Migration v0.0.19

This guide helps you migrate your code from previous versions of the Aurelio SDK to v0.0.19, which introduces significant changes to the extract endpoint.

Key Changes

Deprecated: quality parameter

The quality parameter previously used with values "low" and "high" for both PDF and MP4 file extraction has been deprecated.

New: model parameter

The new model parameter replaces quality and provides more granular control over the extraction process with different model options.

New: Enhanced processing options for video files

For MP4 files, chunking preferences previously set through the quality parameter are now explicitly configured in the processing_options parameter.

Migration Table

File Type	Old Approach	New Approach	Notes
PDF	quality="low"	model="aurelio-base"	Fastest, cheapest option for clean PDFs
PDF	quality="high"	model="docling-base"	Code-based OCR method for high precision
PDF	-	model="gemini-2-flash-lite"	New! State-of-the-art VLM-based extraction
MP4	quality="low", chunk=True	model="aurelio-base", processing_options={"chunking": {"chunker_type": "regex"}}	Basic chunking for videos
MP4	quality="high", chunk=True	model="aurelio-base", processing_options={"chunking": {"chunker_type": "semantic"}}	Semantic chunking for videos

Code Examples

PDF Extraction

Before (pre-v0.0.19):

# Fast extraction for simple PDFs
response = client.extract_file(
    file_path="document.pdf",
    quality="low",
    chunk=True,
    wait=30
)

# Higher quality extraction for complex PDFs
response = client.extract_file(
    file_path="complex_document.pdf",
    quality="high",
    chunk=True,
    wait=30
)

After (v0.0.19+):

# Fast extraction for simple PDFs
response = client.extract_file(
    file_path="document.pdf",
    model="aurelio-base",  # Equivalent to the old quality="low"
    chunk=True,
    wait=30
)

# Higher quality extraction for complex PDFs
response = client.extract_file(
    file_path="complex_document.pdf",
    model="docling-base",  # Equivalent to the old quality="high"
    chunk=True,
    wait=30
)

# NEW: State-of-the-art extraction using VLM
response = client.extract_file(
    file_path="document.pdf",
    model="gemini-2-flash-lite",  # New option not available before
    chunk=True,
    wait=30
)

Video Extraction

Before (pre-v0.0.19):

# Basic video extraction with regex chunking
response = client.extract_file(
    file_path="video.mp4",
    quality="low",
    chunk=True,
    wait=-1
)

# Video extraction with semantic chunking
response = client.extract_file(
    file_path="video.mp4",
    quality="high",
    chunk=True,
    wait=-1
)

After (v0.0.19+):

# Basic video extraction with regex chunking
response = client.extract_file(
    file_path="video.mp4",
    model="aurelio-base",  # Only supported model for videos
    chunk=True,
    wait=-1,
    processing_options={
        "chunking": {
            "chunker_type": "regex"  # Equivalent to old quality="low"
        }
    }
)

# Video extraction with semantic chunking
response = client.extract_file(
    file_path="video.mp4",
    model="aurelio-base",  # Only supported model for videos
    chunk=True,
    wait=-1,
    processing_options={
        "chunking": {
            "chunker_type": "semantic"  # Equivalent to old quality="high"
        }
    }
)

URL Extraction

The changes for extract_url are identical to those for extract_file - replace the quality parameter with the appropriate model parameter, and for videos, specify chunking preferences in processing_options.

VLM-based Extraction (New Feature)

The new gemini-2-flash-lite model uses a Vision Language Model to process PDF content, offering state-of-the-art accuracy. This can be especially valuable for:

• Scanned documents with complex layouts
• Documents with tables, charts, and diagrams
• Documents where context and visual understanding are important

response = client.extract_file(
    file_path="complex_scanned_document.pdf",
    model="gemini-2-flash-lite",
    chunk=True,
    wait=60  # May require longer processing time
)

Note: As mentioned in the OCR in Large Multimodal Models paper, VLMs like Gemini can occasionally hallucinate content. While hallucinations are rare, the model is designed for high-recall, potentially with lower precision than code-based OCR methods.

Pricing Considerations

• aurelio-base pricing is equivalent to the old low quality setting
• Both docling-base and gemini-2-flash-lite are priced equivalent to the old high quality setting

Transition Period

The quality parameter will continue to work during a transition period but will be removed in a future release. We recommend updating your code to use the new model parameter as soon as possible.