chore: bump version to 1.0.1 [skip ci]

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

.github/styles/config/vocabularies/Docling/accept.txt

@@ -0,0 +1,35 @@
+[Dd]ocling
+precommit
+asgi
+async
+(?i)urls
+uvicorn
+[Ww]ebserver
+keyfile
+[Ww]ebsocket(s?)
+[Kk]ubernetes
+UI
+(?i)vllm
+APIs
+[Ss]ubprocesses
+(?i)api
+Kubeflow
+(?i)Jobkit
+(?i)cpu
+(?i)PyTorch
+(?i)CUDA
+(?i)NVIDIA
+(?i)env
+Gradio
+bool
+Ollama
+inbody
+LGTMs
+Dolfi
+Lysak
+Nikos
+Nassar
+Panos
+Vagenas
+Staar
+Livathinos

.github/vale.ini

@@ -0,0 +1,11 @@
+StylesPath = styles
+MinAlertLevel = suggestion
+; Packages = write-good, proselint
+
+Vocab = Docling
+
+[*.md]
+BasedOnStyles = Vale
+
+[CHANGELOG.md]
+BasedOnStyles = 

.pre-commit-config.yaml

@@ -21,6 +21,17 @@ repos:
         pass_filenames: false
         language: system
         files: '\.py$'
+  - repo: https://github.com/errata-ai/vale
+    rev: v3.12.0  # Use latest stable version
+    hooks:
+      - id: vale
+        name: vale sync
+        pass_filenames: false
+        args: [sync, "--config=.github/vale.ini"]
+      - id: vale
+        name: Spell and Style Check with Vale
+        args: ["--config=.github/vale.ini"]
+        files: \.md$
   - repo: https://github.com/astral-sh/uv-pre-commit
     # uv version.
     rev: 0.7.13

CHANGELOG.md

@@ -1,3 +1,25 @@
+## [v1.0.1](https://github.com/docling-project/docling-serve/releases/tag/v1.0.1) - 2025-07-21
+
+### Fix
+
+* Docling update v2.42.0 ([#277](https://github.com/docling-project/docling-serve/issues/277)) ([`8706706`](https://github.com/docling-project/docling-serve/commit/8706706e8797b0a06ec4baa7cf87988311be68b6))
+
+### Documentation
+
+* Typo in README ([#276](https://github.com/docling-project/docling-serve/issues/276)) ([`766adb2`](https://github.com/docling-project/docling-serve/commit/766adb248113c7bd5144d14b3c82929a2ad29f8e))
+
+## [v1.0.0](https://github.com/docling-project/docling-serve/releases/tag/v1.0.0) - 2025-07-14
+
+### Feature
+
+* V1 api with list of sources and target ([#249](https://github.com/docling-project/docling-serve/issues/249)) ([`56e328b`](https://github.com/docling-project/docling-serve/commit/56e328baf76b4bb0476fc6ca820b52034e4f97bf))
+* Use orchestrators from jobkit ([#248](https://github.com/docling-project/docling-serve/issues/248)) ([`daa924a`](https://github.com/docling-project/docling-serve/commit/daa924a77e56d063ef17347dfd8a838872a70529))
+
+### Breaking
+
+* v1 api with list of sources and target ([#249](https://github.com/docling-project/docling-serve/issues/249)) ([`56e328b`](https://github.com/docling-project/docling-serve/commit/56e328baf76b4bb0476fc6ca820b52034e4f97bf))
+* use orchestrators from jobkit ([#248](https://github.com/docling-project/docling-serve/issues/248)) ([`daa924a`](https://github.com/docling-project/docling-serve/commit/daa924a77e56d063ef17347dfd8a838872a70529))
+
 ## [v0.16.1](https://github.com/docling-project/docling-serve/releases/tag/v0.16.1) - 2025-07-07
 
 ### Fix

Containerfile

@@ -42,7 +42,7 @@ ENV \
 
 ARG UV_SYNC_EXTRA_ARGS=""
 
-RUN --mount=from=ghcr.io/astral-sh/uv:0.7.13,source=/uv,target=/bin/uv \
+RUN --mount=from=ghcr.io/astral-sh/uv:0.7.19,source=/uv,target=/bin/uv \
     --mount=type=cache,target=/opt/app-root/src/.cache/uv,uid=1001 \
     --mount=type=bind,source=uv.lock,target=uv.lock \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
@@ -61,7 +61,7 @@ RUN echo "Downloading models..." && \
     chmod -R g=u ${DOCLING_SERVE_ARTIFACTS_PATH}
 
 COPY --chown=1001:0 ./docling_serve ./docling_serve
-RUN --mount=from=ghcr.io/astral-sh/uv:0.7.13,source=/uv,target=/bin/uv \
+RUN --mount=from=ghcr.io/astral-sh/uv:0.7.19,source=/uv,target=/bin/uv \
     --mount=type=cache,target=/opt/app-root/src/.cache/uv,uid=1001 \
     --mount=type=bind,source=uv.lock,target=uv.lock \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \

MAINTAINERS.md

@@ -1,11 +1,11 @@
 # MAINTAINERS
 
-- Christoph Auer - [@cau-git](https://github.com/cau-git)
-- Michele Dolfi - [@dolfim-ibm](https://github.com/dolfim-ibm)
-- Maxim Lysak - [@maxmnemonic](https://github.com/maxmnemonic)
-- Nikos Livathinos - [@nikos-livathinos](https://github.com/nikos-livathinos)
-- Ahmed Nassar - [@nassarofficial](https://github.com/nassarofficial)
-- Panos Vagenas - [@vagenas](https://github.com/vagenas)
-- Peter Staar - [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
+- Christoph Auer - [`@cau-git`](https://github.com/cau-git)
+- Michele Dolfi - [`@dolfim-ibm`](https://github.com/dolfim-ibm)
+- Maxim Lysak - [`@maxmnemonic`](https://github.com/maxmnemonic)
+- Nikos Livathinos - [`@nikos-livathinos`](https://github.com/nikos-livathinos)
+- Ahmed Nassar - [`@nassarofficial`](https://github.com/nassarofficial)
+- Panos Vagenas - [`@vagenas`](https://github.com/vagenas)
+- Peter Staar - [`@PeterStaar-IBM`](https://github.com/PeterStaar-IBM)
 
 Maintainers can be contacted at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).

README.md

@@ -12,9 +12,12 @@ Running [Docling](https://github.com/docling-project/docling) as an API service.
 
 - Learning how to [configure the webserver](./docs/configuration.md)
 - Get to know all [runtime options](./docs/usage.md) of the API
-- Explore usefule [deployment examples](./docs/deployment.md)
+- Explore useful [deployment examples](./docs/deployment.md)
 - And more
 
+> [!NOTE]
+> **Migration to the `v1` API.** Docling Serve now has a stable v1 API. Read more on the [migration to v1](./docs/v1_migration.md).
+
 ## Getting started
 
 Install the `docling-serve` package and run the server.
@@ -39,7 +42,7 @@ Try it out with a simple conversion:
 
 ```bash
 curl -X 'POST' \
-  'http://localhost:5001/v1alpha/convert/source' \
+  'http://localhost:5001/v1/convert/source' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{
@@ -65,9 +68,9 @@ Coming soon: `docling-serve-slim` images will reduce the size by skipping the mo
 
 An easy to use UI is available at the `/ui` endpoint.
 
-![ui-input.png](img/ui-input.png)
+![Input controllers in the UI](img/ui-input.png)
 
-![ui-output.png](img/ui-output.png)
+![Output visualization in the UI](img/ui-output.png)
 
 ## Get help and support
 

docling_serve/__main__.py

@@ -30,6 +30,7 @@ logger = logging.getLogger(__name__)
 def version_callback(value: bool) -> None:
     if value:
         docling_serve_version = importlib.metadata.version("docling_serve")
+        docling_jobkit_version = importlib.metadata.version("docling-jobkit")
         docling_version = importlib.metadata.version("docling")
         docling_core_version = importlib.metadata.version("docling-core")
         docling_ibm_models_version = importlib.metadata.version("docling-ibm-models")
@@ -38,6 +39,7 @@ def version_callback(value: bool) -> None:
         py_impl_version = sys.implementation.cache_tag
         py_lang_version = platform.python_version()
         console.print(f"Docling Serve version: {docling_serve_version}")
+        console.print(f"Docling Jobkit version: {docling_jobkit_version}")
         console.print(f"Docling version: {docling_version}")
         console.print(f"Docling Core version: {docling_core_version}")
         console.print(f"Docling IBM Models version: {docling_ibm_models_version}")

docling_serve/app.py

@@ -11,6 +11,7 @@ from fastapi import (
     BackgroundTasks,
     Depends,
     FastAPI,
+    Form,
     HTTPException,
     Query,
     UploadFile,
@@ -28,16 +29,25 @@ from fastapi.staticfiles import StaticFiles
 from scalar_fastapi import get_scalar_api_reference
 
 from docling.datamodel.base_models import DocumentStream
-
-from docling_serve.datamodel.callback import (
+from docling_jobkit.datamodel.callback import (
     ProgressCallbackRequest,
     ProgressCallbackResponse,
 )
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
+from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
+from docling_jobkit.datamodel.task import Task, TaskSource
+from docling_jobkit.datamodel.task_targets import InBodyTarget, TaskTarget, ZipTarget
+from docling_jobkit.orchestrators.base_orchestrator import (
+    BaseOrchestrator,
+    ProgressInvalid,
+    TaskNotFoundError,
+)
+
+from docling_serve.datamodel.convert import ConvertDocumentsRequestOptions
 from docling_serve.datamodel.requests import (
-    ConvertDocumentFileSourcesRequest,
-    ConvertDocumentHttpSourcesRequest,
     ConvertDocumentsRequest,
+    FileSourceRequest,
+    HttpSourceRequest,
+    TargetName,
 )
 from docling_serve.datamodel.responses import (
     ClearResponse,
@@ -47,17 +57,12 @@ from docling_serve.datamodel.responses import (
     TaskStatusResponse,
     WebsocketMessage,
 )
-from docling_serve.datamodel.task import Task, TaskSource
-from docling_serve.docling_conversion import _get_converter_from_hash
-from docling_serve.engines.async_orchestrator import (
-    BaseAsyncOrchestrator,
-    ProgressInvalid,
-)
-from docling_serve.engines.async_orchestrator_factory import get_async_orchestrator
-from docling_serve.engines.base_orchestrator import TaskNotFoundError
 from docling_serve.helper_functions import FormDepends
+from docling_serve.orchestrator_factory import get_async_orchestrator
+from docling_serve.response_preparation import prepare_response
 from docling_serve.settings import docling_serve_settings
 from docling_serve.storage import get_scratch
+from docling_serve.websocket_notifier import WebsocketNotifier
 
 
 # Set up custom logging as we'll be intermixes with FastAPI/Uvicorn's logging
@@ -95,9 +100,12 @@ _log = logging.getLogger(__name__)
 # Context manager to initialize and clean up the lifespan of the FastAPI app
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    orchestrator = get_async_orchestrator()
     scratch_dir = get_scratch()
 
+    orchestrator = get_async_orchestrator()
+    notifier = WebsocketNotifier(orchestrator)
+    orchestrator.bind_notifier(notifier)
+
     # Warm up processing cache
     if docling_serve_settings.load_models_at_boot:
         await orchestrator.warm_up_caches()
@@ -230,23 +238,27 @@ def create_app():  # noqa: C901
     ########################
 
     async def _enque_source(
-        orchestrator: BaseAsyncOrchestrator, conversion_request: ConvertDocumentsRequest
+        orchestrator: BaseOrchestrator, conversion_request: ConvertDocumentsRequest
     ) -> Task:
         sources: list[TaskSource] = []
-        if isinstance(conversion_request, ConvertDocumentFileSourcesRequest):
-            sources.extend(conversion_request.file_sources)
-        if isinstance(conversion_request, ConvertDocumentHttpSourcesRequest):
-            sources.extend(conversion_request.http_sources)
+        for s in conversion_request.sources:
+            if isinstance(s, FileSourceRequest):
+                sources.append(FileSource.model_validate(s))
+            elif isinstance(s, HttpSourceRequest):
+                sources.append(HttpSource.model_validate(s))
 
         task = await orchestrator.enqueue(
-            sources=sources, options=conversion_request.options
+            sources=sources,
+            options=conversion_request.options,
+            target=conversion_request.target,
         )
         return task
 
     async def _enque_file(
-        orchestrator: BaseAsyncOrchestrator,
+        orchestrator: BaseOrchestrator,
         files: list[UploadFile],
-        options: ConvertDocumentsOptions,
+        options: ConvertDocumentsRequestOptions,
+        target: TaskTarget,
     ) -> Task:
         _log.info(f"Received {len(files)} files for processing.")
 
@@ -258,12 +270,12 @@ def create_app():  # noqa: C901
             name = file.filename if file.filename else f"file{suffix}.pdf"
             file_sources.append(DocumentStream(name=name, stream=buf))
 
-        task = await orchestrator.enqueue(sources=file_sources, options=options)
+        task = await orchestrator.enqueue(
+            sources=file_sources, options=options, target=target
+        )
         return task
 
-    async def _wait_task_complete(
-        orchestrator: BaseAsyncOrchestrator, task_id: str
-    ) -> bool:
+    async def _wait_task_complete(orchestrator: BaseOrchestrator, task_id: str) -> bool:
         start_time = time.monotonic()
         while True:
             task = await orchestrator.task_status(task_id=task_id)
@@ -298,7 +310,7 @@ def create_app():  # noqa: C901
 
     # Convert a document from URL(s)
     @app.post(
-        "/v1alpha/convert/source",
+        "/v1/convert/source",
         response_model=ConvertDocumentResponse,
         responses={
             200: {
@@ -309,36 +321,32 @@ def create_app():  # noqa: C901
     )
     async def process_url(
         background_tasks: BackgroundTasks,
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         conversion_request: ConvertDocumentsRequest,
     ):
         task = await _enque_source(
             orchestrator=orchestrator, conversion_request=conversion_request
         )
-        success = await _wait_task_complete(
+        completed = await _wait_task_complete(
             orchestrator=orchestrator, task_id=task.task_id
         )
 
-        if not success:
+        if not completed:
             # TODO: abort task!
             return HTTPException(
                 status_code=504,
                 detail=f"Conversion is taking too long. The maximum wait time is configure as DOCLING_SERVE_MAX_SYNC_WAIT={docling_serve_settings.max_sync_wait}.",
             )
 
-        result = await orchestrator.task_result(
-            task_id=task.task_id, background_tasks=background_tasks
+        task = await orchestrator.get_raw_task(task_id=task.task_id)
+        response = await prepare_response(
+            task=task, orchestrator=orchestrator, background_tasks=background_tasks
         )
-        if result is None:
-            raise HTTPException(
-                status_code=404,
-                detail="Task result not found. Please wait for a completion status.",
-            )
-        return result
+        return response
 
     # Convert a document from file(s)
     @app.post(
-        "/v1alpha/convert/file",
+        "/v1/convert/file",
         response_model=ConvertDocumentResponse,
         responses={
             200: {
@@ -348,43 +356,41 @@ def create_app():  # noqa: C901
     )
     async def process_file(
         background_tasks: BackgroundTasks,
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         files: list[UploadFile],
         options: Annotated[
-            ConvertDocumentsOptions, FormDepends(ConvertDocumentsOptions)
+            ConvertDocumentsRequestOptions, FormDepends(ConvertDocumentsRequestOptions)
         ],
+        target_type: Annotated[TargetName, Form()] = TargetName.INBODY,
     ):
+        target = InBodyTarget() if target_type == TargetName.INBODY else ZipTarget()
         task = await _enque_file(
-            orchestrator=orchestrator, files=files, options=options
+            orchestrator=orchestrator, files=files, options=options, target=target
         )
-        success = await _wait_task_complete(
+        completed = await _wait_task_complete(
             orchestrator=orchestrator, task_id=task.task_id
         )
 
-        if not success:
+        if not completed:
             # TODO: abort task!
             return HTTPException(
                 status_code=504,
                 detail=f"Conversion is taking too long. The maximum wait time is configure as DOCLING_SERVE_MAX_SYNC_WAIT={docling_serve_settings.max_sync_wait}.",
             )
 
-        result = await orchestrator.task_result(
-            task_id=task.task_id, background_tasks=background_tasks
+        task = await orchestrator.get_raw_task(task_id=task.task_id)
+        response = await prepare_response(
+            task=task, orchestrator=orchestrator, background_tasks=background_tasks
         )
-        if result is None:
-            raise HTTPException(
-                status_code=404,
-                detail="Task result not found. Please wait for a completion status.",
-            )
-        return result
+        return response
 
     # Convert a document from URL(s) using the async api
     @app.post(
-        "/v1alpha/convert/source/async",
+        "/v1/convert/source/async",
         response_model=TaskStatusResponse,
     )
     async def process_url_async(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         conversion_request: ConvertDocumentsRequest,
     ):
         task = await _enque_source(
@@ -402,19 +408,21 @@ def create_app():  # noqa: C901
 
     # Convert a document from file(s) using the async api
     @app.post(
-        "/v1alpha/convert/file/async",
+        "/v1/convert/file/async",
         response_model=TaskStatusResponse,
     )
     async def process_file_async(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         background_tasks: BackgroundTasks,
         files: list[UploadFile],
         options: Annotated[
-            ConvertDocumentsOptions, FormDepends(ConvertDocumentsOptions)
+            ConvertDocumentsRequestOptions, FormDepends(ConvertDocumentsRequestOptions)
         ],
+        target_type: Annotated[TargetName, Form()] = TargetName.INBODY,
     ):
+        target = InBodyTarget() if target_type == TargetName.INBODY else ZipTarget()
         task = await _enque_file(
-            orchestrator=orchestrator, files=files, options=options
+            orchestrator=orchestrator, files=files, options=options, target=target
         )
         task_queue_position = await orchestrator.get_queue_position(
             task_id=task.task_id
@@ -428,11 +436,11 @@ def create_app():  # noqa: C901
 
     # Task status poll
     @app.get(
-        "/v1alpha/status/poll/{task_id}",
+        "/v1/status/poll/{task_id}",
         response_model=TaskStatusResponse,
     )
     async def task_status_poll(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         task_id: str,
         wait: Annotated[
             float, Query(help="Number of seconds to wait for a completed status.")
@@ -452,13 +460,14 @@ def create_app():  # noqa: C901
 
     # Task status websocket
     @app.websocket(
-        "/v1alpha/status/ws/{task_id}",
+        "/v1/status/ws/{task_id}",
     )
     async def task_status_ws(
         websocket: WebSocket,
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         task_id: str,
     ):
+        assert isinstance(orchestrator.notifier, WebsocketNotifier)
         await websocket.accept()
 
         if task_id not in orchestrator.tasks:
@@ -473,7 +482,7 @@ def create_app():  # noqa: C901
         task = orchestrator.tasks[task_id]
 
         # Track active WebSocket connections for this job
-        orchestrator.task_subscribers[task_id].add(websocket)
+        orchestrator.notifier.task_subscribers[task_id].add(websocket)
 
         try:
             task_queue_position = await orchestrator.get_queue_position(task_id=task_id)
@@ -511,11 +520,11 @@ def create_app():  # noqa: C901
             _log.info(f"WebSocket disconnected for job {task_id}")
 
         finally:
-            orchestrator.task_subscribers[task_id].remove(websocket)
+            orchestrator.notifier.task_subscribers[task_id].remove(websocket)
 
     # Task result
     @app.get(
-        "/v1alpha/result/{task_id}",
+        "/v1/result/{task_id}",
         response_model=ConvertDocumentResponse,
         responses={
             200: {
@@ -524,27 +533,26 @@ def create_app():  # noqa: C901
         },
     )
     async def task_result(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         background_tasks: BackgroundTasks,
         task_id: str,
     ):
-        result = await orchestrator.task_result(
-            task_id=task_id, background_tasks=background_tasks
-        )
-        if result is None:
-            raise HTTPException(
-                status_code=404,
-                detail="Task result not found. Please wait for a completion status.",
+        try:
+            task = await orchestrator.get_raw_task(task_id=task_id)
+            response = await prepare_response(
+                task=task, orchestrator=orchestrator, background_tasks=background_tasks
             )
-        return result
+            return response
+        except TaskNotFoundError:
+            raise HTTPException(status_code=404, detail="Task not found.")
 
     # Update task progress
     @app.post(
-        "/v1alpha/callback/task/progress",
+        "/v1/callback/task/progress",
         response_model=ProgressCallbackResponse,
     )
     async def callback_task_progress(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         request: ProgressCallbackRequest,
     ):
         try:
@@ -561,20 +569,22 @@ def create_app():  # noqa: C901
 
     # Offload models
     @app.get(
-        "/v1alpha/clear/converters",
+        "/v1/clear/converters",
         response_model=ClearResponse,
     )
-    async def clear_converters():
-        _get_converter_from_hash.cache_clear()
+    async def clear_converters(
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
+    ):
+        await orchestrator.clear_converters()
         return ClearResponse()
 
     # Clean results
     @app.get(
-        "/v1alpha/clear/results",
+        "/v1/clear/results",
         response_model=ClearResponse,
     )
     async def clear_results(
-        orchestrator: Annotated[BaseAsyncOrchestrator, Depends(get_async_orchestrator)],
+        orchestrator: Annotated[BaseOrchestrator, Depends(get_async_orchestrator)],
         older_then: float = 3600,
     ):
         await orchestrator.clear_results(older_than=older_then)

docling_serve/datamodel/callback.py

@@ -1,50 +0,0 @@
-import enum
-from typing import Annotated, Literal
-
-from pydantic import BaseModel, Field
-
-
-class ProgressKind(str, enum.Enum):
-    SET_NUM_DOCS = "set_num_docs"
-    UPDATE_PROCESSED = "update_processed"
-
-
-class BaseProgress(BaseModel):
-    kind: ProgressKind
-
-
-class ProgressSetNumDocs(BaseProgress):
-    kind: Literal[ProgressKind.SET_NUM_DOCS] = ProgressKind.SET_NUM_DOCS
-
-    num_docs: int
-
-
-class SucceededDocsItem(BaseModel):
-    source: str
-
-
-class FailedDocsItem(BaseModel):
-    source: str
-    error: str
-
-
-class ProgressUpdateProcessed(BaseProgress):
-    kind: Literal[ProgressKind.UPDATE_PROCESSED] = ProgressKind.UPDATE_PROCESSED
-
-    num_processed: int
-    num_succeeded: int
-    num_failed: int
-
-    docs_succeeded: list[SucceededDocsItem]
-    docs_failed: list[FailedDocsItem]
-
-
-class ProgressCallbackRequest(BaseModel):
-    task_id: str
-    progress: Annotated[
-        ProgressSetNumDocs | ProgressUpdateProcessed, Field(discriminator="kind")
-    ]
-
-
-class ProgressCallbackResponse(BaseModel):
-    status: Literal["ack"] = "ack"

docling_serve/datamodel/convert.py

@@ -1,24 +1,13 @@
 # Define the input options for the API
-from typing import Annotated, Any, Optional
+from typing import Annotated
 
-from pydantic import AnyUrl, BaseModel, Field, model_validator
-from typing_extensions import Self
+from pydantic import Field
 
-from docling.datamodel.base_models import InputFormat, OutputFormat
 from docling.datamodel.pipeline_options import (
     EasyOcrOptions,
-    PdfBackend,
-    PictureDescriptionBaseOptions,
-    ProcessingPipeline,
-    TableFormerMode,
-    TableStructureOptions,
-)
-from docling.datamodel.settings import (
-    DEFAULT_PAGE_RANGE,
-    PageRange,
 )
 from docling.models.factories import get_ocr_factory
-from docling_core.types.doc import ImageRefMode
+from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
 
 from docling_serve.settings import docling_serve_settings
 
@@ -28,154 +17,7 @@ ocr_factory = get_ocr_factory(
 ocr_engines_enum = ocr_factory.get_enum()
 
 
-class PictureDescriptionLocal(BaseModel):
-    repo_id: Annotated[
-        str,
-        Field(
-            description="Repository id from the Hugging Face Hub.",
-            examples=[
-                "HuggingFaceTB/SmolVLM-256M-Instruct",
-                "ibm-granite/granite-vision-3.2-2b",
-            ],
-        ),
-    ]
-    prompt: Annotated[
-        str,
-        Field(
-            description="Prompt used when calling the vision-language model.",
-            examples=[
-                "Describe this image in a few sentences.",
-                "This is a figure from a document. Provide a detailed description of it.",
-            ],
-        ),
-    ] = "Describe this image in a few sentences."
-    generation_config: Annotated[
-        dict[str, Any],
-        Field(
-            description="Config from https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig",
-            examples=[{"max_new_tokens": 200, "do_sample": False}],
-        ),
-    ] = {"max_new_tokens": 200, "do_sample": False}
-
-
-class PictureDescriptionApi(BaseModel):
-    url: Annotated[
-        AnyUrl,
-        Field(
-            description="Endpoint which accepts openai-api compatible requests.",
-            examples=[
-                AnyUrl(
-                    "http://localhost:8000/v1/chat/completions"
-                ),  # example of a local vllm api
-                AnyUrl(
-                    "http://localhost:11434/v1/chat/completions"
-                ),  # example of ollama
-            ],
-        ),
-    ]
-    headers: Annotated[
-        dict[str, str],
-        Field(
-            description="Headers used for calling the API endpoint. For example, it could include authentication headers."
-        ),
-    ] = {}
-    params: Annotated[
-        dict[str, Any],
-        Field(
-            description="Model parameters.",
-            examples=[
-                {  # on vllm
-                    "model": "HuggingFaceTB/SmolVLM-256M-Instruct",
-                    "max_completion_tokens": 200,
-                },
-                {  # on vllm
-                    "model": "ibm-granite/granite-vision-3.2-2b",
-                    "max_completion_tokens": 200,
-                },
-                {  # on ollama
-                    "model": "granite3.2-vision:2b"
-                },
-            ],
-        ),
-    ] = {}
-    timeout: Annotated[float, Field(description="Timeout for the API request.")] = 20
-    prompt: Annotated[
-        str,
-        Field(
-            description="Prompt used when calling the vision-language model.",
-            examples=[
-                "Describe this image in a few sentences.",
-                "This is a figures from a document. Provide a detailed description of it.",
-            ],
-        ),
-    ] = "Describe this image in a few sentences."
-
-
-class ConvertDocumentsOptions(BaseModel):
-    from_formats: Annotated[
-        list[InputFormat],
-        Field(
-            description=(
-                "Input format(s) to convert from. String or list of strings. "
-                f"Allowed values: {', '.join([v.value for v in InputFormat])}. "
-                "Optional, defaults to all formats."
-            ),
-            examples=[[v.value for v in InputFormat]],
-        ),
-    ] = list(InputFormat)
-
-    to_formats: Annotated[
-        list[OutputFormat],
-        Field(
-            description=(
-                "Output format(s) to convert to. String or list of strings. "
-                f"Allowed values: {', '.join([v.value for v in OutputFormat])}. "
-                "Optional, defaults to Markdown."
-            ),
-            examples=[
-                [OutputFormat.MARKDOWN],
-                [OutputFormat.MARKDOWN, OutputFormat.JSON],
-                [v.value for v in OutputFormat],
-            ],
-        ),
-    ] = [OutputFormat.MARKDOWN]
-
-    image_export_mode: Annotated[
-        ImageRefMode,
-        Field(
-            description=(
-                "Image export mode for the document (in case of JSON,"
-                " Markdown or HTML). "
-                f"Allowed values: {', '.join([v.value for v in ImageRefMode])}. "
-                "Optional, defaults to Embedded."
-            ),
-            examples=[ImageRefMode.EMBEDDED.value],
-            # pattern="embedded|placeholder|referenced",
-        ),
-    ] = ImageRefMode.EMBEDDED
-
-    do_ocr: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, the bitmap content will be processed using OCR. "
-                "Boolean. Optional, defaults to true"
-            ),
-            # examples=[True],
-        ),
-    ] = True
-
-    force_ocr: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, replace existing text with OCR-generated "
-                "text over content. Boolean. Optional, defaults to false."
-            ),
-            # examples=[False],
-        ),
-    ] = False
-
+class ConvertDocumentsRequestOptions(ConvertDocumentsOptions):
     ocr_engine: Annotated[  # type: ignore
         ocr_engines_enum,
         Field(
@@ -188,57 +30,6 @@ class ConvertDocumentsOptions(BaseModel):
         ),
     ] = ocr_engines_enum(EasyOcrOptions.kind)  # type: ignore
 
-    ocr_lang: Annotated[
-        Optional[list[str]],
-        Field(
-            description=(
-                "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."
-            ),
-            examples=[["fr", "de", "es", "en"]],
-        ),
-    ] = None
-
-    pdf_backend: Annotated[
-        PdfBackend,
-        Field(
-            description=(
-                "The PDF backend to use. String. "
-                f"Allowed values: {', '.join([v.value for v in PdfBackend])}. "
-                f"Optional, defaults to {PdfBackend.DLPARSE_V4.value}."
-            ),
-            examples=[PdfBackend.DLPARSE_V4],
-        ),
-    ] = PdfBackend.DLPARSE_V4
-
-    table_mode: Annotated[
-        TableFormerMode,
-        Field(
-            description=(
-                "Mode to use for table structure, String. "
-                f"Allowed values: {', '.join([v.value for v in TableFormerMode])}. "
-                "Optional, defaults to fast."
-            ),
-            examples=[TableStructureOptions().mode],
-            # pattern="fast|accurate",
-        ),
-    ] = TableStructureOptions().mode
-
-    pipeline: Annotated[
-        ProcessingPipeline,
-        Field(description="Choose the pipeline to process PDF or image files."),
-    ] = ProcessingPipeline.STANDARD
-
-    page_range: Annotated[
-        PageRange,
-        Field(
-            description="Only convert a range of pages. The page number starts at 1.",
-            examples=[DEFAULT_PAGE_RANGE, (1, 4)],
-        ),
-    ] = DEFAULT_PAGE_RANGE
-
     document_timeout: Annotated[
         float,
         Field(
@@ -247,152 +38,3 @@ class ConvertDocumentsOptions(BaseModel):
             le=docling_serve_settings.max_document_timeout,
         ),
     ] = docling_serve_settings.max_document_timeout
-
-    abort_on_error: Annotated[
-        bool,
-        Field(
-            description=(
-                "Abort on error if enabled. Boolean. Optional, defaults to false."
-            ),
-            # examples=[False],
-        ),
-    ] = False
-
-    return_as_file: Annotated[
-        bool,
-        Field(
-            description=(
-                "Return the output as a zip file "
-                "(will happen anyway if multiple files are generated). "
-                "Boolean. Optional, defaults to false."
-            ),
-            examples=[False],
-        ),
-    ] = False
-
-    do_table_structure: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, the table structure will be extracted. "
-                "Boolean. Optional, defaults to true."
-            ),
-            examples=[True],
-        ),
-    ] = True
-
-    include_images: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, images will be extracted from the document. "
-                "Boolean. Optional, defaults to true."
-            ),
-            examples=[True],
-        ),
-    ] = True
-
-    images_scale: Annotated[
-        float,
-        Field(
-            description="Scale factor for images. Float. Optional, defaults to 2.0.",
-            examples=[2.0],
-        ),
-    ] = 2.0
-
-    md_page_break_placeholder: Annotated[
-        str,
-        Field(
-            description="Add this placeholder betweek pages in the markdown output.",
-            examples=["<!-- page-break -->", ""],
-        ),
-    ] = ""
-
-    do_code_enrichment: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, perform OCR code enrichment. "
-                "Boolean. Optional, defaults to false."
-            ),
-            examples=[False],
-        ),
-    ] = False
-
-    do_formula_enrichment: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, perform formula OCR, return LaTeX code. "
-                "Boolean. Optional, defaults to false."
-            ),
-            examples=[False],
-        ),
-    ] = False
-
-    do_picture_classification: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, classify pictures in documents. "
-                "Boolean. Optional, defaults to false."
-            ),
-            examples=[False],
-        ),
-    ] = False
-
-    do_picture_description: Annotated[
-        bool,
-        Field(
-            description=(
-                "If enabled, describe pictures in documents. "
-                "Boolean. Optional, defaults to false."
-            ),
-            examples=[False],
-        ),
-    ] = False
-
-    picture_description_area_threshold: Annotated[
-        float,
-        Field(
-            description="Minimum percentage of the area for a picture to be processed with the models.",
-            examples=[PictureDescriptionBaseOptions().picture_area_threshold],
-        ),
-    ] = PictureDescriptionBaseOptions().picture_area_threshold
-
-    picture_description_local: Annotated[
-        Optional[PictureDescriptionLocal],
-        Field(
-            description="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.",
-            examples=[
-                PictureDescriptionLocal(repo_id="ibm-granite/granite-vision-3.2-2b"),
-                PictureDescriptionLocal(repo_id="HuggingFaceTB/SmolVLM-256M-Instruct"),
-            ],
-        ),
-    ] = None
-
-    picture_description_api: Annotated[
-        Optional[PictureDescriptionApi],
-        Field(
-            description="API details for using a vision-language model in the picture description. This parameter is mutually exclusive with picture_description_local.",
-            examples=[
-                PictureDescriptionApi(
-                    url="http://localhost:11434/v1/chat/completions",
-                    params={"model": "granite3.2-vision:2b"},
-                )
-            ],
-        ),
-    ] = None
-
-    @model_validator(mode="after")
-    def picture_description_exclusivity(self) -> Self:
-        # Validate picture description options
-        if (
-            self.picture_description_local is not None
-            and self.picture_description_api is not None
-        ):
-            raise ValueError(
-                "The parameters picture_description_local and picture_description_api are mutually exclusive, only one of them can be set."
-            )
-
-        return self

docling_serve/datamodel/engines.py

@@ -1,13 +0,0 @@
-import enum
-
-
-class TaskStatus(str, enum.Enum):
-    SUCCESS = "success"
-    PENDING = "pending"
-    STARTED = "started"
-    FAILURE = "failure"
-
-
-class AsyncEngine(str, enum.Enum):
-    LOCAL = "local"
-    KFP = "kfp"

docling_serve/datamodel/kfp.py

@@ -1,7 +0,0 @@
-from pydantic import AnyUrl, BaseModel
-
-
-class CallbackSpec(BaseModel):
-    url: AnyUrl
-    headers: dict[str, str] = {}
-    ca_cert: str = ""

docling_serve/datamodel/requests.py

@@ -1,62 +1,38 @@
-import base64
-from io import BytesIO
-from typing import Annotated, Any, Union
+import enum
+from typing import Annotated, Literal
 
-from pydantic import AnyHttpUrl, BaseModel, Field
+from pydantic import BaseModel, Field
 
-from docling.datamodel.base_models import DocumentStream
+from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
+from docling_jobkit.datamodel.task_targets import InBodyTarget, TaskTarget, ZipTarget
 
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
+from docling_serve.datamodel.convert import ConvertDocumentsRequestOptions
+
+## Sources
 
 
-class DocumentsConvertBase(BaseModel):
-    options: ConvertDocumentsOptions = ConvertDocumentsOptions()
+class FileSourceRequest(FileSource):
+    kind: Literal["file"] = "file"
 
 
-class HttpSource(BaseModel):
-    url: Annotated[
-        AnyHttpUrl,
-        Field(
-            description="HTTP url to process",
-            examples=["https://arxiv.org/pdf/2206.01062"],
-        ),
-    ]
-    headers: Annotated[
-        dict[str, Any],
-        Field(
-            description="Additional headers used to fetch the urls, "
-            "e.g. authorization, agent, etc"
-        ),
-    ] = {}
+class HttpSourceRequest(HttpSource):
+    kind: Literal["http"] = "http"
 
 
-class FileSource(BaseModel):
-    base64_string: Annotated[
-        str,
-        Field(
-            description="Content of the file serialized in base64. "
-            "For example it can be obtained via "
-            "`base64 -w 0 /path/to/file/pdf-to-convert.pdf`."
-        ),
-    ]
-    filename: Annotated[
-        str,
-        Field(description="Filename of the uploaded document", examples=["file.pdf"]),
-    ]
-
-    def to_document_stream(self) -> DocumentStream:
-        buf = BytesIO(base64.b64decode(self.base64_string))
-        return DocumentStream(stream=buf, name=self.filename)
+## Multipart targets
+class TargetName(str, enum.Enum):
+    INBODY = InBodyTarget().kind
+    ZIP = ZipTarget().kind
 
 
-class ConvertDocumentHttpSourcesRequest(DocumentsConvertBase):
-    http_sources: list[HttpSource]
-
-
-class ConvertDocumentFileSourcesRequest(DocumentsConvertBase):
-    file_sources: list[FileSource]
-
-
-ConvertDocumentsRequest = Union[
-    ConvertDocumentFileSourcesRequest, ConvertDocumentHttpSourcesRequest
+## Aliases
+SourceRequestItem = Annotated[
+    FileSourceRequest | HttpSourceRequest, Field(discriminator="kind")
 ]
+
+
+## Complete Source request
+class ConvertDocumentsRequest(BaseModel):
+    options: ConvertDocumentsRequestOptions = ConvertDocumentsRequestOptions()
+    sources: list[SourceRequestItem]
+    target: TaskTarget = InBodyTarget()

docling_serve/datamodel/responses.py

@@ -6,8 +6,7 @@ from pydantic import BaseModel
 from docling.datamodel.document import ConversionStatus, ErrorItem
 from docling.utils.profiling import ProfilingItem
 from docling_core.types.doc import DoclingDocument
-
-from docling_serve.datamodel.task_meta import TaskProcessingMeta
+from docling_jobkit.datamodel.task_meta import TaskProcessingMeta
 
 
 # Status

docling_serve/datamodel/task.py

@@ -1,55 +0,0 @@
-import datetime
-from functools import partial
-from pathlib import Path
-from typing import Optional, Union
-
-from fastapi.responses import FileResponse
-from pydantic import BaseModel, ConfigDict, Field
-
-from docling.datamodel.base_models import DocumentStream
-
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
-from docling_serve.datamodel.engines import TaskStatus
-from docling_serve.datamodel.requests import FileSource, HttpSource
-from docling_serve.datamodel.responses import ConvertDocumentResponse
-from docling_serve.datamodel.task_meta import TaskProcessingMeta
-
-TaskSource = Union[HttpSource, FileSource, DocumentStream]
-
-
-class Task(BaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    task_id: str
-    task_status: TaskStatus = TaskStatus.PENDING
-    sources: list[TaskSource] = []
-    options: Optional[ConvertDocumentsOptions]
-    result: Optional[Union[ConvertDocumentResponse, FileResponse]] = None
-    scratch_dir: Optional[Path] = None
-    processing_meta: Optional[TaskProcessingMeta] = None
-    created_at: datetime.datetime = Field(
-        default_factory=partial(datetime.datetime.now, datetime.timezone.utc)
-    )
-    started_at: Optional[datetime.datetime] = None
-    finished_at: Optional[datetime.datetime] = None
-    last_update_at: datetime.datetime = Field(
-        default_factory=partial(datetime.datetime.now, datetime.timezone.utc)
-    )
-
-    def set_status(self, status: TaskStatus):
-        now = datetime.datetime.now(datetime.timezone.utc)
-        if status == TaskStatus.STARTED and self.started_at is None:
-            self.started_at = now
-        if (
-            status in [TaskStatus.SUCCESS, TaskStatus.FAILURE]
-            and self.finished_at is None
-        ):
-            self.finished_at = now
-
-        self.last_update_at = now
-        self.task_status = status
-
-    def is_completed(self) -> bool:
-        if self.task_status in [TaskStatus.SUCCESS, TaskStatus.FAILURE]:
-            return True
-        return False

docling_serve/datamodel/task_meta.py

@@ -1,8 +0,0 @@
-from pydantic import BaseModel
-
-
-class TaskProcessingMeta(BaseModel):
-    num_docs: int
-    num_processed: int = 0
-    num_succeeded: int = 0
-    num_failed: int = 0

docling_serve/docling_conversion.py

@@ -1,256 +0,0 @@
-import hashlib
-import json
-import logging
-import sys
-from collections.abc import Iterable, Iterator
-from functools import lru_cache
-from pathlib import Path
-from typing import Any, Optional, Union
-
-from fastapi import HTTPException
-
-from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
-from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
-from docling.backend.docling_parse_v4_backend import DoclingParseV4DocumentBackend
-from docling.backend.pdf_backend import PdfDocumentBackend
-from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
-from docling.datamodel.base_models import DocumentStream, InputFormat
-from docling.datamodel.document import ConversionResult
-from docling.datamodel.pipeline_options import (
-    OcrOptions,
-    PdfBackend,
-    PdfPipelineOptions,
-    PictureDescriptionApiOptions,
-    PictureDescriptionVlmOptions,
-    ProcessingPipeline,
-    TableFormerMode,
-    VlmPipelineOptions,
-    smoldocling_vlm_conversion_options,
-    smoldocling_vlm_mlx_conversion_options,
-)
-from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption
-from docling.pipeline.vlm_pipeline import VlmPipeline
-from docling_core.types.doc import ImageRefMode
-
-from docling_serve.datamodel.convert import ConvertDocumentsOptions, ocr_factory
-from docling_serve.helper_functions import _to_list_of_strings
-from docling_serve.settings import docling_serve_settings
-
-_log = logging.getLogger(__name__)
-
-
-# Custom serializer for PdfFormatOption
-# (model_dump_json does not work with some classes)
-def _hash_pdf_format_option(pdf_format_option: PdfFormatOption) -> bytes:
-    data = pdf_format_option.model_dump(serialize_as_any=True)
-
-    # pipeline_options are not fully serialized by model_dump, dedicated pass
-    if pdf_format_option.pipeline_options:
-        data["pipeline_options"] = pdf_format_option.pipeline_options.model_dump(
-            serialize_as_any=True, mode="json"
-        )
-
-    # Replace `pipeline_cls` with a string representation
-    data["pipeline_cls"] = repr(data["pipeline_cls"])
-
-    # Replace `backend` with a string representation
-    data["backend"] = repr(data["backend"])
-
-    # Serialize the dictionary to JSON with sorted keys to have consistent hashes
-    serialized_data = json.dumps(data, sort_keys=True)
-    options_hash = hashlib.sha1(
-        serialized_data.encode(), usedforsecurity=False
-    ).digest()
-    return options_hash
-
-
-# Cache of DocumentConverter objects
-_options_map: dict[bytes, PdfFormatOption] = {}
-
-
-@lru_cache(maxsize=docling_serve_settings.options_cache_size)
-def _get_converter_from_hash(options_hash: bytes) -> DocumentConverter:
-    pdf_format_option = _options_map[options_hash]
-    format_options: dict[InputFormat, FormatOption] = {
-        InputFormat.PDF: pdf_format_option,
-        InputFormat.IMAGE: pdf_format_option,
-    }
-
-    return DocumentConverter(format_options=format_options)
-
-
-def get_converter(pdf_format_option: PdfFormatOption) -> DocumentConverter:
-    options_hash = _hash_pdf_format_option(pdf_format_option)
-    _options_map[options_hash] = pdf_format_option
-    return _get_converter_from_hash(options_hash)
-
-
-def _parse_standard_pdf_opts(
-    request: ConvertDocumentsOptions, artifacts_path: Optional[Path]
-) -> PdfPipelineOptions:
-    try:
-        ocr_options: OcrOptions = ocr_factory.create_options(
-            kind=request.ocr_engine.value,  # type: ignore
-            force_full_page_ocr=request.force_ocr,
-        )
-    except ImportError as err:
-        raise HTTPException(
-            status_code=400,
-            detail="The requested OCR engine"
-            f" (ocr_engine={request.ocr_engine.value})"  # type: ignore
-            " is not available on this system. Please choose another OCR engine "
-            "or contact your system administrator.\n"
-            f"{err}",
-        )
-
-    if request.ocr_lang is not None:
-        if isinstance(request.ocr_lang, str):
-            ocr_options.lang = _to_list_of_strings(request.ocr_lang)
-        else:
-            ocr_options.lang = request.ocr_lang
-
-    pipeline_options = PdfPipelineOptions(
-        artifacts_path=artifacts_path,
-        enable_remote_services=docling_serve_settings.enable_remote_services,
-        document_timeout=request.document_timeout,
-        do_ocr=request.do_ocr,
-        ocr_options=ocr_options,
-        do_table_structure=request.do_table_structure,
-        do_code_enrichment=request.do_code_enrichment,
-        do_formula_enrichment=request.do_formula_enrichment,
-        do_picture_classification=request.do_picture_classification,
-        do_picture_description=request.do_picture_description,
-    )
-    pipeline_options.table_structure_options.mode = TableFormerMode(request.table_mode)
-
-    if request.image_export_mode != ImageRefMode.PLACEHOLDER:
-        pipeline_options.generate_page_images = True
-        if request.image_export_mode == ImageRefMode.REFERENCED:
-            pipeline_options.generate_picture_images = True
-        if request.images_scale:
-            pipeline_options.images_scale = request.images_scale
-
-    if request.picture_description_local is not None:
-        pipeline_options.picture_description_options = (
-            PictureDescriptionVlmOptions.model_validate(
-                request.picture_description_local.model_dump()
-            )
-        )
-
-    if request.picture_description_api is not None:
-        pipeline_options.picture_description_options = (
-            PictureDescriptionApiOptions.model_validate(
-                request.picture_description_api.model_dump()
-            )
-        )
-    pipeline_options.picture_description_options.picture_area_threshold = (
-        request.picture_description_area_threshold
-    )
-
-    return pipeline_options
-
-
-def _parse_backend(request: ConvertDocumentsOptions) -> type[PdfDocumentBackend]:
-    if request.pdf_backend == PdfBackend.DLPARSE_V1:
-        backend: type[PdfDocumentBackend] = DoclingParseDocumentBackend
-    elif request.pdf_backend == PdfBackend.DLPARSE_V2:
-        backend = DoclingParseV2DocumentBackend
-    elif request.pdf_backend == PdfBackend.DLPARSE_V4:
-        backend = DoclingParseV4DocumentBackend
-    elif request.pdf_backend == PdfBackend.PYPDFIUM2:
-        backend = PyPdfiumDocumentBackend
-    else:
-        raise RuntimeError(f"Unexpected PDF backend type {request.pdf_backend}")
-
-    return backend
-
-
-def _parse_vlm_pdf_opts(
-    request: ConvertDocumentsOptions, artifacts_path: Optional[Path]
-) -> VlmPipelineOptions:
-    pipeline_options = VlmPipelineOptions(
-        artifacts_path=artifacts_path,
-        document_timeout=request.document_timeout,
-    )
-    pipeline_options.vlm_options = smoldocling_vlm_conversion_options
-    if sys.platform == "darwin":
-        try:
-            import mlx_vlm  # noqa: F401
-
-            pipeline_options.vlm_options = smoldocling_vlm_mlx_conversion_options
-        except ImportError:
-            _log.warning(
-                "To run SmolDocling faster, please install mlx-vlm:\n"
-                "pip install mlx-vlm"
-            )
-    return pipeline_options
-
-
-# Computes the PDF pipeline options and returns the PdfFormatOption and its hash
-def get_pdf_pipeline_opts(
-    request: ConvertDocumentsOptions,
-) -> PdfFormatOption:
-    artifacts_path: Optional[Path] = None
-    if docling_serve_settings.artifacts_path is not None:
-        if str(docling_serve_settings.artifacts_path.absolute()) == "":
-            _log.info(
-                "artifacts_path is an empty path, model weights will be downloaded "
-                "at runtime."
-            )
-            artifacts_path = None
-        elif docling_serve_settings.artifacts_path.is_dir():
-            _log.info(
-                "artifacts_path is set to a valid directory. "
-                "No model weights will be downloaded at runtime."
-            )
-            artifacts_path = docling_serve_settings.artifacts_path
-        else:
-            _log.warning(
-                "artifacts_path is set to an invalid directory. "
-                "The system will download the model weights at runtime."
-            )
-            artifacts_path = None
-    else:
-        _log.info(
-            "artifacts_path is unset. "
-            "The system will download the model weights at runtime."
-        )
-
-    pipeline_options: Union[PdfPipelineOptions, VlmPipelineOptions]
-    if request.pipeline == ProcessingPipeline.STANDARD:
-        pipeline_options = _parse_standard_pdf_opts(request, artifacts_path)
-        backend = _parse_backend(request)
-        pdf_format_option = PdfFormatOption(
-            pipeline_options=pipeline_options,
-            backend=backend,
-        )
-
-    elif request.pipeline == ProcessingPipeline.VLM:
-        pipeline_options = _parse_vlm_pdf_opts(request, artifacts_path)
-        pdf_format_option = PdfFormatOption(
-            pipeline_cls=VlmPipeline, pipeline_options=pipeline_options
-        )
-    else:
-        raise NotImplementedError(
-            f"The pipeline {request.pipeline} is not implemented."
-        )
-
-    return pdf_format_option
-
-
-def convert_documents(
-    sources: Iterable[Union[Path, str, DocumentStream]],
-    options: ConvertDocumentsOptions,
-    headers: Optional[dict[str, Any]] = None,
-):
-    pdf_format_option = get_pdf_pipeline_opts(options)
-    converter = get_converter(pdf_format_option)
-    results: Iterator[ConversionResult] = converter.convert_all(
-        sources,
-        headers=headers,
-        page_range=options.page_range,
-        max_file_size=docling_serve_settings.max_file_size,
-        max_num_pages=docling_serve_settings.max_num_pages,
-    )
-
-    return results

docling_serve/engines/async_kfp/kfp_pipeline.py

@@ -1,137 +0,0 @@
-# ruff: noqa: E402, UP006, UP035
-
-from typing import Any, Dict, List
-
-from kfp import dsl
-
-PYTHON_BASE_IMAGE = "python:3.12"
-
-
-@dsl.component(
-    base_image=PYTHON_BASE_IMAGE,
-    packages_to_install=[
-        "pydantic",
-        "docling-serve @ git+https://github.com/docling-project/docling-serve@feat-kfp-engine",
-    ],
-    pip_index_urls=["https://download.pytorch.org/whl/cpu", "https://pypi.org/simple"],
-)
-def generate_chunks(
-    run_name: str,
-    request: Dict[str, Any],
-    batch_size: int,
-    callbacks: List[Dict[str, Any]],
-) -> List[List[Dict[str, Any]]]:
-    from pydantic import TypeAdapter
-
-    from docling_serve.datamodel.callback import (
-        ProgressCallbackRequest,
-        ProgressSetNumDocs,
-    )
-    from docling_serve.datamodel.kfp import CallbackSpec
-    from docling_serve.engines.async_kfp.notify import notify_callbacks
-
-    CallbacksListType = TypeAdapter(list[CallbackSpec])
-
-    sources = request["http_sources"]
-    splits = [sources[i : i + batch_size] for i in range(0, len(sources), batch_size)]
-
-    total = sum(len(chunk) for chunk in splits)
-    payload = ProgressCallbackRequest(
-        task_id=run_name, progress=ProgressSetNumDocs(num_docs=total)
-    )
-    notify_callbacks(
-        payload=payload,
-        callbacks=CallbacksListType.validate_python(callbacks),
-    )
-
-    return splits
-
-
-@dsl.component(
-    base_image=PYTHON_BASE_IMAGE,
-    packages_to_install=[
-        "pydantic",
-        "docling-serve @ git+https://github.com/docling-project/docling-serve@feat-kfp-engine",
-    ],
-    pip_index_urls=["https://download.pytorch.org/whl/cpu", "https://pypi.org/simple"],
-)
-def convert_batch(
-    run_name: str,
-    data_splits: List[Dict[str, Any]],
-    request: Dict[str, Any],
-    callbacks: List[Dict[str, Any]],
-    output_path: dsl.OutputPath("Directory"),  # type: ignore
-):
-    from pathlib import Path
-
-    from pydantic import AnyUrl, TypeAdapter
-
-    from docling_serve.datamodel.callback import (
-        FailedDocsItem,
-        ProgressCallbackRequest,
-        ProgressUpdateProcessed,
-        SucceededDocsItem,
-    )
-    from docling_serve.datamodel.convert import ConvertDocumentsOptions
-    from docling_serve.datamodel.kfp import CallbackSpec
-    from docling_serve.datamodel.requests import HttpSource
-    from docling_serve.engines.async_kfp.notify import notify_callbacks
-
-    CallbacksListType = TypeAdapter(list[CallbackSpec])
-
-    convert_options = ConvertDocumentsOptions.model_validate(request["options"])
-    print(convert_options)
-
-    output_dir = Path(output_path)
-    output_dir.mkdir(exist_ok=True, parents=True)
-    docs_succeeded: list[SucceededDocsItem] = []
-    docs_failed: list[FailedDocsItem] = []
-    for source_dict in data_splits:
-        source = HttpSource.model_validate(source_dict)
-        filename = Path(str(AnyUrl(source.url).path)).name
-        output_filename = output_dir / filename
-        print(f"Writing {output_filename}")
-        with output_filename.open("w") as f:
-            f.write(source.model_dump_json())
-        docs_succeeded.append(SucceededDocsItem(source=source.url))
-
-    payload = ProgressCallbackRequest(
-        task_id=run_name,
-        progress=ProgressUpdateProcessed(
-            num_failed=len(docs_failed),
-            num_processed=len(docs_succeeded) + len(docs_failed),
-            num_succeeded=len(docs_succeeded),
-            docs_succeeded=docs_succeeded,
-            docs_failed=docs_failed,
-        ),
-    )
-
-    print(payload)
-    notify_callbacks(
-        payload=payload,
-        callbacks=CallbacksListType.validate_python(callbacks),
-    )
-
-
-@dsl.pipeline()
-def process(
-    batch_size: int,
-    request: Dict[str, Any],
-    callbacks: List[Dict[str, Any]] = [],
-    run_name: str = "",
-):
-    chunks_task = generate_chunks(
-        run_name=run_name,
-        request=request,
-        batch_size=batch_size,
-        callbacks=callbacks,
-    )
-    chunks_task.set_caching_options(False)
-
-    with dsl.ParallelFor(chunks_task.output, parallelism=4) as data_splits:
-        convert_batch(
-            run_name=run_name,
-            data_splits=data_splits,
-            request=request,
-            callbacks=callbacks,
-        )

docling_serve/engines/async_kfp/notify.py

@@ -1,32 +0,0 @@
-import ssl
-
-import certifi
-import httpx
-
-from docling_serve.datamodel.callback import ProgressCallbackRequest
-from docling_serve.datamodel.kfp import CallbackSpec
-
-
-def notify_callbacks(
-    payload: ProgressCallbackRequest,
-    callbacks: list[CallbackSpec],
-):
-    if len(callbacks) == 0:
-        return
-
-    for callback in callbacks:
-        # https://www.python-httpx.org/advanced/ssl/#configuring-client-instances
-        if callback.ca_cert:
-            ctx = ssl.create_default_context(cadata=callback.ca_cert)
-        else:
-            ctx = ssl.create_default_context(cafile=certifi.where())
-
-        try:
-            httpx.post(
-                str(callback.url),
-                headers=callback.headers,
-                json=payload.model_dump(mode="json"),
-                verify=ctx,
-            )
-        except httpx.HTTPError as err:
-            print(f"Error notifying callback {callback.url}: {err}")

docling_serve/engines/async_kfp/orchestrator.py

@@ -1,235 +0,0 @@
-import datetime
-import json
-import logging
-import uuid
-from pathlib import Path
-from typing import Optional
-
-from kfp_server_api.models import V2beta1RuntimeState
-from pydantic import BaseModel, TypeAdapter
-from pydantic_settings import SettingsConfigDict
-
-from docling_serve.datamodel.callback import (
-    ProgressCallbackRequest,
-    ProgressSetNumDocs,
-    ProgressUpdateProcessed,
-)
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
-from docling_serve.datamodel.engines import TaskStatus
-from docling_serve.datamodel.kfp import CallbackSpec
-from docling_serve.datamodel.requests import HttpSource
-from docling_serve.datamodel.task import Task, TaskSource
-from docling_serve.datamodel.task_meta import TaskProcessingMeta
-from docling_serve.engines.async_kfp.kfp_pipeline import process
-from docling_serve.engines.async_orchestrator import (
-    BaseAsyncOrchestrator,
-    ProgressInvalid,
-)
-from docling_serve.settings import docling_serve_settings
-
-_log = logging.getLogger(__name__)
-
-
-class _RunItem(BaseModel):
-    model_config = SettingsConfigDict(arbitrary_types_allowed=True)
-
-    run_id: str
-    state: str
-    created_at: datetime.datetime
-    scheduled_at: datetime.datetime
-    finished_at: datetime.datetime
-
-
-class AsyncKfpOrchestrator(BaseAsyncOrchestrator):
-    def __init__(self):
-        super().__init__()
-        import kfp
-
-        kfp_endpoint = docling_serve_settings.eng_kfp_endpoint
-        if kfp_endpoint is None:
-            raise ValueError("KFP endpoint is required when using the KFP engine.")
-
-        kube_sa_token_path = Path("/run/secrets/kubernetes.io/serviceaccount/token")
-        kube_sa_ca_cert_path = Path(
-            "/run/secrets/kubernetes.io/serviceaccount/service-ca.crt"
-        )
-
-        ssl_ca_cert = docling_serve_settings.eng_kfp_ca_cert_path
-        token = docling_serve_settings.eng_kfp_token
-        if (
-            ssl_ca_cert is None
-            and ".svc" in kfp_endpoint.host
-            and kube_sa_ca_cert_path.exists()
-        ):
-            ssl_ca_cert = str(kube_sa_ca_cert_path)
-        if token is None and kube_sa_token_path.exists():
-            token = kube_sa_token_path.read_text()
-
-        self._client = kfp.Client(
-            host=str(kfp_endpoint),
-            existing_token=token,
-            ssl_ca_cert=ssl_ca_cert,
-            # verify_ssl=False,
-        )
-
-    async def enqueue(
-        self, sources: list[TaskSource], options: ConvertDocumentsOptions
-    ) -> Task:
-        callbacks = []
-        if docling_serve_settings.eng_kfp_self_callback_endpoint is not None:
-            headers = {}
-            if docling_serve_settings.eng_kfp_self_callback_token_path is not None:
-                token = (
-                    docling_serve_settings.eng_kfp_self_callback_token_path.read_text()
-                )
-                headers["Authorization"] = f"Bearer {token}"
-            ca_cert = ""
-            if docling_serve_settings.eng_kfp_self_callback_ca_cert_path is not None:
-                ca_cert = docling_serve_settings.eng_kfp_self_callback_ca_cert_path.read_text()
-            callbacks.append(
-                CallbackSpec(
-                    url=docling_serve_settings.eng_kfp_self_callback_endpoint,
-                    headers=headers,
-                    ca_cert=ca_cert,
-                )
-            )
-
-        CallbacksType = TypeAdapter(list[CallbackSpec])
-        SourcesListType = TypeAdapter(list[HttpSource])
-        http_sources = [s for s in sources if isinstance(s, HttpSource)]
-        # hack: since the current kfp backend is not resolving the job_id placeholder,
-        # we set the run_name and pass it as argument to the job itself.
-        run_name = f"docling-job-{uuid.uuid4()}"
-        kfp_run = self._client.create_run_from_pipeline_func(
-            process,
-            arguments={
-                "batch_size": 10,
-                "sources": SourcesListType.dump_python(http_sources, mode="json"),
-                "options": options.model_dump(mode="json"),
-                "callbacks": CallbacksType.dump_python(callbacks, mode="json"),
-                "run_name": run_name,
-            },
-            run_name=run_name,
-        )
-        task_id = kfp_run.run_id
-
-        task = Task(task_id=task_id, sources=sources, options=options)
-        await self.init_task_tracking(task)
-        return task
-
-    async def _update_task_from_run(self, task_id: str, wait: float = 0.0):
-        run_info = self._client.get_run(run_id=task_id)
-        task = await self.get_raw_task(task_id=task_id)
-        # RUNTIME_STATE_UNSPECIFIED = "RUNTIME_STATE_UNSPECIFIED"
-        # PENDING = "PENDING"
-        # RUNNING = "RUNNING"
-        # SUCCEEDED = "SUCCEEDED"
-        # SKIPPED = "SKIPPED"
-        # FAILED = "FAILED"
-        # CANCELING = "CANCELING"
-        # CANCELED = "CANCELED"
-        # PAUSED = "PAUSED"
-        if run_info.state == V2beta1RuntimeState.SUCCEEDED:
-            task.set_status(TaskStatus.SUCCESS)
-        elif run_info.state == V2beta1RuntimeState.PENDING:
-            task.set_status(TaskStatus.PENDING)
-        elif run_info.state == V2beta1RuntimeState.RUNNING:
-            task.set_status(TaskStatus.STARTED)
-        else:
-            task.set_status(TaskStatus.FAILURE)
-
-    async def task_status(self, task_id: str, wait: float = 0.0) -> Task:
-        await self._update_task_from_run(task_id=task_id, wait=wait)
-        return await self.get_raw_task(task_id=task_id)
-
-    async def _get_pending(self) -> list[_RunItem]:
-        runs: list[_RunItem] = []
-        next_page: Optional[str] = None
-        while True:
-            res = self._client.list_runs(
-                page_token=next_page,
-                page_size=20,
-                filter=json.dumps(
-                    {
-                        "predicates": [
-                            {
-                                "operation": "EQUALS",
-                                "key": "state",
-                                "stringValue": "PENDING",
-                            }
-                        ]
-                    }
-                ),
-            )
-            if res.runs is not None:
-                for run in res.runs:
-                    runs.append(
-                        _RunItem(
-                            run_id=run.run_id,
-                            state=run.state,
-                            created_at=run.created_at,
-                            scheduled_at=run.scheduled_at,
-                            finished_at=run.finished_at,
-                        )
-                    )
-            if res.next_page_token is None:
-                break
-            next_page = res.next_page_token
-        return runs
-
-    async def queue_size(self) -> int:
-        runs = await self._get_pending()
-        return len(runs)
-
-    async def get_queue_position(self, task_id: str) -> Optional[int]:
-        runs = await self._get_pending()
-        for pos, run in enumerate(runs, start=1):
-            if run.run_id == task_id:
-                return pos
-        return None
-
-    async def process_queue(self):
-        return
-
-    async def warm_up_caches(self):
-        return
-
-    async def _get_run_id(self, run_name: str) -> str:
-        res = self._client.list_runs(
-            filter=json.dumps(
-                {
-                    "predicates": [
-                        {
-                            "operation": "EQUALS",
-                            "key": "name",
-                            "stringValue": run_name,
-                        }
-                    ]
-                }
-            ),
-        )
-        if res.runs is not None and len(res.runs) > 0:
-            return res.runs[0].run_id
-        raise RuntimeError(f"Run with {run_name=} not found.")
-
-    async def receive_task_progress(self, request: ProgressCallbackRequest):
-        task_id = await self._get_run_id(run_name=request.task_id)
-        progress = request.progress
-        task = await self.get_raw_task(task_id=task_id)
-
-        if isinstance(progress, ProgressSetNumDocs):
-            task.processing_meta = TaskProcessingMeta(num_docs=progress.num_docs)
-            task.task_status = TaskStatus.STARTED
-
-        elif isinstance(progress, ProgressUpdateProcessed):
-            if task.processing_meta is None:
-                raise ProgressInvalid(
-                    "UpdateProcessed was called before setting the expected number of documents."
-                )
-            task.processing_meta.num_processed += progress.num_processed
-            task.processing_meta.num_succeeded += progress.num_succeeded
-            task.processing_meta.num_failed += progress.num_failed
-            task.task_status = TaskStatus.STARTED
-
-        # TODO: could be moved to BackgroundTask
-        await self.notify_task_subscribers(task_id=task_id)

docling_serve/engines/async_local/orchestrator.py

@@ -1,60 +0,0 @@
-import asyncio
-import logging
-import uuid
-from typing import Optional
-
-from docling.datamodel.base_models import InputFormat
-
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
-from docling_serve.datamodel.task import Task, TaskSource
-from docling_serve.docling_conversion import get_converter, get_pdf_pipeline_opts
-from docling_serve.engines.async_local.worker import AsyncLocalWorker
-from docling_serve.engines.async_orchestrator import BaseAsyncOrchestrator
-from docling_serve.settings import docling_serve_settings
-
-_log = logging.getLogger(__name__)
-
-
-class AsyncLocalOrchestrator(BaseAsyncOrchestrator):
-    def __init__(self):
-        super().__init__()
-        self.task_queue = asyncio.Queue()
-        self.queue_list: list[str] = []
-
-    async def enqueue(
-        self, sources: list[TaskSource], options: ConvertDocumentsOptions
-    ) -> Task:
-        task_id = str(uuid.uuid4())
-        task = Task(task_id=task_id, sources=sources, options=options)
-        await self.init_task_tracking(task)
-
-        self.queue_list.append(task_id)
-        await self.task_queue.put(task_id)
-        return task
-
-    async def queue_size(self) -> int:
-        return self.task_queue.qsize()
-
-    async def get_queue_position(self, task_id: str) -> Optional[int]:
-        return (
-            self.queue_list.index(task_id) + 1 if task_id in self.queue_list else None
-        )
-
-    async def process_queue(self):
-        # Create a pool of workers
-        workers = []
-        for i in range(docling_serve_settings.eng_loc_num_workers):
-            _log.debug(f"Starting worker {i}")
-            w = AsyncLocalWorker(i, self)
-            worker_task = asyncio.create_task(w.loop())
-            workers.append(worker_task)
-
-        # Wait for all workers to complete (they won't, as they run indefinitely)
-        await asyncio.gather(*workers)
-        _log.debug("All workers completed.")
-
-    async def warm_up_caches(self):
-        # Converter with default options
-        pdf_format_option = get_pdf_pipeline_opts(ConvertDocumentsOptions())
-        converter = get_converter(pdf_format_option)
-        converter.initialize_pipeline(InputFormat.PDF)

docling_serve/engines/async_local/worker.py

@@ -1,124 +0,0 @@
-import asyncio
-import logging
-import shutil
-import time
-from typing import TYPE_CHECKING, Any, Optional, Union
-
-from fastapi.responses import FileResponse
-
-from docling.datamodel.base_models import DocumentStream
-
-from docling_serve.datamodel.engines import TaskStatus
-from docling_serve.datamodel.requests import FileSource, HttpSource
-from docling_serve.docling_conversion import convert_documents
-from docling_serve.response_preparation import process_results
-from docling_serve.storage import get_scratch
-
-if TYPE_CHECKING:
-    from docling_serve.engines.async_local.orchestrator import AsyncLocalOrchestrator
-
-_log = logging.getLogger(__name__)
-
-
-class AsyncLocalWorker:
-    def __init__(self, worker_id: int, orchestrator: "AsyncLocalOrchestrator"):
-        self.worker_id = worker_id
-        self.orchestrator = orchestrator
-
-    async def loop(self):
-        _log.debug(f"Starting loop for worker {self.worker_id}")
-        while True:
-            task_id: str = await self.orchestrator.task_queue.get()
-            self.orchestrator.queue_list.remove(task_id)
-
-            if task_id not in self.orchestrator.tasks:
-                raise RuntimeError(f"Task {task_id} not found.")
-            task = self.orchestrator.tasks[task_id]
-
-            try:
-                task.set_status(TaskStatus.STARTED)
-                _log.info(f"Worker {self.worker_id} processing task {task_id}")
-
-                # Notify clients about task updates
-                await self.orchestrator.notify_task_subscribers(task_id)
-
-                # Notify clients about queue updates
-                await self.orchestrator.notify_queue_positions()
-
-                # Define a callback function to send progress updates to the client.
-                # TODO: send partial updates, e.g. when a document in the batch is done
-                def run_conversion():
-                    convert_sources: list[Union[str, DocumentStream]] = []
-                    headers: Optional[dict[str, Any]] = None
-                    for source in task.sources:
-                        if isinstance(source, DocumentStream):
-                            convert_sources.append(source)
-                        elif isinstance(source, FileSource):
-                            convert_sources.append(source.to_document_stream())
-                        elif isinstance(source, HttpSource):
-                            convert_sources.append(str(source.url))
-                            if headers is None and source.headers:
-                                headers = source.headers
-
-                    # Note: results are only an iterator->lazy evaluation
-                    results = convert_documents(
-                        sources=convert_sources,
-                        options=task.options,
-                        headers=headers,
-                    )
-
-                    # The real processing will happen here
-                    work_dir = get_scratch() / task_id
-                    response = process_results(
-                        conversion_options=task.options,
-                        conv_results=results,
-                        work_dir=work_dir,
-                    )
-
-                    if work_dir.exists():
-                        task.scratch_dir = work_dir
-                        if not isinstance(response, FileResponse):
-                            _log.warning(
-                                f"Task {task_id=} produced content in {work_dir=} but the response is not a file."
-                            )
-                            shutil.rmtree(work_dir, ignore_errors=True)
-
-                    return response
-
-                start_time = time.monotonic()
-
-                # Run the prediction in a thread to avoid blocking the event loop.
-                # Get the current event loop
-                # loop = asyncio.get_event_loop()
-                # future = asyncio.run_coroutine_threadsafe(
-                #     run_conversion(),
-                #     loop=loop
-                # )
-                # response = future.result()
-
-                # Run in a thread
-                response = await asyncio.to_thread(
-                    run_conversion,
-                )
-                processing_time = time.monotonic() - start_time
-
-                task.result = response
-                task.sources = []
-                task.options = None
-
-                task.set_status(TaskStatus.SUCCESS)
-                _log.info(
-                    f"Worker {self.worker_id} completed job {task_id} "
-                    f"in {processing_time:.2f} seconds"
-                )
-
-            except Exception as e:
-                _log.error(
-                    f"Worker {self.worker_id} failed to process job {task_id}: {e}"
-                )
-                task.set_status(TaskStatus.FAILURE)
-
-            finally:
-                await self.orchestrator.notify_task_subscribers(task_id)
-                self.orchestrator.task_queue.task_done()
-                _log.debug(f"Worker {self.worker_id} completely done with {task_id}")

docling_serve/engines/async_orchestrator.py

@@ -1,127 +0,0 @@
-import asyncio
-import datetime
-import logging
-import shutil
-from typing import Union
-
-from fastapi import BackgroundTasks, WebSocket
-from fastapi.responses import FileResponse
-
-from docling_serve.datamodel.callback import ProgressCallbackRequest
-from docling_serve.datamodel.engines import TaskStatus
-from docling_serve.datamodel.responses import (
-    ConvertDocumentResponse,
-    MessageKind,
-    TaskStatusResponse,
-    WebsocketMessage,
-)
-from docling_serve.datamodel.task import Task
-from docling_serve.engines.base_orchestrator import (
-    BaseOrchestrator,
-    OrchestratorError,
-    TaskNotFoundError,
-)
-from docling_serve.settings import docling_serve_settings
-
-_log = logging.getLogger(__name__)
-
-
-class ProgressInvalid(OrchestratorError):
-    pass
-
-
-class BaseAsyncOrchestrator(BaseOrchestrator):
-    def __init__(self):
-        self.tasks: dict[str, Task] = {}
-        self.task_subscribers: dict[str, set[WebSocket]] = {}
-
-    async def init_task_tracking(self, task: Task):
-        task_id = task.task_id
-        self.tasks[task.task_id] = task
-        self.task_subscribers[task_id] = set()
-
-    async def get_raw_task(self, task_id: str) -> Task:
-        if task_id not in self.tasks:
-            raise TaskNotFoundError()
-        return self.tasks[task_id]
-
-    async def task_status(self, task_id: str, wait: float = 0.0) -> Task:
-        return await self.get_raw_task(task_id=task_id)
-
-    async def task_result(
-        self, task_id: str, background_tasks: BackgroundTasks
-    ) -> Union[ConvertDocumentResponse, FileResponse, None]:
-        try:
-            task = await self.get_raw_task(task_id=task_id)
-            if task.is_completed() and docling_serve_settings.single_use_results:
-                if task.scratch_dir is not None:
-                    background_tasks.add_task(
-                        shutil.rmtree, task.scratch_dir, ignore_errors=True
-                    )
-
-                async def _remove_task_impl():
-                    await asyncio.sleep(docling_serve_settings.result_removal_delay)
-                    await self.delete_task(task_id=task.task_id)
-
-                async def _remove_task():
-                    asyncio.create_task(_remove_task_impl())  # noqa: RUF006
-
-                background_tasks.add_task(_remove_task)
-
-            return task.result
-        except TaskNotFoundError:
-            return None
-
-    async def delete_task(self, task_id: str):
-        _log.info(f"Deleting {task_id=}")
-        if task_id in self.task_subscribers:
-            for websocket in self.task_subscribers[task_id]:
-                await websocket.close()
-
-            del self.task_subscribers[task_id]
-
-        if task_id in self.tasks:
-            del self.tasks[task_id]
-
-    async def clear_results(self, older_than: float = 0.0):
-        cutoff_time = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
-            seconds=older_than
-        )
-
-        tasks_to_delete = [
-            task_id
-            for task_id, task in self.tasks.items()
-            if task.finished_at is not None and task.finished_at < cutoff_time
-        ]
-        for task_id in tasks_to_delete:
-            await self.delete_task(task_id=task_id)
-
-    async def notify_task_subscribers(self, task_id: str):
-        if task_id not in self.task_subscribers:
-            raise RuntimeError(f"Task {task_id} does not have a subscribers list.")
-
-        task = await self.get_raw_task(task_id=task_id)
-        task_queue_position = await self.get_queue_position(task_id)
-        msg = TaskStatusResponse(
-            task_id=task.task_id,
-            task_status=task.task_status,
-            task_position=task_queue_position,
-            task_meta=task.processing_meta,
-        )
-        for websocket in self.task_subscribers[task_id]:
-            await websocket.send_text(
-                WebsocketMessage(message=MessageKind.UPDATE, task=msg).model_dump_json()
-            )
-            if task.is_completed():
-                await websocket.close()
-
-    async def notify_queue_positions(self):
-        for task_id in self.task_subscribers.keys():
-            # notify only pending tasks
-            if self.tasks[task_id].task_status != TaskStatus.PENDING:
-                continue
-
-            await self.notify_task_subscribers(task_id)
-
-    async def receive_task_progress(self, request: ProgressCallbackRequest):
-        raise NotImplementedError()

docling_serve/engines/async_orchestrator_factory.py

@@ -1,21 +0,0 @@
-from functools import lru_cache
-
-from docling_serve.datamodel.engines import AsyncEngine
-from docling_serve.engines.async_orchestrator import BaseAsyncOrchestrator
-from docling_serve.settings import docling_serve_settings
-
-
-@lru_cache
-def get_async_orchestrator() -> BaseAsyncOrchestrator:
-    if docling_serve_settings.eng_kind == AsyncEngine.LOCAL:
-        from docling_serve.engines.async_local.orchestrator import (
-            AsyncLocalOrchestrator,
-        )
-
-        return AsyncLocalOrchestrator()
-    elif docling_serve_settings.eng_kind == AsyncEngine.KFP:
-        from docling_serve.engines.async_kfp.orchestrator import AsyncKfpOrchestrator
-
-        return AsyncKfpOrchestrator()
-
-    raise RuntimeError(f"Engine {docling_serve_settings.eng_kind} not recognized.")

docling_serve/engines/base_orchestrator.py

@@ -1,55 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional, Union
-
-from fastapi import BackgroundTasks
-from fastapi.responses import FileResponse
-
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
-from docling_serve.datamodel.responses import ConvertDocumentResponse
-from docling_serve.datamodel.task import Task, TaskSource
-
-
-class OrchestratorError(Exception):
-    pass
-
-
-class TaskNotFoundError(OrchestratorError):
-    pass
-
-
-class BaseOrchestrator(ABC):
-    @abstractmethod
-    async def enqueue(
-        self, sources: list[TaskSource], options: ConvertDocumentsOptions
-    ) -> Task:
-        pass
-
-    @abstractmethod
-    async def queue_size(self) -> int:
-        pass
-
-    @abstractmethod
-    async def get_queue_position(self, task_id: str) -> Optional[int]:
-        pass
-
-    @abstractmethod
-    async def task_status(self, task_id: str, wait: float = 0.0) -> Task:
-        pass
-
-    @abstractmethod
-    async def task_result(
-        self, task_id: str, background_tasks: BackgroundTasks
-    ) -> Union[ConvertDocumentResponse, FileResponse, None]:
-        pass
-
-    @abstractmethod
-    async def clear_results(self, older_than: float = 0.0):
-        pass
-
-    @abstractmethod
-    async def process_queue(self):
-        pass
-
-    @abstractmethod
-    async def warm_up_caches(self):
-        pass

docling_serve/gradio_ui.py

@@ -241,7 +241,7 @@ def wait_task_finish(task_id: str, return_as_file: bool):
     while not task_finished:
         try:
             response = httpx.get(
-                f"{get_api_endpoint()}/v1alpha/status/poll/{task_id}?wait=5",
+                f"{get_api_endpoint()}/v1/status/poll/{task_id}?wait=5",
                 verify=ssl_ctx,
                 timeout=15,
             )
@@ -264,7 +264,7 @@ def wait_task_finish(task_id: str, return_as_file: bool):
     if conversion_sucess:
         try:
             response = httpx.get(
-                f"{get_api_endpoint()}/v1alpha/result/{task_id}",
+                f"{get_api_endpoint()}/v1/result/{task_id}",
                 timeout=15,
                 verify=ssl_ctx,
             )
@@ -296,8 +296,11 @@ def process_url(
     do_picture_classification,
     do_picture_description,
 ):
+    target = {"kind": "zip" if return_as_file else "inbody"}
     parameters = {
-        "http_sources": [{"url": source} for source in input_sources.split(",")],
+        "sources": [
+            {"kind": "http", "url": source} for source in input_sources.split(",")
+        ],
         "options": {
             "to_formats": to_formats,
             "image_export_mode": image_export_mode,
@@ -309,24 +312,24 @@ def process_url(
             "pdf_backend": pdf_backend,
             "table_mode": table_mode,
             "abort_on_error": abort_on_error,
-            "return_as_file": return_as_file,
             "do_code_enrichment": do_code_enrichment,
             "do_formula_enrichment": do_formula_enrichment,
             "do_picture_classification": do_picture_classification,
             "do_picture_description": do_picture_description,
         },
+        "target": target,
     }
     if (
-        not parameters["http_sources"]
-        or len(parameters["http_sources"]) == 0
-        or parameters["http_sources"][0]["url"] == ""
+        not parameters["sources"]
+        or len(parameters["sources"]) == 0
+        or parameters["sources"][0]["url"] == ""
     ):
         logger.error("No input sources provided.")
         raise gr.Error("No input sources provided.", print_exception=False)
     try:
         ssl_ctx = get_ssl_context()
         response = httpx.post(
-            f"{get_api_endpoint()}/v1alpha/convert/source/async",
+            f"{get_api_endpoint()}/v1/convert/source/async",
             json=parameters,
             verify=ssl_ctx,
             timeout=60,
@@ -372,11 +375,13 @@ def process_file(
         logger.error("No files provided.")
         raise gr.Error("No files provided.", print_exception=False)
     files_data = [
-        {"base64_string": file_to_base64(file), "filename": file.name} for file in files
+        {"kind": "file", "base64_string": file_to_base64(file), "filename": file.name}
+        for file in files
     ]
+    target = {"kind": "zip" if return_as_file else "inbody"}
 
     parameters = {
-        "file_sources": files_data,
+        "sources": files_data,
         "options": {
             "to_formats": to_formats,
             "image_export_mode": image_export_mode,
@@ -394,12 +399,13 @@ def process_file(
             "do_picture_classification": do_picture_classification,
             "do_picture_description": do_picture_description,
         },
+        "target": target,
     }
 
     try:
         ssl_ctx = get_ssl_context()
         response = httpx.post(
-            f"{get_api_endpoint()}/v1alpha/convert/source/async",
+            f"{get_api_endpoint()}/v1/convert/source/async",
             json=parameters,
             verify=ssl_ctx,
             timeout=60,

docling_serve/orchestrator_factory.py

@@ -0,0 +1,52 @@
+from functools import lru_cache
+
+from docling_jobkit.orchestrators.base_orchestrator import BaseOrchestrator
+
+from docling_serve.settings import AsyncEngine, docling_serve_settings
+
+
+@lru_cache
+def get_async_orchestrator() -> BaseOrchestrator:
+    if docling_serve_settings.eng_kind == AsyncEngine.LOCAL:
+        from docling_jobkit.convert.manager import (
+            DoclingConverterManager,
+            DoclingConverterManagerConfig,
+        )
+        from docling_jobkit.orchestrators.local.orchestrator import (
+            LocalOrchestrator,
+            LocalOrchestratorConfig,
+        )
+
+        local_config = LocalOrchestratorConfig(
+            num_workers=docling_serve_settings.eng_loc_num_workers,
+        )
+
+        cm_config = DoclingConverterManagerConfig(
+            artifacts_path=docling_serve_settings.artifacts_path,
+            options_cache_size=docling_serve_settings.options_cache_size,
+            enable_remote_services=docling_serve_settings.enable_remote_services,
+            allow_external_plugins=docling_serve_settings.allow_external_plugins,
+            max_num_pages=docling_serve_settings.max_num_pages,
+            max_file_size=docling_serve_settings.max_file_size,
+        )
+        cm = DoclingConverterManager(config=cm_config)
+
+        return LocalOrchestrator(config=local_config, converter_manager=cm)
+    elif docling_serve_settings.eng_kind == AsyncEngine.KFP:
+        from docling_jobkit.orchestrators.kfp.orchestrator import (
+            KfpOrchestrator,
+            KfpOrchestratorConfig,
+        )
+
+        kfp_config = KfpOrchestratorConfig(
+            endpoint=docling_serve_settings.eng_kfp_endpoint,
+            token=docling_serve_settings.eng_kfp_token,
+            ca_cert_path=docling_serve_settings.eng_kfp_ca_cert_path,
+            self_callback_endpoint=docling_serve_settings.eng_kfp_self_callback_endpoint,
+            self_callback_token_path=docling_serve_settings.eng_kfp_self_callback_token_path,
+            self_callback_ca_cert_path=docling_serve_settings.eng_kfp_self_callback_ca_cert_path,
+        )
+
+        return KfpOrchestrator(config=kfp_config)
+
+    raise RuntimeError(f"Engine {docling_serve_settings.eng_kind} not recognized.")

docling_serve/response_preparation.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 import os
 import shutil
@@ -6,15 +7,22 @@ from collections.abc import Iterable
 from pathlib import Path
 from typing import Union
 
-from fastapi import HTTPException
+from fastapi import BackgroundTasks, HTTPException
 from fastapi.responses import FileResponse
 
 from docling.datamodel.base_models import OutputFormat
 from docling.datamodel.document import ConversionResult, ConversionStatus
 from docling_core.types.doc import ImageRefMode
+from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
+from docling_jobkit.datamodel.task import Task
+from docling_jobkit.datamodel.task_targets import InBodyTarget, TaskTarget
+from docling_jobkit.orchestrators.base_orchestrator import (
+    BaseOrchestrator,
+)
 
-from docling_serve.datamodel.convert import ConvertDocumentsOptions
 from docling_serve.datamodel.responses import ConvertDocumentResponse, DocumentResponse
+from docling_serve.settings import docling_serve_settings
+from docling_serve.storage import get_scratch
 
 _log = logging.getLogger(__name__)
 
@@ -32,7 +40,9 @@ def _export_document_as_content(
     document = DocumentResponse(filename=conv_res.input.file.name)
 
     if conv_res.status == ConversionStatus.SUCCESS:
-        new_doc = conv_res.document._make_copy_with_refmode(Path(), image_mode)
+        new_doc = conv_res.document._make_copy_with_refmode(
+            Path(), image_mode, page_no=None
+        )
 
         # Create the different formats
         if export_json:
@@ -118,7 +128,7 @@ def _export_documents_as_files(
             if export_doctags:
                 fname = output_dir / f"{doc_filename}.doctags"
                 _log.info(f"writing Doc Tags output to {fname}")
-                conv_res.document.save_as_document_tokens(filename=fname)
+                conv_res.document.save_as_doctags(filename=fname)
 
         else:
             _log.warning(f"Document {conv_res.input.file} failed to convert.")
@@ -132,6 +142,7 @@ def _export_documents_as_files(
 
 def process_results(
     conversion_options: ConvertDocumentsOptions,
+    target: TaskTarget,
     conv_results: Iterable[ConversionResult],
     work_dir: Path,
 ) -> Union[ConvertDocumentResponse, FileResponse]:
@@ -168,7 +179,7 @@ def process_results(
     export_doctags = OutputFormat.DOCTAGS in conversion_options.to_formats
 
     # Only 1 document was processed, and we are not returning it as a file
-    if len(conv_results) == 1 and not conversion_options.return_as_file:
+    if len(conv_results) == 1 and isinstance(target, InBodyTarget):
         conv_res = conv_results[0]
         document = _export_document_as_content(
             conv_res,
@@ -230,3 +241,47 @@ def process_results(
         )
 
     return response
+
+
+async def prepare_response(
+    task: Task, orchestrator: BaseOrchestrator, background_tasks: BackgroundTasks
+):
+    if task.results is None:
+        raise HTTPException(
+            status_code=404,
+            detail="Task result not found. Please wait for a completion status.",
+        )
+    assert task.options is not None
+
+    work_dir = get_scratch() / task.task_id
+    response = process_results(
+        conversion_options=task.options,
+        target=task.target,
+        conv_results=task.results,
+        work_dir=work_dir,
+    )
+
+    if work_dir.exists():
+        task.scratch_dir = work_dir
+        if not isinstance(response, FileResponse):
+            _log.warning(
+                f"Task {task.task_id=} produced content in {work_dir=} but the response is not a file."
+            )
+            shutil.rmtree(work_dir, ignore_errors=True)
+
+    if docling_serve_settings.single_use_results:
+        if task.scratch_dir is not None:
+            background_tasks.add_task(
+                shutil.rmtree, task.scratch_dir, ignore_errors=True
+            )
+
+        async def _remove_task_impl():
+            await asyncio.sleep(docling_serve_settings.result_removal_delay)
+            await orchestrator.delete_task(task_id=task.task_id)
+
+        async def _remove_task():
+            asyncio.create_task(_remove_task_impl())  # noqa: RUF006
+
+        background_tasks.add_task(_remove_task)
+
+    return response

docling_serve/settings.py

@@ -1,3 +1,4 @@
+import enum
 import sys
 from pathlib import Path
 from typing import Optional, Union
@@ -6,8 +7,6 @@ from pydantic import AnyUrl, model_validator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 from typing_extensions import Self
 
-from docling_serve.datamodel.engines import AsyncEngine
-
 
 class UvicornSettings(BaseSettings):
     model_config = SettingsConfigDict(
@@ -26,6 +25,11 @@ class UvicornSettings(BaseSettings):
     workers: Union[int, None] = None
 
 
+class AsyncEngine(str, enum.Enum):
+    LOCAL = "local"
+    KFP = "kfp"
+
+
 class DoclingServeSettings(BaseSettings):
     model_config = SettingsConfigDict(
         env_prefix="DOCLING_SERVE_",

docling_serve/websocket_notifier.py

@@ -0,0 +1,54 @@
+from fastapi import WebSocket
+
+from docling_jobkit.datamodel.task_meta import TaskStatus
+from docling_jobkit.orchestrators.base_notifier import BaseNotifier
+from docling_jobkit.orchestrators.base_orchestrator import BaseOrchestrator
+
+from docling_serve.datamodel.responses import (
+    MessageKind,
+    TaskStatusResponse,
+    WebsocketMessage,
+)
+
+
+class WebsocketNotifier(BaseNotifier):
+    def __init__(self, orchestrator: BaseOrchestrator):
+        super().__init__(orchestrator)
+        self.task_subscribers: dict[str, set[WebSocket]] = {}
+
+    async def add_task(self, task_id: str):
+        self.task_subscribers[task_id] = set()
+
+    async def remove_task(self, task_id: str):
+        if task_id in self.task_subscribers:
+            for websocket in self.task_subscribers[task_id]:
+                await websocket.close()
+
+            del self.task_subscribers[task_id]
+
+    async def notify_task_subscribers(self, task_id: str):
+        if task_id not in self.task_subscribers:
+            raise RuntimeError(f"Task {task_id} does not have a subscribers list.")
+
+        task = await self.orchestrator.get_raw_task(task_id=task_id)
+        task_queue_position = await self.orchestrator.get_queue_position(task_id)
+        msg = TaskStatusResponse(
+            task_id=task.task_id,
+            task_status=task.task_status,
+            task_position=task_queue_position,
+            task_meta=task.processing_meta,
+        )
+        for websocket in self.task_subscribers[task_id]:
+            await websocket.send_text(
+                WebsocketMessage(message=MessageKind.UPDATE, task=msg).model_dump_json()
+            )
+            if task.is_completed():
+                await websocket.close()
+
+    async def notify_queue_positions(self):
+        for task_id in self.task_subscribers.keys():
+            # notify only pending tasks
+            if self.orchestrator.tasks[task_id].task_status != TaskStatus.PENDING:
+                continue
+
+            await self.notify_task_subscribers(task_id)

docs/README.md

@@ -6,3 +6,4 @@ This documentation pages explore the webserver configurations, runtime options,
 - [Advance usage](./usage.md)
 - [Deployment](./deployment.md)
 - [Development](./development.md)
+- [`v1` migration](./v1_migration.md)

docs/configuration.md

@@ -7,7 +7,7 @@ server and the actual app-specific configurations.
 
  > [!WARNING]
 > When the server is running with `reload` or with multiple `workers`, uvicorn
-> will spawn multiple subprocessed. This invalidates all the values configured
+> will spawn multiple subprocesses. This invalidates all the values configured
 > via the CLI command line options. Please use environment variables in this
 > type of deployments.
 
@@ -36,7 +36,7 @@ THe following table describes the options to configure the Docling Serve app.
 | CLI option | ENV | Default | Description |
 | -----------|-----|---------|-------------|
 | `--artifacts-path` | `DOCLING_SERVE_ARTIFACTS_PATH` | unset | If set to a valid directory, the model weights will be loaded from this path |
-|  | `DOCLING_SERVE_STATIC_PATH` | unset | If set to a valid directory, the static assets for the docs and ui will be loaded from this path |
+|  | `DOCLING_SERVE_STATIC_PATH` | unset | If set to a valid directory, the static assets for the docs and UI will be loaded from this path |
 |  | `DOCLING_SERVE_SCRATCH_PATH` |  | If set, this directory will be used as scratch workspace, e.g. storing the results before they get requested. If unset, a temporary created is created for this purpose. |
 | `--enable-ui` | `DOCLING_SERVE_ENABLE_UI` | `false` | Enable the demonstrator UI. |
 |  | `DOCLING_SERVE_ENABLE_REMOTE_SERVICES` | `false` | Allow pipeline components making remote connections. For example, this is needed when using a vision-language model via APIs. |
@@ -76,6 +76,6 @@ The following table describes the options to configure the Docling Serve KFP eng
 | `DOCLING_SERVE_ENG_KFP_ENDPOINT` |  | Must be set to the Kubeflow Pipeline endpoint. When using the in-cluster deployment, make sure to use the cluster endpoint, e.g. `https://NAME.NAMESPACE.svc.cluster.local:8888`  |
 | `DOCLING_SERVE_ENG_KFP_TOKEN` |  | The authentication token for KFP. For in-cluster deployment, the app will load automatically the token of the ServiceAccount. |
 | `DOCLING_SERVE_ENG_KFP_CA_CERT_PATH` |  | Path to the CA certificates for the KFP endpoint. For in-cluster deployment, the app will load automatically the internal CA. |
-| `DOCLING_SERVE_ENG_KFP_SELF_CALLBACK_ENDPOINT` |  | If set, it enables internal callbacks providing status update of the KFP job. Usually something like `https://NAME.NAMESPACE.svc.cluster.local:5001/v1alpha/callback/task/progress`. |
+| `DOCLING_SERVE_ENG_KFP_SELF_CALLBACK_ENDPOINT` |  | If set, it enables internal callbacks providing status update of the KFP job. Usually something like `https://NAME.NAMESPACE.svc.cluster.local:5001/v1/callback/task/progress`. |
 | `DOCLING_SERVE_ENG_KFP_SELF_CALLBACK_TOKEN_PATH` |  | The token used for authenticating the progress callback. For cluster-internal workloads, use `/run/secrets/kubernetes.io/serviceaccount/token`. |
 | `DOCLING_SERVE_ENG_KFP_SELF_CALLBACK_CA_CERT_PATH` |  | The CA certificate for the progress callback. For cluster-inetrnal workloads, use `/var/run/secrets/kubernetes.io/serviceaccount/service-ca.crt`. |

docs/deployment.md

@@ -30,7 +30,7 @@ For using the API:
 ```sh
 # Make a test query
 curl -X 'POST' \
-  "localhost:5001/v1alpha/convert/source/async" \
+  "localhost:5001/v1/convert/source/async" \
   -H "accept: application/json" \
   -H "Content-Type: application/json" \
   -d '{
@@ -148,7 +148,7 @@ oc port-forward svc/docling-serve 5001:5001
 
 # Make a test query
 curl -X 'POST' \
-  "localhost:5001/v1alpha/convert/source/async" \
+  "localhost:5001/v1/convert/source/async" \
   -H "accept: application/json" \
   -H "Content-Type: application/json" \
   -d '{
@@ -184,7 +184,7 @@ OCP_AUTH_TOKEN=$(oc whoami --show-token)
 
 # Make a test query
 curl -X 'POST' \
-  "${DOCLING_ROUTE}/v1alpha/convert/source/async" \
+  "${DOCLING_ROUTE}/v1/convert/source/async" \
   -H "Authorization: Bearer ${OCP_AUTH_TOKEN}" \
   -H "accept: application/json" \
   -H "Content-Type: application/json" \
@@ -218,7 +218,7 @@ DOCLING_ROUTE="https://$(oc get routes $DOCLING_NAME --template={{.spec.host}})"
 
 # Make a test query, store the cookie and taskid
 task_id=$(curl -s -X 'POST' \
-    "${DOCLING_ROUTE}/v1alpha/convert/source/async" \
+    "${DOCLING_ROUTE}/v1/convert/source/async" \
     -H "accept: application/json" \
     -H "Content-Type: application/json" \
     -d '{
@@ -230,7 +230,7 @@ task_id=$(curl -s -X 'POST' \
 ```sh
 # Grab the taskid and cookie to check the task status
 curl -v -X 'GET' \
-  "${DOCLING_ROUTE}/v1alpha/status/poll/$task_id?wait=0" \
+  "${DOCLING_ROUTE}/v1/status/poll/$task_id?wait=0" \
   -H "accept: application/json" \
   -b "cookies.txt"
 ```

docs/pre-loading-models.md

@@ -74,7 +74,7 @@ This document provides examples for pre-loading docling models to a persistent v
     Manifest example: [docling-model-cache-job.yaml](./deploy-examples/docling-model-cache-job.yaml)
 
 3. Now we can mount volume in the docling-serve deployment and set env `DOCLING_SERVE_ARTIFACTS_PATH` to point to it.
-    Following additions to deploymeny should be made:
+    Following additions to deployment should be made:
 
     ```yaml
     spec:
@@ -98,6 +98,6 @@ This document provides examples for pre-loading docling models to a persistent v
 
     Make sure that value of `DOCLING_SERVE_ARTIFACTS_PATH` is the same as where models were downloaded and where volume is mounted.
 
-    Now when docling-serve is executing tasks, the underlying docling installation will load model weights from mouted volume.
+    Now when docling-serve is executing tasks, the underlying docling installation will load model weights from mounted volume.
 
     Manifest example: [docling-model-cache-deployment.yaml](./deploy-examples/docling-model-cache-deployment.yaml)

docs/usage.md

@@ -9,7 +9,7 @@ On top of the source of file (see below), both endpoints support the same parame
 - `from_formats` (List[str]): Input format(s) to convert from. Allowed values: `docx`, `pptx`, `html`, `image`, `pdf`, `asciidoc`, `md`. Defaults to all formats.
 - `to_formats` (List[str]): Output format(s) to convert to. Allowed values: `md`, `json`, `html`, `text`, `doctags`. Defaults to `md`.
 - `pipeline` (str). The choice of which pipeline to use. Allowed values are `standard` and `vlm`. Defaults to `standard`.
-- `page_range` (tuple). If speficied, only convert a range of pages. The page number starts at 1.
+- `page_range` (tuple). If specified, only convert a range of pages. The page number starts at 1.
 - `do_ocr` (bool): If enabled, the bitmap content will be processed using OCR. Defaults to `True`.
 - `image_export_mode`: Image export mode for the document (only in case of JSON, Markdown or HTML). Allowed values: embedded, placeholder, referenced. Optional, defaults to `embedded`.
 - `force_ocr` (bool): If enabled, replace any existing text with OCR-generated text over the full content. Defaults to `False`.
@@ -18,7 +18,6 @@ On top of the source of file (see below), both endpoints support the same parame
 - `pdf_backend` (str): PDF backend to use. Allowed values: `pypdfium2`, `dlparse_v1`, `dlparse_v2`, `dlparse_v4`. Defaults to `dlparse_v4`.
 - `table_mode` (str): Table mode to use. Allowed values: `fast`, `accurate`. Defaults to `fast`.
 - `abort_on_error` (bool): If enabled, abort on error. Defaults to false.
-- `return_as_file` (boo): If enabled, return the output as a file. Defaults to false.
 - `md_page_break_placeholder` (str): Add this placeholder between pages in the markdown output.
 - `do_table_structure` (bool): If enabled, the table structure will be extracted. Defaults to true.
 - `do_code_enrichment` (bool): If enabled, perform OCR code enrichment. Defaults to false.
@@ -26,8 +25,8 @@ On top of the source of file (see below), both endpoints support the same parame
 - `do_picture_classification` (bool): If enabled, classify pictures in documents. Defaults to false.
 - `do_picture_description` (bool): If enabled, describe pictures in documents. Defaults to false.
 - `picture_description_area_threshold` (float): Minimum percentage of the area for a picture to be processed with the models. Defaults to 0.05.
-- `picture_description_local` (dict): 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.
-- `picture_description_api` (dict): API details for using a vision-language model in the picture description. This parameter is mutually exclusive with picture_description_local.
+- `picture_description_local` (dict): 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`.
+- `picture_description_api` (dict): API details for using a vision-language model in the picture description. This parameter is mutually exclusive with `picture_description_local`.
 - `include_images` (bool): If enabled, images will be extracted from the document. Defaults to false.
 - `images_scale` (float): Scale factor for images. Defaults to 2.0.
 
@@ -35,7 +34,7 @@ On top of the source of file (see below), both endpoints support the same parame
 
 ### Source endpoint
 
-The endpoint is `/v1alpha/convert/source`, listening for POST requests of JSON payloads.
+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.
@@ -66,7 +65,6 @@ Simple payload example:
     "pdf_backend": "dlparse_v2",
     "table_mode": "fast",
     "abort_on_error": false,
-    "return_as_file": false,
   },
   "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
 }
@@ -80,7 +78,7 @@ Simple payload example:
 
 ```sh
 curl -X 'POST' \
-  'http://localhost:5001/v1alpha/convert/source' \
+  'http://localhost:5001/v1/convert/source' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{
@@ -109,7 +107,6 @@ curl -X 'POST' \
     "pdf_backend": "dlparse_v2",
     "table_mode": "fast",
     "abort_on_error": false,
-    "return_as_file": false,
     "do_table_structure": true,
     "include_images": true,
     "images_scale": 2
@@ -127,7 +124,7 @@ curl -X 'POST' \
 import httpx
 
 async_client = httpx.AsyncClient(timeout=60.0)
-url = "http://localhost:5001/v1alpha/convert/source"
+url = "http://localhost:5001/v1/convert/source"
 payload = {
   "options": {
     "from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"],
@@ -140,7 +137,6 @@ payload = {
     "pdf_backend": "dlparse_v2",
     "table_mode": "fast",
     "abort_on_error": False,
-    "return_as_file": False,
   },
   "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}]
 }
@@ -179,7 +175,7 @@ cat <<EOF > /tmp/request_body.json
 EOF
 
 # 3. POST the request to the docling service
-curl -X POST "localhost:5001/v1alpha/convert/source" \
+curl -X POST "localhost:5001/v1/convert/source" \
      -H "Content-Type: application/json" \
      -d @/tmp/request_body.json
 ```
@@ -188,14 +184,14 @@ curl -X POST "localhost:5001/v1alpha/convert/source" \
 
 ### File endpoint
 
-The endpoint is: `/v1alpha/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.
+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.
 
 <details>
 <summary>CURL example:</summary>
 
 ```sh
 curl -X 'POST' \
-  'http://127.0.0.1:5001/v1alpha/convert/file' \
+  'http://127.0.0.1:5001/v1/convert/file' \
   -H 'accept: application/json' \
   -H 'Content-Type: multipart/form-data' \
   -F 'ocr_engine=easyocr' \
@@ -211,7 +207,6 @@ curl -X 'POST' \
   -F 'abort_on_error=false' \
   -F 'to_formats=md' \
   -F 'to_formats=text' \
-  -F 'return_as_file=false' \
   -F 'do_ocr=true'
 ```
 
@@ -224,7 +219,7 @@ curl -X 'POST' \
 import httpx
 
 async_client = httpx.AsyncClient(timeout=60.0)
-url = "http://localhost:5001/v1alpha/convert/file"
+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"],
@@ -236,7 +231,6 @@ parameters = {
 "pdf_backend": "dlparse_v2",
 "table_mode": "fast",
 "abort_on_error": False,
-"return_as_file": False
 }
 
 current_dir = os.path.dirname(__file__)
@@ -313,7 +307,7 @@ Example URLs are:
     }
     ```
 
-- `http://localhost:11434/v1/chat/completions` for the local ollama api, with example `picture_description_api`:
+- `http://localhost:11434/v1/chat/completions` for the local Ollama api, with example `picture_description_api`:
   - the `granite3.2-vision:2b` model
 
     ```json
@@ -354,19 +348,19 @@ The response can be a JSON Document or a File.
   `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 `return_as_file` to True, the response will be a zip file.
-- If multiple files are generated (multiple inputs, or one input but multiple outputs with `return_as_file` True), the response will be a zip file.
+- 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 `/v1alpha/convert/source` and `/v1alpha/convert/file` endpoints are available as asynchronous variants.
+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 stabilities and allows the client application logic to easily interleave conversion with other tasks.
+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 /v1alpha/convert/source/async` when providing the input as sources.
-- `POST /v1alpha/convert/file/async` when providing the input as multipart-form files.
+- `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:
 
@@ -383,7 +377,7 @@ The response format is a task detail:
 
 For checking the progress of the conversion task and wait for its completion, use the endpoint:
 
-- `GET /v1alpha/status/poll/{task_id}`
+- `GET /v1/status/poll/{task_id}`
 
 <details>
 <summary>Example waiting loop:</summary>
@@ -408,9 +402,9 @@ while task["task_status"] not in ("success", "failure"):
 ### Subscribe with websockets
 
 Using websocket you can get the client application being notified about updates of the conversion task.
-To start the websocker connection, use the endpoint:
+To start the websocket connection, use the endpoint:
 
-- `/v1alpha/status/ws/{task_id}`
+- `/v1/status/ws/{task_id}`
 
 Websocket messages are JSON object with the following structure:
 
@@ -423,12 +417,12 @@ Websocket messages are JSON object with the following structure:
 ```
 
 <details>
-<summary>Example websocker usage:</summary>
+<summary>Example websocket usage:</summary>
 
 ```python
 from websockets.sync.client import connect
 
-uri = f"ws://{base_url}/v1alpha/status/ws/{task['task_id']}"
+uri = f"ws://{base_url}/v1/status/ws/{task['task_id']}"
 with connect(uri) as websocket:
     for message in websocket:
         try:
@@ -447,4 +441,4 @@ with connect(uri) as websocket:
 
 When the task is completed, the result can be fetched with the endpoint:
 
-- `GET /v1alpha/result/{task_id}`
+- `GET /v1/result/{task_id}`

docs/v1_migration.md

@@ -0,0 +1,80 @@
+# Migration to the `v1` API
+
+Docling Serve from the initial prototype `v1alpha` API to the stable `v1` API.
+This page provides simple instructions to upgrade your application to the new API.
+
+## API changes
+
+The breaking changes introduced in the `v1` release of Docling Serve are designed to provide a stable schema which
+allows the project to provide new capabilities as new type of input sources, targets and also the definition of callback for event-driven applications.
+
+### Endpoint names
+
+All endpoints are renamed from `/v1alpha/` to `/v1/`.
+
+### Sources
+
+When using the `/v1/convert/source` endpoint, input documents have to be specified with the `sources: []` argument, which is replacing the usage of `file_sources` and `http_sources`.
+
+Old version:
+
+```jsonc
+{
+    "options": {},  // conversion options
+    "file_sources": [  // input documents provided as base64-encoded strings
+        {"base64_string": "abc123...", "filename": "file.pdf"}
+    ],
+    "http_sources": [  // input documents provided as http urls
+        {"url": "https://..."}
+    ]
+}
+```
+
+New version:
+
+```jsonc
+{
+    "options": {},  // conversion options
+    "sources": [
+        // input document provided as base64-encoded string
+        {"kind": "kind", "base64_string": "abc123...", "filename": "file.pdf"},
+        // input document provided as http urls
+        {"kind": "http", "url": "https://..."},
+    ]
+}
+```
+
+### Targets
+
+Switching between output formats, i.e. from the JSON inbody response to the zip archive response, users have to specify the `target` argument, which is replacing the usage of `options.return_as_file`.
+
+Old version:
+
+```jsonc
+{
+    "options": {
+        "return_as_file": true  // <-- to be removed
+    },
+    // ...
+}
+```
+
+New version:
+
+```jsonc
+{
+    "options": {},
+    "target": {"kind": "zip"},  // <-- add this
+    // ...
+}
+```
+
+## Continue with the old API
+
+If you are not able to apply the changes above to your application, please consider pinning of the previous `v0.x` container images, e.g.
+
+```sh
+podman run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=1 quay.io/docling-project/docling-serve:v0.16.1
+```
+
+_Note that the old prototype API will not be supported in new `v1.x` versions._

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "docling-serve"
-version = "0.16.1"  # DO NOT EDIT, updated automatically
+version = "1.0.1"  # DO NOT EDIT, updated automatically
 description = "Running Docling as a service"
 license = {text = "MIT"}
 authors = [
@@ -23,7 +23,7 @@ readme = "README.md"
 classifiers = [
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
-    # "Development Status :: 5 - Production/Stable",
+    "Development Status :: 5 - Production/Stable",
     "Intended Audience :: Developers",
     "Typing :: Typed",
     "Programming Language :: Python :: 3",
@@ -34,12 +34,11 @@ classifiers = [
 ]
 requires-python = ">=3.10"
 dependencies = [
-    "docling[vlm]~=2.38",
+    "docling~=2.38",
     "docling-core>=2.32.0",
-    "mlx-vlm~=0.1.12; sys_platform == 'darwin' and platform_machine == 'arm64'",
+    "docling-jobkit[kfp,vlm]~=1.1",
     "fastapi[standard]~=0.115",
     "httpx~=0.28",
-    "kfp[kubernetes]>=2.10.0",
     "pydantic~=2.10",
     "pydantic-settings~=2.4",
     "python-multipart>=0.0.14,<0.1.0",
@@ -129,6 +128,8 @@ torchvision = [
   { index = "pytorch-cu126", group = "cu126" },
   { index = "pytorch-cu128", group = "cu128" },
 ]
+# docling-jobkit = { git = "https://github.com/docling-project/docling-jobkit/", rev = "refactor" }
+# docling-jobkit = { path = "../docling-jobkit", editable = true }
 
 [[tool.uv.index]]
 name = "pytorch-pypi"
@@ -223,7 +224,7 @@ ignore = [
 max-complexity = 15
 
 [tool.ruff.lint.isort.sections]
-"docling" = ["docling", "docling_core"]
+"docling" = ["docling", "docling_core", "docling_jobkit"]
 
 [tool.ruff.lint.isort]
 combine-as-imports = true

tests/test_1-file-all-outputs.py

@@ -16,7 +16,7 @@ async def async_client():
 @pytest.mark.asyncio
 async def test_convert_file(async_client):
     """Test convert single file to all outputs"""
-    url = "http://localhost:5001/v1alpha/convert/file"
+    url = "http://localhost:5001/v1/convert/file"
     options = {
         "from_formats": [
             "docx",
@@ -37,7 +37,6 @@ async def test_convert_file(async_client):
         "pdf_backend": "dlparse_v2",
         "table_mode": "fast",
         "abort_on_error": False,
-        "return_as_file": False,
     }
 
     current_dir = os.path.dirname(__file__)

tests/test_1-file-async.py

@@ -17,13 +17,12 @@ async def async_client():
 async def test_convert_url(async_client):
     """Test convert URL to all outputs"""
 
-    base_url = "http://localhost:5001/v1alpha"
+    base_url = "http://localhost:5001/v1"
     payload = {
         "to_formats": ["md", "json", "html"],
         "image_export_mode": "placeholder",
         "ocr": False,
         "abort_on_error": False,
-        "return_as_file": False,
     }
 
     file_path = Path(__file__).parent / "2206.01062v1.pdf"

tests/test_1-url-all-outputs.py

@@ -15,7 +15,7 @@ async def async_client():
 @pytest.mark.asyncio
 async def test_convert_url(async_client):
     """Test convert URL to all outputs"""
-    url = "http://localhost:5001/v1alpha/convert/source"
+    url = "http://localhost:5001/v1/convert/source"
     payload = {
         "options": {
             "from_formats": [
@@ -37,9 +37,8 @@ async def test_convert_url(async_client):
             "pdf_backend": "dlparse_v2",
             "table_mode": "fast",
             "abort_on_error": False,
-            "return_as_file": False,
         },
-        "http_sources": [{"url": "https://arxiv.org/pdf/2206.01062"}],
+        "sources": [{"kind": "http", "url": "https://arxiv.org/pdf/2206.01062"}],
     }
     print(json.dumps(payload, indent=2))
 

tests/test_1-url-async-ws.py

@@ -20,14 +20,13 @@ async def test_convert_url(async_client: httpx.AsyncClient):
     doc_filename = Path("tests/2408.09869v5.pdf")
     encoded_doc = base64.b64encode(doc_filename.read_bytes()).decode()
 
-    base_url = "http://localhost:5001/v1alpha"
+    base_url = "http://localhost:5001/v1"
     payload = {
         "options": {
             "to_formats": ["md", "json"],
             "image_export_mode": "placeholder",
             "ocr": True,
             "abort_on_error": False,
-            "return_as_file": False,
             # "do_picture_description": True,
             # "picture_description_api": {
             #     "url": "http://localhost:11434/v1/chat/completions",
@@ -39,8 +38,14 @@ async def test_convert_url(async_client: httpx.AsyncClient):
             #     "repo_id": "HuggingFaceTB/SmolVLM-256M-Instruct",
             # },
         },
-        # "http_sources": [{"url": "https://arxiv.org/pdf/2501.17887"}],
-        "file_sources": [{"base64_string": encoded_doc, "filename": doc_filename.name}],
+        # "sources": [{"kind": "http", "url": "https://arxiv.org/pdf/2501.17887"}],
+        "sources": [
+            {
+                "kind": "file",
+                "base64_string": encoded_doc,
+                "filename": doc_filename.name,
+            }
+        ],
     }
     # print(json.dumps(payload, indent=2))
 
@@ -52,7 +57,7 @@ async def test_convert_url(async_client: httpx.AsyncClient):
 
     task = response.json()
 
-    uri = f"ws://localhost:5001/v1alpha/status/ws/{task['task_id']}"
+    uri = f"ws://localhost:5001/v1/status/ws/{task['task_id']}"
     with connect(uri) as websocket:
         for message in websocket:
             print(message)

tests/test_1-url-async.py

@@ -25,16 +25,15 @@ async def test_convert_url(async_client):
         "https://arxiv.org/pdf/2311.18481",
     ]
 
-    base_url = "http://localhost:5001/v1alpha"
+    base_url = "http://localhost:5001/v1"
     payload = {
         "options": {
             "to_formats": ["md", "json"],
             "image_export_mode": "placeholder",
             "ocr": True,
             "abort_on_error": False,
-            "return_as_file": False,
         },
-        "http_sources": [{"url": random.choice(example_docs)}],
+        "sources": [{"kind": "http", "url": random.choice(example_docs)}],
     }
     print(json.dumps(payload, indent=2))
 

tests/test_2-files-all-outputs.py

@@ -15,7 +15,7 @@ async def async_client():
 @pytest.mark.asyncio
 async def test_convert_file(async_client):
     """Test convert single file to all outputs"""
-    url = "http://localhost:5001/v1alpha/convert/file"
+    url = "http://localhost:5001/v1/convert/file"
     options = {
         "from_formats": [
             "docx",
@@ -36,7 +36,6 @@ async def test_convert_file(async_client):
         "pdf_backend": "dlparse_v2",
         "table_mode": "fast",
         "abort_on_error": False,
-        "return_as_file": False,
     }
 
     current_dir = os.path.dirname(__file__)

tests/test_2-urls-all-outputs.py

@@ -13,7 +13,7 @@ async def async_client():
 @pytest.mark.asyncio
 async def test_convert_url(async_client):
     """Test convert URL to all outputs"""
-    url = "http://localhost:5001/v1alpha/convert/source"
+    url = "http://localhost:5001/v1/convert/source"
     payload = {
         "options": {
             "from_formats": [
@@ -35,12 +35,12 @@ async def test_convert_url(async_client):
             "pdf_backend": "dlparse_v2",
             "table_mode": "fast",
             "abort_on_error": False,
-            "return_as_file": False,
         },
-        "http_sources": [
-            {"url": "https://arxiv.org/pdf/2206.01062"},
-            {"url": "https://arxiv.org/pdf/2408.09869"},
+        "sources": [
+            {"kind": "http", "url": "https://arxiv.org/pdf/2206.01062"},
+            {"kind": "http", "url": "https://arxiv.org/pdf/2408.09869"},
         ],
+        "target": {"kind": "zip"},
     }
 
     response = await async_client.post(url, json=payload)

tests/test_2-urls-async-all-outputs.py

@@ -16,7 +16,7 @@ async def async_client():
 @pytest.mark.asyncio
 async def test_convert_url(async_client):
     """Test convert URL to all outputs"""
-    base_url = "http://localhost:5001/v1alpha"
+    base_url = "http://localhost:5001/v1"
     payload = {
         "options": {
             "from_formats": [
@@ -38,12 +38,12 @@ async def test_convert_url(async_client):
             "pdf_backend": "dlparse_v2",
             "table_mode": "fast",
             "abort_on_error": False,
-            "return_as_file": False,
         },
-        "http_sources": [
-            {"url": "https://arxiv.org/pdf/2206.01062"},
-            {"url": "https://arxiv.org/pdf/2408.09869"},
+        "sources": [
+            {"kind": "http", "url": "https://arxiv.org/pdf/2206.01062"},
+            {"kind": "http", "url": "https://arxiv.org/pdf/2408.09869"},
         ],
+        "target": {"kind": "zip"},
     }
 
     response = await async_client.post(f"{base_url}/convert/source/async", json=payload)

tests/test_fastapi_endpoints.py

@@ -45,7 +45,7 @@ async def test_health(client: AsyncClient):
 async def test_convert_file(client: AsyncClient):
     """Test convert single file to all outputs"""
 
-    endpoint = "/v1alpha/convert/file"
+    endpoint = "/v1/convert/file"
     options = {
         "from_formats": [
             "docx",
@@ -66,7 +66,6 @@ async def test_convert_file(client: AsyncClient):
         "pdf_backend": "dlparse_v2",
         "table_mode": "fast",
         "abort_on_error": False,
-        "return_as_file": False,
     }
 
     current_dir = os.path.dirname(__file__)

tests/test_file_opts.py

@@ -40,7 +40,7 @@ async def client(app):
 async def test_convert_file(client: AsyncClient):
     """Test convert single file to all outputs"""
 
-    endpoint = "/v1alpha/convert/file"
+    endpoint = "/v1/convert/file"
     options = {
         "to_formats": ["md", "json"],
         "image_export_mode": "placeholder",

tests/test_options_serialization.py

@@ -1,54 +0,0 @@
-from docling_serve.datamodel.convert import (
-    ConvertDocumentsOptions,
-    PictureDescriptionApi,
-)
-from docling_serve.docling_conversion import (
-    _hash_pdf_format_option,
-    get_pdf_pipeline_opts,
-)
-
-
-def test_options_cache_key():
-    hashes = set()
-
-    opts = ConvertDocumentsOptions()
-    pipeline_opts = get_pdf_pipeline_opts(opts)
-    hash = _hash_pdf_format_option(pipeline_opts)
-    assert hash not in hashes
-    hashes.add(hash)
-
-    opts.do_picture_description = True
-    pipeline_opts = get_pdf_pipeline_opts(opts)
-    hash = _hash_pdf_format_option(pipeline_opts)
-    # pprint(pipeline_opts.pipeline_options.model_dump(serialize_as_any=True))
-    assert hash not in hashes
-    hashes.add(hash)
-
-    opts.picture_description_api = PictureDescriptionApi(
-        url="http://localhost",
-        params={"model": "mymodel"},
-        prompt="Hello 1",
-    )
-    pipeline_opts = get_pdf_pipeline_opts(opts)
-    hash = _hash_pdf_format_option(pipeline_opts)
-    # pprint(pipeline_opts.pipeline_options.model_dump(serialize_as_any=True))
-    assert hash not in hashes
-    hashes.add(hash)
-
-    opts.picture_description_api = PictureDescriptionApi(
-        url="http://localhost",
-        params={"model": "your-model"},
-        prompt="Hello 1",
-    )
-    pipeline_opts = get_pdf_pipeline_opts(opts)
-    hash = _hash_pdf_format_option(pipeline_opts)
-    # pprint(pipeline_opts.pipeline_options.model_dump(serialize_as_any=True))
-    assert hash not in hashes
-    hashes.add(hash)
-
-    opts.picture_description_api.prompt = "World"
-    pipeline_opts = get_pdf_pipeline_opts(opts)
-    hash = _hash_pdf_format_option(pipeline_opts)
-    # pprint(pipeline_opts.pipeline_options.model_dump(serialize_as_any=True))
-    assert hash not in hashes
-    hashes.add(hash)

tests/test_results_clear.py

@@ -43,10 +43,16 @@ async def convert_file(client: AsyncClient):
         "options": {
             "to_formats": ["json"],
         },
-        "file_sources": [{"base64_string": encoded_doc, "filename": doc_filename.name}],
+        "sources": [
+            {
+                "kind": "file",
+                "base64_string": encoded_doc,
+                "filename": doc_filename.name,
+            }
+        ],
     }
 
-    response = await client.post("/v1alpha/convert/source/async", json=payload)
+    response = await client.post("/v1/convert/source/async", json=payload)
     assert response.status_code == 200, "Response should be 200 OK"
 
     task = response.json()
@@ -54,7 +60,7 @@ async def convert_file(client: AsyncClient):
     print(json.dumps(task, indent=2))
 
     while task["task_status"] not in ("success", "failure"):
-        response = await client.get(f"/v1alpha/status/poll/{task['task_id']}")
+        response = await client.get(f"/v1/status/poll/{task['task_id']}")
         assert response.status_code == 200, "Response should be 200 OK"
         task = response.json()
         print(f"{task['task_status']=}")
@@ -78,26 +84,26 @@ async def test_clear_results(client: AsyncClient):
     task = await convert_file(client)
 
     # Get result once
-    result_response = await client.get(f"/v1alpha/result/{task['task_id']}")
+    result_response = await client.get(f"/v1/result/{task['task_id']}")
     assert result_response.status_code == 200, "Response should be 200 OK"
     print("Result 1 ok.")
     result = result_response.json()
     assert result["document"]["json_content"]["schema_name"] == "DoclingDocument"
 
     # Get result twice
-    result_response = await client.get(f"/v1alpha/result/{task['task_id']}")
+    result_response = await client.get(f"/v1/result/{task['task_id']}")
     assert result_response.status_code == 200, "Response should be 200 OK"
     print("Result 2 ok.")
     result = result_response.json()
     assert result["document"]["json_content"]["schema_name"] == "DoclingDocument"
 
     # Clear
-    clear_response = await client.get("/v1alpha/clear/results?older_then=0")
+    clear_response = await client.get("/v1/clear/results?older_then=0")
     assert clear_response.status_code == 200, "Response should be 200 OK"
     print("Clear ok.")
 
     # Get deleted result
-    result_response = await client.get(f"/v1alpha/result/{task['task_id']}")
+    result_response = await client.get(f"/v1/result/{task['task_id']}")
     assert result_response.status_code == 404, "Response should be removed"
     print("Result was no longer found.")
 
@@ -113,7 +119,7 @@ async def test_delay_remove(client: AsyncClient):
     task = await convert_file(client)
 
     # Get result once
-    result_response = await client.get(f"/v1alpha/result/{task['task_id']}")
+    result_response = await client.get(f"/v1/result/{task['task_id']}")
     assert result_response.status_code == 200, "Response should be 200 OK"
     print("Result ok.")
     result = result_response.json()
@@ -123,5 +129,5 @@ async def test_delay_remove(client: AsyncClient):
     await asyncio.sleep(10)
 
     # Get deleted result
-    result_response = await client.get(f"/v1alpha/result/{task['task_id']}")
+    result_response = await client.get(f"/v1/result/{task['task_id']}")
     assert result_response.status_code == 404, "Response should be removed"