docstrings: `chat_models` consistency (#9227)

Updated docstrings into the consistent format.

libs/langchain/langchain/chat_models/anthropic.py

@@ -22,7 +22,7 @@ from langchain.schema.output import ChatGenerationChunk
 
 
 class ChatAnthropic(BaseChatModel, _AnthropicCommon):
-    """Anthropic's large language chat model.
+    """`Anthropic` chat large language models.
 
     To use, you should have the ``anthropic`` python package installed, and the
     environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass

libs/langchain/langchain/chat_models/anyscale.py

@@ -28,7 +28,7 @@ DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
 
 
 class ChatAnyscale(ChatOpenAI):
-    """Wrapper around Anyscale Chat large language models.
+    """`Anyscale` Chat large language models.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``ANYSCALE_API_KEY`` set with your API key.

libs/langchain/langchain/chat_models/azure_openai.py

@@ -14,7 +14,7 @@ logger = logging.getLogger(__name__)
 
 
 class AzureChatOpenAI(ChatOpenAI):
-    """Wrapper around Azure OpenAI Chat Completion API.
+    """`Azure OpenAI` Chat Completion API.
 
     To use this class you
     must have a deployed model on Azure OpenAI. Use `deployment_name` in the

libs/langchain/langchain/chat_models/azureml_endpoint.py

@@ -17,7 +17,7 @@ from langchain.utils import get_from_dict_or_env
 
 
 class LlamaContentFormatter(ContentFormatterBase):
-    """Content formatter for LLaMa"""
+    """Content formatter for `LLaMA`."""
 
     SUPPORTED_ROLES = ["user", "assistant", "system"]
 
@@ -66,7 +66,7 @@ class LlamaContentFormatter(ContentFormatterBase):
 
 
 class AzureMLChatOnlineEndpoint(SimpleChatModel):
-    """Azure ML Chat Online Endpoint models.
+    """`AzureML` Chat models API.
 
     Example:
         .. code-block:: python

libs/langchain/langchain/chat_models/base.py

@@ -51,7 +51,7 @@ def _get_verbosity() -> bool:
 
 
 class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
-    """Base class for chat models."""
+    """Base class for Chat models."""
 
     cache: Optional[bool] = None
     """Whether to cache the response."""

libs/langchain/langchain/chat_models/google_palm.py

@@ -38,7 +38,7 @@ logger = logging.getLogger(__name__)
 
 
 class ChatGooglePalmError(Exception):
-    """Error raised when there is an issue with the Google PaLM API."""
+    """Error with the `Google PaLM` API."""
 
 
 def _truncate_at_stop_tokens(
@@ -214,7 +214,7 @@ async def achat_with_retry(llm: ChatGooglePalm, **kwargs: Any) -> Any:
 
 
 class ChatGooglePalm(BaseChatModel, BaseModel):
-    """Wrapper around Google's PaLM Chat API.
+    """`Google PaLM` Chat models API.
 
     To use you must have the google.generativeai Python package installed and
     either:

libs/langchain/langchain/chat_models/jinachat.py

@@ -140,8 +140,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 
 class JinaChat(BaseChatModel):
-    """Wrapper for Jina AI's LLM service, providing cost-effective
-    image chat capabilities.
+    """`Jina AI` Chat models API.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``JINACHAT_API_KEY`` set to your API key, which you

libs/langchain/langchain/chat_models/litellm.py

@@ -46,7 +46,7 @@ logger = logging.getLogger(__name__)
 
 
 class ChatLiteLLMException(Exception):
-    """Error raised when there is an issue with the LiteLLM I/O Library"""
+    """Error with the `LiteLLM I/O` library"""
 
 
 def _truncate_at_stop_tokens(
@@ -65,7 +65,7 @@ def _truncate_at_stop_tokens(
 
 
 class FunctionMessage(BaseMessage):
-    """A Message for passing the result of executing a function back to a model."""
+    """Message for passing the result of executing a function back to a model."""
 
     name: str
     """The name of the function that was executed."""
@@ -77,6 +77,8 @@ class FunctionMessage(BaseMessage):
 
 
 class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
+    """Message Chunk for passing the result of executing a function back to a model."""
+
     pass
 
 
@@ -187,7 +189,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 
 class ChatLiteLLM(BaseChatModel):
-    """Wrapper around the LiteLLM Model I/O library.
+    """`LiteLLM` Chat models API.
 
     To use you must have the google.generativeai Python package installed and
     either:

libs/langchain/langchain/chat_models/mlflow_ai_gateway.py

@@ -29,7 +29,7 @@ logger = logging.getLogger(__name__)
 # Ignoring type because below is valid pydantic code
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"  [call-arg]
 class ChatParams(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """Parameters for the MLflow AI Gateway LLM."""
+    """Parameters for the `MLflow AI Gateway` LLM."""
 
     temperature: float = 0.0
     candidate_count: int = 1
@@ -39,8 +39,7 @@ class ChatParams(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
 
 
 class ChatMLflowAIGateway(BaseChatModel):
-    """
-    Wrapper around chat LLMs in the MLflow AI Gateway.
+    """`MLflow AI Gateway` chat models API.
 
     To use, you should have the ``mlflow[gateway]`` python package installed.
     For more information, see https://mlflow.org/docs/latest/gateway/index.html.

libs/langchain/langchain/chat_models/openai.py

@@ -118,7 +118,7 @@ def _convert_delta_to_message_chunk(
 
 
 class ChatOpenAI(BaseChatModel):
-    """Wrapper around OpenAI Chat large language models.
+    """`OpenAI` Chat large language models API.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``OPENAI_API_KEY`` set with your API key.

libs/langchain/langchain/chat_models/promptlayer_openai.py

@@ -12,7 +12,7 @@ from langchain.schema.messages import BaseMessage
 
 
 class PromptLayerChatOpenAI(ChatOpenAI):
-    """Wrapper around OpenAI Chat large language models and PromptLayer.
+    """`PromptLayer` and `OpenAI` Chat large language models API.
 
     To use, you should have the ``openai`` and ``promptlayer`` python
     package installed, and the environment variable ``OPENAI_API_KEY``

libs/langchain/langchain/chat_models/vertexai.py

@@ -93,7 +93,7 @@ def _parse_examples(examples: List[BaseMessage]) -> List["InputOutputTextPair"]:
 
 
 class ChatVertexAI(_VertexAICommon, BaseChatModel):
-    """Wrapper around Vertex AI large language models."""
+    """`Vertex AI` Chat large language models API."""
 
     model_name: str = "chat-bison"