CourseContentExtractorParams.md

CourseContentExtractorParams

Parameters for the course content extractor.

Properties

Name	Type	Description	Notes
extractor_type	str	Discriminator field. Must be 'course_content_extractor'.	[optional] [default to 'course_content_extractor']
target_segment_duration_ms	int	Target duration for video segments in milliseconds.	[optional] [default to 120000]
min_segment_duration_ms	int	Minimum duration for video segments in milliseconds.	[optional] [default to 30000]
segmentation_method	str	Video segmentation method: 'scene', 'srt', or 'time'.	[optional] [default to 'scene']
scene_detection_threshold	float	Scene detection sensitivity (0.0-1.0).	[optional] [default to 0.3]
use_whisper_asr	bool	Use Whisper ASR for transcription instead of SRT subtitles.	[optional] [default to True]
expand_to_granular_docs	bool	Expand each segment into multiple granular documents.	[optional] [default to True]
ocr_frames_per_segment	int	Number of frames to OCR per video segment.	[optional] [default to 3]
pdf_extraction_mode	str	How to extract PDF content: 'per_page' or 'per_element'.	[optional] [default to 'per_element']
pdf_render_dpi	int	DPI for rendering PDF pages/elements as images.	[optional] [default to 150]
detect_code_in_pdf	bool	Whether to detect code blocks in PDF text.	[optional] [default to True]
segment_functions	bool	Whether to segment code files into individual functions.	[optional] [default to True]
supported_languages	List[str]	Programming languages to extract from code archives.	[optional]
run_text_embedding	bool	Generate E5 text embeddings (1024D) for transcripts and text.	[optional] [default to True]
run_code_embedding	bool	Generate Jina Code embeddings (768D) for code snippets.	[optional] [default to True]
run_visual_embedding	bool	Generate SigLIP visual embeddings (768D) for video frames.	[optional] [default to True]
run_structure_embedding	bool	Generate DINOv2 visual structure embeddings (768D) for layout comparison.	[optional] [default to True]
visual_embedding_use_case	str	Content type preset for visual embedding strategy.	[optional] [default to 'lecture']
extract_screen_text	bool	Run OCR on video frames to extract on-screen text.	[optional] [default to True]
generate_thumbnails	bool	Generate thumbnail images for each learning unit.	[optional] [default to True]
use_cdn	bool	Use CDN for thumbnail delivery.	[optional] [default to False]
run_vlm_frame_analysis	bool	Run VLM on video frame thumbnails to extract structured fields: frame_type, page_context, ui_labels, workflow_steps, config_options. Enables drift detection and UI comparison use cases.	[optional] [default to False]
vlm_provider	str	VLM provider: 'google' (Gemini API) or 'vllm' (local GPU with Qwen2.5-VL).	[optional] [default to 'google']
vlm_model	str	VLM model. For google: 'gemini-2.5-flash'. For vllm: 'Qwen/Qwen2.5-VL-7B-Instruct'.	[optional] [default to 'gemini-2.5-flash']
enrich_with_llm	bool	Use Gemini to generate summaries and enhance descriptions.	[optional] [default to False]
llm_prompt	str	Prompt for LLM enrichment when enrich_with_llm=True.	[optional] [default to 'Summarize this educational content segment, highlighting key concepts.']

Example

from mixpeek.models.course_content_extractor_params import CourseContentExtractorParams

# TODO update the JSON string below
json = "{}"
# create an instance of CourseContentExtractorParams from a JSON string
course_content_extractor_params_instance = CourseContentExtractorParams.from_json(json)
# print the JSON string representation of the object
print(CourseContentExtractorParams.to_json())

# convert the object into a dict
course_content_extractor_params_dict = course_content_extractor_params_instance.to_dict()
# create an instance of CourseContentExtractorParams from a dict
course_content_extractor_params_from_dict = CourseContentExtractorParams.from_dict(course_content_extractor_params_dict)

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