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feat: Add support for custom LLM describers #1449
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This commit introduces a new `llm_describber` callback to provide a flexible and extensible way to integrate any multimodal Large Language Model for image descriptions. Key changes: - The `MarkItDown` class now accepts an `llm_describber` function in its constructor. This callback is propagated to all relevant converters. - The `ImageConverter`, `PptxConverter`, and `llm_caption` function have been updated to prioritize the `llm_describber` callback if provided, while maintaining backward compatibility with the existing `llm_client` and `llm_model` implementation. - A new test file, `test_llm_describber.py`, has been added to verify that the `llm_describber` is correctly called. - The `README.md` file has been updated to document the new `llm_describber` functionality with an example.
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This PR addresses issue #1129 by introducing a flexible and extensible way to use any multimodal Large Language Model for image descriptions, not just OpenAI models.
Instead of hardcoding support for specific LLM providers like Gemini or Claude, this PR introduces a new
llm_describbercallback function. This allows users to define their own logic for generating image descriptions, making the library compatible with any LLM provider.Key Changes:
MarkItDownclass now accepts anllm_describberfunction in its constructor.llm_describberis propagated to all converters that handle image descriptions (ImageConverter,PptxConverter).llm_describberis not provided, the library falls back to the existingllm_client/llm_modellogic for OpenAI models.llm_describberis called correctly.README.mdto document the new functionality.Example Usage:
This approach provides maximum flexibility and avoids the need to add specific support for each new LLM provider in the future.