FEATURE: Enhance embedding functionality with batch and image support. #55
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cyrus2281
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Nov 1, 2024
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| for query in queries: | ||
| embeddings.append(self.component.embed_query(query)) | ||
| return {"embeddings": embeddings} |
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[CMT] LangChain doesn't expose the function to do batch in single calls, so had to loop here
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SonarQube Quality Gate
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LG |
alimosaed
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Nov 4, 2024
| embedding_type = data.get("type", "document") | ||
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| embeddings = None | ||
| items = [items] if type(items) != list else items |
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[SUG]: You can improve the validation step before getting an error in the next steps. Maybe check None and/or check the content of items based on types (document, image or query).
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What is the purpose of this change?
The purpose of this change is to enhance the LangChain embeddings component by adding support for batch processing of embeddings and extending its functionality to include image embeddings. This allows for more efficient handling of multiple embeddings in a single operation and broadens the use cases to include image data alongside text data.
How is this accomplished?
This is accomplished by modifying the input schema to accept a list of items instead of a single text input and introducing methods to handle different types of embeddings (document, query, and image). The invoke method has been refactored to process all item types consistently, delegating the actual embedding process to specialized methods for each type.
Anything reviews should focus on/be aware of?
Reviewers should focus on the changes to the input and output schemas and how the invoke method handles the embedding operations for different types of data. Ensure that the batch processing logic works as intended for all supported embedding types and that the new image embedding functionality is properly integrated.
Changes are backward incompatible, but no one is using this component yet