REST API

The docling-serve HTTP API. Examples target a local server at http://localhost:5001; on Docling for IBM watsonx use your service base URL and key.

Endpoints

The full, interactive schema is always available at /docs (OpenAPI / Swagger) on any running server.

Authentication

If DOCLING_SERVE_API_KEY is set on the server, send it on every request:

-H "X-Api-Key: <YOUR_KEY>"

Python client

Docling ships a Python client for the API server, so you don't have to hand-roll HTTP calls. It takes the service URL and an optional API key, and returns the same ConversionResult as the local DocumentConverter:

from docling.service_client import DoclingServiceClient
from docling.datamodel.service.options import ConvertDocumentsOptions

with DoclingServiceClient(url="http://localhost:5001") as client:
    result = client.convert(
        source="https://arxiv.org/pdf/2501.17887",
        options=ConvertDocumentsOptions(to_formats=["md"]),
    )

print(result.document.export_to_markdown())

source accepts an HTTP/HTTPS URL string, a local pathlib.Path, or a DocumentStream; use convert_all([...]) to stream multiple conversion results. The options are the same conversion options shown below. See the examples for more recipes.

Conversion options (common)

Pass these in the JSON body under options. This is the common subset — the authoritative, full schema is the live OpenAPI docs at /docs on any running server.

For full-page VLM conversion models see Vision models; for picture-description models see Enrichment features and Model catalog.

Example: convert a URL (async)

curl -X POST "http://localhost:5001/v1/convert/source/async" \
  -H "Content-Type: application/json" \
  -d '{"http_sources": [{"url": "https://arxiv.org/pdf/2501.17887"}]}'

For a synchronous call use /v1/convert/source with the same body.

Example: upload a file (multipart)

The /v1/convert/file endpoint accepts one or more files as multipart/form-data, with options as form fields:

curl -X POST "http://localhost:5001/v1/convert/file" \
  -H "Content-Type: multipart/form-data" \
  -F "files=@2206.01062v1.pdf;type=application/pdf" \
  -F "from_formats=pdf" \
  -F "to_formats=md" \
  -F "do_ocr=true" \
  -F "image_export_mode=embedded" \
  -F "table_mode=fast"

Base64 file upload

To send a file inline instead of multipart, POST to /v1/convert/source with file_sources. For large files, write the request body to a temp file and pass it with -d @file to avoid the shell's "Argument list too long" error:

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

# 2. build the request body
cat > /tmp/request_body.json <<EOF
{
  "file_sources": [{ "base64_string": "${B64_DATA}", "filename": "document.pdf" }]
}
EOF

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

Response format

A single-file conversion returns JSON:

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

Only the *_content fields you requested via to_formats are populated. processing_time is in seconds; timings carries per-component detail when enabled. If you request the zip target, or the job produces multiple files, the response is a zip archive instead of JSON.

Asynchronous API

Both convert endpoints have /async variants. Submitting returns a task descriptor:

{
  "task_id": "<task_id>",
  "task_status": "pending",   // pending | started | success | failure
  "task_position": 1,
  "task_meta": null
}

Poll until done — GET /v1/status/poll/{task_id}:

import time
import httpx

base_url = "http://localhost:5001/v1"
task = response.json()  # from the /async submission

while task["task_status"] not in ("success", "failure"):
    task = httpx.get(f"{base_url}/status/poll/{task['task_id']}").json()
    time.sleep(5)

Subscribe via WebSocket — /v1/status/ws/{task_id} — for push updates (messages are JSON with message = connection | update | error plus the task object).

Fetch the result when finished — GET /v1/result/{task_id}.

Picture description

When picture-description (image captioning) enrichment is on, select the model with picture_description_preset (a named preset) or, for full control, picture_description_custom_config. The model can run locally (in-process) or via a remote OpenAI-compatible API endpoint; the remote path requires launching the server with DOCLING_SERVE_ENABLE_REMOTE_SERVICES=true.

See Enrichment features for picture-description options, and Model catalog for available models.