docs: docstrings langchain_community update (#14889)

Addded missed docstrings. Fixed inconsistency in docstrings.

**Note** CC @efriis 
There were PR errors on
`langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py`
But, I didn't touch this file in this PR! Can it be some cache problems?
I fixed this error.

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

@@ -32,30 +32,44 @@ from langchain_community.utilities.github import GitHubAPIWrapper
 
 
 class NoInput(BaseModel):
+    """Schema for operations that do not require any input."""
+
     no_input: str = Field("", description="No input required, e.g. `` (empty string).")
 
 
 class GetIssue(BaseModel):
+    """Schema for operations that require an issue number as input."""
+
     issue_number: int = Field(0, description="Issue number as an integer, e.g. `42`")
 
 
 class CommentOnIssue(BaseModel):
+    """Schema for operations that require a comment as input."""
+
     input: str = Field(..., description="Follow the required formatting.")
 
 
 class GetPR(BaseModel):
+    """Schema for operations that require a PR number as input."""
+
     pr_number: int = Field(0, description="The PR number as an integer, e.g. `12`")
 
 
 class CreatePR(BaseModel):
+    """Schema for operations that require a PR title and body as input."""
+
     formatted_pr: str = Field(..., description="Follow the required formatting.")
 
 
 class CreateFile(BaseModel):
+    """Schema for operations that require a file path and content as input."""
+
     formatted_file: str = Field(..., description="Follow the required formatting.")
 
 
 class ReadFile(BaseModel):
+    """Schema for operations that require a file path as input."""
+
     formatted_filepath: str = Field(
         ...,
         description=(
@@ -66,12 +80,16 @@ class ReadFile(BaseModel):
 
 
 class UpdateFile(BaseModel):
+    """Schema for operations that require a file path and content as input."""
+
     formatted_file_update: str = Field(
         ..., description="Strictly follow the provided rules."
     )
 
 
 class DeleteFile(BaseModel):
+    """Schema for operations that require a file path as input."""
+
     formatted_filepath: str = Field(
         ...,
         description=(
@@ -84,6 +102,8 @@ class DeleteFile(BaseModel):
 
 
 class DirectoryPath(BaseModel):
+    """Schema for operations that require a directory path as input."""
+
     input: str = Field(
         "",
         description=(
@@ -94,12 +114,16 @@ class DirectoryPath(BaseModel):
 
 
 class BranchName(BaseModel):
+    """Schema for operations that require a branch name as input."""
+
     branch_name: str = Field(
         ..., description="The name of the branch, e.g. `my_branch`."
     )
 
 
 class SearchCode(BaseModel):
+    """Schema for operations that require a search query as input."""
+
     search_query: str = Field(
         ...,
         description=(
@@ -110,6 +134,8 @@ class SearchCode(BaseModel):
 
 
 class CreateReviewRequest(BaseModel):
+    """Schema for operations that require a username as input."""
+
     username: str = Field(
         ...,
         description="GitHub username of the user being requested, e.g. `my_username`.",
@@ -117,6 +143,8 @@ class CreateReviewRequest(BaseModel):
 
 
 class SearchIssuesAndPRs(BaseModel):
+    """Schema for operations that require a search query as input."""
+
     search_query: str = Field(
         ...,
         description="Natural language search query, e.g. `My issue title or topic`.",

libs/community/langchain_community/callbacks/tracers/comet.py

@@ -50,10 +50,15 @@ def import_comet_llm_api() -> SimpleNamespace:
 
 
 class CometTracer(BaseTracer):
+    """Comet Tracer."""
+
     def __init__(self, **kwargs: Any) -> None:
+        """Initialize the Comet Tracer."""
         super().__init__(**kwargs)
         self._span_map: Dict["UUID", "Span"] = {}
+        """Map from run id to span."""
         self._chains_map: Dict["UUID", "Chain"] = {}
+        """Map from run id to chain."""
         self._initialize_comet_modules()
 
     def _initialize_comet_modules(self) -> None:

libs/community/langchain_community/chat_models/fireworks.py

@@ -259,6 +259,16 @@ class ChatFireworks(BaseChatModel):
 def conditional_decorator(
     condition: bool, decorator: Callable[[Any], Any]
 ) -> Callable[[Any], Any]:
+    """Define conditional decorator.
+
+    Args:
+        condition: The condition.
+        decorator: The decorator.
+
+    Returns:
+        The decorated function.
+    """
+
     def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
         if condition:
             return decorator(func)
@@ -281,6 +291,7 @@ def completion_with_retry(
 
     @conditional_decorator(use_retry, retry_decorator)
     def _completion_with_retry(**kwargs: Any) -> Any:
+        """Use tenacity to retry the completion call."""
         return fireworks.client.ChatCompletion.create(
             **kwargs,
         )

libs/community/langchain_community/chat_models/meta.py

@@ -24,6 +24,8 @@ def _convert_one_message_to_text_llama(message: BaseMessage) -> str:
 
 
 def convert_messages_to_prompt_llama(messages: List[BaseMessage]) -> str:
+    """Convert a list of messages to a prompt for llama."""
+
     return "\n".join(
         [_convert_one_message_to_text_llama(message) for message in messages]
     )

libs/community/langchain_community/chat_models/tongyi.py

@@ -53,6 +53,8 @@ logger = logging.getLogger(__name__)
 
 
 def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+    """Convert a dict to a message."""
+
     role = _dict["role"]
     if role == "user":
         return HumanMessage(content=_dict["content"])
@@ -72,6 +74,8 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
 
 
 def convert_message_to_dict(message: BaseMessage) -> dict:
+    """Convert a message to a dict."""
+
     message_dict: Dict[str, Any]
     if isinstance(message, ChatMessage):
         message_dict = {"role": message.role, "content": message.content}

libs/community/langchain_community/chat_models/volcengine_maas.py

@@ -32,6 +32,8 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 
 def convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
+    """Convert a dict to a message."""
+
     content = _dict.get("choice", {}).get("message", {}).get("content", "")
     return AIMessage(content=content)
 

libs/community/langchain_community/document_loaders/docusaurus.py

@@ -5,11 +5,12 @@ from langchain_community.document_loaders.sitemap import SitemapLoader
 
 
 class DocusaurusLoader(SitemapLoader):
-    """
-    Loader that leverages the SitemapLoader to loop through the generated pages of a
+    """Load from Docusaurus Documentation.
+
+    It leverages the SitemapLoader to loop through the generated pages of a
     Docusaurus Documentation website and extracts the content by looking for specific
     HTML tags. By default, the parser searches for the main content of the Docusaurus
-    page, which is normally the <article>. You also have the option to define your own
+    page, which is normally the <article>. You can also define your own
     custom HTML tags by providing them as a list, for example: ["div", ".main", "a"].
     """
 
@@ -19,8 +20,8 @@ class DocusaurusLoader(SitemapLoader):
         custom_html_tags: Optional[List[str]] = None,
         **kwargs: Any,
     ):
-        """
-        Initialize DocusaurusLoader
+        """Initialize DocusaurusLoader
+
         Args:
             url: The base URL of the Docusaurus website.
             custom_html_tags: Optional custom html tags to extract content from pages.
@@ -39,7 +40,7 @@ class DocusaurusLoader(SitemapLoader):
         )
 
     def _parsing_function(self, content: Any) -> str:
-        """Parses specific elements from a Docusarus page."""
+        """Parses specific elements from a Docusaurus page."""
         relevant_elements = content.select(",".join(self.custom_html_tags))
 
         for element in relevant_elements:

libs/community/langchain_community/document_loaders/lakefs.py

@@ -13,6 +13,8 @@ from langchain_community.document_loaders.unstructured import UnstructuredBaseLo
 
 
 class LakeFSClient:
+    """Client for lakeFS."""
+
     def __init__(
         self,
         lakefs_access_key: str,
@@ -126,6 +128,8 @@ class LakeFSLoader(BaseLoader):
 
 
 class UnstructuredLakeFSLoader(UnstructuredBaseLoader):
+    """Load from `lakeFS` as unstructured data."""
+
     def __init__(
         self,
         url: str,
@@ -135,7 +139,7 @@ class UnstructuredLakeFSLoader(UnstructuredBaseLoader):
         presign: bool = True,
         **unstructured_kwargs: Any,
     ):
-        """
+        """Initialize UnstructuredLakeFSLoader.
 
         Args:
 

libs/community/langchain_community/document_loaders/rspace.py

@@ -9,11 +9,9 @@ from langchain_community.document_loaders.base import BaseLoader
 
 
 class RSpaceLoader(BaseLoader):
-    """
-    Loads  content from RSpace notebooks, folders, documents or PDF Gallery files into
-    Langchain documents.
+    """Load content from RSpace notebooks, folders, documents or PDF Gallery files.
 
-    Maps  RSpace document <-> Langchain Document in 1-1. PDFs are imported using PyPDF.
+    Map RSpace document <-> Langchain Document in 1-1. PDFs are imported using PyPDF.
 
     Requirements are rspace_client (`pip install rspace_client`) and PyPDF if importing
      PDF docs (`pip install pypdf`).
@@ -45,7 +43,7 @@ class RSpaceLoader(BaseLoader):
 
     @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that API key and URL exists in environment."""
+        """Validate that API key and URL exist in environment."""
         values["api_key"] = get_from_dict_or_env(values, "api_key", "RSPACE_API_KEY")
         values["url"] = get_from_dict_or_env(values, "url", "RSPACE_URL")
         if "global_id" not in values or values["global_id"] is None:

libs/community/langchain_community/document_transformers/beautiful_soup_transformer.py

@@ -137,6 +137,15 @@ class BeautifulSoupTransformer(BaseDocumentTransformer):
 
 
 def get_navigable_strings(element: Any) -> Iterator[str]:
+    """Get all navigable strings from a BeautifulSoup element.
+
+    Args:
+        element: A BeautifulSoup element.
+
+    Returns:
+        A generator of strings.
+    """
+
     from bs4 import NavigableString, Tag
 
     for child in cast(Tag, element).children:

libs/community/langchain_community/llms/fireworks.py

@@ -209,6 +209,16 @@ class Fireworks(BaseLLM):
 def conditional_decorator(
     condition: bool, decorator: Callable[[Any], Any]
 ) -> Callable[[Any], Any]:
+    """Conditionally apply a decorator.
+
+    Args:
+        condition: A boolean indicating whether to apply the decorator.
+        decorator: A decorator function.
+
+    Returns:
+        A decorator function.
+    """
+
     def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
         if condition:
             return decorator(func)

libs/community/langchain_community/llms/textgen.py

@@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 
 class TextGen(LLM):
-    """text-generation-webui models.
+    """Text generation models from WebUI.
 
     To use, you should have the text-generation-webui installed, a model loaded,
     and --api added as a command-line option.

libs/community/langchain_community/llms/titan_takeoff_pro.py

@@ -10,6 +10,8 @@ from langchain_community.llms.utils import enforce_stop_tokens
 
 
 class TitanTakeoffPro(LLM):
+    """Titan Takeoff Pro is a language model that can be used to generate text."""
+
     base_url: Optional[str] = "http://localhost:3000"
     """Specifies the baseURL to use for the Titan Takeoff Pro API.
     Default = http://localhost:3000.

libs/community/langchain_community/llms/together.py

@@ -17,7 +17,7 @@ logger = logging.getLogger(__name__)
 
 
 class Together(LLM):
-    """Wrapper around Together AI models.
+    """LLM models from `Together`.
 
     To use, you'll need an API key which you can find here:
     https://api.together.xyz/settings/api-keys. This can be passed in as init param

libs/community/langchain_community/llms/xinference.py

@@ -9,7 +9,8 @@ if TYPE_CHECKING:
 
 
 class Xinference(LLM):
-    """Wrapper for accessing Xinference's large-scale model inference service.
+    """`Xinference` large-scale model inference service.
+
     To use, you should have the xinference library installed:
 
     .. code-block:: bash

libs/community/langchain_community/retrievers/bedrock.py

@@ -7,15 +7,19 @@ from langchain_core.retrievers import BaseRetriever
 
 
 class VectorSearchConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
+    """Configuration for vector search."""
+
     numberOfResults: int = 4
 
 
 class RetrievalConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
+    """Configuration for retrieval."""
+
     vectorSearchConfiguration: VectorSearchConfig
 
 
 class AmazonKnowledgeBasesRetriever(BaseRetriever):
-    """A retriever class for `Amazon Bedrock Knowledge Bases`.
+    """`Amazon Bedrock Knowledge Bases` retrieval.
 
     See https://aws.amazon.com/bedrock/knowledge-bases for more info.
 

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

@@ -10,6 +10,8 @@ from langchain_community.utilities.arxiv import ArxivAPIWrapper
 
 
 class ArxivInput(BaseModel):
+    """Input for the Arxiv tool."""
+
     query: str = Field(description="search query to look up")
 
 

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

@@ -11,6 +11,8 @@ from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIW
 
 
 class DDGInput(BaseModel):
+    """Input for the DuckDuckGo search tool."""
+
     query: str = Field(description="search query to look up")
 
 

libs/community/langchain_community/tools/e2b_data_analysis/unparse.py

@@ -728,6 +728,14 @@ class Unparser:
 
 
 def roundtrip(filename, output=sys.stdout):
+    """Parse a file and pretty-print it to output.
+
+    The output is formatted as valid Python source code.
+
+    Args:
+        filename: The name of the file to parse.
+        output: The output stream to write to.
+    """
     with open(filename, "rb") as pyfile:
         encoding = tokenize.detect_encoding(pyfile.readline)[0]
     with open(filename, "r", encoding=encoding) as pyfile:

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

@@ -13,6 +13,8 @@ from langchain_community.llms.gradient_ai import TrainResult
 
 @runtime_checkable
 class TrainableLLM(Protocol):
+    """Protocol for trainable language models."""
+
     @abstractmethod
     def train_unsupervised(
         self,
@@ -31,6 +33,8 @@ class TrainableLLM(Protocol):
 
 
 class Memorize(BaseTool):
+    """Tool that trains a language model."""
+
     name: str = "Memorize"
     description: str = (
         "Useful whenever you observed novel information "

libs/community/langchain_community/tools/slack/get_channel.py

@@ -8,6 +8,8 @@ from langchain_community.tools.slack.base import SlackBaseTool
 
 
 class SlackGetChannel(SlackBaseTool):
+    """Tool that gets Slack channel information."""
+
     name: str = "get_channelid_name_dict"
     description: str = "Use this tool to get channelid-name dict."
 

libs/community/langchain_community/tools/slack/get_message.py

@@ -18,6 +18,8 @@ class SlackGetMessageSchema(BaseModel):
 
 
 class SlackGetMessage(SlackBaseTool):
+    """Tool that gets Slack messages."""
+
     name: str = "get_messages"
     description: str = "Use this tool to get messages from a channel."
 

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

@@ -13,6 +13,8 @@ from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
 
 
 class TavilyInput(BaseModel):
+    """Input for the Tavily tool."""
+
     query: str = Field(description="search query to look up")
 
 

libs/community/langchain_community/utilities/google_finance.py

@@ -7,6 +7,7 @@ from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 
 class GoogleFinanceAPIWrapper(BaseModel):
     """Wrapper for SerpApi's Google Finance API
+
     You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
     The wrapper uses the SerpApi.com python package:
     https://serpapi.com/integrations/python

libs/community/langchain_community/utilities/google_jobs.py

@@ -7,6 +7,7 @@ from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 
 class GoogleJobsAPIWrapper(BaseModel):
     """Wrapper for SerpApi's Google Scholar API
+
     You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
     The wrapper uses the SerpApi.com python package:
     https://serpapi.com/integrations/python

libs/community/langchain_community/utilities/nasa.py

@@ -8,6 +8,8 @@ IMAGE_AND_VIDEO_LIBRARY_URL = "https://images-api.nasa.gov"
 
 
 class NasaAPIWrapper(BaseModel):
+    """Wrapper for NASA API."""
+
     def get_media(self, query: str) -> str:
         params = json.loads(query)
         if params.get("q"):

libs/community/langchain_community/utils/openai.py

@@ -6,5 +6,6 @@ from packaging.version import parse
 
 
 def is_openai_v1() -> bool:
+    """Return whether OpenAI API is v1 or more."""
     _version = parse(version("openai"))
     return _version.major >= 1

libs/community/langchain_community/vectorstores/pgvecto_rs.py

@@ -23,6 +23,8 @@ class _ORMBase(DeclarativeBase):
 
 
 class PGVecto_rs(VectorStore):
+    """VectorStore backed by pgvecto_rs."""
+
     _engine: sqlalchemy.engine.Engine
     _table: Type[_ORMBase]
     _embedding: Embeddings
@@ -35,6 +37,16 @@ class PGVecto_rs(VectorStore):
         collection_name: str,
         new_table: bool = False,
     ) -> None:
+        """Initialize a PGVecto_rs vectorstore.
+
+        Args:
+            embedding: Embeddings to use.
+            dimension: Dimension of the embeddings.
+            db_url: Database URL.
+            collection_name: Name of the collection.
+            new_table: Whether to create a new table or connect to an existing one.
+              Defaults to False.
+        """
         try:
             from pgvecto_rs.sqlalchemy import Vector
         except ImportError as e:

libs/community/langchain_community/vectorstores/sklearn.py

@@ -127,7 +127,7 @@ class SKLearnVectorStoreException(RuntimeError):
 
 class SKLearnVectorStore(VectorStore):
     """Simple in-memory vector store based on the `scikit-learn` library
-    `NearestNeighbors` implementation."""
+    `NearestNeighbors`."""
 
     def __init__(
         self,

libs/community/langchain_community/vectorstores/surrealdb.py

@@ -201,7 +201,6 @@ class SurrealDBStore(VectorStore):
         where vector::similarity::cosine(embedding,{embedding}) >= {score_threshold}
         order by similarity desc LIMIT {k}
         """.format(**args)
-
         results = await self.sdb.query(query)
 
         if len(results) == 0:

libs/community/langchain_community/vectorstores/tencentvectordb.py

@@ -64,7 +64,7 @@ class IndexParams:
 
 
 class TencentVectorDB(VectorStore):
-    """Initialize wrapper around the tencent vector database.
+    """Tencent VectorDB as a vector store.
 
     In order to use this you need to have a database instance.
     See the following documentation for details:

libs/community/langchain_community/vectorstores/tiledb.py

@@ -37,23 +37,34 @@ def dependable_tiledb_import() -> Any:
 
 
 def get_vector_index_uri_from_group(group: Any) -> str:
+    """Get the URI of the vector index."""
     return group[VECTOR_INDEX_NAME].uri
 
 
 def get_documents_array_uri_from_group(group: Any) -> str:
+    """Get the URI of the documents array from group.
+
+    Args:
+        group: TileDB group object.
+
+    Returns:
+        URI of the documents array.
+    """
     return group[DOCUMENTS_ARRAY_NAME].uri
 
 
 def get_vector_index_uri(uri: str) -> str:
+    """Get the URI of the vector index."""
     return f"{uri}/{VECTOR_INDEX_NAME}"
 
 
 def get_documents_array_uri(uri: str) -> str:
+    """Get the URI of the documents array."""
     return f"{uri}/{DOCUMENTS_ARRAY_NAME}"
 
 
 class TileDB(VectorStore):
-    """Wrapper around TileDB vector database.
+    """TileDB vector store.
 
     To use, you should have the ``tiledb-vector-search`` python package installed.
 

libs/community/langchain_community/vectorstores/timescalevector.py

@@ -37,8 +37,7 @@ _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain_store"
 
 
 class TimescaleVector(VectorStore):
-    """VectorStore implementation using the timescale vector client to store vectors
-    in Postgres.
+    """Timescale Postgres vector store
 
     To use, you should have the ``timescale_vector`` python package installed.
 

libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py

@@ -11,7 +11,9 @@ if TYPE_CHECKING:
 
 
 class PromptInjectionException(ValueError):
-    def __init__(self, message="Prompt injection attack detected", score: float = 1.0):
+    def __init__(
+        self, message: str = "Prompt injection attack detected", score: float = 1.0
+    ):
         self.message = message
         self.score = score
 
@@ -83,7 +85,7 @@ class HuggingFaceInjectionIdentifier(BaseTool):
 
     def _run(self, query: str) -> str:
         """Use the tool."""
-        result = self.model(query)
+        result = self.model(query)  # type: ignore
         score = (
             result[0]["score"]
             if result[0]["label"] == self.injection_label