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064be93edf
# What does this PR do? This PR adds similar to `llms` a SageMaker-powered `embeddings` class. This is helpful if you want to leverage Hugging Face models on SageMaker for creating your indexes. I added a example into the [docs/modules/indexes/examples/embeddings.ipynb](https://github.com/hwchase17/langchain/compare/master...philschmid:add-sm-embeddings?expand=1#diff-e82629e2894974ec87856aedd769d4bdfe400314b03734f32bee5990bc7e8062) document. The example currently includes some `_### TEMPORARY: Showing how to deploy a SageMaker Endpoint from a Hugging Face model ###_ ` code showing how you can deploy a sentence-transformers to SageMaker and then run the methods of the embeddings class. @hwchase17 please let me know if/when i should remove the `_### TEMPORARY: Showing how to deploy a SageMaker Endpoint from a Hugging Face model ###_` in the description i linked to a detail blog on how to deploy a Sentence Transformers so i think we don't need to include those steps here. I also reused the `ContentHandlerBase` from `langchain.llms.sagemaker_endpoint` and changed the output type to `any` since it is depending on the implementation. |
1 year ago | |
|---|---|---|
| .. | ||
| __init__.py | 1 year ago | |
| base.py | 2 years ago | |
| cohere.py | 1 year ago | |
| fake.py | 1 year ago | |
| huggingface.py | 1 year ago | |
| huggingface_hub.py | 2 years ago | |
| openai.py | 1 year ago | |
| sagemaker_endpoint.py | 1 year ago | |
| self_hosted.py | 1 year ago | |
| self_hosted_hugging_face.py | 1 year ago | |
| tensorflow_hub.py | 1 year ago | |