 📈, and 2) agentic orchestration on top to solve use cases like deep research, report generation, and document workflows end-to-end. Our llamas will continue to love all sorts of data (we have 600+ integrations on the open-source framework!), but they now especially love automating paperwork 🦙📄. If you would also love to automate paperwork, come check out our new website and come talk to us! Site: Developer Hub:
3:07

I’ve excited to announce a brand-new website and documentation hub 💫 that solidifies our evolution towards automating knowledge work over your documents. You might’ve followed us since the “RAG framework” days. Even then, the biggest challenge users faced was figuring out how to actually ingest an entire collection of unstructured docs (.pdf, .pptx, .docx, and more) for chatbot/agentic workflow use cases. Over the past year we’ve progressively built up incredibly deep tech around document parsing, extraction, and indexing - while teaching developers how to build various workflows on top. We’re now going all in on documents, and we’re the only company that has both 1) SOTA document processing and file management 📈, and 2) agentic orchestration on top to solve use cases like deep research, report generation, and document workflows end-to-end. Our llamas will continue to love all sorts of data (we have 600+ integrations on the open-source framework!), but they now especially love automating paperwork 🦙📄. If you would also love to automate paperwork, come check out our new website and come talk to us! Site: Developer Hub:

Jerry Liu

25,710 просмотров • 9 месяцев назад

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:
1:09

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:

Jerry Liu

46,748 просмотров • 8 месяцев назад

Today, we're introducing our document parser built specifically for RAG. The parser combines the best vision, OCR, and vision language models to deliver unmatched accuracy. Try it for free today—the first 500+ pages are on us! 🧵 1/
1:51

Today, we're introducing our document parser built specifically for RAG. The parser combines the best vision, OCR, and vision language models to deliver unmatched accuracy. Try it for free today—the first 500+ pages are on us! 🧵 1/

Douwe Kiela

1,308,593 просмотров • 1 год назад

Our document parsing is really good You can see for yourself with our new feature💫: convert complex PDFs with tables, charts, multi-column layouts to clean markdown/JSON representations through our new clickable templates! 1. We convert complex tables with gaps into clean markdown structures 2. We parse chart + line graphs into interpretable 2d tables Check out LlamaCloud: Come talk to us:
0:42

Our document parsing is really good You can see for yourself with our new feature💫: convert complex PDFs with tables, charts, multi-column layouts to clean markdown/JSON representations through our new clickable templates! 1. We convert complex tables with gaps into clean markdown structures 2. We parse chart + line graphs into interpretable 2d tables Check out LlamaCloud: Come talk to us:

Jerry Liu

18,029 просмотров • 4 месяцев назад

Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:
0:51

Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:

Databricks

19,428 просмотров • 2 месяцев назад

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:
2:52

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 просмотров • 2 лет назад