style: repo linting pass (#33089)

enable docstring-code-format

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py

@@ -326,7 +326,8 @@ class ChatHuggingFace(BaseChatModel):
         .. code-block:: python
 
             from huggingface_hub import login
-            login() # You will be prompted for your HF key, which will then be saved locally
+
+            login()  # You will be prompted for your HF key, which will then be saved locally
 
     Key init args — completion params:
         llm: `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`, `HuggingFaceHub`, or
@@ -447,9 +448,13 @@ class ChatHuggingFace(BaseChatModel):
 
         .. code-block:: python
 
-            [{'name': 'GetPopulation',
-              'args': {'location': 'Los Angeles, CA'},
-              'id': '0'}]
+            [
+                {
+                    "name": "GetPopulation",
+                    "args": {"location": "Los Angeles, CA"},
+                    "id": "0",
+                }
+            ]
 
     Response metadata
         .. code-block:: python
@@ -458,10 +463,13 @@ class ChatHuggingFace(BaseChatModel):
             ai_msg.response_metadata
 
         .. code-block:: python
-            {'token_usage': ChatCompletionOutputUsage(completion_tokens=100,
-            prompt_tokens=8, total_tokens=108),
-             'model': '',
-             'finish_reason': 'length'}
+            {
+                "token_usage": ChatCompletionOutputUsage(
+                    completion_tokens=100, prompt_tokens=8, total_tokens=108
+                ),
+                "model": "",
+                "finish_reason": "length",
+            }
 
     """  # noqa: E501
 

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py

@@ -28,12 +28,12 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             from langchain_huggingface import HuggingFaceEmbeddings
 
             model_name = "sentence-transformers/all-mpnet-base-v2"
-            model_kwargs = {'device': 'cpu'}
-            encode_kwargs = {'normalize_embeddings': False}
+            model_kwargs = {"device": "cpu"}
+            encode_kwargs = {"normalize_embeddings": False}
             hf = HuggingFaceEmbeddings(
                 model_name=model_name,
                 model_kwargs=model_kwargs,
-                encode_kwargs=encode_kwargs
+                encode_kwargs=encode_kwargs,
             )
 
     """

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py

@@ -23,6 +23,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain_huggingface import HuggingFaceEndpointEmbeddings
+
             model = "sentence-transformers/all-mpnet-base-v2"
             hf = HuggingFaceEndpointEmbeddings(
                 model=model,

libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py

@@ -45,7 +45,7 @@ class HuggingFaceEndpoint(LLM):
                 typical_p=0.95,
                 temperature=0.01,
                 repetition_penalty=1.03,
-                huggingfacehub_api_token="my-api-key"
+                huggingfacehub_api_token="my-api-key",
             )
             print(llm.invoke("What is Deep Learning?"))
 
@@ -63,7 +63,7 @@ class HuggingFaceEndpoint(LLM):
                 repetition_penalty=1.03,
                 callbacks=callbacks,
                 streaming=True,
-                huggingfacehub_api_token="my-api-key"
+                huggingfacehub_api_token="my-api-key",
             )
             print(llm.invoke("What is Deep Learning?"))
 
@@ -73,7 +73,7 @@ class HuggingFaceEndpoint(LLM):
                 provider="novita",
                 max_new_tokens=100,
                 do_sample=False,
-                huggingfacehub_api_token="my-api-key"
+                huggingfacehub_api_token="my-api-key",
             )
             print(llm.invoke("What is Deep Learning?"))
 

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py

@@ -61,7 +61,10 @@ class HuggingFacePipeline(BaseLLM):
             tokenizer = AutoTokenizer.from_pretrained(model_id)
             model = AutoModelForCausalLM.from_pretrained(model_id)
             pipe = pipeline(
-                "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
+                "text-generation",
+                model=model,
+                tokenizer=tokenizer,
+                max_new_tokens=10,
             )
             hf = HuggingFacePipeline(pipeline=pipe)