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Let's talk parsing charts 📊📈. Last week we released ParseBench, the first document OCR benchmark for AI agents. New in ParseBench: ChartDataPointMatch. Most document look at a chart and OCR the caption. Agents need the actual numbers. That's the gap between "OCR'd the text around the chart" and "actually... show more

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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.

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 görüntüleme • 6 ay önce

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:

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

108,403 görüntüleme • 6 ay önce

It's raining OCR models again! This time, OpenBMB released MiniCPM-OCR, a 1B-parameter model for document parsing I made a Hugging Face Space so you can compare its outputs against Baidu's new Unlimited OCR v2 Find the link below

It's raining OCR models again! This time, OpenBMB released MiniCPM-OCR, a 1B-parameter model for document parsing I made a Hugging Face Space so you can compare its outputs against Baidu's new Unlimited OCR v2 Find the link below

Niels Rogge

26,661 görüntüleme • 3 gün önce

Document OCR benchmarks are still an open problem Existing document OCR benchmarks are either too narrowly focused on a specific type (e.g. FinTabNet, ChartQA), or on documents that aren’t reflective of real-world tasks (e.g. OmniDocBench, OlmOCR-bench on over academic papers) ParseBench is a step towards solving this problem. * It tries to comprehensively cover real-world document distributions within the enterprise. * It contains comprehensive evaluations across 5 different dimensions (tables, charts, content faithfulness, formatting, grounding). * It tries to use metrics that optimize for agent semantic understanding rather than structural similarity. We released this yesterday, and there’s a TON of content: 1. Whitepaper 2. HF dataset 3. Github repo 4. Blog 5. Video And today, we’re excited to feature our home page website for ParseBench 💫 come check it out! Take a look at some of our other materials if you’re interested: Blog: Paper:

Document OCR benchmarks are still an open problem Existing document OCR benchmarks are either too narrowly focused on a specific type (e.g. FinTabNet, ChartQA), or on documents that aren’t reflective of real-world tasks (e.g. OmniDocBench, OlmOCR-bench on over academic papers) ParseBench is a step towards solving this problem. * It tries to comprehensively cover real-world document distributions within the enterprise. * It contains comprehensive evaluations across 5 different dimensions (tables, charts, content faithfulness, formatting, grounding). * It tries to use metrics that optimize for agent semantic understanding rather than structural similarity. We released this yesterday, and there’s a TON of content: 1. Whitepaper 2. HF dataset 3. Github repo 4. Blog 5. Video And today, we’re excited to feature our home page website for ParseBench 💫 come check it out! Take a look at some of our other materials if you’re interested: Blog: Paper:

Jerry Liu

21,657 görüntüleme • 6 ay önce

Most AI OCR tools can extract text from a document. But here's the real problem: How do you know if the extracted information is actually correct? OpenBMB just introduced something interesting with MiniCPM-OCR. It combines two specialized AI models: → MiniCPM-OCR 1.0: A compact 1.3B-parameter model that parses scanned documents, complex tables, formulas, and even tilted or curved pages. → MiniCPM-OCR Jev: A dedicated AI quality inspector that evaluates OCR outputs and helps identify potential errors before human review. And the official benchmarks are impressive: • 93.68 on Real5-OmniDocBench. • OpenBMB reports a 55% improvement in end-to-end processing efficiency with DSpark acceleration under its benchmark configuration. • Jev achieves an AUROC of 0.936 on JevOCRBench, approaching Qwen3.5-397B-A17B at 0.938. The interesting part isn't just extracting information faster. It's building a workflow where AI can parse documents and help determine which results need a second look. Jev doesn't automatically fix mistakes, but it can help make document-processing workflows more reliable. Open-source models, practical deployment options, and an interesting direction for document AI. Try the official global demo: GitHub: Hugging Face: -MiniCPM-OCR 1.0： -MiniCPM-OCR Jev： #MiniCPM #OCR #OpenSourceAI

Most AI OCR tools can extract text from a document. But here's the real problem: How do you know if the extracted information is actually correct? OpenBMB just introduced something interesting with MiniCPM-OCR. It combines two specialized AI models: → MiniCPM-OCR 1.0: A compact 1.3B-parameter model that parses scanned documents, complex tables, formulas, and even tilted or curved pages. → MiniCPM-OCR Jev: A dedicated AI quality inspector that evaluates OCR outputs and helps identify potential errors before human review. And the official benchmarks are impressive: • 93.68 on Real5-OmniDocBench. • OpenBMB reports a 55% improvement in end-to-end processing efficiency with DSpark acceleration under its benchmark configuration. • Jev achieves an AUROC of 0.936 on JevOCRBench, approaching Qwen3.5-397B-A17B at 0.938. The interesting part isn't just extracting information faster. It's building a workflow where AI can parse documents and help determine which results need a second look. Jev doesn't automatically fix mistakes, but it can help make document-processing workflows more reliable. Open-source models, practical deployment options, and an interesting direction for document AI. Try the official global demo: GitHub: Hugging Face: -MiniCPM-OCR 1.0： -MiniCPM-OCR Jev： #MiniCPM #OCR #OpenSourceAI

Andy Marlow

26,329 görüntüleme • 5 gün önce

Announcing: Agentic Document Extraction! PDF files represent information visually - via layout, charts, graphs, etc. - and are more than just text. Unlike traditional OCR and most PDF-to-text approaches, which focus on extracting the text, an agentic approach lets us break a document down into components and reason about them, resulting in more accurate extraction of the underlying meaning for RAG and other applications. Watch the video for details.

Announcing: Agentic Document Extraction! PDF files represent information visually - via layout, charts, graphs, etc. - and are more than just text. Unlike traditional OCR and most PDF-to-text approaches, which focus on extracting the text, an agentic approach lets us break a document down into components and reason about them, resulting in more accurate extraction of the underlying meaning for RAG and other applications. Watch the video for details.

Andrew Ng

689,842 görüntüleme • 1 yıl önce

i'm flattered by the shoutout, but there's no free lunch for document parsing Cohere parse starts at $1.50 / 1k pages, which is indeed on the cheaper end of doc OCR solutions, but it scores ~50% when averaged across all 5 ParseBench dimensions: * It lacks high-quality visual grounding, doing coarse segmentation for tables and images, but lacking finer-grained bboxes at the word/cell/line-level. this is quite important for any sensitive AI application that requires precise citations back to the source. * It lacks general visual / chart parsing capabilities, which are prevalent across finance, manufacturing, healthcare, and more * to its credit, it does a decent job over tables (87%) compared to other comparable solutions at the price point our cost-effective mode in LlamaParse starts at 0.375c / page and is more consistently strong across all dimensions. also there's various ways to bring it down with volume discounts if you're interested in learning more about various document parsing modes and comparing tradeoffs in price/accuracy, come talk to us! LlamaParse:

i'm flattered by the shoutout, but there's no free lunch for document parsing Cohere parse starts at $1.50 / 1k pages, which is indeed on the cheaper end of doc OCR solutions, but it scores ~50% when averaged across all 5 ParseBench dimensions: * It lacks high-quality visual grounding, doing coarse segmentation for tables and images, but lacking finer-grained bboxes at the word/cell/line-level. this is quite important for any sensitive AI application that requires precise citations back to the source. * It lacks general visual / chart parsing capabilities, which are prevalent across finance, manufacturing, healthcare, and more * to its credit, it does a decent job over tables (87%) compared to other comparable solutions at the price point our cost-effective mode in LlamaParse starts at 0.375c / page and is more consistently strong across all dimensions. also there's various ways to bring it down with volume discounts if you're interested in learning more about various document parsing modes and comparing tradeoffs in price/accuracy, come talk to us! LlamaParse:

Jerry Liu

25,896 görüntüleme • 1 ay önce

Jev as a reviewer for OCR! OpenBMB and ModelBest just released MiniCPM-OCR 1.0 for document parsing and MiniCPM-OCR Jev for reviewing the reliability of those outputs. That second part is what I find more interesting. OCR errors are often hard to catch once the output looks plausible. A number can be slightly wrong, a formula can be misread, or a table cell can shift, and the result can still look completely reasonable when it moves into the next step of the workflow. MiniCPM-OCR Jev sits after the parser and evaluates how reliable those outputs are across text, formulas, and tables. It does not rewrite or correct the OCR result. Its job is to score the output and help decide which blocks can move forward and which ones should be sent for human review. So the workflow becomes: Document → Layout detection → OCR parser → Jev review → Human review where needed On the parsing side, MiniCPM-OCR 1.0 is a 1.3B model that supports scanned documents, photographed pages, curved inputs, tilted documents, tables, formulas, and key information extraction. OpenBMB reports 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, while DSpark improves end-to-end throughput by 62% in their benchmark setup. On the review side, MiniCPM-OCR Jev reaches 93.5 AUROC on their JevOCRBench, close to Qwen3.5-397B-A17B at 93.8 and above JEV-35B at 90.1 in the official evaluation. The useful part here is that you no longer have to treat the whole OCR output as equally reliable. You can review it block by block and route only the uncertain parts for another look. I tested both models on my own documents to see how well Jev catches OCR outputs that look right but are actually wrong. I’ve shared the GitHub repo below!

Jev as a reviewer for OCR! OpenBMB and ModelBest just released MiniCPM-OCR 1.0 for document parsing and MiniCPM-OCR Jev for reviewing the reliability of those outputs. That second part is what I find more interesting. OCR errors are often hard to catch once the output looks plausible. A number can be slightly wrong, a formula can be misread, or a table cell can shift, and the result can still look completely reasonable when it moves into the next step of the workflow. MiniCPM-OCR Jev sits after the parser and evaluates how reliable those outputs are across text, formulas, and tables. It does not rewrite or correct the OCR result. Its job is to score the output and help decide which blocks can move forward and which ones should be sent for human review. So the workflow becomes: Document → Layout detection → OCR parser → Jev review → Human review where needed On the parsing side, MiniCPM-OCR 1.0 is a 1.3B model that supports scanned documents, photographed pages, curved inputs, tilted documents, tables, formulas, and key information extraction. OpenBMB reports 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, while DSpark improves end-to-end throughput by 62% in their benchmark setup. On the review side, MiniCPM-OCR Jev reaches 93.5 AUROC on their JevOCRBench, close to Qwen3.5-397B-A17B at 93.8 and above JEV-35B at 90.1 in the official evaluation. The useful part here is that you no longer have to treat the whole OCR output as equally reliable. You can review it block by block and route only the uncertain parts for another look. I tested both models on my own documents to see how well Jev catches OCR outputs that look right but are actually wrong. I’ve shared the GitHub repo below!

Sumanth

21,117 görüntüleme • 6 gün önce

Should you rely on public benchmark scores when choosing a model? Vik Paruchuri argues "no" in this talk. To illustrate this, he shows specific problems found in popular OCR benchmarks: ParseBench: • A test expects 310 gigawatts of solar-panel capacity when the correct value is 303. Additionally, 73% of its chart tests ask for values that aren't printed on the chart. (4:51) • A test requires "Parish Priest:" to be bold even though those words aren't bold in the source document. (15:33) • The scorer fails to recognize heading tags placed on separate lines. (16:15) • dots.mocr ranks last among six models even though it beats the first-ranked model on content and tables. This is an example of how overall benchmark score can hide important differences in model capabilities. (14:48) olmOCR-bench: • A model correctly preserves a line break in a document and gets penalized for it. (10:30) • A test expects "Republican Party" when a document says "Republican party". Copying the capitalization correctly makes the model fail. (10:49) • A 700-line postprocessor that rewrites model output adds 1 to 2 points, more than many gaps between models. (12:12) OmniDocBench: • This benchmark uses edit distance, which penalizes a correct table written in HTML more heavily than a wrong number in Markdown. So the format matters more than the answer! (6:58) This is just the tip of the iceberg, the video contains more examples. Chapters: 0:00 Choosing a model when every vendor claims to be #1 1:54 How OCR benchmark scores improve without better OCR 5:30 Metric design and tests that reject correct answers 11:33 Postprocessing, saturation, and misleading model rankings 12:40 How to slice benchmarks by your task and check for bias 16:53 How to build fair tests with verified answers and consistent scoring 19:05 How to test OCR models on your own documents 24:10 Benchmark marketing, private evals, and how to choose a model

Should you rely on public benchmark scores when choosing a model? Vik Paruchuri argues "no" in this talk. To illustrate this, he shows specific problems found in popular OCR benchmarks: ParseBench: • A test expects 310 gigawatts of solar-panel capacity when the correct value is 303. Additionally, 73% of its chart tests ask for values that aren't printed on the chart. (4:51) • A test requires "Parish Priest:" to be bold even though those words aren't bold in the source document. (15:33) • The scorer fails to recognize heading tags placed on separate lines. (16:15) • dots.mocr ranks last among six models even though it beats the first-ranked model on content and tables. This is an example of how overall benchmark score can hide important differences in model capabilities. (14:48) olmOCR-bench: • A model correctly preserves a line break in a document and gets penalized for it. (10:30) • A test expects "Republican Party" when a document says "Republican party". Copying the capitalization correctly makes the model fail. (10:49) • A 700-line postprocessor that rewrites model output adds 1 to 2 points, more than many gaps between models. (12:12) OmniDocBench: • This benchmark uses edit distance, which penalizes a correct table written in HTML more heavily than a wrong number in Markdown. So the format matters more than the answer! (6:58) This is just the tip of the iceberg, the video contains more examples. Chapters: 0:00 Choosing a model when every vendor claims to be #1 1:54 How OCR benchmark scores improve without better OCR 5:30 Metric design and tests that reject correct answers 11:33 Postprocessing, saturation, and misleading model rankings 12:40 How to slice benchmarks by your task and check for bias 16:53 How to build fair tests with verified answers and consistent scoring 19:05 How to test OCR models on your own documents 24:10 Benchmark marketing, private evals, and how to choose a model

Hamel Husain

10,612 görüntüleme • 2 gün önce

The first chart usually raises the next question. Ask it in the same chat and /visualize answers with a new chart.

The first chart usually raises the next question. Ask it in the same chat and /visualize answers with a new chart.

Cursor

83,464 görüntüleme • 27 gün önce

OCR can process characters but it doesn’t understand pixels. OCR has no way to reason about the headers, totals, or checkboxes found in tables, invoices, or forms. In our course with LandingAI, "Document AI: From OCR to Agentic Doc Extraction," we build agents to address these failure modes by breaking documents into pieces, applying the right tools, and mapping information to expected formats. Learn more and enroll today:

OCR can process characters but it doesn’t understand pixels. OCR has no way to reason about the headers, totals, or checkboxes found in tables, invoices, or forms. In our course with LandingAI, "Document AI: From OCR to Agentic Doc Extraction," we build agents to address these failure modes by breaking documents into pieces, applying the right tools, and mapping information to expected formats. Learn more and enroll today:

DeepLearning.AI

21,554 görüntüleme • 9 ay önce

🎬 MiniCPM-OCR in action — running locally on iPhone Duo and iPad. Parse fast and accurately. Guard the results. All in your hands. 🙌 Handwritten notes, complex formulas, newspapers, poor-quality scans—real documents put OCR to the test. 📄 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. It achieves 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, combining accurate document parsing with efficient inference. 🛡️ MiniCPM-OCR Jev — A 2B decision model that checks OCR accuracy. It approaches Qwen3.5-397B-A17B on JevOCRBench and works with outputs from different OCR parsers. Watch it compare parsed text, formulas, and tables with the original image and assign 0–1 correctness scores—helping you pick better results and identify what needs human review. 🔓 Open weights. Deploy your way. Run with llama.cpp, vLLM, SGLang, or Ollama. Customize with open-source fine-tuning code. Watch it in action. 👇 🔗 GitHub: 🤗 Hugging Face: 👉 Try the Demo： Use the API：

🎬 MiniCPM-OCR in action — running locally on iPhone Duo and iPad. Parse fast and accurately. Guard the results. All in your hands. 🙌 Handwritten notes, complex formulas, newspapers, poor-quality scans—real documents put OCR to the test. 📄 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. It achieves 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, combining accurate document parsing with efficient inference. 🛡️ MiniCPM-OCR Jev — A 2B decision model that checks OCR accuracy. It approaches Qwen3.5-397B-A17B on JevOCRBench and works with outputs from different OCR parsers. Watch it compare parsed text, formulas, and tables with the original image and assign 0–1 correctness scores—helping you pick better results and identify what needs human review. 🔓 Open weights. Deploy your way. Run with llama.cpp, vLLM, SGLang, or Ollama. Customize with open-source fine-tuning code. Watch it in action. 👇 🔗 GitHub: 🤗 Hugging Face: 👉 Try the Demo： Use the API：

OpenBMB

344,220 görüntüleme • 7 gün önce

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:

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 görüntüleme • 7 ay önce

Our core mission today is using AI to solve document OCR. All of our product offerings, from commercial (LlamaParse) to open-source (LiteParse, ParseBench), are fully aligned towards solving this problem. Introducing our revamped website 👇

Our core mission today is using AI to solve document OCR. All of our product offerings, from commercial (LlamaParse) to open-source (LiteParse, ParseBench), are fully aligned towards solving this problem. Introducing our revamped website 👇

Jerry Liu

26,905 görüntüleme • 6 ay önce

From recognizing text to understanding documents. 📄 Meet Xiaomi-OCR-0, an open-source document understanding model with just 0.8B parameters. It brings document parsing, key information extraction, and OCR-oriented visual question answering into one lightweight model. It scores 95.24 on Real5-OmniDocBench and 83.2 on average across five OCR-oriented VQA benchmarks. On OmniDocBench v1.6, Xiaomi-OCR-0 ranks first in document parsing performance among models with fewer than 1B parameters. Model weights, inference code, an interactive demo, MCP service, and Agent Skill are now open source. 👇 🤗 Huggingface: 🔗 GitHub: 📄 Demo: #XiaomiAI #OCR #OpenSource

From recognizing text to understanding documents. 📄 Meet Xiaomi-OCR-0, an open-source document understanding model with just 0.8B parameters. It brings document parsing, key information extraction, and OCR-oriented visual question answering into one lightweight model. It scores 95.24 on Real5-OmniDocBench and 83.2 on average across five OCR-oriented VQA benchmarks. On OmniDocBench v1.6, Xiaomi-OCR-0 ranks first in document parsing performance among models with fewer than 1B parameters. Model weights, inference code, an interactive demo, MCP service, and Agent Skill are now open source. 👇 🤗 Huggingface: 🔗 GitHub: 📄 Demo: #XiaomiAI #OCR #OpenSource

Xiaomi Tech

150,715 görüntüleme • 5 gün önce

hongjoong about ‘bad’ entered the melon chart🥺 🐿️ today was a ver special day for ateez. 🧑🏻 oh really? 🐿️ at the last chorus part, there’s san’s choreography. that part was hugely vital in korea these days. and after we debuted, this is the first time our song entered the melon chart. 🧑🏻 oh really? 🐿️ yeah, for the first time. 🧑🏻 i see. 🐿️ we always talk in our group, “it’s really hard to get on the melon chart, even more than billboard chart.” 🧑🏻 i mean only ateez, no other group would think that the melon chart is harder. it’s funny.. 🐿️ yeah… but.. luckily and also with everyone’s effort, it's very worth it that entered a huge korean chart for the first time. 🧑🏻 right. 🐿️ and also we got many good results these days with new song. i’m so happy. at the same time i have many thoughts about how and where to go for the next step 🧑🏻 i see.

hongjoong about ‘bad’ entered the melon chart🥺 🐿️ today was a ver special day for ateez. 🧑🏻 oh really? 🐿️ at the last chorus part, there’s san’s choreography. that part was hugely vital in korea these days. and after we debuted, this is the first time our song entered the melon chart. 🧑🏻 oh really? 🐿️ yeah, for the first time. 🧑🏻 i see. 🐿️ we always talk in our group, “it’s really hard to get on the melon chart, even more than billboard chart.” 🧑🏻 i mean only ateez, no other group would think that the melon chart is harder. it’s funny.. 🐿️ yeah… but.. luckily and also with everyone’s effort, it's very worth it that entered a huge korean chart for the first time. 🧑🏻 right. 🐿️ and also we got many good results these days with new song. i’m so happy. at the same time i have many thoughts about how and where to go for the next step 🧑🏻 i see.

Irene | AhgaTiny

57,975 görüntüleme • 3 ay önce

.jlo’s “On The Floor" re-enters the Official UK Singles Chart at #41 this week for the first time in 15 years. — The song originally peaked at #1 in 2011, and became her third #1 song on the chart.

.jlo’s “On The Floor" re-enters the Official UK Singles Chart at #41 this week for the first time in 15 years. — The song originally peaked at #1 in 2011, and became her third #1 song on the chart.

ᴛʜᴇ ʟᴇɢᴇɴᴅᴀʀʏ ᴊʟᴏ

17,583 görüntüleme • 5 ay önce

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:

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 🦙

143,198 görüntüleme • 2 yıl önce

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:

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 görüntüleme • 7 ay önce