style: address Sphinx double-backtick snippet syntax (#33389)

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

@@ -889,9 +889,9 @@ class ChatHuggingFace(BaseChatModel):
 
             method: The method for steering model generation, one of:
 
-                - ``'function_calling'``: uses tool-calling features.
-                - ``'json_schema'``: uses dedicated structured output features.
-                - ``'json_mode'``: uses JSON mode.
+                - `'function_calling'`: uses tool-calling features.
+                - `'json_schema'`: uses dedicated structured output features.
+                - `'json_mode'`: uses JSON mode.
 
             include_raw:
                 If `False` then only the parsed structured output is returned. If
@@ -899,7 +899,7 @@ class ChatHuggingFace(BaseChatModel):
                 then both the raw model response (a BaseMessage) and the parsed model
                 response will be returned. If an error occurs during output parsing it
                 will be caught and returned as well. The final output is always a dict
-                with keys ``'raw'``, ``'parsed'``, and ``'parsing_error'``.
+                with keys `'raw'`, `'parsed'`, and `'parsing_error'`.
 
             kwargs:
                 Additional parameters to pass to the underlying LLM's
@@ -909,16 +909,16 @@ class ChatHuggingFace(BaseChatModel):
         Returns:
             A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
 
-            If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
-            an instance of ``schema`` (i.e., a Pydantic object).
+            If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
+            an instance of `schema` (i.e., a Pydantic object).
 
-            Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
+            Otherwise, if `include_raw` is False then Runnable outputs a dict.
 
-            If ``include_raw`` is True, then Runnable outputs a dict with keys:
+            If `include_raw` is True, then Runnable outputs a dict with keys:
 
-            - ``'raw'``: BaseMessage
-            - ``'parsed'``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
-            - ``'parsing_error'``: BaseException | None
+            - `'raw'`: BaseMessage
+            - `'parsed'`: None if there was a parsing error, otherwise the type depends on the `schema` as described above.
+            - `'parsing_error'`: BaseException | None
 
         """  # noqa: E501
         _ = kwargs.pop("strict", None)

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

@@ -20,7 +20,7 @@ _MIN_OPTIMUM_VERSION = "1.22"
 class HuggingFaceEmbeddings(BaseModel, Embeddings):
     """HuggingFace sentence_transformers embedding models.
 
-    To use, you should have the ``sentence_transformers`` python package installed.
+    To use, you should have the `sentence_transformers` python package installed.
 
     Example:
         .. code-block:: python

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

@@ -15,8 +15,8 @@ VALID_TASKS = ("feature-extraction",)
 class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
     """HuggingFaceHub embedding models.
 
-    To use, you should have the ``huggingface_hub`` python package installed, and the
-    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
+    To use, you should have the `huggingface_hub` python package installed, and the
+    environment variable `HUGGINGFACEHUB_API_TOKEN` set with your API token, or pass
     it as a named parameter to the constructor.
 
     Example:
@@ -39,7 +39,7 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
     """Model name to use."""
     provider: str | None = 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: str | None = None
     """Huggingfacehub repository id, for backward compatibility."""

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

@@ -29,8 +29,8 @@ VALID_TASKS = (
 class HuggingFaceEndpoint(LLM):
     """Hugging Face Endpoint. This works with any model that supports text generation (i.e. text completion) task.
 
-    To use this class, you should have installed the ``huggingface_hub`` package, and
-    the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token,
+    To use this class, you should have installed the `huggingface_hub` package, and
+    the environment variable `HUGGINGFACEHUB_API_TOKEN` set with your API token,
     or given as a named parameter to the constructor.
 
     Example:

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

@@ -37,7 +37,7 @@ logger = logging.getLogger(__name__)
 class HuggingFacePipeline(BaseLLM):
     """HuggingFace Pipeline API.
 
-    To use, you should have the ``transformers`` python package installed.
+    To use, you should have the `transformers` python package installed.
 
     Only supports `text-generation`, `text2text-generation`, `image-text-to-text`,
     `summarization` and `translation`  for now.