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huggingface[fix]: HuggingFaceEndpointEmbeddings model parameter passing error when async embed (#27953)

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This change refines the handling of _model_kwargs in POST requests.
Instead of nesting _model_kwargs as a dictionary under the parameters
key, it is now directly unpacked and merged into the request's JSON
payload. This ensures that the model parameters are passed correctly and
avoids unnecessary nesting.E. g.:

```python
import asyncio

from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings

embedding_input = ["This input will get multiplied" * 10000]

embeddings = HuggingFaceEndpointEmbeddings(
    model="http://127.0.0.1:8081/embed",
    model_kwargs={"truncate": True},
)

# Truncated parameters in synchronized methods are handled correctly
embeddings.embed_documents(texts=embedding_input)
# The truncate parameter is not handled correctly in the asynchronous method,
# and 413 Request Entity Too Large is returned.
asyncio.run(embeddings.aembed_documents(texts=embedding_input))
```

Co-authored-by: af su <saf@zjuici.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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af su 2024-11-21 03:08:56 +08:00 committed by GitHub
parent 923ef85105
commit 7c7ee07d30
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libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py
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@ -127,7 +127,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
texts = [text.replace("\n", " ") for text in texts] texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {} _model_kwargs = self.model_kwargs or {}
responses = await self.async_client.post( responses = await self.async_client.post(
json={"inputs": texts, "parameters": _model_kwargs}, task=self.task json={"inputs": texts, **_model_kwargs}, task=self.task
) )
return json.loads(responses.decode()) return json.loads(responses.decode())