community[patch]: docstrings update (#20301)

Added missed docstrings. Format docstings to the consistent form.

libs/community/langchain_community/agent_toolkits/connery/toolkit.py

@@ -9,7 +9,7 @@ from langchain_community.tools.connery import ConneryService
 
 class ConneryToolkit(BaseToolkit):
     """
-    A LangChain Toolkit with a list of Connery Actions as tools.
+    Toolkit with a list of Connery Actions as tools.
     """
 
     tools: List[BaseTool]

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -348,7 +348,7 @@ def create_openapi_agent(
     allow_dangerous_requests: bool = False,
     **kwargs: Any,
 ) -> Any:
-    """Instantiate OpenAI API planner and controller for a given spec.
+    """Construct an OpenAI API planner and controller for a given spec.
 
     Inject credentials via requests_wrapper.
 

libs/community/langchain_community/callbacks/flyte_callback.py

@@ -91,7 +91,7 @@ def analyze_text(
 
 
 class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
-    """This callback handler that is used within a Flyte task."""
+    """Callback handler that is used within a Flyte task."""
 
     def __init__(self) -> None:
         """Initialize callback handler."""

libs/community/langchain_community/callbacks/streamlit/mutable_expander.py

@@ -9,14 +9,14 @@ if TYPE_CHECKING:
 
 
 class ChildType(Enum):
-    """The enumerator of the child type."""
+    """Enumerator of the child type."""
 
     MARKDOWN = "MARKDOWN"
     EXCEPTION = "EXCEPTION"
 
 
 class ChildRecord(NamedTuple):
-    """The child record as a NamedTuple."""
+    """Child record as a NamedTuple."""
 
     type: ChildType
     kwargs: Dict[str, Any]
@@ -24,7 +24,7 @@ class ChildRecord(NamedTuple):
 
 
 class MutableExpander:
-    """A Streamlit expander that can be renamed and dynamically expanded/collapsed."""
+    """Streamlit expander that can be renamed and dynamically expanded/collapsed."""
 
     def __init__(self, parent_container: DeltaGenerator, label: str, expanded: bool):
         """Create a new MutableExpander.
@@ -51,7 +51,7 @@ class MutableExpander:
 
     @property
     def label(self) -> str:
-        """The expander's label string."""
+        """Expander's label string."""
         return self._label
 
     @property

libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py

@@ -40,7 +40,7 @@ class LLMThoughtState(Enum):
 
 
 class ToolRecord(NamedTuple):
-    """The tool record as a NamedTuple."""
+    """Tool record as a NamedTuple."""
 
     name: str
     input_str: str

libs/community/langchain_community/chat_message_histories/sql.py

@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 
 
 class BaseMessageConverter(ABC):
-    """Class that converts BaseMessage to the SQLAlchemy model."""
+    """Convert BaseMessage to the SQLAlchemy model."""
 
     @abstractmethod
     def from_sql_model(self, sql_message: Any) -> BaseMessage:

libs/community/langchain_community/chat_models/bedrock.py

@@ -196,7 +196,7 @@ _message_type_lookups = {"human": "user", "ai": "assistant"}
 
 
 class BedrockChat(BaseChatModel, BedrockBase):
-    """A chat model that uses the Bedrock API."""
+    """Chat model that uses the Bedrock API."""
 
     @property
     def _llm_type(self) -> str:

libs/community/langchain_community/chat_models/huggingface.py

@@ -26,8 +26,7 @@ DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful, and honest assistant."
 
 
 class ChatHuggingFace(BaseChatModel):
-    """
+    """Hugging Face LLMs as ChatModels.
-    Wrapper for using Hugging Face LLM's as ChatModels.
 
     Works with `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`,
     and `HuggingFaceHub` LLMs.

libs/community/langchain_community/chat_models/litellm.py

@@ -161,7 +161,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 
 class ChatLiteLLM(BaseChatModel):
-    """A chat model that uses the LiteLLM API."""
+    """Chat model that uses the LiteLLM API."""
 
     client: Any  #: :meta private:
     model: str = "gpt-3.5-turbo"

libs/community/langchain_community/chat_models/minimax.py

@@ -37,7 +37,7 @@ def _parse_chat_history(history: List[BaseMessage]) -> List:
 
 
 class MiniMaxChat(MinimaxCommon, BaseChatModel):
-    """Wrapper around Minimax large language models.
+    """MiniMax large language models.
 
     To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
     ``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to

libs/community/langchain_community/chat_models/moonshot.py

@@ -9,7 +9,7 @@ from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, Moonsho
 
 
 class MoonshotChat(MoonshotCommon, ChatOpenAI):  # type: ignore[misc]
-    """Wrapper around Moonshot large language models.
+    """Moonshot large language models.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``MOONSHOT_API_KEY`` set with your API key.

libs/community/langchain_community/chat_models/pai_eas_endpoint.py

@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 
 
 class PaiEasChatEndpoint(BaseChatModel):
-    """Eas LLM Service chat model API.
+    """Alibaba Cloud PAI-EAS LLM Service chat model API.
 
         To use, must have a deployed eas chat llm service on AliCloud. One can set the
     environment variable ``eas_service_url`` and ``eas_service_token`` set with your eas

libs/community/langchain_community/chat_models/premai.py

@@ -159,7 +159,7 @@ def _messages_to_prompt_dict(
 
 
 class ChatPremAI(BaseChatModel, BaseModel):
-    """Use any LLM provider with Prem and Langchain.
+    """PremAI Chat models.
 
     To use, you will need to have an API key. You can find your existing API Key
     or generate a new one here: https://app.premai.io/api_keys/
@@ -357,6 +357,7 @@ def create_prem_retry_decorator(
     max_retries: int = 1,
     run_manager: Optional[Union[CallbackManagerForLLMRun]] = None,
 ) -> Callable[[Any], Any]:
+    """Create a retry decorator for PremAI API errors."""
     import premai.models
 
     errors = [

libs/community/langchain_community/chat_models/solar.py

@@ -10,7 +10,7 @@ from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon
 
 
 class SolarChat(SolarCommon, ChatOpenAI):  # type: ignore[misc]
-    """Wrapper around Solar large language models.
+    """Solar large language models.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``SOLAR_API_KEY`` set with your API key.

libs/community/langchain_community/chat_models/sparkllm.py

@@ -89,7 +89,7 @@ def _convert_delta_to_message_chunk(
 
 
 class ChatSparkLLM(BaseChatModel):
-    """Wrapper around iFlyTek's Spark large language model.
+    """iFlyTek Spark large language model.
 
     To use, you should pass `app_id`, `api_key`, `api_secret`
     as a named parameter to the constructor OR set environment

libs/community/langchain_community/chat_models/tongyi.py

@@ -61,6 +61,7 @@ logger = logging.getLogger(__name__)
 def convert_dict_to_message(
     _dict: Mapping[str, Any], is_chunk: bool = False
 ) -> Union[BaseMessage, BaseMessageChunk]:
+    """Convert a dict to a message."""
     role = _dict["role"]
     content = _dict["content"]
     if role == "user":
@@ -88,6 +89,7 @@ def convert_dict_to_message(
 
 
 def convert_message_chunk_to_message(message_chunk: BaseMessageChunk) -> BaseMessage:
+    """Convert a message chunk to a message."""
     if isinstance(message_chunk, HumanMessageChunk):
         return HumanMessage(content=message_chunk.content)
     elif isinstance(message_chunk, AIMessageChunk):

libs/community/langchain_community/chat_models/yandex.py

@@ -53,7 +53,7 @@ def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]:
 
 
 class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):
-    """Wrapper around YandexGPT large language models.
+    """YandexGPT large language models.
 
     There are two authentication options for the service account
     with the ``ai.languageModels.user`` role:

libs/community/langchain_community/chat_models/zhipuai.py

@@ -42,6 +42,7 @@ ZHIPUAI_API_BASE = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
 
 @contextmanager
 def connect_sse(client: Any, method: str, url: str, **kwargs: Any) -> Iterator:
+    """Connect to a server-sent event stream."""
     from httpx_sse import EventSource
 
     with client.stream(method, url, **kwargs) as response:
@@ -52,6 +53,7 @@ def connect_sse(client: Any, method: str, url: str, **kwargs: Any) -> Iterator:
 async def aconnect_sse(
     client: Any, method: str, url: str, **kwargs: Any
 ) -> AsyncIterator:
+    """Async connect to a server-sent event stream."""
     from httpx_sse import EventSource
 
     async with client.stream(method, url, **kwargs) as response:

libs/community/langchain_community/docstore/arbitrary_fn.py

@@ -6,7 +6,7 @@ from langchain_community.docstore.base import Docstore
 
 
 class DocstoreFn(Docstore):
-    """Langchain Docstore via arbitrary lookup function.
+    """Docstore via arbitrary lookup function.
 
     This is useful when:
      * it's expensive to construct an InMemoryDocstore/dict

libs/community/langchain_community/docstore/wikipedia.py

@@ -9,7 +9,7 @@ from langchain_community.docstore.base import Docstore
 
 
 class Wikipedia(Docstore):
-    """Wrapper around wikipedia API."""
+    """Wikipedia API."""
 
     def __init__(self) -> None:
         """Check that wikipedia package is installed."""

libs/community/langchain_community/document_compressors/openvino_rerank.py

@@ -9,6 +9,8 @@ from langchain_core.pydantic_v1 import Field
 
 
 class RerankRequest:
+    """Request for reranking."""
+
     def __init__(self, query: Any = None, passages: Any = None):
         self.query = query
         self.passages = passages if passages is not None else []

libs/community/langchain_community/document_loaders/assemblyai.py

@@ -29,8 +29,7 @@ class TranscriptFormat(Enum):
 
 
 class AssemblyAIAudioTranscriptLoader(BaseLoader):
-    """
+    """Load AssemblyAI audio transcripts.
-    Loader for AssemblyAI audio transcripts.
 
     It uses the AssemblyAI API to transcribe audio files
     and loads the transcribed text into one or more Documents,
@@ -110,7 +109,7 @@ class AssemblyAIAudioTranscriptLoader(BaseLoader):
 
 class AssemblyAIAudioLoaderById(BaseLoader):
     """
-    Loader for AssemblyAI audio transcripts.
+    Load AssemblyAI audio transcripts.
 
     It uses the AssemblyAI API to get an existing transcription
     and loads the transcribed text into one or more Documents,

libs/community/langchain_community/document_loaders/bilibili.py

@@ -15,7 +15,7 @@ AV_PATTERN = re.compile(r"av[0-9]+")
 
 class BiliBiliLoader(BaseLoader):
     """
-    Loader for fetching transcripts from BiliBili videos.
+    Load fetching transcripts from BiliBili videos.
     """
 
     def __init__(

libs/community/langchain_community/document_transformers/long_context_reorder.py

@@ -21,7 +21,9 @@ def _litm_reordering(documents: List[Document]) -> List[Document]:
 
 
 class LongContextReorder(BaseDocumentTransformer, BaseModel):
-    """Lost in the middle:
+    """Reorder long context.
+
+    Lost in the middle:
     Performance degrades when models must access relevant information
     in the middle of long contexts.
     See: https://arxiv.org/abs//2307.03172"""

libs/community/langchain_community/document_transformers/nuclia_text_transform.py

@@ -9,7 +9,8 @@ from langchain_community.tools.nuclia.tool import NucliaUnderstandingAPI
 
 
 class NucliaTextTransformer(BaseDocumentTransformer):
-    """
+    """Nuclia Text Transformer.
+
     The Nuclia Understanding API splits into paragraphs and sentences,
     identifies entities, provides a summary of the text and generates
     embeddings for all sentences.

libs/community/langchain_community/embeddings/aleph_alpha.py

@@ -183,7 +183,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
 
 
 class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding):
-    """The symmetric version of the Aleph Alpha's semantic embeddings.
+    """Symmetric version of the Aleph Alpha's semantic embeddings.
 
     The main difference is that here, both the documents and
     queries are embedded with a SemanticRepresentation.Symmetric

libs/community/langchain_community/embeddings/databricks.py

@@ -12,7 +12,7 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
 
 
 class DatabricksEmbeddings(MlflowEmbeddings):
-    """Wrapper around embeddings LLMs in Databricks.
+    """Databricks embeddings.
 
     To use, you should have the ``mlflow`` python package installed.
     For more information, see https://mlflow.org/docs/latest/llms/deployments.

libs/community/langchain_community/embeddings/infinity_local.py

@@ -13,7 +13,9 @@ logger = getLogger(__name__)
 
 
 class InfinityEmbeddingsLocal(BaseModel, Embeddings):
-    """Optimized Embedding models https://github.com/michaelfeil/infinity
+    """Optimized Infinity embedding models.
+
+    https://github.com/michaelfeil/infinity
     This class deploys a local Infinity instance to embed text.
     The class requires async usage.
 

libs/community/langchain_community/embeddings/javelin_ai_gateway.py

@@ -12,8 +12,7 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
 
 
 class JavelinAIGatewayEmbeddings(Embeddings, BaseModel):
-    """
+    """Javelin AI Gateway embeddings.
-    Wrapper around embeddings LLMs in the Javelin AI Gateway.
 
     To use, you should have the ``javelin_sdk`` python package installed.
     For more information, see https://docs.getjavelin.io

libs/community/langchain_community/embeddings/mlflow_gateway.py

@@ -13,8 +13,7 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
 
 
 class MlflowAIGatewayEmbeddings(Embeddings, BaseModel):
-    """
+    """MLflow AI Gateway embeddings.
-    Wrapper around embeddings LLMs in the MLflow AI Gateway.
 
     To use, you should have the ``mlflow[gateway]`` python package installed.
     For more information, see https://mlflow.org/docs/latest/gateway/index.html.

libs/community/langchain_community/embeddings/nemo.py

@@ -42,6 +42,8 @@ def is_endpoint_live(url: str, headers: Optional[dict], payload: Any) -> bool:
 
 
 class NeMoEmbeddings(BaseModel, Embeddings):
+    """NeMo embedding models."""
+
     batch_size: int = 16
     model: str = "NV-Embed-QA-003"
     api_endpoint_url: str = "http://localhost:8088/v1/embeddings"

libs/community/langchain_community/graphs/networkx_graph.py

@@ -7,7 +7,7 @@ KG_TRIPLE_DELIMITER = "<|>"
 
 
 class KnowledgeTriple(NamedTuple):
-    """A triple in the graph."""
+    """Knowledge triple in the graph."""
 
     subject: str
     predicate: str

libs/community/langchain_community/indexes/base.py

@@ -8,7 +8,7 @@ NAMESPACE_UUID = uuid.UUID(int=1984)
 
 
 class RecordManager(ABC):
-    """An abstract base class representing the interface for a record manager."""
+    """Abstract base class for a record manager."""
 
     def __init__(
         self,

libs/community/langchain_community/llms/cloudflare_workersai.py

@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 
 
 class CloudflareWorkersAI(LLM):
-    """Langchain LLM class to help to access Cloudflare Workers AI service.
+    """Cloudflare Workers AI service.
 
     To use, you must provide an API token and
     account ID to access Cloudflare Workers AI, and

libs/community/langchain_community/llms/databricks.py

@@ -161,7 +161,7 @@ class _DatabricksClusterDriverProxyClient(_DatabricksClientBase):
 
 
 def get_repl_context() -> Any:
-    """Gets the notebook REPL context if running inside a Databricks notebook.
+    """Get the notebook REPL context if running inside a Databricks notebook.
     Returns None otherwise.
     """
     try:
@@ -175,7 +175,7 @@ def get_repl_context() -> Any:
 
 
 def get_default_host() -> str:
-    """Gets the default Databricks workspace hostname.
+    """Get the default Databricks workspace hostname.
     Raises an error if the hostname cannot be automatically determined.
     """
     host = os.getenv("DATABRICKS_HOST")
@@ -195,7 +195,7 @@ def get_default_host() -> str:
 
 
 def get_default_api_token() -> str:
-    """Gets the default Databricks personal access token.
+    """Get the default Databricks personal access token.
     Raises an error if the token cannot be automatically determined.
     """
     if api_token := os.getenv("DATABRICKS_TOKEN"):

libs/community/langchain_community/llms/edenai.py

@@ -18,7 +18,7 @@ logger = logging.getLogger(__name__)
 
 
 class EdenAI(LLM):
-    """Wrapper around edenai models.
+    """EdenAI models.
 
     To use, you should have
     the environment variable ``EDENAI_API_KEY`` set with your API token.

libs/community/langchain_community/llms/human.py

@@ -33,9 +33,7 @@ def _collect_user_input(
 
 
 class HumanInputLLM(LLM):
-    """
+    """User input as the response."""
-    It returns user input as the response.
-    """
 
     input_func: Callable = Field(default_factory=lambda: _collect_user_input)
     prompt_func: Callable[[str], None] = Field(default_factory=lambda: _display_prompt)

libs/community/langchain_community/llms/ipex_llm.py

@@ -12,7 +12,7 @@ logger = logging.getLogger(__name__)
 
 
 class IpexLLM(LLM):
-    """Wrapper around the IpexLLM model
+    """IpexLLM model.
 
     Example:
         .. code-block:: python

libs/community/langchain_community/llms/konko.py

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 
 
 class Konko(LLM):
-    """Wrapper around Konko AI models.
+    """Konko AI models.
 
     To use, you'll need an API key. This can be passed in as init param
     ``konko_api_key`` or set as environment variable ``KONKO_API_KEY``.

libs/community/langchain_community/llms/layerup_security.py

@@ -9,6 +9,14 @@ logger = logging.getLogger(__name__)
 
 
 def default_guardrail_violation_handler(violation: dict) -> str:
+    """Default guardrail violation handler.
+
+    Args:
+        violation (dict): The violation dictionary.
+
+    Returns:
+        str: The canned response.
+    """
     if violation.get("canned_response"):
         return violation["canned_response"]
     guardrail_name = (
@@ -22,6 +30,8 @@ def default_guardrail_violation_handler(violation: dict) -> str:
 
 
 class LayerupSecurity(LLM):
+    """Layerup Security LLM service."""
+
     llm: LLM
     layerup_api_key: str
     layerup_api_base_url: str = "https://api.uselayerup.com/v1"

libs/community/langchain_community/llms/loading.py

@@ -34,6 +34,7 @@ def load_llm_from_config(config: dict, **kwargs: Any) -> BaseLLM:
 
 
 def load_llm(file: Union[str, Path], **kwargs: Any) -> BaseLLM:
+    """Load LLM from a file."""
     # Convert file to Path object.
     if isinstance(file, str):
         file_path = Path(file)

libs/community/langchain_community/llms/mlflow.py

@@ -9,7 +9,7 @@ from langchain_core.pydantic_v1 import Field, PrivateAttr
 
 
 class Mlflow(LLM):
-    """Wrapper around completions LLMs in MLflow.
+    """MLflow LLM service.
 
     To use, you should have the `mlflow[genai]` python package installed.
     For more information, see https://mlflow.org/docs/latest/llms/deployments.

libs/community/langchain_community/llms/mlflow_ai_gateway.py

@@ -21,8 +21,7 @@ class Params(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
 
 
 class MlflowAIGateway(LLM):
-    """
+    """MLflow AI Gateway LLMs.
-    Wrapper around completions LLMs in the MLflow AI Gateway.
 
     To use, you should have the ``mlflow[gateway]`` python package installed.
     For more information, see https://mlflow.org/docs/latest/gateway/index.html.

libs/community/langchain_community/llms/moonshot.py

@@ -31,6 +31,8 @@ class _MoonshotClient(BaseModel):
 
 
 class MoonshotCommon(BaseModel):
+    """Common parameters for Moonshot LLMs."""
+
     _client: _MoonshotClient
     base_url: str = MOONSHOT_SERVICE_URL_BASE
     moonshot_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")

libs/community/langchain_community/llms/opaqueprompts.py

@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 
 
 class OpaquePrompts(LLM):
-    """An LLM wrapper that uses OpaquePrompts to sanitize prompts.
+    """LLM that uses OpaquePrompts to sanitize prompts.
 
     Wraps another LLM and sanitizes prompts before passing it to the LLM, then
         de-sanitizes the response.

libs/community/langchain_community/llms/promptlayer_openai.py

@@ -124,7 +124,7 @@ class PromptLayerOpenAI(OpenAI):
 
 
 class PromptLayerOpenAIChat(OpenAIChat):
-    """Wrapper around OpenAI large language models.
+    """PromptLayer OpenAI large language models.
 
     To use, you should have the ``openai`` and ``promptlayer`` python
     package installed, and the environment variable ``OPENAI_API_KEY``

libs/community/langchain_community/llms/sagemaker_endpoint.py

@@ -15,8 +15,7 @@ OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]], Iterato
 
 
 class LineIterator:
-    """
+    """Parse the byte stream input.
-    A helper class for parsing the byte stream input.
 
     The output of the model will be in the following format:
 
@@ -74,7 +73,7 @@ class LineIterator:
 
 
 class ContentHandlerBase(Generic[INPUT_TYPE, OUTPUT_TYPE]):
-    """A handler class to transform input from LLM to a
+    """Handler class to transform input from LLM to a
     format that SageMaker endpoint expects.
 
     Similarly, the class handles transforming output from the

libs/community/langchain_community/llms/sparkllm.py

@@ -24,7 +24,7 @@ logger = logging.getLogger(__name__)
 
 
 class SparkLLM(LLM):
-    """Wrapper around iFlyTek's Spark large language model.
+    """iFlyTek Spark large language model.
 
     To use, you should pass `app_id`, `api_key`, `api_secret`
     as a named parameter to the constructor OR set environment

libs/community/langchain_community/llms/titan_takeoff.py

@@ -10,7 +10,7 @@ from langchain_community.llms.utils import enforce_stop_tokens
 
 
 class TitanTakeoff(LLM):
-    """Wrapper around Titan Takeoff APIs."""
+    """Titan Takeoff API LLMs."""
 
     base_url: str = "http://localhost:8000"
     """Specifies the baseURL to use for the Titan Takeoff API. 

libs/community/langchain_community/llms/vertexai.py

@@ -40,12 +40,12 @@ stream_completion_with_retry = None
 
 
 def is_codey_model(model_name: str) -> bool:
-    """Returns True if the model name is a Codey model."""
+    """Return True if the model name is a Codey model."""
     return "code" in model_name
 
 
 def is_gemini_model(model_name: str) -> bool:
-    """Returns True if the model name is a Gemini model."""
+    """Return True if the model name is a Gemini model."""
     return model_name is not None and "gemini" in model_name
 
 
@@ -397,7 +397,7 @@ class VertexAI(_VertexAICommon, BaseLLM):
     alternative_import="langchain_google_vertexai.VertexAIModelGarden",
 )
 class VertexAIModelGarden(_VertexAIBase, BaseLLM):
-    """Large language models served from Vertex AI Model Garden."""
+    """Vertex AI Model Garden large language models."""
 
     client: "PredictionServiceClient" = None  #: :meta private:
     async_client: "PredictionServiceAsyncClient" = None  #: :meta private:

libs/community/langchain_community/retrievers/arcee.py

@@ -10,7 +10,7 @@ from langchain_community.utilities.arcee import ArceeWrapper, DALMFilter
 
 
 class ArceeRetriever(BaseRetriever):
-    """Retriever for Arcee's Domain Adapted Language Models (DALMs).
+    """Arcee Domain Adapted Language Models (DALMs) retriever.
 
     To use, set the ``ARCEE_API_KEY`` environment variable with your Arcee API key,
     or pass ``arcee_api_key`` as a named parameter.

libs/community/langchain_community/retrievers/you.py

@@ -11,7 +11,8 @@ from langchain_community.utilities import YouSearchAPIWrapper
 
 
 class YouRetriever(BaseRetriever, YouSearchAPIWrapper):
-    """`You` retriever that uses You.com's search API.
+    """You.com Search API retriever.
+
     It wraps results() to get_relevant_documents
     It uses all YouSearchAPIWrapper arguments without any change.
     """

libs/community/langchain_community/tools/cogniswitch/tool.py

@@ -8,8 +8,8 @@ from langchain_core.tools import BaseTool
 
 
 class CogniswitchKnowledgeRequest(BaseTool):
-    """
+    """Tool that uses the Cogniswitch service to answer questions.
-    A tool for interacting with the Cogniswitch service to answer questions.
+
     name: str = "cogniswitch_knowledge_request"
     description: str = (
         "A wrapper around cogniswitch service to answer the question
@@ -81,9 +81,9 @@ class CogniswitchKnowledgeRequest(BaseTool):
 
 
 class CogniswitchKnowledgeStatus(BaseTool):
-    """
+    """Tool that uses the Cogniswitch services to get the
-    A cogniswitch tool for interacting with the Cogniswitch services to know the
      status of the document or url uploaded.
+
     name: str = "cogniswitch_knowledge_status"
     description: str = (
         "A wrapper around cogniswitch services to know the status of
@@ -181,8 +181,8 @@ class CogniswitchKnowledgeStatus(BaseTool):
 
 
 class CogniswitchKnowledgeSourceFile(BaseTool):
-    """
+    """Tool that uses the Cogniswitch services to store data from file.
-    A cogniswitch tool for interacting with the Cogniswitch services to store data.
+
     name: str = "cogniswitch_knowledge_source_file"
     description: str = (
         "This calls the CogniSwitch services to analyze & store data from a file.
@@ -294,8 +294,8 @@ class CogniswitchKnowledgeSourceFile(BaseTool):
 
 
 class CogniswitchKnowledgeSourceURL(BaseTool):
-    """
+    """Tool that uses the Cogniswitch services to store data from a URL.
-    A cogniswitch tool for interacting with the Cogniswitch services to store data.
+
     name: str = "cogniswitch_knowledge_source_url"
     description: str = (
         "This calls the CogniSwitch services to analyze & store data from a url.

libs/community/langchain_community/tools/connery/service.py

@@ -10,8 +10,8 @@ from langchain_community.tools.connery.tool import ConneryAction
 
 
 class ConneryService(BaseModel):
-    """
+    """Service for interacting with the Connery Runner API.
-    A service for interacting with the Connery Runner API.
+
     It gets the list of available actions from the Connery Runner,
     wraps them in ConneryAction Tools and returns them to the user.
     It also provides a method for running the actions.

libs/community/langchain_community/tools/connery/tool.py

@@ -13,9 +13,7 @@ from langchain_community.tools.connery.models import Action, Parameter
 
 
 class ConneryAction(BaseTool):
-    """
+    """Connery Action tool."""
-    A LangChain Tool wrapping a Connery Action.
-    """
 
     name: str
     description: str

libs/community/langchain_community/tools/office365/events_search.py

@@ -54,7 +54,7 @@ class SearchEventsInput(BaseModel):
 
 
 class O365SearchEvents(O365BaseTool):
-    """Class for searching calendar events in Office 365
+    """Search calendar events in Office 365.
 
     Free, but setup is required
     """

libs/community/langchain_community/tools/office365/messages_search.py

@@ -53,9 +53,9 @@ class SearchEmailsInput(BaseModel):
 
 
 class O365SearchEmails(O365BaseTool):
-    """Class for searching email messages in Office 365
+    """Search email messages in Office 365.
 
-    Free, but setup is required
+    Free, but setup is required.
     """
 
     name: str = "messages_search"

libs/community/langchain_community/tools/office365/send_message.py

@@ -32,7 +32,7 @@ class SendMessageSchema(BaseModel):
 
 
 class O365SendMessage(O365BaseTool):
-    """Tool for sending an email in Office 365."""
+    """Send an email in Office 365."""
 
     name: str = "send_email"
     description: str = (

libs/community/langchain_community/tools/office365/utils.py

@@ -36,7 +36,7 @@ def clean_body(body: str) -> str:
 
 
 def authenticate() -> Account:
-    """Authenticate using the Microsoft Grah API"""
+    """Authenticate using the Microsoft Graph API"""
     try:
         from O365 import Account
     except ImportError as e:

libs/community/langchain_community/tools/you/tool.py

@@ -12,11 +12,13 @@ from langchain_community.utilities.you import YouSearchAPIWrapper
 
 
 class YouInput(BaseModel):
+    """Input schema for the you.com tool."""
+
     query: str = Field(description="should be a search query")
 
 
 class YouSearchTool(BaseTool):
-    """Tool that searches the you.com API"""
+    """Tool that searches the you.com API."""
 
     name = "you_search"
     description = (

libs/community/langchain_community/tools/zapier/tool.py

@@ -82,8 +82,9 @@ from langchain_community.utilities.zapier import ZapierNLAWrapper
 
 
 class ZapierNLARunAction(BaseTool):
-    """
+    """Tool to run a specific action from the user's exposed actions.
-    Args:
+
+    Params:
         action_id: a specific action ID (from list actions) of the action to execute
             (the set api_key must be associated with the action owner)
         instructions: a natural language instruction string for using the action
@@ -167,11 +168,7 @@ ZapierNLARunAction.__doc__ = (
 
 
 class ZapierNLAListActions(BaseTool):
-    """
+    """Tool to list all exposed actions for the user."""
-    Args:
-        None
-
-    """
 
     name: str = "ZapierNLA_list_actions"
     description: str = BASE_ZAPIER_TOOL_PROMPT + (

libs/community/langchain_community/utilities/astradb.py

@@ -14,6 +14,8 @@ if TYPE_CHECKING:
 
 
 class SetupMode(Enum):
+    """Setup mode for AstraDBEnvironment as enumerator."""
+
     SYNC = 1
     ASYNC = 2
     OFF = 3

libs/community/langchain_community/utilities/pebblo.py

@@ -62,7 +62,7 @@ logger = logging.getLogger(__name__)
 
 
 class Runtime(BaseModel):
-    """This class represents a Runtime.
+    """Pebblo Runtime.
 
     Args:
         type (Optional[str]): Runtime type. Defaults to ""
@@ -90,7 +90,7 @@ class Runtime(BaseModel):
 
 
 class Framework(BaseModel):
-    """This class represents a Framework instance.
+    """Pebblo Framework instance.
 
     Args:
         name (str): Name of the Framework.
@@ -102,7 +102,7 @@ class Framework(BaseModel):
 
 
 class App(BaseModel):
-    """This class represents an AI application.
+    """Pebblo AI application.
 
     Args:
         name (str): Name of the app.
@@ -124,7 +124,7 @@ class App(BaseModel):
 
 
 class Doc(BaseModel):
-    """This class represents a pebblo document.
+    """Pebblo document.
 
     Args:
         name (str): Name of app originating this document.
@@ -148,8 +148,8 @@ class Doc(BaseModel):
 
 
 def get_full_path(path: str) -> str:
-    """Return absolute local path for a local file/directory,
+    """Return an absolute local path for a local file/directory,
-    for network related path, return as is.
+    for a network related path, return as is.
 
     Args:
         path (str): Relative path to be resolved.
@@ -184,7 +184,7 @@ def get_loader_type(loader: str) -> str:
 
 
 def get_loader_full_path(loader: BaseLoader) -> str:
-    """Return absolute source path of source of loader based on the
+    """Return an absolute source path of source of loader based on the
     keys present in Document object from loader.
 
     Args:
@@ -266,7 +266,7 @@ def get_runtime() -> Tuple[Framework, Runtime]:
 
 
 def get_ip() -> str:
-    """Fetch local runtime ip address
+    """Fetch local runtime ip address.
 
     Returns:
         str: IP address

libs/community/langchain_community/utilities/vertexai.py

@@ -22,7 +22,7 @@ def create_retry_decorator(
         Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
     ] = None,
 ) -> Callable[[Any], Any]:
-    """Creates a retry decorator for Vertex / Palm LLMs."""
+    """Create a retry decorator for Vertex / Palm LLMs."""
     import google.api_core
 
     errors = [
@@ -82,7 +82,7 @@ def init_vertexai(
 
 
 def get_client_info(module: Optional[str] = None) -> "ClientInfo":
-    r"""Returns a custom user agent header.
+    r"""Return a custom user agent header.
 
     Args:
         module (Optional[str]):
@@ -109,7 +109,7 @@ def get_client_info(module: Optional[str] = None) -> "ClientInfo":
 
 
 def load_image_from_gcs(path: str, project: Optional[str] = None) -> "Image":
-    """Loads im Image from GCS."""
+    """Load an image from Google Cloud Storage."""
     try:
         from google.cloud import storage
     except ImportError:

libs/community/langchain_community/utilities/you.py

@@ -29,7 +29,7 @@ class YouHit(YouHitMetadata):
 
 
 class YouAPIOutput(BaseModel):
-    """The output from you.com api"""
+    """Output from you.com API."""
 
     hits: List[YouHit] = Field(
         description="A list of dictionaries containing the results"
@@ -37,7 +37,7 @@ class YouAPIOutput(BaseModel):
 
 
 class YouDocument(BaseModel):
-    """The output of parsing one snippet"""
+    """Output of parsing one snippet."""
 
     page_content: str = Field(description="One snippet of text")
     metadata: YouHitMetadata

libs/community/langchain_community/utils/ernie_functions.py

@@ -28,7 +28,7 @@ def convert_pydantic_to_ernie_function(
     name: Optional[str] = None,
     description: Optional[str] = None,
 ) -> FunctionDescription:
-    """Converts a Pydantic model to a function description for the Ernie API."""
+    """Convert a Pydantic model to a function description for the Ernie API."""
     schema = dereference_refs(model.schema())
     schema.pop("definitions", None)
     return {
@@ -44,7 +44,7 @@ def convert_pydantic_to_ernie_tool(
     name: Optional[str] = None,
     description: Optional[str] = None,
 ) -> ToolDescription:
-    """Converts a Pydantic model to a function description for the Ernie API."""
+    """Convert a Pydantic model to a function description for the Ernie API."""
     function = convert_pydantic_to_ernie_function(
         model, name=name, description=description
     )

libs/community/langchain_community/utils/google.py

@@ -5,7 +5,7 @@ from typing import Any, Optional
 
 
 def get_client_info(module: Optional[str] = None) -> Any:
-    r"""Returns a custom user agent header.
+    r"""Return a custom user agent header.
 
     Args:
         module (Optional[str]):

libs/community/langchain_community/vectorstores/neo4j_vector.py

@@ -139,7 +139,7 @@ def remove_lucene_chars(text: str) -> str:
 
 def dict_to_yaml_str(input_dict: Dict, indent: int = 0) -> str:
     """
-    Converts a dictionary to a YAML-like string without using external libraries.
+    Convert a dictionary to a YAML-like string without using external libraries.
 
     Parameters:
     - input_dict (dict): The dictionary to convert.
@@ -165,6 +165,8 @@ def dict_to_yaml_str(input_dict: Dict, indent: int = 0) -> str:
 def combine_queries(
     input_queries: List[Tuple[str, Dict[str, Any]]], operator: str
 ) -> Tuple[str, Dict[str, Any]]:
+    """Combine multiple queries with an operator."""
+
     # Initialize variables to hold the combined query and parameters
     combined_query: str = ""
     combined_params: Dict = {}
@@ -197,8 +199,7 @@ def combine_queries(
 def collect_params(
     input_data: List[Tuple[str, Dict[str, str]]],
 ) -> Tuple[List[str], Dict[str, Any]]:
-    """
+    """Transform the input data into the desired format.
-    Transform the input data into the desired format.
 
     Args:
     - input_data (list of tuples): Input data to transform.
@@ -324,6 +325,15 @@ def _handle_field_filter(
 
 
 def construct_metadata_filter(filter: Dict[str, Any]) -> Tuple[str, Dict]:
+    """Construct a metadata filter.
+
+    Args:
+        filter: A dictionary representing the filter condition.
+
+    Returns:
+        Tuple[str, Dict]
+    """
+
     if isinstance(filter, dict):
         if len(filter) == 1:
             # The only operators allowed at the top level are $AND and $OR

libs/community/langchain_community/vectorstores/redis/filters.py

@@ -100,7 +100,7 @@ def check_operator_misuse(func: Callable) -> Callable:
 
 
 class RedisTag(RedisFilterField):
-    """A RedisFilterField representing a tag in a Redis index."""
+    """RedisFilterField representing a tag in a Redis index."""
 
     OPERATORS: Dict[RedisFilterOperator, str] = {
         RedisFilterOperator.EQ: "==",
@@ -192,7 +192,7 @@ class RedisTag(RedisFilterField):
 
 
 class RedisNum(RedisFilterField):
-    """A RedisFilterField representing a numeric field in a Redis index."""
+    """RedisFilterField representing a numeric field in a Redis index."""
 
     OPERATORS: Dict[RedisFilterOperator, str] = {
         RedisFilterOperator.EQ: "==",
@@ -311,7 +311,7 @@ class RedisNum(RedisFilterField):
 
 
 class RedisText(RedisFilterField):
-    """A RedisFilterField representing a text field in a Redis index."""
+    """RedisFilterField representing a text field in a Redis index."""
 
     OPERATORS: Dict[RedisFilterOperator, str] = {
         RedisFilterOperator.EQ: "==",
@@ -381,7 +381,7 @@ class RedisText(RedisFilterField):
 
 
 class RedisFilterExpression:
-    """A logical expression of RedisFilterFields.
+    """Logical expression of RedisFilterFields.
 
     RedisFilterExpressions can be combined using the & and | operators to create
     complex logical expressions that evaluate to the Redis Query language.

libs/community/langchain_community/vectorstores/redis/schema.py

@@ -285,7 +285,7 @@ class RedisModel(BaseModel):
 def read_schema(
     index_schema: Optional[Union[Dict[str, List[Any]], str, os.PathLike]],
 ) -> Dict[str, Any]:
-    """Reads in the index schema from a dict or yaml file.
+    """Read in the index schema from a dict or yaml file.
 
     Check if it is a dict and return RedisModel otherwise, check if it's a path and
     read in the file assuming it's a yaml file and return a RedisModel

libs/community/langchain_community/vectorstores/sklearn.py

@@ -42,7 +42,7 @@ class BaseSerializer(ABC):
 
 
 class JsonSerializer(BaseSerializer):
-    """Serializes data in json using the json package from python standard library."""
+    """Serialize data in JSON using the json package from python standard library."""
 
     @classmethod
     def extension(cls) -> str:
@@ -58,7 +58,7 @@ class JsonSerializer(BaseSerializer):
 
 
 class BsonSerializer(BaseSerializer):
-    """Serializes data in binary json using the `bson` python package."""
+    """Serialize data in Binary JSON using the `bson` python package."""
 
     def __init__(self, persist_path: str) -> None:
         super().__init__(persist_path)
@@ -78,7 +78,7 @@ class BsonSerializer(BaseSerializer):
 
 
 class ParquetSerializer(BaseSerializer):
-    """Serializes data in `Apache Parquet` format using the `pyarrow` package."""
+    """Serialize data in `Apache Parquet` format using the `pyarrow` package."""
 
     def __init__(self, persist_path: str) -> None:
         super().__init__(persist_path)

libs/community/langchain_community/vectorstores/sqlitevss.py

@@ -24,7 +24,8 @@ logger = logging.getLogger(__name__)
 
 
 class SQLiteVSS(VectorStore):
-    """Wrapper around SQLite with vss extension as a vector database.
+    """SQLite with VSS extension as a vector database.
+
     To use, you should have the ``sqlite-vss`` python package installed.
     Example:
         .. code-block:: python

libs/community/langchain_community/vectorstores/tidb_vector.py

@@ -10,6 +10,8 @@ DEFAULT_TiDB_VECTOR_TABLE_NAME = "langchain_vector"
 
 
 class TiDBVectorStore(VectorStore):
+    """TiDB Vector Store."""
+
     def __init__(
         self,
         connection_string: str,

libs/community/langchain_community/vectorstores/vald.py

@@ -12,7 +12,7 @@ from langchain_community.vectorstores.utils import maximal_marginal_relevance
 
 
 class Vald(VectorStore):
-    """Wrapper around Vald vector database.
+    """Vald vector database.
 
     To use, you should have the ``vald-client-python`` python package installed.
 

libs/community/langchain_community/vectorstores/vdms.py

@@ -77,8 +77,7 @@ def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
 
 
 def VDMS_Client(host: str = "localhost", port: int = 55555) -> vdms.vdms:
-    """
+    """VDMS client for the VDMS server.
-    Wrapper to initiate and connect a VDMS client to a VDMS server
 
     Args:
         host: IP or hostname of VDMS server
@@ -98,7 +97,7 @@ def VDMS_Client(host: str = "localhost", port: int = 55555) -> vdms.vdms:
 
 
 class VDMS(VectorStore):
-    """Wrapper around Intel Lab's VDMS for vector-store workloads.
+    """Intel Lab's VDMS for vector-store workloads.
 
     To use, you should have both:
     - the ``vdms`` python package installed
@@ -1534,6 +1533,8 @@ def _check_descriptor_exists_by_id(
 
 
 def embedding2bytes(embedding: Union[List[float], None]) -> Union[bytes, None]:
+    """Convert embedding to bytes."""
+
     blob = None
     if embedding is not None:
         emb = np.array(embedding, dtype="float32")

libs/community/langchain_community/vectorstores/vectara.py

@@ -18,7 +18,8 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class SummaryConfig:
-    """
+    """Configuration for summary generation.
+
     is_enabled: True if summary is enabled, False otherwise
     max_results: maximum number of results to summarize
     response_lang: requested language for the summary
@@ -34,7 +35,8 @@ class SummaryConfig:
 
 @dataclass
 class MMRConfig:
-    """
+    """Configuration for Maximal Marginal Relevance (MMR) search.
+
     is_enabled: True if MMR is enabled, False otherwise
     mmr_k: number of results to fetch for MMR, defaults to 50
     diversity_bias: number between 0 and 1 that determines the degree
@@ -53,7 +55,8 @@ class MMRConfig:
 
 @dataclass
 class VectaraQueryConfig:
-    """
+    """Configuration for Vectara query.
+
     k: Number of Documents to return. Defaults to 10.
     lambda_val: lexical match parameter for hybrid search.
     filter Dictionary of argument(s) to filter on metadata. For example a
@@ -566,7 +569,7 @@ class Vectara(VectorStore):
 
 
 class VectaraRetriever(VectorStoreRetriever):
-    """Retriever class for `Vectara`."""
+    """Retriever for `Vectara`."""
 
     vectorstore: Vectara
     """Vectara vectorstore."""

libs/community/langchain_community/vectorstores/yellowbrick.py

@@ -23,7 +23,8 @@ logger = logging.getLogger(__name__)
 
 
 class Yellowbrick(VectorStore):
-    """Wrapper around Yellowbrick as a vector database.
+    """Yellowbrick as a vector database.
+
     Example:
         .. code-block:: python
             from langchain_community.vectorstores import Yellowbrick