Release Notes

v2.0.1

The HuggingFaceTGIGenerator and HuggingFaceTGIChatGenerator components have been modified to be compatible with huggingface_hub>=0.22.0.
If you use these components, you may need to upgrade the huggingface_hub library. To do this, run the following command in your environment: pip install "huggingface_hub>=0.22.0"

Adds streaming_callback parameter to HuggingFaceLocalGenerator, allowing users to handle streaming responses.
Introduce a new SparseEmbedding class which can be used to store a sparse vector representation of a Document. It will be instrumental to support Sparse Embedding Retrieval with the subsequent introduction of Sparse Embedders and Sparse Embedding Retrievers.

Set max_new_tokens default to 512 in Hugging Face generators.
In Jupyter notebooks, the image of the Pipeline will no longer be displayed automatically. The textual representation of the Pipeline will be displayed.
To display the Pipeline image, use the show method of the Pipeline object.

The test_comparison_in test case in the base document store tests used to always pass, no matter how the in filtering logic was implemented in document stores. With the fix, the in logic is actually tested. Some tests might start to fail for document stores that don't implement the in filter correctly.
Put HFTokenStreamingHandler in a lazy import block in HuggingFaceLocalGenerator. This fixed some breaking core-integrations.
Fixes Pipeline.run() logic so Components that have all their inputs with a default are run in the correct order. This happened we gather a list of Components to run internally when running the Pipeline in the order they are added during creation of the Pipeline. This caused some Components to run before they received all their inputs.
Fixes HuggingFaceTEITextEmbedder returning an embedding of incorrect shape when used with a Text-Embedding-Inference endpoint deployed using Docker.
Add the @component decorator to HuggingFaceTGIChatGenerator. The lack of this decorator made it impossible to use the HuggingFaceTGIChatGenerator in a pipeline.