Use term keyword according to the official python doc glossary (#11338)

- **Description:** use term keyword according to the official python doc
glossary, see https://docs.python.org/3/glossary.html
  - **Issue:** not applicable
  - **Dependencies:** not applicable
  - **Tag maintainer:** @hwchase17
  - **Twitter handle:** vreyespue

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -85,7 +85,7 @@ import InputMessages from "@snippets/get_started/quickstart/input_messages.mdx"
 
 <InputMessages/>
 
-For both these methods, you can also pass in parameters as key word arguments.
+For both these methods, you can also pass in parameters as keyword arguments.
 For example, you could pass in `temperature=0` to adjust the temperature that is used from what the object was configured with.
 Whatever values are passed in during run time will always override what the object was configured with.
 

docs/snippets/modules/agents/get_started.mdx

@@ -54,7 +54,7 @@ prompt = ChatPromptTemplate.from_messages([
 ```
 
 How does the agent know what tools it can use?
-Those are passed in as a separate argument, so we can bind those as key word arguments to the LLM.
+Those are passed in as a separate argument, so we can bind those as keyword arguments to the LLM.
 
 ```python
 from langchain.tools.render import format_tool_to_openai_function

libs/langchain/langchain/agents/initialize.py

@@ -30,9 +30,9 @@ def initialize_agent(
         callback_manager: CallbackManager to use. Global callback manager is used if
             not provided. Defaults to None.
         agent_path: Path to serialized agent to use.
-        agent_kwargs: Additional key word arguments to pass to the underlying agent
+        agent_kwargs: Additional keyword arguments to pass to the underlying agent
         tags: Tags to apply to the traced runs.
-        **kwargs: Additional key word arguments passed to the agent executor
+        **kwargs: Additional keyword arguments passed to the agent executor
 
     Returns:
         An agent executor

libs/langchain/langchain/agents/loading.py

@@ -42,7 +42,7 @@ def load_agent_from_config(
         config: Config dict to load agent from.
         llm: Language model to use as the agent.
         tools: List of tools this agent has access to.
-        **kwargs: Additional key word arguments passed to the agent executor.
+        **kwargs: Additional keyword arguments passed to the agent executor.
 
     Returns:
         An agent executor.
@@ -92,7 +92,7 @@ def load_agent(
 
     Args:
         path: Path to the agent file.
-        **kwargs: Additional key word arguments passed to the agent executor.
+        **kwargs: Additional keyword arguments passed to the agent executor.
 
     Returns:
         An agent executor.

libs/langchain/langchain/chat_models/azureml_endpoint.py

@@ -93,7 +93,7 @@ class AzureMLChatOnlineEndpoint(SimpleChatModel):
     the endpoint"""
 
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     @validator("http_client", always=True, allow_reuse=True)
     @classmethod

libs/langchain/langchain/embeddings/bedrock.py

@@ -59,7 +59,7 @@ class BedrockEmbeddings(BaseModel, Embeddings):
     equivalent to the modelId property in the list-foundation-models api"""
 
     model_kwargs: Optional[Dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     endpoint_url: Optional[str] = None
     """Needed if you don't want to default to us-east-1 endpoint"""

libs/langchain/langchain/embeddings/huggingface.py

@@ -45,9 +45,9 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     """Path to store models. 
     Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass when calling the `encode` method of the model."""
+    """Keyword arguments to pass when calling the `encode` method of the model."""
     multi_process: bool = False
     """Run encode() on multiple GPUs."""
 
@@ -133,9 +133,9 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
     """Path to store models. 
     Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass when calling the `encode` method of the model."""
+    """Keyword arguments to pass when calling the `encode` method of the model."""
     embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
     """Instruction to use for embedding documents."""
     query_instruction: str = DEFAULT_QUERY_INSTRUCTION
@@ -212,9 +212,9 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
     """Path to store models.
     Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Key word arguments to pass when calling the `encode` method of the model."""
+    """Keyword arguments to pass when calling the `encode` method of the model."""
     query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
     """Instruction to use for embedding query."""
 

libs/langchain/langchain/embeddings/huggingface_hub.py

@@ -33,7 +33,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
     task: Optional[str] = "feature-extraction"
     """Task to call the model with."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     huggingfacehub_api_token: Optional[str] = None
 

libs/langchain/langchain/embeddings/sagemaker_endpoint.py

@@ -90,7 +90,7 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
     """  # noqa: E501
 
     model_kwargs: Optional[Dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     endpoint_kwargs: Optional[Dict] = None
     """Optional attributes passed to the invoke_endpoint

libs/langchain/langchain/embeddings/self_hosted_hugging_face.py

@@ -86,7 +86,7 @@ class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings):
     model_load_fn: Callable = load_embedding_model
     """Function to load the model remotely on the server."""
     load_fn_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model load function."""
+    """Keyword arguments to pass to the model load function."""
     inference_fn: Callable = _embed_documents
     """Inference function to extract the embeddings."""
 

libs/langchain/langchain/llms/amazon_api_gateway.py

@@ -36,7 +36,7 @@ class AmazonAPIGateway(LLM):
     """API Gateway HTTP Headers to send, e.g. for authentication"""
 
     model_kwargs: Optional[Dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
     """The content handler class that provides an input and

libs/langchain/langchain/llms/anyscale.py

@@ -36,7 +36,7 @@ class Anyscale(LLM):
     """
 
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model. Reserved for future use"""
+    """Keyword arguments to pass to the model. Reserved for future use"""
 
     anyscale_service_url: Optional[str] = None
     anyscale_service_route: Optional[str] = None

libs/langchain/langchain/llms/azureml_endpoint.py

@@ -230,7 +230,7 @@ class AzureMLOnlineEndpoint(LLM, BaseModel):
     the endpoint"""
 
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     @validator("http_client", always=True, allow_reuse=True)
     @classmethod

libs/langchain/langchain/llms/bedrock.py

@@ -147,7 +147,7 @@ class BedrockBase(BaseModel, ABC):
     equivalent to the modelId property in the list-foundation-models api"""
 
     model_kwargs: Optional[Dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     endpoint_url: Optional[str] = None
     """Needed if you don't want to default to us-east-1 endpoint"""

libs/langchain/langchain/llms/chatglm.py

@@ -28,7 +28,7 @@ class ChatGLM(LLM):
     endpoint_url: str = "http://127.0.0.1:8000/"
     """Endpoint URL to use."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     max_token: int = 20000
     """Max token allowed to pass to the model."""
     temperature: float = 0.1

libs/langchain/langchain/llms/deepsparse.py

@@ -28,7 +28,7 @@ class DeepSparse(LLM):
     """The path to a model file or directory or the name of a SparseZoo model stub."""
 
     model_config: Optional[Dict[str, Any]] = None
-    """Key word arguments passed to the pipeline construction.
+    """Keyword arguments passed to the pipeline construction.
     Common parameters are sequence_length, prompt_sequence_length"""
 
     generation_config: Union[None, str, Dict] = None

libs/langchain/langchain/llms/gradient_ai.py

@@ -54,7 +54,7 @@ class GradientLLM(LLM):
     """
 
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     gradient_api_url: str = "https://api.gradient.ai/api"
     """Endpoint URL to use."""

libs/langchain/langchain/llms/huggingface_endpoint.py

@@ -39,7 +39,7 @@ class HuggingFaceEndpoint(LLM):
     """Task to call the model with.
     Should be a task that returns `generated_text` or `summary_text`."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     huggingfacehub_api_token: Optional[str] = None
 

libs/langchain/langchain/llms/huggingface_hub.py

@@ -33,7 +33,7 @@ class HuggingFaceHub(LLM):
     """Task to call the model with.
     Should be a task that returns `generated_text` or `summary_text`."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     huggingfacehub_api_token: Optional[str] = None
 

libs/langchain/langchain/llms/huggingface_pipeline.py

@@ -53,9 +53,9 @@ class HuggingFacePipeline(BaseLLM):
     model_id: str = DEFAULT_MODEL_ID
     """Model name to use."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments passed to the model."""
+    """Keyword arguments passed to the model."""
     pipeline_kwargs: Optional[dict] = None
-    """Key word arguments passed to the pipeline."""
+    """Keyword arguments passed to the pipeline."""
     batch_size: int = DEFAULT_BATCH_SIZE
     """Batch size to use when passing multiple documents to generate."""
 

libs/langchain/langchain/llms/mosaicml.py

@@ -53,7 +53,7 @@ class MosaicML(LLM):
     inject_instruction_format: bool = False
     """Whether to inject the instruction format into the prompt."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     retry_sleep: float = 1.0
     """How long to try sleeping for if a rate limit is encountered"""
 

libs/langchain/langchain/llms/octoai_endpoint.py

@@ -40,7 +40,7 @@ class OctoAIEndpoint(LLM):
     """Endpoint URL to use."""
 
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     octoai_api_token: Optional[str] = None
     """OCTOAI API Token"""

libs/langchain/langchain/llms/openllm.py

@@ -88,7 +88,7 @@ class OpenLLM(LLM):
     """Initialize this LLM instance in current process by default. Should 
     only set to False when using in conjunction with BentoML Service."""
     llm_kwargs: Dict[str, Any]
-    """Key word arguments to be passed to openllm.LLM"""
+    """Keyword arguments to be passed to openllm.LLM"""
 
     _runner: Optional[openllm.LLMRunner] = PrivateAttr(default=None)
     _client: Union[

libs/langchain/langchain/llms/sagemaker_endpoint.py

@@ -171,7 +171,7 @@ class SagemakerEndpoint(LLM):
     """
 
     model_kwargs: Optional[Dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
 
     endpoint_kwargs: Optional[Dict] = None
     """Optional attributes passed to the invoke_endpoint

libs/langchain/langchain/llms/self_hosted.py

@@ -132,7 +132,7 @@ class SelfHostedPipeline(LLM):
     model_load_fn: Callable
     """Function to load the model remotely on the server."""
     load_fn_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model load function."""
+    """Keyword arguments to pass to the model load function."""
     model_reqs: List[str] = ["./", "torch"]
     """Requirements to install on hardware to inference the model."""
 

libs/langchain/langchain/llms/self_hosted_hugging_face.py

@@ -158,7 +158,7 @@ class SelfHostedHuggingFaceLLM(SelfHostedPipeline):
     device: int = 0
     """Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc."""
     model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
+    """Keyword arguments to pass to the model."""
     hardware: Any
     """Remote hardware to send the inference function to."""
     model_reqs: List[str] = ["./", "transformers", "torch"]

libs/langchain/langchain/llms/xinference.py

@@ -80,7 +80,7 @@ class Xinference(LLM):
     model_uid: Optional[str]
     """UID of the launched model"""
     model_kwargs: Dict[str, Any]
-    """Key word arguments to be passed to xinference.LLM"""
+    """Keyword arguments to be passed to xinference.LLM"""
 
     def __init__(
         self,

libs/langchain/langchain/retrievers/kendra.py

@@ -53,7 +53,7 @@ Dates are also represented as str.
 
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
 class Highlight(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
-    """Information that highlights the key words in the excerpt."""
+    """Information that highlights the keywords in the excerpt."""
 
     BeginOffset: int
     """The zero-based location in the excerpt where the highlight starts."""

libs/langchain/tests/unit_tests/test_formatting.py

@@ -20,7 +20,7 @@ def test_does_not_allow_args() -> None:
 
 
 def test_does_not_allow_extra_kwargs() -> None:
-    """Test formatting does not allow extra key word arguments."""
+    """Test formatting does not allow extra keyword arguments."""
     template = "This is a {foo} test."
     with pytest.raises(KeyError):
         formatter.format(template, foo="good", bar="oops")