docs(examples): processing parquet file of images (#2641)

* add example processing parquet file of images

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

* vlm using vllm api

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

* use openvino and add more docs

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

* add default input file

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

* change default to standard for running in CI

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

* use simple rapidocr without openvino in the CI example

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

---------

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

docs/examples/parquet_images.py

@@ -0,0 +1,184 @@
+# %% [markdown]
+# What this example does
+# - Run a batch conversion on a parquet file with an image column.
+#
+# Requirements
+# - Python 3.9+
+# - Install Docling: `pip install docling`
+#
+# How to run
+# - `python docs/examples/parquet_images.py FILE`
+#
+# The parquet file should be in the format similar to the ViDoRe V3 dataset.
+# https://huggingface.co/collections/vidore/vidore-benchmark-v3
+#
+# For example:
+# - https://huggingface.co/datasets/vidore/vidore_v3_hr/blob/main/corpus/test-00000-of-00001.parquet
+#
+# ### Start models with vllm
+# ```console
+# vllm serve ibm-granite/granite-docling-258M \
+#   --host 127.0.0.1 --port 8000 \
+#   --max-num-seqs 512 \
+#   --max-num-batched-tokens 8192 \
+#   --enable-chunked-prefill \
+#   --gpu-memory-utilization 0.9
+# ```
+# %%
+
+import io
+import time
+from pathlib import Path
+from typing import Annotated, Literal
+
+import pyarrow.parquet as pq
+import typer
+from PIL import Image
+
+from docling.datamodel import vlm_model_specs
+from docling.datamodel.base_models import ConversionStatus, DocumentStream, InputFormat
+from docling.datamodel.pipeline_options import (
+    PdfPipelineOptions,
+    PipelineOptions,
+    RapidOcrOptions,
+    VlmPipelineOptions,
+)
+from docling.datamodel.pipeline_options_vlm_model import ApiVlmOptions, ResponseFormat
+from docling.datamodel.settings import settings
+from docling.document_converter import DocumentConverter, ImageFormatOption
+from docling.pipeline.base_pipeline import ConvertPipeline
+from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
+from docling.pipeline.vlm_pipeline import VlmPipeline
+
+
+def process_document(
+    images: list[Image.Image], chunk_idx: int, doc_converter: DocumentConverter
+):
+    """Builds a tall image and sends it through Docling."""
+
+    print(f"\n--- Processing chunk {chunk_idx} with {len(images)} images ---")
+
+    # Convert images to mode RGB (TIFF pages must match)
+    rgb_images = [im.convert("RGB") for im in images]
+
+    # First image is the base frame
+    first = rgb_images[0]
+    rest = rgb_images[1:]
+
+    # Create multi-page TIFF using PIL frames
+    buf = io.BytesIO()
+    first.save(
+        buf,
+        format="TIFF",
+        save_all=True,
+        append_images=rest,
+        compression="tiff_deflate",  # good compression, optional
+    )
+    buf.seek(0)
+
+    # Docling conversion
+    doc_stream = DocumentStream(name=f"doc_{chunk_idx}.tiff", stream=buf)
+
+    start_time = time.time()
+    conv_result = doc_converter.convert(doc_stream)
+    runtime = time.time() - start_time
+
+    assert conv_result.status == ConversionStatus.SUCCESS
+
+    pages = len(conv_result.pages)
+    print(
+        f"Chunk {chunk_idx} converted in {runtime:.2f} sec ({pages / runtime:.2f} pages/s)."
+    )
+
+
+def run(
+    filename: Annotated[Path, typer.Argument()] = Path(
+        "docs/examples/data/vidore_v3_hr-slice.parquet"
+    ),
+    doc_size: int = 192,
+    batch_size: int = 64,
+    pipeline: Literal["standard", "vlm"] = "standard",
+):
+    if pipeline == "standard":
+        pipeline_cls: type[ConvertPipeline] = StandardPdfPipeline
+        pipeline_options: PipelineOptions = PdfPipelineOptions(
+            # ocr_options=RapidOcrOptions(backend="openvino"),
+            ocr_batch_size=batch_size,
+            layout_batch_size=batch_size,
+            table_batch_size=4,
+        )
+    elif pipeline == "vlm":
+        settings.perf.page_batch_size = batch_size
+        pipeline_cls = VlmPipeline
+        vlm_options = ApiVlmOptions(
+            url="http://localhost:8000/v1/chat/completions",
+            params=dict(
+                model=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS.repo_id,
+                max_tokens=4096,
+                skip_special_tokens=True,
+            ),
+            prompt=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS.prompt,
+            timeout=90,
+            scale=1.0,
+            temperature=0.0,
+            concurrency=batch_size,
+            stop_strings=["</doctag>", "<|end_of_text|>"],
+            response_format=ResponseFormat.DOCTAGS,
+        )
+        pipeline_options = VlmPipelineOptions(
+            vlm_options=vlm_options,
+            enable_remote_services=True,  # required when using a remote inference service.
+        )
+    else:
+        raise RuntimeError(f"Pipeline {pipeline} not available.")
+
+    doc_converter = DocumentConverter(
+        format_options={
+            InputFormat.IMAGE: ImageFormatOption(
+                pipeline_cls=pipeline_cls,
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+
+    start_time = time.time()
+    doc_converter.initialize_pipeline(InputFormat.IMAGE)
+    init_runtime = time.time() - start_time
+    print(f"Pipeline initialized in {init_runtime:.2f} seconds.")
+
+    # ------------------------------------------------------------
+    # Open parquet file in streaming mode
+    # ------------------------------------------------------------
+    pf = pq.ParquetFile(filename)
+
+    image_buffer = []  # holds up to doc_size images
+    chunk_idx = 0
+
+    # ------------------------------------------------------------
+    # Stream batches from parquet
+    # ------------------------------------------------------------
+    for batch in pf.iter_batches(batch_size=batch_size, columns=["image"]):
+        col = batch.column("image")
+
+        # Extract Python objects (PIL images)
+        # Arrow stores them as Python objects inside an ObjectArray
+        for i in range(len(col)):
+            img_dict = col[i].as_py()  # {"bytes": ..., "path": ...}
+            pil_image = Image.open(io.BytesIO(img_dict["bytes"]))
+            image_buffer.append(pil_image)
+
+            # If enough images gathered → process one doc
+            if len(image_buffer) == doc_size:
+                process_document(image_buffer, chunk_idx, doc_converter)
+                image_buffer.clear()
+                chunk_idx += 1
+
+    # ------------------------------------------------------------
+    # Process trailing images (last partial chunk)
+    # ------------------------------------------------------------
+    if image_buffer:
+        process_document(image_buffer, chunk_idx, doc_converter)
+
+
+if __name__ == "__main__":
+    typer.run(run)

mkdocs.yml

@@ -118,6 +118,7 @@ nav:
     - ⚡️ GPU optimization:
       - "Standard pipeline": examples/gpu_standard_pipeline.py
       - "VLM pipeline": examples/gpu_vlm_pipeline.py
+      - "Parquet benchmark": examples/parquet_images.py
     - 🗂️ More examples:
       - examples/dpk-ingest-chunk-tokenize.ipynb
       - examples/rag_azuresearch.ipynb