utilization topic_generator.py Examples can be systematized to address the issue:
- Structured Output: Configuration model returns results in JSON format with topic relevance scores
- contextual enhancement: Built-in Google search tool automatically supplements domain background knowledge
- batch file: By
stream_contentProcessing multiple research propositions simultaneously - parameter tuning: Adjustments
num_topicscap (a poem)temperatureControl of diversity generation
Sample code:async for part in processor(["量子计算临床应用"], num_topics=5):
print(part.json())
5 sub-directions of research and their theoretical basis are available at once.
This answer comes from the articleGenAI Processors: lightweight Python library supports efficient parallel processing of multimodal contentThe
May not be reproduced without permission:AI productivity tools " How can we improve the quality and efficiency of theme generation in academic research?
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