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huggingface: Add ipex support to HuggingFaceEmbeddings (#29386)

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ONNX and OpenVINO models are available by specifying the `backend`
argument (the model is loaded using `optimum`
https://github.com/huggingface/optimum)

```python
from langchain_huggingface import HuggingFaceEmbeddings

embedding = HuggingFaceEmbeddings(
    model_name=model_id,
    model_kwargs={"backend": "onnx"},
)
```

With this PR we also enable the IPEX backend 



```python
from langchain_huggingface import HuggingFaceEmbeddings

embedding = HuggingFaceEmbeddings(
    model_name=model_id,
    model_kwargs={"backend": "ipex"},
)
```
This commit is contained in:
Ella Charlaix 2025-02-08 00:21:09 +01:00 committed by GitHub
parent 3eaf561561
commit c401254770
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1 changed files with 31 additions and 1 deletions
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32
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
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@ -3,8 +3,17 @@ from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
from ..utils.import_utils import (
IMPORT_ERROR,
is_ipex_available,
is_optimum_intel_available,
is_optimum_intel_version,
)
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
_MIN_OPTIMUM_VERSION = "1.22"
class HuggingFaceEmbeddings(BaseModel, Embeddings):
"""HuggingFace sentence_transformers embedding models.
@ -61,7 +70,28 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
"Please install it with `pip install sentence-transformers`."
) from exc
self._client = sentence_transformers.SentenceTransformer(
if self.model_kwargs.get("backend", "torch") == "ipex":
if not is_optimum_intel_available() or not is_ipex_available():
raise ImportError(
f'Backend: ipex {IMPORT_ERROR.format("optimum[ipex]")}'
)
if is_optimum_intel_version("<", _MIN_OPTIMUM_VERSION):
raise ImportError(
f"Backend: ipex requires optimum-intel>="
f"{_MIN_OPTIMUM_VERSION}. You can install it with pip: "
"`pip install --upgrade --upgrade-strategy eager "
"`optimum[ipex]`."
)
from optimum.intel import IPEXSentenceTransformer # type: ignore[import]
model_cls = IPEXSentenceTransformer
else:
model_cls = sentence_transformers.SentenceTransformer
self._client = model_cls(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)