Extract key information from texts and documents programmatically. Generate concise, informative summaries that capture the meaning of text using large language models (LLM) in Python. The applications of software text summarization are vast and varied. Text summarization can be used to provide up-to-date information on current events. By integrating text summarization function into your software, you will not only increase productivity, but also improve decision making by accessing important data as quickly as possible.
The interaction between Aspose.Words and Large Language Models is built on a REST architecture. This approach provides reliable and secure communication between your Python via .NET application and various AI services. To set up authentication, you will need to specify your private API key and the `endpoint` of the AI service that provides the models you need (GoogleAiModel, OpenAiModel). For a full list of supported LLM types, see the API Reference.
Experience the future of intelligent text processing in Python today!
pip install aspose-words
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doc = aw.Document("Document.docx")
api_key = os.getenv("API_KEY")
# Use OpenAI or Google generative language models.
model = aw.ai.AiModel.create(aw.ai.AiModelType.GPT_4O_MINI).with_api_key(api_key).as_open_ai_model()
options = aw.ai.SummarizeOptions()
options.summary_length = aw.ai.SummaryLength.SHORT
summary = model.summarize(doc, options)
summary.save("Output.pdf")
We host our Python packages in PyPi repositories. Please follow the step-by-step instructions on how to install "Aspose.Words for Python via .NET" to your developer environment.
This package is compatible with Python ≥3.5 and <3.12. If you develop software for Linux, please have a look at additional requirements for gcc and libpython in Product Documentation.