docstrings langchain update

libs/langchain/langchain/agents/agent.py

@@ -390,10 +390,7 @@ async def aplan(
         intermediate_steps: List[Tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
-    ) -> Union[
-        AgentAction,
-        AgentFinish,
-    ]:
+    ) -> Union[AgentAction, AgentFinish,]:
         """Given input, decided what to do.
 
         Args:
@@ -442,10 +439,7 @@ def plan(
         intermediate_steps: List[Tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
-    ) -> Union[
-        List[AgentAction],
-        AgentFinish,
-    ]:
+    ) -> Union[List[AgentAction], AgentFinish,]:
         """Given input, decided what to do.
 
         Args:
@@ -466,10 +460,7 @@ async def aplan(
         intermediate_steps: List[Tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
-    ) -> Union[
-        List[AgentAction],
-        AgentFinish,
-    ]:
+    ) -> Union[List[AgentAction], AgentFinish,]:
         """Given input, decided what to do.
 
         Args:

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -55,8 +55,12 @@ def _get_chat_history(chat_history: List[CHAT_TURN_TYPE]) -> str:
 
 
 class InputType(BaseModel):
+    """Input type for ConversationalRetrievalChain."""
+
     question: str
+    """The question to answer."""
     chat_history: List[CHAT_TURN_TYPE] = Field(default_factory=list)
+    """The chat history to use for retrieval."""
 
 
 class BaseConversationalRetrievalChain(Chain):

libs/langchain/langchain/chains/openai_tools/extraction.py

@@ -19,6 +19,16 @@ def create_extraction_chain_pydantic(
     llm: BaseLanguageModel,
     system_message: str = _EXTRACTION_TEMPLATE,
 ) -> Runnable:
+    """Creates a chain that extracts information from a passage.
+
+    Args:
+        pydantic_schemas: The schema of the entities to extract.
+        llm: The language model to use.
+        system_message: The system message to use for extraction.
+
+    Returns:
+        A runnable that extracts information from a passage.
+    """
     if not isinstance(pydantic_schemas, list):
         pydantic_schemas = [pydantic_schemas]
     prompt = ChatPromptTemplate.from_messages(

libs/langchain/langchain/retrievers/self_query/redis.py

@@ -34,7 +34,7 @@
 
 
 class RedisTranslator(Visitor):
-    """Translate"""
+    """Visitor for translating structured queries to Redis filter expressions."""
 
     allowed_comparators = (
         Comparator.EQ,

libs/langchain/langchain/smith/evaluation/config.py

@@ -55,6 +55,8 @@ def get_kwargs(self) -> Dict[str, Any]:
 
 
 class SingleKeyEvalConfig(EvalConfig):
+    """Configuration for a run evaluator that only requires a single key."""
+
     reference_key: Optional[str] = None
     """The key in the dataset run to use as the reference string.
     If not provided, we will attempt to infer automatically."""

libs/langchain/langchain/text_splitter.py

@@ -420,7 +420,8 @@ def split_text(self, text: str) -> List[Document]:
                 if stripped_line.startswith(sep) and (
                     # Header with no text OR header is followed by space
                     # Both are valid conditions that sep is being used a header
-                    len(stripped_line) == len(sep) or stripped_line[len(sep)] == " "
+                    len(stripped_line) == len(sep)
+                    or stripped_line[len(sep)] == " "
                 ):
                     # Ensure we are tracking the header as metadata
                     if name is not None:

libs/langchain/langchain/tools/retriever.py

@@ -5,6 +5,8 @@
 
 
 class RetrieverInput(BaseModel):
+    """Input to the retriever."""
+
     query: str = Field(description="query to look up in retriever")