diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7fa55ff..93deebb 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -137,6 +137,23 @@ More detailed options are described in the [uv documentation](https://docs.astra
 uv add NAME
 ```
 
+### Developing and testing the RQ engine
+
+Here are the setup steps for the local development and testing of the RQ engine.
+
+Launch the local redis container:
+
+```sh
+docker run -d --name redis -p 6379:6379 redis
+```
+
+Launch the workers (to be repeated when developing the worker code):
+
+```sh
+uv run python docling_jobkit/orchestrators/rq/worker.py
+```
+
+
 ## Coding style guidelines
 
 We use the following tools to enforce code style:
diff --git a/docling_jobkit/convert/chunking.py b/docling_jobkit/convert/chunking.py
new file mode 100644
index 0000000..1825860
--- /dev/null
+++ b/docling_jobkit/convert/chunking.py
@@ -0,0 +1,324 @@
+import hashlib
+import logging
+import threading
+import time
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional
+
+from pydantic import BaseModel, Field
+
+from docling.datamodel.base_models import ConversionStatus
+from docling.datamodel.document import ConversionResult
+from docling_core.transforms.chunker import BaseChunker
+from docling_core.transforms.chunker.hierarchical_chunker import (
+    ChunkingSerializerProvider,
+    DocChunk,
+    HierarchicalChunker,
+)
+from docling_core.transforms.chunker.hybrid_chunker import HybridChunker
+from docling_core.transforms.chunker.tokenizer.huggingface import (
+    HuggingFaceTokenizer,
+)
+from docling_core.types.doc.document import DoclingDocument, ImageRefMode
+
+from docling_jobkit.convert.results import _export_document_as_content
+from docling_jobkit.datamodel.chunking import (
+    BaseChunkerOptions,
+    HierarchicalChunkerOptions,
+    HybridChunkerOptions,
+)
+from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
+from docling_jobkit.datamodel.result import (
+    ChunkedDocumentResult,
+    ChunkedDocumentResultItem,
+    DoclingTaskResult,
+    ExportDocumentResponse,
+    ExportResult,
+)
+from docling_jobkit.datamodel.task import Task
+from docling_jobkit.datamodel.task_targets import InBodyTarget
+
+_log = logging.getLogger(__name__)
+
+
+class MarkdownTableSerializerProvider(ChunkingSerializerProvider):
+    """Serializer provider that uses markdown table format for table serialization."""
+
+    def get_serializer(self, doc):
+        """Get a serializer that uses markdown table format."""
+        from docling_core.transforms.chunker.hierarchical_chunker import (
+            ChunkingDocSerializer,
+        )
+        from docling_core.transforms.serializer.markdown import MarkdownTableSerializer
+
+        return ChunkingDocSerializer(
+            doc=doc,
+            table_serializer=MarkdownTableSerializer(),
+        )
+
+
+class DocumentChunkerConfig(BaseModel):
+    """Configuration for DocumentChunker."""
+
+    cache_size: int = Field(
+        default=10,
+        gt=0,
+        le=100,
+        description="Maximum number of chunker instances to cache",
+    )
+
+
+class DocumentChunkerManager:
+    """Handles document chunking for RAG workflows using chunkers from docling-core."""
+
+    def __init__(self, config: Optional[DocumentChunkerConfig] = None):
+        self.config = config or DocumentChunkerConfig()
+        self._cache_lock = threading.Lock()
+        self._options_map: dict[bytes, BaseChunkerOptions] = {}
+        self._get_chunker_from_cache = self._create_chunker_cache()
+
+    def _create_chunker_cache(self):
+        """Create LRU cache for chunker instances."""
+
+        @lru_cache(maxsize=self.config.cache_size)
+        def _get_chunker_from_cache(cache_key: bytes) -> BaseChunker:
+            try:
+                options = self._options_map[cache_key]
+
+                # Create serializer provider based on markdown table option
+                if options.use_markdown_tables:
+                    serializer_provider: ChunkingSerializerProvider = (
+                        MarkdownTableSerializerProvider()
+                    )
+                else:
+                    serializer_provider = ChunkingSerializerProvider()
+
+                if isinstance(options, HybridChunkerOptions):
+                    # Create tokenizer
+                    tokenizer_name = options.tokenizer
+                    tokenizer_obj = HuggingFaceTokenizer.from_pretrained(
+                        model_name=tokenizer_name,
+                        max_tokens=options.max_tokens,
+                    )
+
+                    chunker: BaseChunker = HybridChunker(
+                        tokenizer=tokenizer_obj,
+                        merge_peers=options.merge_peers,
+                        serializer_provider=serializer_provider,
+                    )
+                elif isinstance(options, HierarchicalChunkerOptions):
+                    chunker = HierarchicalChunker(
+                        serializer_provider=serializer_provider
+                    )
+                else:
+                    raise RuntimeError(f"Unknown chunker {options.chunker}.")
+
+                return chunker
+
+            except ImportError as e:
+                _log.error(f"Missing dependencies for document chunking: {e}")
+                raise ImportError(
+                    "Document chunking requires docling-core with chunking dependencies. "
+                    "Install with: pip install 'docling-core[chunking]'"
+                ) from e
+            except (ValueError, TypeError, AttributeError) as e:
+                _log.error(f"Invalid chunking configuration: {e}")
+                raise ValueError(f"Invalid chunking options: {e}") from e
+            except (OSError, ConnectionError) as e:
+                _log.error(f"Resource or connection error during chunker creation: {e}")
+                raise RuntimeError(
+                    f"Failed to initialize chunker resources: {e}"
+                ) from e
+
+        return _get_chunker_from_cache
+
+    def _get_chunker(self, options: BaseChunkerOptions) -> BaseChunker:
+        """Get or create a cached BaseChunker instance."""
+        # Create a cache key based on chunking options using the same pattern as the repo
+        cache_key = self._generate_cache_key(options)
+
+        with self._cache_lock:
+            self._options_map[cache_key] = options
+            return self._get_chunker_from_cache(cache_key)
+
+    def _generate_cache_key(self, options: BaseChunkerOptions) -> bytes:
+        """Generate a deterministic cache key from chunking options."""
+        serialized_data = options.model_dump_json(serialize_as_any=True)
+        options_hash = hashlib.sha1(
+            serialized_data.encode(), usedforsecurity=False
+        ).digest()
+        return options_hash
+
+    def clear_cache(self):
+        """Clear the chunker cache."""
+        with self._cache_lock:
+            self._get_chunker_from_cache.cache_clear()
+
+    def chunk_document(
+        self,
+        document: DoclingDocument,
+        filename: str,
+        options: BaseChunkerOptions,
+    ) -> Iterable[ChunkedDocumentResultItem]:
+        """Chunk a document using chunker from docling-core."""
+
+        chunker = self._get_chunker(options)
+
+        chunks = list(chunker.chunk(document))
+
+        # Convert chunks to response format
+        chunk_items: list[ChunkedDocumentResultItem] = []
+        for i, chunk in enumerate(chunks):
+            page_numbers: List[int] = []
+            metadata: Dict[str, Any] = {}
+
+            doc_chunk = DocChunk.model_validate(chunk)
+
+            # Extract page numbers and doc_items refs
+            page_numbers = []
+            doc_items = []
+            for item in doc_chunk.meta.doc_items:
+                doc_items.append(item.self_ref)
+                for prov in item.prov:
+                    page_numbers.append(prov.page_no)
+
+            # Remove duplicates and sort
+            page_numbers = sorted(set(page_numbers))
+
+            # Store additional metadata
+            if doc_chunk.meta.origin:
+                metadata["origin"] = doc_chunk.meta.origin
+
+            # Get the text
+            text = chunker.contextualize(doc_chunk)
+
+            # Compute the number of tokens
+            num_tokens: int | None = None
+            if isinstance(chunker, HybridChunker):
+                num_tokens = chunker.tokenizer.count_tokens(text)
+
+            # Create chunk item
+            chunk_item = ChunkedDocumentResultItem(
+                filename=filename,
+                chunk_index=i,
+                text=text,
+                raw_text=doc_chunk.text if options.include_raw_text else None,
+                num_tokens=num_tokens,
+                headings=doc_chunk.meta.headings,
+                captions=doc_chunk.meta.captions,
+                doc_items=doc_items,
+                page_numbers=page_numbers,
+                metadata=metadata,
+            )
+            chunk_items.append(chunk_item)
+
+        return chunk_items
+
+
+def process_chunk_results(
+    task: Task,
+    conv_results: Iterable[ConversionResult],
+    work_dir: Path,
+) -> DoclingTaskResult:
+    # Let's start by processing the documents
+    start_time = time.monotonic()
+    chunking_options = task.chunking_options or HybridChunkerOptions()
+    conversion_options = task.convert_options or ConvertDocumentsOptions()
+
+    # We have some results, let's prepare the response
+    task_result: ChunkedDocumentResult
+    chunks: list[ChunkedDocumentResultItem] = []
+    documents: list[ExportResult] = []
+    num_succeeded = 0
+    num_failed = 0
+
+    # TODO: DocumentChunkerManager should be initialized outside for really working as a cache
+    chunker_manager = DocumentChunkerManager()
+    for conv_res in conv_results:
+        errors = conv_res.errors
+        filename = conv_res.input.file.name
+        if conv_res.status == ConversionStatus.SUCCESS:
+            try:
+                chunks.extend(
+                    chunker_manager.chunk_document(
+                        document=conv_res.document,
+                        filename=filename,
+                        options=chunking_options,
+                    )
+                )
+                num_succeeded += 1
+            except Exception as e:
+                _log.exception(
+                    f"Document chunking failed for {conv_res.input.file}: {e}",
+                    stack_info=True,
+                )
+                num_failed += 1
+                # TODO: for propagating errors we have first to allow other component_type in the Docling class.
+                # errors = [
+                #     *errors,
+                #     ErrorItem(
+                #         component_type="chunking",
+                #         module_name=type(e).__name__,
+                #         error_message=str(e),
+                #     ),
+                # ]
+
+        else:
+            _log.warning(f"Document {conv_res.input.file} failed to convert.")
+            num_failed += 1
+
+        if (
+            isinstance(task.target, InBodyTarget)
+            and task.chunking_export_options.include_converted_doc
+        ):
+            if conversion_options.image_export_mode == ImageRefMode.REFERENCED:
+                raise RuntimeError("InBodyTarget cannot use REFERENCED image mode.")
+
+            doc_content = _export_document_as_content(
+                conv_res,
+                export_json=True,
+                export_doctags=False,
+                export_html=False,
+                export_md=False,
+                export_txt=False,
+                image_mode=conversion_options.image_export_mode,
+                md_page_break_placeholder=conversion_options.md_page_break_placeholder,
+            )
+        else:
+            doc_content = ExportDocumentResponse(filename=filename)
+
+        doc_result = ExportResult(
+            content=doc_content,
+            status=conv_res.status,
+            timings=conv_res.timings,
+            errors=errors,
+        )
+
+        documents.append(doc_result)
+
+    num_total = num_succeeded + num_failed
+    processing_time = time.monotonic() - start_time
+    _log.info(
+        f"Processed {num_total} docs generating {len(chunks)} chunks in {processing_time:.2f} seconds."
+    )
+
+    if isinstance(task.target, InBodyTarget):
+        task_result = ChunkedDocumentResult(
+            chunks=chunks,
+            documents=documents,
+            processing_time=processing_time,
+            chunking_info=chunking_options.model_dump(mode="json"),
+        )
+
+    # Multiple documents were processed, or we are forced returning as a file
+    else:
+        raise NotImplementedError("Saving chunks to a file is not yet supported.")
+
+    return DoclingTaskResult(
+        result=task_result,
+        processing_time=processing_time,
+        num_succeeded=num_succeeded,
+        num_failed=num_failed,
+        num_converted=num_total,
+    )
diff --git a/docling_jobkit/convert/results.py b/docling_jobkit/convert/results.py
index c726d2b..7373b73 100644
--- a/docling_jobkit/convert/results.py
+++ b/docling_jobkit/convert/results.py
@@ -11,16 +11,16 @@
 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.result import (
-    ConvertDocumentResult,
+    DoclingTaskResult,
     ExportDocumentResponse,
     ExportResult,
     RemoteTargetResult,
     ResultType,
     ZipArchiveResult,
 )
-from docling_jobkit.datamodel.task_targets import InBodyTarget, PutTarget, TaskTarget
+from docling_jobkit.datamodel.task import Task
+from docling_jobkit.datamodel.task_targets import InBodyTarget, PutTarget
 
 _log = logging.getLogger(__name__)
 
@@ -146,12 +146,15 @@ def _export_documents_as_files(
     return success_count, failure_count, conv_result
 
 
-def process_results(
-    conversion_options: ConvertDocumentsOptions,
-    target: TaskTarget,
+def process_export_results(
+    task: Task,
     conv_results: Iterable[ConversionResult],
     work_dir: Path,
-) -> ConvertDocumentResult:
+) -> DoclingTaskResult:
+    conversion_options = task.convert_options
+    if conversion_options is None:
+        raise RuntimeError("process_export_results called without task.convert_options")
+
     # Let's start by processing the documents
     start_time = time.monotonic()
 
@@ -179,8 +182,9 @@ 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 isinstance(target, InBodyTarget):
+    if len(conv_results) == 1 and isinstance(task.target, InBodyTarget):
         conv_res = conv_results[0]
+
         content = _export_document_as_content(
             conv_res,
             export_json=export_json,
@@ -191,7 +195,6 @@ def process_results(
             image_mode=conversion_options.image_export_mode,
             md_page_break_placeholder=conversion_options.md_page_break_placeholder,
         )
-
         task_result = ExportResult(
             content=content,
             status=conv_res.status,
@@ -199,6 +202,7 @@ def process_results(
             timings=conv_res.timings,
             errors=conv_res.errors,
         )
+
         num_succeeded = 1 if conv_res.status == ConversionStatus.SUCCESS else 0
         num_failed = 1 if conv_res.status != ConversionStatus.SUCCESS else 0
 
@@ -232,10 +236,10 @@ def process_results(
             root_dir=output_dir,
         )
 
-        if isinstance(target, PutTarget):
+        if isinstance(task.target, PutTarget):
             try:
                 with file_path.open("rb") as file_data:
-                    r = httpx.put(str(target.url), files={"file": file_data})
+                    r = httpx.put(str(task.target.url), files={"file": file_data})
                     r.raise_for_status()
                 task_result = RemoteTargetResult()
             except Exception as exc:
@@ -247,7 +251,7 @@ def process_results(
         else:
             task_result = ZipArchiveResult(content=file_path.read_bytes())
 
-    return ConvertDocumentResult(
+    return DoclingTaskResult(
         result=task_result,
         processing_time=processing_time,
         num_succeeded=num_succeeded,
diff --git a/docling_jobkit/datamodel/chunking.py b/docling_jobkit/datamodel/chunking.py
new file mode 100644
index 0000000..8a3e56e
--- /dev/null
+++ b/docling_jobkit/datamodel/chunking.py
@@ -0,0 +1,78 @@
+import enum
+from typing import Annotated, Literal, Optional
+
+from pydantic import BaseModel, Field
+
+
+class ChunkingExportOptions(BaseModel):
+    """Export options for the response of the chunking task."""
+
+    include_converted_doc: bool = False
+
+
+class ChunkerType(str, enum.Enum):
+    """Choice of the chunkers available in Docling."""
+
+    HIERARCHICAL = "hierarchical"
+    HYBRID = "hybrid"
+
+
+class BaseChunkerOptions(BaseModel):
+    """Configuration options for document chunking using Docling chunkers."""
+
+    chunker: ChunkerType
+
+    use_markdown_tables: Annotated[
+        bool,
+        Field(
+            description="Use markdown table format instead of triplets for table serialization.",
+        ),
+    ] = False
+
+    include_raw_text: Annotated[
+        bool,
+        Field(
+            description="Include both raw_text and text (contextualized) in response. If False, only text is included.",
+        ),
+    ] = False
+
+
+class HierarchicalChunkerOptions(BaseChunkerOptions):
+    """Configuration options for the HierarchicalChunker."""
+
+    chunker: Literal[ChunkerType.HIERARCHICAL] = ChunkerType.HIERARCHICAL
+
+
+class HybridChunkerOptions(BaseChunkerOptions):
+    """Configuration options for the HybridChunker."""
+
+    chunker: Literal[ChunkerType.HYBRID] = ChunkerType.HYBRID
+
+    max_tokens: Annotated[
+        Optional[int],
+        Field(
+            description="Maximum number of tokens per chunk. When left to none, the value is automatically extracted from the tokenizer.",
+        ),
+    ] = None
+
+    tokenizer: Annotated[
+        str,
+        Field(
+            description="HuggingFace model name for custom tokenization. If not specified, uses 'sentence-transformers/all-MiniLM-L6-v2' as default.",
+            examples=[
+                "Qwen/Qwen3-Embedding-0.6B",
+                "sentence-transformers/all-MiniLM-L6-v2",
+            ],
+        ),
+    ] = "sentence-transformers/all-MiniLM-L6-v2"
+
+    merge_peers: Annotated[
+        bool,
+        Field(
+            description="Merge undersized successive chunks with same headings.",
+        ),
+    ] = True
+
+
+# Union of all chunking options in the factory
+ChunkingOptionType = HybridChunkerOptions | HierarchicalChunkerOptions
diff --git a/docling_jobkit/datamodel/result.py b/docling_jobkit/datamodel/result.py
index 81caba8..e03eb3f 100644
--- a/docling_jobkit/datamodel/result.py
+++ b/docling_jobkit/datamodel/result.py
@@ -1,10 +1,11 @@
+import warnings
 from typing import Annotated, Literal, Optional
 
 from pydantic import BaseModel, Field
 
-from docling.datamodel.document import ConversionStatus, ErrorItem
+from docling.datamodel.base_models import ConversionStatus, ErrorItem
 from docling.utils.profiling import ProfilingItem
-from docling_core.types.doc import DoclingDocument
+from docling_core.types.doc.document import DoclingDocument
 
 
 class ExportDocumentResponse(BaseModel):
@@ -39,14 +40,75 @@ class RemoteTargetResult(BaseModel):
     kind: Literal["RemoteTargetResult"] = "RemoteTargetResult"
 
 
+class ChunkedDocumentResultItem(BaseModel):
+    """A single chunk of a document with its metadata and content."""
+
+    filename: str
+    chunk_index: int
+    text: Annotated[
+        str,
+        Field(
+            description="The chunk text with structural context (headers, formatting)"
+        ),
+    ]
+    raw_text: Annotated[
+        str | None,
+        Field(
+            description="Raw chunk text without additional formatting or context",
+        ),
+    ] = None
+    num_tokens: Annotated[
+        int | None,
+        Field(
+            description="Number of tokens in the text, if the chunker is aware of tokens"
+        ),
+    ] = None
+    headings: Annotated[
+        list[str] | None, Field(description="List of headings for this chunk")
+    ] = None
+    captions: Annotated[
+        list[str] | None,
+        Field(
+            description="List of captions for this chunk (e.g. for pictures and tables)",
+        ),
+    ] = None
+    doc_items: Annotated[list[str], Field(description="List of doc items references")]
+    page_numbers: Annotated[
+        list[int] | None,
+        Field(description="Page numbers where this chunk content appears"),
+    ] = None
+    metadata: Annotated[
+        dict | None, Field(description="Additional metadata associated with this chunk")
+    ] = None
+
+
+class ChunkedDocumentResult(BaseModel):
+    kind: Literal["ChunkedDocumentResponse"] = "ChunkedDocumentResponse"
+    chunks: list[ChunkedDocumentResultItem]
+    documents: list[ExportResult]
+    chunking_info: Optional[dict] = None
+
+
 ResultType = Annotated[
-    ExportResult | ZipArchiveResult | RemoteTargetResult, Field(discriminator="kind")
+    ExportResult | ZipArchiveResult | RemoteTargetResult | ChunkedDocumentResult,
+    Field(discriminator="kind"),
 ]
 
 
-class ConvertDocumentResult(BaseModel):
+class DoclingTaskResult(BaseModel):
     result: ResultType
     processing_time: float
     num_converted: int
     num_succeeded: int
     num_failed: int
+
+
+class ConvertDocumentResult(DoclingTaskResult):
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            "ConvertDocumentResult is deprecated and will be removed in a future version. "
+            "Use DoclingTaskResult instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(*args, **kwargs)
diff --git a/docling_jobkit/datamodel/task.py b/docling_jobkit/datamodel/task.py
index 63f4bdb..8f6a8d2 100644
--- a/docling_jobkit/datamodel/task.py
+++ b/docling_jobkit/datamodel/task.py
@@ -1,15 +1,20 @@
 import datetime
+import warnings
 from functools import partial
-from typing import Optional, Union
+from typing import Annotated, Optional, Union
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from docling.datamodel.base_models import DocumentStream
 
+from docling_jobkit.datamodel.chunking import (
+    ChunkingExportOptions,
+    ChunkingOptionType,
+)
 from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
 from docling_jobkit.datamodel.s3_coords import S3Coordinates
-from docling_jobkit.datamodel.task_meta import TaskProcessingMeta, TaskStatus
+from docling_jobkit.datamodel.task_meta import TaskProcessingMeta, TaskStatus, TaskType
 from docling_jobkit.datamodel.task_targets import InBodyTarget, TaskTarget
 
 TaskSource = Union[HttpSource, FileSource, DocumentStream, S3Coordinates]
@@ -19,10 +24,24 @@ class Task(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     task_id: str
+    task_type: TaskType = TaskType.CONVERT
     task_status: TaskStatus = TaskStatus.PENDING
     sources: list[TaskSource] = []
     target: TaskTarget = InBodyTarget()
-    options: Optional[ConvertDocumentsOptions]
+    options: Annotated[
+        Optional[ConvertDocumentsOptions],
+        Field(
+            description="Deprecated, use conversion_options instead.",
+            deprecated="Use conversion_options instead.",
+            exclude=True,
+        ),
+    ] = None
+    convert_options: Optional[ConvertDocumentsOptions] = None
+    chunking_options: Annotated[
+        Optional[ChunkingOptionType],
+        Field(discriminator="chunker"),
+    ] = None
+    chunking_export_options: ChunkingExportOptions = ChunkingExportOptions()
     # scratch_dir: Optional[Path] = None
     processing_meta: Optional[TaskProcessingMeta] = None
     created_at: datetime.datetime = Field(
@@ -34,6 +53,19 @@ class Task(BaseModel):
         default_factory=partial(datetime.datetime.now, datetime.timezone.utc)
     )
 
+    @model_validator(mode="before")
+    def handle_deprecated_options(cls, values):
+        # Warn users if they pass the deprecated field
+        if "options" in values and "conversion_options" not in values:
+            warnings.warn(
+                "'options' is deprecated and will be removed in a future version. "
+                "Use 'conversion_options' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            values["conversion_options"] = values["options"]
+        return values
+
     def set_status(self, status: TaskStatus):
         now = datetime.datetime.now(datetime.timezone.utc)
         if status == TaskStatus.STARTED and self.started_at is None:
diff --git a/docling_jobkit/datamodel/task_meta.py b/docling_jobkit/datamodel/task_meta.py
index 2327b33..c49b74b 100644
--- a/docling_jobkit/datamodel/task_meta.py
+++ b/docling_jobkit/datamodel/task_meta.py
@@ -10,6 +10,11 @@ class TaskStatus(str, enum.Enum):
     FAILURE = "failure"
 
 
+class TaskType(str, enum.Enum):
+    CONVERT = "convert"
+    CHUNK = "chunk"
+
+
 class TaskProcessingMeta(BaseModel):
     num_docs: int
     num_processed: int = 0
diff --git a/docling_jobkit/orchestrators/base_orchestrator.py b/docling_jobkit/orchestrators/base_orchestrator.py
index 2e65538..beed78f 100644
--- a/docling_jobkit/orchestrators/base_orchestrator.py
+++ b/docling_jobkit/orchestrators/base_orchestrator.py
@@ -4,9 +4,11 @@
 from typing import TYPE_CHECKING, Optional
 
 from docling_jobkit.datamodel.callback import ProgressCallbackRequest
+from docling_jobkit.datamodel.chunking import BaseChunkerOptions, ChunkingExportOptions
 from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
-from docling_jobkit.datamodel.result import ConvertDocumentResult
+from docling_jobkit.datamodel.result import DoclingTaskResult
 from docling_jobkit.datamodel.task import Task, TaskSource
+from docling_jobkit.datamodel.task_meta import TaskType
 from docling_jobkit.datamodel.task_targets import TaskTarget
 
 if TYPE_CHECKING:
@@ -39,8 +41,12 @@ def bind_notifier(self, notifier: "BaseNotifier"):
     async def enqueue(
         self,
         sources: list[TaskSource],
-        options: ConvertDocumentsOptions,
         target: TaskTarget,
+        task_type: TaskType = TaskType.CONVERT,
+        options: ConvertDocumentsOptions | None = None,
+        convert_options: ConvertDocumentsOptions | None = None,
+        chunking_options: BaseChunkerOptions | None = None,
+        chunking_export_options: ChunkingExportOptions | None = None,
     ) -> Task:
         pass
 
@@ -86,7 +92,7 @@ async def task_status(self, task_id: str, wait: float = 0.0) -> Task:
     async def task_result(
         self,
         task_id: str,
-    ) -> Optional[ConvertDocumentResult]:
+    ) -> Optional[DoclingTaskResult]:
         pass
 
     async def delete_task(self, task_id: str):
diff --git a/docling_jobkit/orchestrators/kfp/orchestrator.py b/docling_jobkit/orchestrators/kfp/orchestrator.py
index a22a50c..87b44a8 100644
--- a/docling_jobkit/orchestrators/kfp/orchestrator.py
+++ b/docling_jobkit/orchestrators/kfp/orchestrator.py
@@ -2,6 +2,7 @@
 import json
 import logging
 import uuid
+import warnings
 from pathlib import Path
 from typing import Optional
 
@@ -15,12 +16,13 @@
     ProgressSetNumDocs,
     ProgressUpdateProcessed,
 )
+from docling_jobkit.datamodel.chunking import BaseChunkerOptions, ChunkingExportOptions
 from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
 from docling_jobkit.datamodel.http_inputs import HttpSource
-from docling_jobkit.datamodel.result import ConvertDocumentResult
+from docling_jobkit.datamodel.result import DoclingTaskResult
 from docling_jobkit.datamodel.s3_coords import S3Coordinates
 from docling_jobkit.datamodel.task import Task, TaskSource
-from docling_jobkit.datamodel.task_meta import TaskProcessingMeta, TaskStatus
+from docling_jobkit.datamodel.task_meta import TaskProcessingMeta, TaskStatus, TaskType
 from docling_jobkit.datamodel.task_targets import S3Target, TaskTarget
 from docling_jobkit.kfp_pipeline.docling_s3in_s3out import inputs_s3in_s3out
 from docling_jobkit.orchestrators.base_orchestrator import (
@@ -88,9 +90,23 @@ def __init__(self, config: KfpOrchestratorConfig):
     async def enqueue(
         self,
         sources: list[TaskSource],
-        options: ConvertDocumentsOptions,
         target: TaskTarget,
+        task_type: TaskType = TaskType.CONVERT,
+        options: ConvertDocumentsOptions | None = None,
+        convert_options: ConvertDocumentsOptions | None = None,
+        chunking_options: BaseChunkerOptions | None = None,
+        chunking_export_options: ChunkingExportOptions | None = None,
     ) -> Task:
+        if options is not None and convert_options is None:
+            convert_options = options
+            warnings.warn(
+                "'options' is deprecated and will be removed in a future version. "
+                "Use 'conversion_options' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        if convert_options is None:
+            raise RuntimeError("convert_options is required.")
         callbacks = []
         if self.config.self_callback_endpoint is not None:
             headers = {}
@@ -121,7 +137,7 @@ async def enqueue(
             kfp_run = self._client.create_run_from_pipeline_func(
                 inputs_s3in_s3out,
                 arguments={
-                    "convertion_options": options.model_dump(),
+                    "convertion_options": convert_options.model_dump(),
                     "source": {
                         "endpoint": s3_source.endpoint,
                         "access_key": s3_source.access_key.get_secret_value(),
@@ -147,7 +163,7 @@ async def enqueue(
                 arguments={
                     "batch_size": 10,
                     "sources": SourcesListType.dump_python(http_sources, mode="json"),
-                    "options": options.model_dump(mode="json"),
+                    "options": convert_options.model_dump(mode="json"),
                     "callbacks": CallbacksType.dump_python(callbacks, mode="json"),
                     "run_name": run_name,
                 },
@@ -233,7 +249,7 @@ async def get_queue_position(self, task_id: str) -> Optional[int]:
     async def task_result(
         self,
         task_id: str,
-    ) -> Optional[ConvertDocumentResult]:
+    ) -> Optional[DoclingTaskResult]:
         raise NotImplementedError()
 
     async def process_queue(self):
diff --git a/docling_jobkit/orchestrators/local/orchestrator.py b/docling_jobkit/orchestrators/local/orchestrator.py
index 8093b41..e8eb487 100644
--- a/docling_jobkit/orchestrators/local/orchestrator.py
+++ b/docling_jobkit/orchestrators/local/orchestrator.py
@@ -2,6 +2,7 @@
 import logging
 import tempfile
 import uuid
+import warnings
 from pathlib import Path
 from typing import Optional
 
@@ -10,9 +11,11 @@
 from docling.datamodel.base_models import InputFormat
 
 from docling_jobkit.convert.manager import DoclingConverterManager
+from docling_jobkit.datamodel.chunking import BaseChunkerOptions, ChunkingExportOptions
 from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
-from docling_jobkit.datamodel.result import ConvertDocumentResult
+from docling_jobkit.datamodel.result import DoclingTaskResult
 from docling_jobkit.datamodel.task import Task, TaskSource, TaskTarget
+from docling_jobkit.datamodel.task_meta import TaskType
 from docling_jobkit.orchestrators.base_orchestrator import (
     BaseOrchestrator,
 )
@@ -38,7 +41,7 @@ def __init__(
         self.task_queue: asyncio.Queue[str] = asyncio.Queue()
         self.queue_list: list[str] = []
         self.cm = converter_manager
-        self._task_results: dict[str, ConvertDocumentResult] = {}
+        self._task_results: dict[str, DoclingTaskResult] = {}
         self.scratch_dir = self.config.scratch_dir or Path(
             tempfile.mkdtemp(prefix="docling_")
         )
@@ -46,11 +49,32 @@ def __init__(
     async def enqueue(
         self,
         sources: list[TaskSource],
-        options: ConvertDocumentsOptions,
         target: TaskTarget,
+        task_type: TaskType = TaskType.CONVERT,
+        options: ConvertDocumentsOptions | None = None,
+        convert_options: ConvertDocumentsOptions | None = None,
+        chunking_options: BaseChunkerOptions | None = None,
+        chunking_export_options: ChunkingExportOptions | None = None,
     ) -> Task:
+        if options is not None and convert_options is None:
+            convert_options = options
+            warnings.warn(
+                "'options' is deprecated and will be removed in a future version. "
+                "Use 'conversion_options' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         task_id = str(uuid.uuid4())
-        task = Task(task_id=task_id, sources=sources, options=options, target=target)
+        chunking_export_options = chunking_export_options or ChunkingExportOptions()
+        task = Task(
+            task_id=task_id,
+            task_type=task_type,
+            sources=sources,
+            convert_options=convert_options,
+            chunking_options=chunking_options,
+            chunking_export_options=chunking_export_options,
+            target=target,
+        )
         await self.init_task_tracking(task)
 
         self.queue_list.append(task_id)
@@ -68,7 +92,7 @@ async def get_queue_position(self, task_id: str) -> Optional[int]:
     async def task_result(
         self,
         task_id: str,
-    ) -> Optional[ConvertDocumentResult]:
+    ) -> Optional[DoclingTaskResult]:
         if task_id not in self._task_results:
             return None
         return self._task_results[task_id]
diff --git a/docling_jobkit/orchestrators/local/worker.py b/docling_jobkit/orchestrators/local/worker.py
index 6b03838..a71bfe4 100644
--- a/docling_jobkit/orchestrators/local/worker.py
+++ b/docling_jobkit/orchestrators/local/worker.py
@@ -6,11 +6,12 @@
 
 from docling.datamodel.base_models import DocumentStream
 
+from docling_jobkit.convert.chunking import process_chunk_results
 from docling_jobkit.convert.manager import DoclingConverterManager
-from docling_jobkit.convert.results import process_results
+from docling_jobkit.convert.results import process_export_results
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
-from docling_jobkit.datamodel.result import ConvertDocumentResult
-from docling_jobkit.datamodel.task_meta import TaskStatus
+from docling_jobkit.datamodel.result import DoclingTaskResult
+from docling_jobkit.datamodel.task_meta import TaskStatus, TaskType
 
 if TYPE_CHECKING:
     from docling_jobkit.orchestrators.local.orchestrator import LocalOrchestrator
@@ -60,7 +61,7 @@ async def loop(self):
 
                 # 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() -> ConvertDocumentResult:
+                def run_task() -> DoclingTaskResult:
                     convert_sources: list[Union[str, DocumentStream]] = []
                     headers: Optional[dict[str, Any]] = None
                     for source in task.sources:
@@ -76,17 +77,24 @@ def run_conversion() -> ConvertDocumentResult:
                     # Note: results are only an iterator->lazy evaluation
                     conv_results = cm.convert_documents(
                         sources=convert_sources,
-                        options=task.options,
+                        options=task.convert_options,
                         headers=headers,
                     )
 
                     # The real processing will happen here
-                    processed_results = process_results(
-                        conversion_options=task.options,
-                        target=task.target,
-                        conv_results=conv_results,
-                        work_dir=workdir,
-                    )
+                    processed_results: DoclingTaskResult
+                    if task.task_type == TaskType.CONVERT:
+                        processed_results = process_export_results(
+                            task=task,
+                            conv_results=conv_results,
+                            work_dir=workdir,
+                        )
+                    elif task.task_type == TaskType.CHUNK:
+                        processed_results = process_chunk_results(
+                            task=task,
+                            conv_results=conv_results,
+                            work_dir=workdir,
+                        )
 
                     return processed_results
 
@@ -101,7 +109,7 @@ def run_conversion() -> ConvertDocumentResult:
 
                 # Run in a thread
                 task_result = await asyncio.to_thread(
-                    run_conversion,
+                    run_task,
                 )
                 self.orchestrator._task_results[task_id] = task_result
                 task.sources = []
diff --git a/docling_jobkit/orchestrators/rq/orchestrator.py b/docling_jobkit/orchestrators/rq/orchestrator.py
index 5ed4221..0bfef89 100644
--- a/docling_jobkit/orchestrators/rq/orchestrator.py
+++ b/docling_jobkit/orchestrators/rq/orchestrator.py
@@ -2,6 +2,7 @@
 import base64
 import logging
 import uuid
+import warnings
 from pathlib import Path
 from typing import Optional
 
@@ -14,11 +15,12 @@
 
 from docling.datamodel.base_models import DocumentStream
 
+from docling_jobkit.datamodel.chunking import BaseChunkerOptions, ChunkingExportOptions
 from docling_jobkit.datamodel.convert import ConvertDocumentsOptions
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
-from docling_jobkit.datamodel.result import ConvertDocumentResult
+from docling_jobkit.datamodel.result import DoclingTaskResult
 from docling_jobkit.datamodel.task import Task, TaskSource, TaskTarget
-from docling_jobkit.datamodel.task_meta import TaskStatus
+from docling_jobkit.datamodel.task_meta import TaskStatus, TaskType
 from docling_jobkit.orchestrators.base_orchestrator import (
     BaseOrchestrator,
     TaskNotFoundError,
@@ -70,9 +72,21 @@ async def notify_end_job(self, task_id):
     async def enqueue(
         self,
         sources: list[TaskSource],
-        options: ConvertDocumentsOptions,
         target: TaskTarget,
+        task_type: TaskType = TaskType.CONVERT,
+        options: ConvertDocumentsOptions | None = None,
+        convert_options: ConvertDocumentsOptions | None = None,
+        chunking_options: BaseChunkerOptions | None = None,
+        chunking_export_options: ChunkingExportOptions | None = None,
     ) -> Task:
+        if options is not None and convert_options is None:
+            convert_options = options
+            warnings.warn(
+                "'options' is deprecated and will be removed in a future version. "
+                "Use 'conversion_options' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         task_id = str(uuid.uuid4())
         rq_sources: list[HttpSource | FileSource] = []
         for source in sources:
@@ -83,11 +97,20 @@ async def enqueue(
                 )
             elif isinstance(source, (HttpSource | FileSource)):
                 rq_sources.append(source)
-        task = Task(task_id=task_id, sources=rq_sources, options=options, target=target)
+        chunking_export_options = chunking_export_options or ChunkingExportOptions()
+        task = Task(
+            task_id=task_id,
+            task_type=task_type,
+            sources=rq_sources,
+            convert_options=convert_options,
+            chunking_options=chunking_options,
+            chunking_export_options=chunking_export_options,
+            target=target,
+        )
         self.tasks.update({task.task_id: task})
-        task_data = task.model_dump(mode="json")
+        task_data = task.model_dump(mode="json", serialize_as_any=True)
         self._rq_queue.enqueue(
-            "docling_jobkit.orchestrators.rq.worker.conversion_task",
+            "docling_jobkit.orchestrators.rq.worker.docling_task",
             kwargs={"task_data": task_data},
             job_id=task_id,
             timeout=14400,
@@ -144,12 +167,12 @@ async def get_queue_position(self, task_id: str) -> Optional[int]:
     async def task_result(
         self,
         task_id: str,
-    ) -> Optional[ConvertDocumentResult]:
+    ) -> Optional[DoclingTaskResult]:
         if task_id not in self._task_result_keys:
             return None
         result_key = self._task_result_keys[task_id]
         packed = await self._async_redis_conn.get(result_key)
-        result = ConvertDocumentResult.model_validate(
+        result = DoclingTaskResult.model_validate(
             msgpack.unpackb(packed, raw=False, strict_map_key=False)
         )
         return result
diff --git a/docling_jobkit/orchestrators/rq/worker.py b/docling_jobkit/orchestrators/rq/worker.py
index 8285d44..b90f5be 100644
--- a/docling_jobkit/orchestrators/rq/worker.py
+++ b/docling_jobkit/orchestrators/rq/worker.py
@@ -9,15 +9,16 @@
 
 from docling.datamodel.base_models import DocumentStream
 
+from docling_jobkit.convert.chunking import process_chunk_results
 from docling_jobkit.convert.manager import (
     DoclingConverterManager,
     DoclingConverterManagerConfig,
 )
-from docling_jobkit.convert.results import process_results
+from docling_jobkit.convert.results import process_export_results
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
-from docling_jobkit.datamodel.result import ConvertDocumentResult
+from docling_jobkit.datamodel.result import DoclingTaskResult
 from docling_jobkit.datamodel.task import Task
-from docling_jobkit.datamodel.task_meta import TaskStatus
+from docling_jobkit.datamodel.task_meta import TaskStatus, TaskType
 from docling_jobkit.orchestrators.rq.orchestrator import (
     RQOrchestrator,
     RQOrchestratorConfig,
@@ -86,7 +87,7 @@ def perform_job(self, job, queue):
             raise
 
 
-def conversion_task(
+def docling_task(
     task_data: dict,
     conversion_manager: DoclingConverterManager,
     orchestrator_config: RQOrchestratorConfig,
@@ -127,20 +128,27 @@ def conversion_task(
 
         if not conversion_manager:
             raise RuntimeError("No converter")
-        if not task.options:
+        if not task.convert_options:
             raise RuntimeError("No conversion options")
         conv_results = conversion_manager.convert_documents(
             sources=convert_sources,
-            options=task.options,
+            options=task.convert_options,
             headers=headers,
         )
 
-        processed_results: ConvertDocumentResult = process_results(
-            conversion_options=task.options,
-            target=task.target,
-            conv_results=conv_results,
-            work_dir=workdir,
-        )
+        processed_results: DoclingTaskResult
+        if task.task_type == TaskType.CONVERT:
+            processed_results = process_export_results(
+                task=task,
+                conv_results=conv_results,
+                work_dir=workdir,
+            )
+        elif task.task_type == TaskType.CHUNK:
+            processed_results = process_chunk_results(
+                task=task,
+                conv_results=conv_results,
+                work_dir=workdir,
+            )
         safe_data = make_msgpack_safe(processed_results.model_dump())
         packed = msgpack.packb(safe_data, use_bin_type=True)
         result_key = f"{orchestrator_config.results_prefix}:{task_id}"
diff --git a/tests/test_chunking.py b/tests/test_chunking.py
new file mode 100644
index 0000000..7c84637
--- /dev/null
+++ b/tests/test_chunking.py
@@ -0,0 +1,180 @@
+import tempfile
+from pathlib import Path
+from unittest.mock import Mock
+
+from docling.datamodel.base_models import ConversionStatus
+from docling.datamodel.document import ConversionResult, InputDocument
+
+from docling_jobkit.convert.chunking import (
+    DocumentChunkerManager,
+    process_chunk_results,
+)
+from docling_jobkit.datamodel.chunking import (
+    HierarchicalChunkerOptions,
+    HybridChunkerOptions,
+)
+from docling_jobkit.datamodel.result import (
+    ChunkedDocumentResult,
+    ExportDocumentResponse,
+    ExportResult,
+)
+from docling_jobkit.datamodel.task import Task
+from docling_jobkit.datamodel.task_meta import TaskType
+from docling_jobkit.datamodel.task_targets import InBodyTarget
+
+
+class TestDocumentChunker:
+    """Test cases for DocumentChunker functionality."""
+
+    def test_chunker_initialization(self):
+        """Test that DocumentChunker can be initialized."""
+        chunker = DocumentChunkerManager()
+        assert chunker is not None
+        assert chunker.config.cache_size == 10  # Default cache size
+        assert chunker._get_chunker_from_cache is not None
+
+    def test_chunker_custom_config(self):
+        """Test DocumentChunker with custom configuration."""
+        from docling_jobkit.convert.chunking import DocumentChunkerConfig
+
+        config = DocumentChunkerConfig(cache_size=5)
+        chunker = DocumentChunkerManager(config=config)
+        assert chunker.config.cache_size == 5
+
+    def test_chunking_options_defaults(self):
+        """Test HybridChunkerOptions with default values."""
+        options = HybridChunkerOptions()
+        assert options.max_tokens is None
+        assert options.tokenizer == "sentence-transformers/all-MiniLM-L6-v2"
+        assert options.use_markdown_tables is False
+        assert options.merge_peers is True
+        assert options.include_raw_text is False
+
+    def test_chunking_options_custom_values(self):
+        """Test HybridChunkerOptions with custom values."""
+        options = HybridChunkerOptions(
+            max_tokens=1024,
+            tokenizer="custom/tokenizer",
+            use_markdown_tables=True,
+            merge_peers=False,
+            include_raw_text=True,
+        )
+        assert options.max_tokens == 1024
+        assert options.tokenizer == "custom/tokenizer"
+        assert options.use_markdown_tables is True
+        assert options.merge_peers is False
+        assert options.include_raw_text is True
+
+    def test_chunk_conversion_result_failure(self):
+        """Test chunking with failed conversion result."""
+        # Create failed conversion result with minimal required fields
+        failed_result = Mock(spec=ConversionResult)
+        failed_result.input = Mock(spec=InputDocument)
+        failed_result.input.file = Path("file.pdf")
+        failed_result.status = ConversionStatus.FAILURE
+        failed_result.errors = []
+        failed_result.timings = {}
+
+        workdir = tempfile.mkdtemp()
+
+        task = Task(
+            task_id="abc",
+            task_type=TaskType.CHUNK,
+            chunking_options=HybridChunkerOptions(),
+            target=InBodyTarget(),
+        )
+        task_result = process_chunk_results(
+            task=task,
+            conv_results=[failed_result],
+            work_dir=workdir,
+        )
+        result = task_result.result
+
+        assert isinstance(result, ChunkedDocumentResult)
+        assert result.documents[0].status == ConversionStatus.FAILURE
+        assert len(result.chunks) == 0
+
+
+class TestChunkedDocumentResponse:
+    """Test cases for ChunkedDocumentResponse model."""
+
+    def test_chunked_response_creation(self):
+        """Test creating a ChunkedDocumentResponse."""
+        response = ChunkedDocumentResult(
+            chunks=[],
+            documents=[
+                ExportResult(
+                    content=ExportDocumentResponse(filename="file.pdf"),
+                    status=ConversionStatus.SUCCESS,
+                )
+            ],
+            chunking_info={"total_chunks": 0},
+        )
+
+        assert response.documents[0].status == ConversionStatus.SUCCESS
+        assert response.chunking_info == {"total_chunks": 0}
+
+    def test_chunked_response_with_chunks(self):
+        """Test ChunkedDocumentResponse with actual chunks."""
+        from docling_jobkit.datamodel.result import ChunkedDocumentResultItem
+
+        chunk = ChunkedDocumentResultItem(
+            filename="test.pdf",
+            chunk_index=0,
+            text="Test content",
+            num_tokens=4,
+            headings=["Heading 1"],
+            doc_items=["#/tests/1"],
+            page_numbers=[1],
+        )
+
+        response = ChunkedDocumentResult(
+            chunks=[chunk],
+            documents=[
+                ExportResult(
+                    content=ExportDocumentResponse(filename="file.pdf"),
+                    status=ConversionStatus.SUCCESS,
+                )
+            ],
+            chunking_info={"total_chunks": 1},
+        )
+
+        assert len(response.chunks) == 1
+        assert response.chunks[0].filename == "test.pdf"
+        assert response.chunks[0].text == "Test content"
+
+    def test_cache_key_generation(self):
+        """Test that cache key generation is deterministic and uses SHA1."""
+        chunker = DocumentChunkerManager()
+
+        options1 = HybridChunkerOptions(
+            max_tokens=512,
+            tokenizer="test-tokenizer",
+            merge_peers=True,
+            use_markdown_tables=False,
+        )
+        options2 = HybridChunkerOptions(
+            max_tokens=512,
+            tokenizer="test-tokenizer",
+            merge_peers=True,
+            use_markdown_tables=False,
+        )
+        options3 = HybridChunkerOptions(
+            max_tokens=1024,  # Different value
+            tokenizer="test-tokenizer",
+            merge_peers=True,
+            use_markdown_tables=False,
+        )
+        options4 = HierarchicalChunkerOptions()
+
+        key1 = chunker._generate_cache_key(options1)
+        key2 = chunker._generate_cache_key(options2)
+        key3 = chunker._generate_cache_key(options3)
+        key4 = chunker._generate_cache_key(options4)
+
+        # Same options should generate same key
+        assert key1 == key2
+        # Different options should generate different key
+        assert key1 != key3
+        assert key1 != key4
+        assert key3 != key4
diff --git a/tests/test_local_orchestrator.py b/tests/test_local_orchestrator.py
index e0479ed..87bc762 100644
--- a/tests/test_local_orchestrator.py
+++ b/tests/test_local_orchestrator.py
@@ -22,14 +22,20 @@
     DoclingConverterManager,
     DoclingConverterManagerConfig,
 )
+from docling_jobkit.datamodel.chunking import (
+    BaseChunkerOptions,
+    HierarchicalChunkerOptions,
+    HybridChunkerOptions,
+)
 from docling_jobkit.datamodel.convert import (
     ConvertDocumentsOptions,
     VlmModelApi,
     VlmModelLocal,
 )
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
-from docling_jobkit.datamodel.result import ExportResult
-from docling_jobkit.datamodel.task import TaskSource
+from docling_jobkit.datamodel.result import ChunkedDocumentResult, ExportResult
+from docling_jobkit.datamodel.task import Task, TaskSource
+from docling_jobkit.datamodel.task_meta import TaskType
 from docling_jobkit.datamodel.task_targets import InBodyTarget
 from docling_jobkit.orchestrators.local.orchestrator import (
     LocalOrchestrator,
@@ -196,7 +202,7 @@ async def test_convert_url(orchestrator: LocalOrchestrator, test_option: TestOpt
 
     task = await orchestrator.enqueue(
         sources=sources,
-        options=options,
+        convert_options=options,
         target=InBodyTarget(),
     )
 
@@ -220,7 +226,7 @@ async def test_convert_file(orchestrator: LocalOrchestrator):
 
     task = await orchestrator.enqueue(
         sources=sources,
-        options=options,
+        convert_options=options,
         target=InBodyTarget(),
     )
 
@@ -247,7 +253,7 @@ async def test_replicated_convert(replicated_orchestrator: LocalOrchestrator):
     for _ in range(NUM_TASKS):
         task = await replicated_orchestrator.enqueue(
             sources=sources,
-            options=options,
+            convert_options=options,
             target=InBodyTarget(),
         )
 
@@ -258,3 +264,44 @@ async def test_replicated_convert(replicated_orchestrator: LocalOrchestrator):
     assert isinstance(task_result.result, ExportResult)
 
     assert task_result.result.status == ConversionStatus.SUCCESS
+
+
+@pytest.mark.parametrize(
+    "chunking_options", [HybridChunkerOptions(), HierarchicalChunkerOptions()]
+)
+async def test_chunk_file(
+    orchestrator: LocalOrchestrator, chunking_options: BaseChunkerOptions
+):
+    conversion_options = ConvertDocumentsOptions(to_formats=[])
+
+    doc_filename = Path(__file__).parent / "2206.01062v1-pg4.pdf"
+    encoded_doc = base64.b64encode(doc_filename.read_bytes()).decode()
+
+    sources: list[TaskSource] = []
+    sources.append(FileSource(base64_string=encoded_doc, filename=doc_filename.name))
+
+    task: Task = await orchestrator.enqueue(
+        task_type=TaskType.CHUNK,
+        sources=sources,
+        convert_options=conversion_options,
+        chunking_options=chunking_options,
+        target=InBodyTarget(),
+    )
+
+    await _wait_task_complete(orchestrator, task.task_id)
+    task_result = await orchestrator.task_result(task_id=task.task_id)
+
+    assert task_result is not None
+    assert isinstance(task_result.result, ChunkedDocumentResult)
+
+    assert len(task_result.result.documents) == 1
+    assert (
+        task_result.result.documents[0].content.json_content is None
+    )  # by default no document
+    assert len(task_result.result.chunks) > 1
+
+    if isinstance(chunking_options, HybridChunkerOptions):
+        assert task_result.result.chunks[0].num_tokens > 0
+
+    if isinstance(chunking_options, HierarchicalChunkerOptions):
+        assert task_result.result.chunks[0].num_tokens is None
diff --git a/tests/test_rq_orchestrator.py b/tests/test_rq_orchestrator.py
index b755d9c..45ad55b 100644
--- a/tests/test_rq_orchestrator.py
+++ b/tests/test_rq_orchestrator.py
@@ -11,13 +11,19 @@
 import pytest_asyncio
 
 from docling.datamodel.base_models import ConversionStatus
+from docling_core.types.doc import DoclingDocument
 
+from docling_jobkit.datamodel.chunking import (
+    ChunkingExportOptions,
+    HybridChunkerOptions,
+)
 from docling_jobkit.datamodel.convert import (
     ConvertDocumentsOptions,
 )
 from docling_jobkit.datamodel.http_inputs import FileSource, HttpSource
-from docling_jobkit.datamodel.result import ExportResult
-from docling_jobkit.datamodel.task import TaskSource
+from docling_jobkit.datamodel.result import ChunkedDocumentResult, ExportResult
+from docling_jobkit.datamodel.task import Task, TaskSource
+from docling_jobkit.datamodel.task_meta import TaskType
 from docling_jobkit.datamodel.task_targets import InBodyTarget
 from docling_jobkit.orchestrators.base_orchestrator import BaseOrchestrator
 from docling_jobkit.orchestrators.rq.orchestrator import (
@@ -87,7 +93,7 @@ async def test_convert_url(orchestrator: RQOrchestrator, test_option: TestOption
 
     task = await orchestrator.enqueue(
         sources=sources,
-        options=options,
+        convert_options=options,
         target=InBodyTarget(),
     )
 
@@ -111,7 +117,7 @@ async def test_convert_file(orchestrator: RQOrchestrator):
 
     task = await orchestrator.enqueue(
         sources=sources,
-        options=options,
+        convert_options=options,
         target=InBodyTarget(),
     )
 
@@ -122,3 +128,41 @@ async def test_convert_file(orchestrator: RQOrchestrator):
     assert isinstance(task_result.result, ExportResult)
 
     assert task_result.result.status == ConversionStatus.SUCCESS
+
+
+@pytest.mark.parametrize("include_converted_doc", [False, True])
+async def test_chunk_file(orchestrator: RQOrchestrator, include_converted_doc: bool):
+    conversion_options = ConvertDocumentsOptions()
+    chunking_options = HybridChunkerOptions()
+    export_options = ChunkingExportOptions(include_converted_doc=include_converted_doc)
+
+    doc_filename = Path(__file__).parent / "2206.01062v1-pg4.pdf"
+    encoded_doc = base64.b64encode(doc_filename.read_bytes()).decode()
+
+    sources: list[TaskSource] = []
+    sources.append(FileSource(base64_string=encoded_doc, filename=doc_filename.name))
+
+    task: Task = await orchestrator.enqueue(
+        task_type=TaskType.CHUNK,
+        sources=sources,
+        convert_options=conversion_options,
+        chunking_options=chunking_options,
+        chunking_export_options=export_options,
+        target=InBodyTarget(),
+    )
+
+    await _wait_task_complete(orchestrator, task.task_id)
+    task_result = await orchestrator.task_result(task_id=task.task_id)
+
+    assert task_result is not None
+    assert isinstance(task_result.result, ChunkedDocumentResult)
+
+    assert len(task_result.result.documents) == 1
+    assert len(task_result.result.chunks) > 1
+
+    if include_converted_doc:
+        DoclingDocument.model_validate(
+            task_result.result.documents[0].content.json_content
+        )
+    else:
+        task_result.result.documents[0].content.json_content is None
diff --git a/uv.lock b/uv.lock
index bb46231..c034249 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'darwin'",
@@ -30,27 +30,27 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/c4/25/969456a95a90ed38f73f68d0f0915bdf1d76145d05054c59ad587b171150/accelerate-1.9.0.tar.gz", hash = "sha256:0e8c61f81af7bf37195b6175a545ed292617dd90563c88f49020aea5b6a0b47f", size = 383234 }
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+    { url = "https://files.pythonhosted.org/packages/9f/1c/a17fb513aeb684fb83bef5f395910f53103ab30308bbdd77fd66d6698c46/accelerate-1.9.0-py3-none-any.whl", hash = "sha256:c24739a97ade1d54af4549a65f8b6b046adc87e2b3e4d6c66516e32c53d5a8f1", size = 367073 },
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