github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-09-08 22:42:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

huggingface[patch]: ruff fixes and rules (#31912)

Browse Source 
* bump ruff deps
* add more thorough ruff rules
* fix said rules
This commit is contained in:
Mason Daugherty
2025-07-08 10:07:57 -04:00
committed by GitHub
parent 06ab2972e3
commit 750721b4c3
15 changed files with 283 additions and 196 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
View File

@@ -1,9 +1,11 @@
from __future__ import annotations
from typing import Any, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
from ..utils.import_utils import (
from langchain_huggingface.utils.import_utils import (
IMPORT_ERROR,
is_ipex_available,
is_optimum_intel_available,
@@ -33,12 +35,13 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
model_name: str = Field(default=DEFAULT_MODEL_NAME, alias="model")
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
@@ -46,12 +49,12 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
encode_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the documents of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
query_encode_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the query of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
multi_process: bool = False
@@ -65,24 +68,25 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
try:
import sentence_transformers # type: ignore[import]
except ImportError as exc:
raise ImportError(
msg = (
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence-transformers`."
) from exc
)
raise ImportError(msg) from exc
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]")}'
)
msg = f'Backend: ipex {IMPORT_ERROR.format("optimum[ipex]")}'
raise ImportError(msg)
if is_optimum_intel_version("<", _MIN_OPTIMUM_VERSION):
raise ImportError(
msg = (
f"Backend: ipex requires optimum-intel>="
f"{_MIN_OPTIMUM_VERSION}. You can install it with pip: "
"`pip install --upgrade --upgrade-strategy eager "
"`optimum[ipex]`."
)
raise ImportError(msg)
from optimum.intel import IPEXSentenceTransformer # type: ignore[import]
@@ -104,21 +108,21 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
def _embed(
self, texts: list[str], encode_kwargs: dict[str, Any]
) -> list[list[float]]:
"""
Embed a text using the HuggingFace transformer model.
"""Embed a text using the HuggingFace transformer model.
Args:
texts: The list of texts to embed.
encode_kwargs: Keyword arguments to pass when calling the
`encode` method for the documents of the SentenceTransformer
encode method.
encode method.
Returns:
List of embeddings, one for each text.
"""
import sentence_transformers # type: ignore[import]
texts = list(map(lambda x: x.replace("\n", " "), texts))
texts = [x.replace("\n", " ") for x in texts]
if self.multi_process:
pool = self._client.start_multi_process_pool()
embeddings = self._client.encode_multi_process(texts, pool)
@@ -127,16 +131,17 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
embeddings = self._client.encode(
texts,
show_progress_bar=self.show_progress,
**encode_kwargs, # type: ignore
**encode_kwargs,
)
if isinstance(embeddings, list):
raise TypeError(
msg = (
"Expected embeddings to be a Tensor or a numpy array, "
"got a list instead."
)
raise TypeError(msg)
return embeddings.tolist()
return embeddings.tolist() # type: ignore[return-type]
def embed_documents(self, texts: list[str]) -> list[list[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
@@ -146,6 +151,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
Returns:
List of embeddings, one for each text.
"""
return self._embed(texts, self.encode_kwargs)
@@ -157,6 +163,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
Returns:
Embeddings for the text.
"""
embed_kwargs = (
self.query_encode_kwargs

21
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py
View File

@@ -1,3 +1,5 @@
from __future__ import annotations
import os
from typing import Any, Optional
@@ -27,6 +29,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
"""
client: Any = None #: :meta private:
@@ -35,7 +38,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
"""Model name to use."""
provider: Optional[str] = None
"""Name of the provider to use for inference with the model specified in
``repo_id``. e.g. "sambanova". if not specified, defaults to HF Inference API.
``repo_id``. e.g. "sambanova". if not specified, defaults to HF Inference API.
available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
repo_id: Optional[str] = None
"""Huggingfacehub repository id, for backward compatibility."""
@@ -87,18 +90,20 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
)
if self.task not in VALID_TASKS:
raise ValueError(
msg = (
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
raise ValueError(msg)
self.client = client
self.async_client = async_client
except ImportError:
raise ImportError(
msg = (
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
raise ImportError(msg)
return self
def embed_documents(self, texts: list[str]) -> list[list[float]]:
@@ -109,6 +114,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
@@ -125,6 +131,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
@@ -142,9 +149,9 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
Returns:
Embeddings for the text.
"""
response = self.embed_documents([text])[0]
return response
return self.embed_documents([text])[0]
async def aembed_query(self, text: str) -> list[float]:
"""Async Call to HuggingFaceHub's embedding endpoint for embedding query text.
@@ -154,6 +161,6 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
Returns:
Embeddings for the text.
"""
response = (await self.aembed_documents([text]))[0]
return response
return (await self.aembed_documents([text]))[0]