docling-serve/docs/usage.md

Usage

The API provides two endpoints: one for urls, one for files. This is necessary to send files directly in binary format instead of base64-encoded strings.

Common parameters

On top of the source of file (see below), both endpoints support the same parameters.

ConvertDocumentsRequestOptions

Field Name	Type	Description
from_formats	List[InputFormat]	Input format(s) to convert from. String or list of strings. Allowed values: docx, pptx, html, image, pdf, asciidoc, md, csv, xlsx, xml_uspto, xml_jats, xml_xbrl, mets_gbs, json_docling, audio, vtt, latex. Optional, defaults to all formats.
to_formats	List[OutputFormat]	Output format(s) to convert to. String or list of strings. Allowed values: md, json, yaml, html, html_split_page, text, doctags. Optional, defaults to Markdown.
image_export_mode	ImageRefMode	Image export mode for the document (in case of JSON, Markdown or HTML). Allowed values: placeholder, embedded, referenced. Optional, defaults to Embedded.
do_ocr	bool	If enabled, the bitmap content will be processed using OCR. Boolean. Optional, defaults to true
force_ocr	bool	If enabled, replace existing text with OCR-generated text over content. Boolean. Optional, defaults to false.
ocr_engine	ocr_engines_enum	The OCR engine to use. String. Allowed values: auto, easyocr, ocrmac, rapidocr, tesserocr, tesseract. Optional, defaults to easyocr.
ocr_lang	List[str] or NoneType	List of languages used by the OCR engine. Note that each OCR engine has different values for the language names. String or list of strings. Optional, defaults to empty.
pdf_backend	PdfBackend	The PDF backend to use. String. Allowed values: pypdfium2, docling_parse, dlparse_v1, dlparse_v2, dlparse_v4. Optional, defaults to docling_parse.
table_mode	TableFormerMode	Mode to use for table structure, String. Allowed values: fast, accurate. Optional, defaults to accurate.
table_cell_matching	bool	If true, matches table cells predictions back to PDF cells. Can break table output if PDF cells are merged across table columns. If false, let table structure model define the text cells, ignore PDF cells.
pipeline	ProcessingPipeline	Choose the pipeline to process PDF or image files.
page_range	Tuple	Only convert a range of pages. The page number starts at 1.
document_timeout	float	The timeout for processing each document, in seconds.
abort_on_error	bool	Abort on error if enabled. Boolean. Optional, defaults to false.
do_table_structure	bool	If enabled, the table structure will be extracted. Boolean. Optional, defaults to true.
include_images	bool	If enabled, images will be extracted from the document. Boolean. Optional, defaults to true.
images_scale	float	Scale factor for images. Float. Optional, defaults to 2.0.
md_page_break_placeholder	str	Add this placeholder between pages in the markdown output.
do_code_enrichment	bool	If enabled, perform OCR code enrichment. Boolean. Optional, defaults to false.
do_formula_enrichment	bool	If enabled, perform formula OCR, return LaTeX code. Boolean. Optional, defaults to false.
do_picture_classification	bool	If enabled, classify pictures in documents. Boolean. Optional, defaults to false.
do_chart_extraction	bool	If enabled, extract numeric data from charts. Boolean. Optional, defaults to false.
do_picture_description	bool	If enabled, describe pictures in documents. Boolean. Optional, defaults to false.
picture_description_area_threshold	float	Minimum percentage of the area for a picture to be processed with the models.
picture_description_local	PictureDescriptionLocal or NoneType	DEPRECATED: Options for running a local vision-language model in the picture description. The parameters refer to a model hosted on Hugging Face. This parameter is mutually exclusive with picture_description_api. Please migrate to picture_description_preset or picture_description_custom_config.
picture_description_api	PictureDescriptionApi or NoneType	DEPRECATED: API details for using a vision-language model in the picture description. This parameter is mutually exclusive with picture_description_local. Please migrate to picture_description_preset or picture_description_custom_config.
vlm_pipeline_model	VlmModelType or NoneType	DEPRECATED: Preset of local and API models for the vlm pipeline. This parameter is mutually exclusive with vlm_pipeline_model_local and vlm_pipeline_model_api. Use the other options for more parameters. Please migrate to vlm_pipeline_preset or vlm_pipeline_custom_config.
vlm_pipeline_model_local	VlmModelLocal or NoneType	DEPRECATED: Options for running a local vision-language model for the vlm pipeline. The parameters refer to a model hosted on Hugging Face. This parameter is mutually exclusive with vlm_pipeline_model_api and vlm_pipeline_model. Please migrate to vlm_pipeline_preset or vlm_pipeline_custom_config.
vlm_pipeline_model_api	VlmModelApi or NoneType	DEPRECATED: API details for using a vision-language model for the vlm pipeline. This parameter is mutually exclusive with vlm_pipeline_model_local and vlm_pipeline_model. Please migrate to vlm_pipeline_preset or vlm_pipeline_custom_config.
vlm_pipeline_preset	str or NoneType	Preset ID to use (e.g., "default", "granite_docling"). Use "default" for stable, admin-controlled configuration.
picture_description_preset	str or NoneType	Preset ID for picture description.
code_formula_preset	str or NoneType	Preset ID for code/formula extraction.
vlm_pipeline_custom_config	VlmConvertOptions or dict or NoneType	Custom VLM configuration including model spec and engine options. Only available if admin allows it. Must include 'model_spec' and 'engine_options'.
picture_description_custom_config	PictureDescriptionVlmEngineOptions or dict or NoneType	Custom picture description configuration including model spec and engine options.
code_formula_custom_config	CodeFormulaVlmOptions or dict or NoneType	Custom code/formula extraction configuration including model spec and engine options.
table_structure_custom_config	Dict[str, Any] or NoneType	Custom configuration for table structure model. Use this to specify a non-default kind with its options. The 'kind' field in the config dict determines which table structure implementation to use. If not specified, uses the default kind with preset configuration.
layout_custom_config	Dict[str, Any] or NoneType	Custom configuration for layout model. Use this to specify a non-default kind with its options. The 'kind' field in the config dict determines which layout implementation to use. If not specified, uses the default kind with preset configuration.

CodeFormulaVlmOptions

Field Name	Type	Description
engine_options	BaseVlmEngineOptions	Runtime configuration (transformers, mlx, api, etc.)
model_spec	VlmModelSpec	Model specification with runtime-specific overrides
scale	float	Image scaling factor for preprocessing
max_size	int or NoneType	Maximum image dimension (width or height)
extract_code	bool	Extract code blocks
extract_formulas	bool	Extract mathematical formulas

VlmModelSpec

Field Name	Type	Description
name	str	Human-readable model name
default_repo_id	str	Default HuggingFace repository ID
revision	str	Default model revision
prompt	str	Prompt template for this model
response_format	ResponseFormat	Expected response format from the model
supported_engines	Set or NoneType	Set of supported engines (None = all supported)
engine_overrides	Dict[VlmEngineType, EngineModelConfig]	Engine-specific configuration overrides
api_overrides	Dict[VlmEngineType, ApiModelConfig]	API-specific configuration overrides
trust_remote_code	bool	Whether to trust remote code for this model
stop_strings	List[str]	Stop strings for generation
max_new_tokens	int	Maximum number of new tokens to generate

BaseVlmEngineOptions

Field Name	Type	Description
engine_type	VlmEngineType	Type of inference engine to use

PictureDescriptionVlmEngineOptions

Field Name	Type	Description
batch_size	int	Number of images to process in a single batch during picture description. Higher values improve throughput but increase memory usage. Adjust based on available GPU/CPU memory.
scale	float	Scaling factor for image resolution before processing. Higher values (e.g., 2.0) provide more detail for the vision model but increase processing time and memory. Range: 0.5-4.0 typical.
picture_area_threshold	float	Minimum picture area as fraction of page area (0.0-1.0) to trigger description. Pictures smaller than this threshold are skipped. Use lower values (e.g., 0.01) to describe small images.
classification_allow	List[PictureClassificationLabel] or NoneType	List of picture classification labels to allow for description. Only pictures classified with these labels will be processed. If None, all picture types are allowed unless explicitly denied. Use to focus description on specific image types (e.g., diagrams, charts).
classification_deny	List[PictureClassificationLabel] or NoneType	List of picture classification labels to exclude from description. Pictures classified with these labels will be skipped. If None, no picture types are denied unless not in allow list. Use to exclude unwanted image types (e.g., decorative images, logos).
classification_min_confidence	float	Minimum classification confidence score (0.0-1.0) required for a picture to be processed. Pictures with classification confidence below this threshold are skipped. Higher values ensure only confidently classified images are described. Range: 0.0 (no filtering) to 1.0 (maximum confidence).
engine_options	BaseVlmEngineOptions	Runtime configuration (transformers, mlx, api, etc.)
model_spec	VlmModelSpec	Model specification with runtime-specific overrides
prompt	str	Prompt template for the vision model. Customize to control description style, detail level, or focus.
generation_config	Dict[str, Any]	Generation configuration for text generation. Controls output length, sampling strategy, temperature, etc.

VlmConvertOptions

Field Name	Type	Description
engine_options	BaseVlmEngineOptions	Runtime configuration (transformers, mlx, api, etc.)
model_spec	VlmModelSpec	Model specification with runtime-specific overrides
scale	float	Image scaling factor for preprocessing
max_size	int or NoneType	Maximum image dimension (width or height)
batch_size	int	Batch size for processing multiple pages
force_backend_text	bool	Force use of backend text extraction instead of VLM

VlmModelApi

Field Name	Type	Description
url	AnyUrl	Endpoint which accepts openai-api compatible requests.
headers	Dict[str, str]	Headers used for calling the API endpoint. For example, it could include authentication headers.
params	Dict[str, Any]	Model parameters.
timeout	float	Timeout for the API request.
concurrency	int	Maximum number of concurrent requests to the API.
prompt	str	Prompt used when calling the vision-language model.
scale	float	Scale factor of the images used.
response_format	ResponseFormat	Type of response generated by the model.
temperature	float	Temperature parameter controlling the reproducibility of the result.

VlmModelLocal

Field Name	Type	Description
repo_id	str	Repository id from the Hugging Face Hub.
prompt	str	Prompt used when calling the vision-language model.
scale	float	Scale factor of the images used.
response_format	ResponseFormat	Type of response generated by the model.
inference_framework	InferenceFramework	Inference framework to use.
transformers_model_type	TransformersModelType	Type of transformers auto-model to use.
extra_generation_config	Dict[str, Any]	Config from https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig
temperature	float	Temperature parameter controlling the reproducibility of the result.

PictureDescriptionApi

Field Name	Type	Description
url	AnyUrl	Endpoint which accepts openai-api compatible requests.
headers	Dict[str, str]	Headers used for calling the API endpoint. For example, it could include authentication headers.
params	Dict[str, Any]	Model parameters.
timeout	float	Timeout for the API request.
concurrency	int	Maximum number of concurrent requests to the API.
prompt	str	Prompt used when calling the vision-language model.

PictureDescriptionLocal

Field Name	Type	Description
repo_id	str	Repository id from the Hugging Face Hub.
prompt	str	Prompt used when calling the vision-language model.
generation_config	Dict[str, Any]	Config from https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig

Authentication

When authentication is activated (see the parameter DOCLING_SERVE_API_KEY in configuration.md), all the API requests must provide the header X-Api-Key with the correct secret key.

Convert endpoints

Source endpoint

The endpoint is /v1/convert/source, listening for POST requests of JSON payloads.

On top of the above parameters, you must send the URL(s) of the document you want process with either the http_sources or file_sources fields. The first is fetching URL(s) (optionally using with extra headers), the second allows to provide documents as base64-encoded strings. No options is required, they can be partially or completely omitted.

Simple payload example:

{
  "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
}

Complete payload example:

{
  "options": {
    "from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"],
    "to_formats": ["md", "json", "html", "text", "doctags"],
    "image_export_mode": "placeholder",
    "do_ocr": true,
    "force_ocr": false,
    "ocr_engine": "easyocr",
    "ocr_lang": ["en"],
    "pdf_backend": "dlparse_v2",
    "table_mode": "fast",
    "abort_on_error": false,
  },
  "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
}

CURL example:

curl -X 'POST' \
  'http://localhost:5001/v1/convert/source' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "options": {
    "from_formats": [
      "docx",
      "pptx",
      "html",
      "image",
      "pdf",
      "asciidoc",
      "md",
      "xlsx"
    ],
    "to_formats": ["md", "json", "html", "text", "doctags"],
    "image_export_mode": "placeholder",
    "do_ocr": true,
    "force_ocr": false,
    "ocr_engine": "easyocr",
    "ocr_lang": [
      "fr",
      "de",
      "es",
      "en"
    ],
    "pdf_backend": "dlparse_v2",
    "table_mode": "fast",
    "abort_on_error": false,
    "do_table_structure": true,
    "include_images": true,
    "images_scale": 2
  },
  "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
}'

Python example:

import httpx

async_client = httpx.AsyncClient(timeout=60.0)
url = "http://localhost:5001/v1/convert/source"
payload = {
  "options": {
    "from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"],
    "to_formats": ["md", "json", "html", "text", "doctags"],
    "image_export_mode": "placeholder",
    "do_ocr": True,
    "force_ocr": False,
    "ocr_engine": "easyocr",
    "ocr_lang": "en",
    "pdf_backend": "dlparse_v2",
    "table_mode": "fast",
    "abort_on_error": False,
  },
  "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
}

response = await async_client_client.post(url, json=payload)

data = response.json()

File as base64

The file_sources argument in the endpoint allows to send files as base64-encoded strings. When your PDF or other file type is too large, encoding it and passing it inline to curl can lead to an “Argument list too long” error on some systems. To avoid this, we write the JSON request body to a file and have curl read from that file.

CURL steps:

# 1. Base64-encode the file
B64_DATA=$(base64 -w 0 /path/to/file/pdf-to-convert.pdf)

# 2. Build the JSON with your options
cat <<EOF > /tmp/request_body.json
{
  "options": {
  },
  "file_sources": [{
    "base64_string": "${B64_DATA}",
    "filename": "pdf-to-convert.pdf"
  }]
}
EOF

# 3. POST the request to the docling service
curl -X POST "localhost:5001/v1/convert/source" \
     -H "Content-Type: application/json" \
     -d @/tmp/request_body.json

File endpoint

The endpoint is: /v1/convert/file, listening for POST requests of Form payloads (necessary as the files are sent as multipart/form data). You can send one or multiple files.

CURL example:

curl -X 'POST' \
  'http://127.0.0.1:5001/v1/convert/file' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'ocr_engine=easyocr' \
  -F 'pdf_backend=dlparse_v2' \
  -F 'from_formats=pdf' \
  -F 'from_formats=docx' \
  -F 'force_ocr=false' \
  -F 'image_export_mode=embedded' \
  -F 'ocr_lang=en' \
  -F 'ocr_lang=pl' \
  -F 'table_mode=fast' \
  -F 'files=@2206.01062v1.pdf;type=application/pdf' \
  -F 'abort_on_error=false' \
  -F 'to_formats=md' \
  -F 'to_formats=text' \
  -F 'do_ocr=true'

Python example:

import httpx

async_client = httpx.AsyncClient(timeout=60.0)
url = "http://localhost:5001/v1/convert/file"
parameters = {
"from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"],
"to_formats": ["md", "json", "html", "text", "doctags"],
"image_export_mode": "placeholder",
"do_ocr": True,
"force_ocr": False,
"ocr_engine": "easyocr",
"ocr_lang": ["en"],
"pdf_backend": "dlparse_v2",
"table_mode": "fast",
"abort_on_error": False,
}

current_dir = os.path.dirname(__file__)
file_path = os.path.join(current_dir, '2206.01062v1.pdf')

files = {
    'files': ('2206.01062v1.pdf', open(file_path, 'rb'), 'application/pdf'),
}

response = await async_client.post(url, files=files, data=parameters)
assert response.status_code == 200, "Response should be 200 OK"

data = response.json()

Picture description options

When the picture description enrichment is activated, users may specify which model and which execution mode to use for this task. There are two choices for the execution mode: local will run the vision-language model directly, api will invoke an external API endpoint.

The local option is specified with:

{
  "picture_description_local": {
    "repo_id": "",  // Repository id from the Hugging Face Hub.
    "generation_config": {"max_new_tokens": 200, "do_sample": false},  // HF generation config.
    "prompt": "Describe this image in a few sentences. ",  // Prompt used when calling the vision-language model.
  }
}

The possible values for generation_config are documented in the Hugging Face text generation docs.

The api option is specified with:

{
  "picture_description_api": {
    "url": "",  // Endpoint which accepts openai-api compatible requests.
    "headers": {},  // Headers used for calling the API endpoint. For example, it could include authentication headers.
    "params": {},  // Model parameters.
    "timeout": 20,  // Timeout for the API request.
    "prompt": "Describe this image in a few sentences. ",  // Prompt used when calling the vision-language model.
  }
}

Example URLs are:

• http://localhost:8000/v1/chat/completions for the local vllm api, with example picture_description_api:

  • the HuggingFaceTB/SmolVLM-256M-Instruct model

    {
      "url": "http://localhost:8000/v1/chat/completions",
      "params": {
        "model": "HuggingFaceTB/SmolVLM-256M-Instruct",
        "max_completion_tokens": 200,
      }
    }
    

  • the ibm-granite/granite-vision-3.2-2b model

    {
      "url": "http://localhost:8000/v1/chat/completions",
      "params": {
        "model": "ibm-granite/granite-vision-3.2-2b",
        "max_completion_tokens": 200,
      }
    }
    

• http://localhost:11434/v1/chat/completions for the local Ollama api, with example picture_description_api:

  • the granite3.2-vision:2b model

    {
      "url": "http://localhost:11434/v1/chat/completions",
      "params": {
        "model": "granite3.2-vision:2b"
      }
    }
    

Note that when using picture_description_api, the server must be launched with DOCLING_SERVE_ENABLE_REMOTE_SERVICES=true.

Response format

The response can be a JSON Document or a File.

• If you process only one file, the response will be a JSON document with the following format:

  {
    "document": {
      "md_content": "",
      "json_content": {},
      "html_content": "",
      "text_content": "",
      "doctags_content": ""
      },
    "status": "<success|partial_success|skipped|failure>",
    "processing_time": 0.0,
    "timings": {},
    "errors": []
  }
  

  Depending on the value you set in output_formats, the different items will be populated with their respective results or empty.

  processing_time is the Docling processing time in seconds, and timings (when enabled in the backend) provides the detailed timing of all the internal Docling components.

• If you set the parameter target to the zip mode, the response will be a zip file.

• If multiple files are generated (multiple inputs, or one input but multiple outputs with the zip target mode), the response will be a zip file.

Asynchronous API

Both /v1/convert/source and /v1/convert/file endpoints are available as asynchronous variants. The advantage of the asynchronous endpoints is the possible to interrupt the connection, check for the progress update and fetch the result. This approach is more resilient against network instabilities and allows the client application logic to easily interleave conversion with other tasks.

Launch an asynchronous conversion with:

• POST /v1/convert/source/async when providing the input as sources.
• POST /v1/convert/file/async when providing the input as multipart-form files.

The response format is a task detail:

{
  "task_id": "<task_id>",  // the task_id which can be used for the next operations
  "task_status": "pending|started|success|failure",  // the task status
  "task_position": 1,  // the position in the queue
  "task_meta": null,  // metadata e.g. how many documents are in the total job and how many have been converted
}

Polling status

For checking the progress of the conversion task and wait for its completion, use the endpoint:

• GET /v1/status/poll/{task_id}

Example waiting loop:

import time
import httpx

# ...
# response from the async task submission
task = response.json()

while task["task_status"] not in ("success", "failure"):
    response = httpx.get(f"{base_url}/status/poll/{task['task_id']}")
    task = response.json()

    time.sleep(5)

Subscribe with websockets

Using websocket you can get the client application being notified about updates of the conversion task. To start the websocket connection, use the endpoint:

• /v1/status/ws/{task_id}

Websocket messages are JSON object with the following structure:

{
  "message": "connection|update|error",  // type of message being sent
  "task": {},  // the same content of the task description
  "error": "",  // description of the error
}

Example websocket usage:

from websockets.sync.client import connect

uri = f"ws://{base_url}/v1/status/ws/{task['task_id']}"
with connect(uri) as websocket:
    for message in websocket:
        try:
            payload = json.loads(message)
            if payload["message"] == "error":
                break
            if payload["message"] == "update" and payload["task"]["task_status"] in ("success", "failure"):
                break
        except:
          break

Fetch results

When the task is completed, the result can be fetched with the endpoint:

• GET /v1/result/{task_id}