DataEyesAI
home
home
  1. Document OCR Parsing API
  • ​​Quick Start
    • Overview
    • Authorization
    • Online Debugging
    • Integration Guide
  • API Reference​​
    • Error Codes
    • HTTP Notes
  • Search / Reader Product
    • Web Reader API​​
      • Web Reader API
      • Web Reader API(HK)
    • Web Search API​​
      • Modal Card API
        • Weather
          • All City ID
          • Weather Query API
      • Web Search API
      • Video Search api
      • Trending Search API
    • Document OCR Parsing API
      • fiel upload
        POST
      • URL Parsing
        POST
  • AI Models API
    • Base Url
    • Configuration Guide for Various Plugins
    • System interface
      • Query account information
    • List models
      • Models
    • OpenAI format (supports major original models)
      • Chat (Response)
        • Create Network Search
        • Create Model Response GPT-5 Enable Thinking
        • Create Function Call
        • Create Model Response
        • Create Model Response (Streaming Return)
        • Create Model Response (Control Thinking Length)
      • ChatGPT Interface
        • Audio
          • Audio to text gpt-4o-transcribe
          • GPT-4o-audio
          • Audio to text whisper-1
          • Audio to text gpt-4o-transcribe
          • Create voice gpt-4o-mini-tts
        • Chat
          • Create chat-based image recognition (non-streaming)
          • Create chat-based image recognition (streaming)
          • Create chat-based image recognition (streaming) best64
          • Official N test
          • Create structured output
          • Control the effort level of the inference model
          • Create chat function call
          • deepseek-ocr recognition
          • Create chat completion (non-stream)
        • Completions
          • ChatGPT automatic completion
          • Create completion
      • Image
        • Edit image
        • Create chat completion (streaming)
        • Create chat completion (qwen-mt-turbo)
        • Create chat completion with deepseek v3.1 level of reasoning (streaming)
      • Audio
        • Speech recognition
        • Speech synthesis
        • Official Function Calling invocation
        • Create chat-generated images (non-streaming)
      • Embedding
        • Text embeddings
    • Anthropic format
      • Chat
      • Chat(prompt cache)
      • Streaming response
      • Chat (deep reasoning)
      • Tool invocation (function call)
      • Analyze image
    • Google Gemini interface
      • Native format
        • Text-to-image + control over aspect ratio + clarity
        • Generate image
        • Text generation
        • Text generation - stream
        • Text generation + reasoning - stream
        • Image generation
        • Formatted output
        • Function call
        • Document understanding
        • URL context [native format]
        • Code execution
        • Video understanding
        • URL context
        • Video understanding - url [native format]
        • Imagen 4
        • Audio understanding
        • Embeddings
        • Chat
        • Edit image
      • Image-to-image Base64 request method
        • Multi-image fusion slice generation with gemini-3-pro-image-preview, controlling aspect ratio and clarity
        • Image editing
        • Single image gemini-3-pro-image-preview, controlling aspect ratio and clarity.
        • Image generation( gemini-2.5-flash-image)
        • Image generation gemini-2.5-flash-image, controlling aspect ratio.
        • Image understanding
      • Image-to-image URL request returns URL request format OpenAI
        • Single image generation with gemini-3-pro-image-preview, controlling aspect ratio and clarity.
        • Multi-image fusion slice generation with gemini-3-pro-image-preview, controlling aspect ratio and clarity.
        • Image understanding
    • NanoBanana
      • OpenAI request
        • Edit image
        • OpenAI image format
      • Gemini request
        • Generate image
        • Edit image
    • Midjourney format
      • Task query interface
      • Upload image
      • Get seed (Seed)
      • Submit Imagine task
      • Query tasks based on ID list
      • FaceSwap
      • Execute Action operation
      • /mj/submit/blend
      • Submit Describe task
      • Submit Modal
      • Refresh link
      • Edit image
      • Query task status by task ID
      • Get the seed of the task image
    • Doubao - Video Generation
      • Text-to-video example
      • Image-to-video example
      • Query a single task
    • Doubao - Painting
      • doubao-seededit-3-0-i2i-250628
      • doubao-seedream-4-0-250828 - text-to-image
      • doubao-seedream-4-0-250828 - image-to-image
      • doubao-seedream-4-0-250828 - multi-image generation
    • Rerank Reordering Model
      • Rerank
    • Text-to-Music Suno
      • Task Submission
        • Generate Song (Inspiration Mode)
        • Generate Song (Custom Mode)
        • Generate Song (Continuation Mode)
        • Generate Song (Singer Style)
        • Generate Song (Secondary Creation from Uploaded Song)
        • Generate Song (Song Stitching)
        • Generate Lyrics
        • Song Stitching
      • Query Interface
        • Batch Retrieve Tasks
        • Query Single Task
    • Video Model
      • Veo-Video Generation
        • OpenAI Video Format (Recommended)
          • OpenAI Creates Video with Images
          • OpenAI Query Task
          • OpenAI Download Video
      • Sora-Video Generation
        • OpenAI Official Video Format (Recommended)
          • sora-2/sora-2-pro
            • OpenAI Query Task
            • OpenAI Download Video
            • OpenAI Creates Video with Images
            • OpenAI Creates Video (with Character)
            • OpenAI Edit Video
        • Chat Format
          • Create Video
          • Create video + image
          • Continuously modify and generate video
      • Kling -Video Generation
        • Text-to-video
        • Image-to-video
        • Query task (free)
      • Wan -Video Generation
        • Create video with image Wan
        • Query video
      • MiniMax -Video Generation
        • Text-to-video generation task
        • Image-to-video task
        • Query video generation task status
        • Video download
      • Vidu -Video Generation
        • Generates video
        • Query
  • ​​FAQ​​
    • Data Updates
  • Change Log​​
home
home
  1. Document OCR Parsing API

fiel upload

Search/Read product
https://api.dataeyes.ai
Search/Read product
https://api.dataeyes.ai
POST
/v1/document/parse
Document Parsing Interface, supports parsing document content through URL or file upload, and returns the text in Markdown format.
Request Method:
The interface supports two request modes:
1.URL mode: Specify the document address to be parsed through the URL parameter.
2.File upload mode: Directly upload the PDF file for parsing.

Request

Header Params

Body Params multipart/form-data

Responses

🟢200成功
application/json
Body

Example
{
    "code": 0,
    "data": {
        "metadata": {
            "url": "https://arxiv.org/pdf/2201.00019",
            "title": "2201.00019v2",
            "description": "",
            "keywords": "",
            "siteName": "",
            "favicon": "",
            "statusCode": 200,
            "contentType": "application/pdf",
            "cached": 0
        },
        "html": "",
        "markdown": "# FACET: A new long-lived particle detector in the very forward region of the CMS experiment\n\nS. Cerci,a,1 D. Sunar Cerci,a,1 D. Lazic,b G. Landsberg,c,2 F. Cerutti,d M. Sabaté-Gilarte,d M.G. Albrow,e,2 J. Berryhill,e D.R. Green,e J. Hirschauer,e S. Kulkarni,f J.E. Brücken,g L. Emediato,h A. Mestvirishvili,h J. Nachtman,h Y. Onel,h A. Penzo,h O. Aydilek,i B. Hacisahinoglu,i S. Ozkorucuklu,i,2 H. Sert,i C. Simsek,i C. Zorbilmez,i I. Hos, $^ { j , 1 }$ N. Hadley,k A. Skuja,k M. Du,l R. Fang,l Z. Liu,l B. Isildak $^ { m , 1 }$ and V.Q. Tran $^ { n , o }$\n\naDepartment of Physics, Adiyaman University, 02040, Adiyaman, Turkey   \nbPhysics Department, Boston University, 590 Commonwealth Ave, Boston, MA 02215, U.S.A. cDepartment of Physics, Brown University, 182 Hope St, Providence, RI 02912, U.S.A.   \ndCERN, 1211 Geneva 23, Switzerland   \neFermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, U.S.A.   \nf Institute of Physics, NAWI Graz, University of Graz, Universitätsplatz 5, A-8010 Graz, Austria gHelsinki Institute of Physics, University of Helsinki, Gustaf Hällströmin katu 2, 00560 Helsinki, Finland   \n$h$ Department of Physics and Astronomy, University of Iowa, 203 Van Allen Hall, Iowa City, IA 52242, U.S.A.   \niPhysics Department, Istanbul University, Vezneciler Caddesi, 34134, Istanbul, Turkey   \n$j$ Department of Engineering Sciences, Istanbul University-Cerrahpasa, 34320 Avcilar, Istanbul, Turkey   \nkDepartment of Physics, University of Maryland, College Park, MD 20742, U.S.A.   \nlDepartment of Physics, Nanjing University, Nanjing 210093, China   \n$_ { m }$ Department of Natural and Mathematical Sciences, Ozyegin University, Orman Sk 13, 34794, Istanbul, Turkey   \nnTsung Dao Lee Institute, Shanghai Jiao Tong University, Shanghai 200240, China   \noFaculty of Fundamental Sciences, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi 12116, Vietnam   \nE-mail: Salim.Cerci@cern.ch, Deniz.Sunar.Cerci@cern.ch,   \nDragoslav.Lazic@cern.ch, Greg.Landsberg@cern.ch,   \nFrancesco.Cerutti@cern.ch, Marta.Sabate.Gilarte@cern.ch,   \nalbrow@fnal.gov, Jeffrey.Berryhill@cern.ch, dgreen@fnal.gov,   \njhirsch@fnal.gov, suchita.kulkarni@uni-graz.at,   \njens.brucken@helsinki.fi, lregisem@cern.ch,   \nAlexi.Mestvirishvili@cern.ch, Jane.Nachtman@cern.ch,   \nyasar-onel@uiowa.edu, Aldo.Penzo@cern.ch, Orhan.Aydilek@cern.ch,   \nBurak.Hacisahinoglu@cern.ch, Suat.Ozkorucuklu@cern.ch,   \nHale.Sert@cern.ch, Cagdas.Simsek@cern.ch, Caglar.Zorbilmez@cern.ch,   \nIlknur.Hos@cern.ch, hadley@umd.edu, skuja@umd.edu,   \nmg1722004@smail.nju.edu.cn, 141150012@smail.nju.edu.cn,   \nzuoweiliu@nju.edu.cn, Bora.Isildak@cern.ch, vqtran@sjtu.edu.cn\n\nAbstract: We describe a proposal to add a set of very forward detectors to the CMS experiment for the high-luminosity era of the Large Hadron Collider to search for beyond the standard model long-lived particles, such as dark photons, heavy neutral leptons, axion-like particles, and dark Higgs bosons. The proposed subsystem is called FACET for ForwardAperture CMS ExTension, and will be sensitive to any particles that can penetrate at least 50 m of magnetized iron and decay in an 18 m long, 1 m diameter vacuum pipe. The decay products will be measured in detectors using identical technology to the planned CMS Phase-2 upgrade.\n\nKeywords: Beyond Standard Model, Exotics, Hadron-Hadron Scattering, Dark Matter, Particle and Resonance Production\n\n# Contents\n\n1 Introduction 1\n\n2 FACET as a New Subsystem of CMS 3\n\n3 Sensitivity to Long-Lived Particles 5\n\n3.1 Dark Photons   \n3.2 Heavy Neutral Leptons   \n3.3 Axion-Like Particles   \n3.4 Dark Higgs Bosons 9\n\n4 Triggers 10\n\n5 Backgrounds 11\n\n6 Summary 13\n\n7 Acknowledgments 13\n\n# 1 Introduction\n\nThe existence of long-lived particles (LLPs), i.e., particles with the proper lifetime $c \\tau$ in a macroscopic range, is predicted in many models of physics beyond the standard model (BSM). Generally, LLPs are naturally expected in models with small mass splittings between the adjacent states (e.g., in supersymmetric models) or with suppressed couplings to standard model (SM) particles. In particular, LLPs are often present in particle dark matter (DM) models, where they serve as portals between the DM and SM particles. The existence of DM is well established from astronomical observations and cosmology. It is generally assumed that DM consists of BSM particles . While searches for such particles in the TeV mass range continue at the CERN Large Hadron Collider (LHC), the possibility that new particle
Previous
Trending Search API
Next
URL Parsing