docstrings agents (#7866)

Added/Updated docstrings for `agents`
@baskaryan

langchain/agents/agent.py

@@ -43,7 +43,7 @@ logger = logging.getLogger(__name__)
 
 
 class BaseSingleActionAgent(BaseModel):
-    """Base Agent class."""
+    """Base Single Action Agent class."""
 
     @property
     def return_values(self) -> List[str]:
@@ -179,7 +179,7 @@ class BaseSingleActionAgent(BaseModel):
 
 
 class BaseMultiActionAgent(BaseModel):
-    """Base Agent class."""
+    """Base Multi Action Agent class."""
 
     @property
     def return_values(self) -> List[str]:
@@ -200,7 +200,7 @@ class BaseMultiActionAgent(BaseModel):
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
-                along with observations
+                along with the observations.
             callbacks: Callbacks to run.
             **kwargs: User inputs.
 
@@ -219,7 +219,7 @@ class BaseMultiActionAgent(BaseModel):
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
-                along with observations
+                along with the observations.
             callbacks: Callbacks to run.
             **kwargs: User inputs.
 
@@ -299,18 +299,30 @@ class BaseMultiActionAgent(BaseModel):
 
 
 class AgentOutputParser(BaseOutputParser):
+    """Base class for parsing agent output into agent action/finish."""
+
     @abstractmethod
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         """Parse text into agent action/finish."""
 
 
 class LLMSingleActionAgent(BaseSingleActionAgent):
+    """Base class for single action agents."""
+
     llm_chain: LLMChain
+    """LLMChain to use for agent."""
     output_parser: AgentOutputParser
+    """Output parser to use for agent."""
     stop: List[str]
+    """List of strings to stop on."""
 
     @property
     def input_keys(self) -> List[str]:
+        """Return the input keys.
+
+        Returns:
+            List of input keys.
+        """
         return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
 
     def dict(self, **kwargs: Any) -> Dict:
@@ -329,7 +341,7 @@ class LLMSingleActionAgent(BaseSingleActionAgent):
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
-                along with observations
+                along with the observations.
             callbacks: Callbacks to run.
             **kwargs: User inputs.
 
@@ -377,7 +389,7 @@ class LLMSingleActionAgent(BaseSingleActionAgent):
 
 
 class Agent(BaseSingleActionAgent):
-    """Class responsible for calling the language model and deciding the action.
+    """Agent that calls the language model and deciding the action.
 
     This is driven by an LLMChain. The prompt in the LLMChain MUST include
     a variable called "agent_scratchpad" where the agent can put its
@@ -599,8 +611,12 @@ class Agent(BaseSingleActionAgent):
 
 
 class ExceptionTool(BaseTool):
+    """Tool that just returns the query."""
+
     name = "_Exception"
+    """Name of the tool."""
     description = "Exception tool"
+    """Description of the tool."""
 
     def _run(
         self,
@@ -618,7 +634,7 @@ class ExceptionTool(BaseTool):
 
 
 class AgentExecutor(Chain):
-    """Consists of an agent using tools."""
+    """Agent that is using tools."""
 
     agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
     """The agent to run for creating a plan and determining actions

langchain/agents/agent_toolkits/base.py

@@ -8,7 +8,7 @@ from langchain.tools import BaseTool
 
 
 class BaseToolkit(BaseModel, ABC):
-    """Class representing a collection of related tools."""
+    """Base Toolkit representing a collection of related tools."""
 
     @abstractmethod
     def get_tools(self) -> List[BaseTool]:

langchain/agents/agent_toolkits/csv/base.py

@@ -1,4 +1,3 @@
-"""Agent for working with csv files."""
 from typing import Any, List, Optional, Union
 
 from langchain.agents.agent import AgentExecutor

langchain/agents/agent_toolkits/file_management/toolkit.py

@@ -1,4 +1,3 @@
-"""Toolkit for interacting with the local filesystem."""
 from __future__ import annotations
 
 from typing import List, Optional

langchain/agents/agent_toolkits/jira/toolkit.py

@@ -1,4 +1,3 @@
-"""Jira Toolkit."""
 from typing import List
 
 from langchain.agents.agent_toolkits.base import BaseToolkit

langchain/agents/agent_toolkits/json/toolkit.py

@@ -1,4 +1,3 @@
-"""Toolkit for interacting with a JSON spec."""
 from __future__ import annotations
 
 from typing import List

langchain/agents/agent_toolkits/nla/tool.py

@@ -1,4 +1,4 @@
-"""Tool for interacting with a single API with natural language efinition."""
+"""Tool for interacting with a single API with natural language definition."""
 
 
 from typing import Any, Optional

langchain/agents/agent_toolkits/nla/toolkit.py

@@ -1,4 +1,3 @@
-"""Toolkit for interacting with API's using natural language."""
 from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence
@@ -15,7 +14,7 @@ from langchain.tools.plugin import AIPlugin
 
 
 class NLAToolkit(BaseToolkit):
-    """Natural Language API Toolkit Definition."""
+    """Natural Language API Toolkit."""
 
     nla_tools: Sequence[NLATool] = Field(...)
     """List of API Endpoint Tools."""

langchain/agents/agent_toolkits/office365/toolkit.py

@@ -18,7 +18,7 @@ if TYPE_CHECKING:
 
 
 class O365Toolkit(BaseToolkit):
-    """Toolkit for interacting with Office365."""
+    """Toolkit for interacting with Office 365."""
 
     account: Account = Field(default_factory=authenticate)
 

langchain/agents/agent_toolkits/openapi/base.py

@@ -30,7 +30,7 @@ def create_openapi_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a json agent from an LLM and tools."""
+    """Construct an OpenAPI agent from an LLM and tools."""
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(
         tools,

langchain/agents/agent_toolkits/openapi/planner.py

@@ -46,6 +46,7 @@ from langchain.tools.requests.tool import BaseRequestsTool
 # information in the response.
 # However, the goal for now is to have only a single inference step.
 MAX_RESPONSE_LENGTH = 5000
+"""Maximum length of the response to be returned."""
 
 
 def _get_default_llm_chain(prompt: BasePromptTemplate) -> LLMChain:
@@ -63,12 +64,18 @@ def _get_default_llm_chain_factory(
 
 
 class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
+    """Requests GET tool with LLM-instructed extraction of truncated responses."""
+
     name = "requests_get"
+    """Tool name."""
     description = REQUESTS_GET_TOOL_DESCRIPTION
+    """Tool description."""
     response_length: Optional[int] = MAX_RESPONSE_LENGTH
+    """Maximum length of the response to be returned."""
     llm_chain: LLMChain = Field(
         default_factory=_get_default_llm_chain_factory(PARSING_GET_PROMPT)
     )
+    """LLMChain used to extract the response."""
 
     def _run(self, text: str) -> str:
         try:
@@ -87,13 +94,18 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
 
 
 class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
-    name = "requests_post"
-    description = REQUESTS_POST_TOOL_DESCRIPTION
+    """Requests POST tool with LLM-instructed extraction of truncated responses."""
 
+    name = "requests_post"
+    """Tool name."""
+    description = REQUESTS_POST_TOOL_DESCRIPTION
+    """Tool description."""
     response_length: Optional[int] = MAX_RESPONSE_LENGTH
+    """Maximum length of the response to be returned."""
     llm_chain: LLMChain = Field(
         default_factory=_get_default_llm_chain_factory(PARSING_POST_PROMPT)
     )
+    """LLMChain used to extract the response."""
 
     def _run(self, text: str) -> str:
         try:
@@ -111,13 +123,18 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
 
 
 class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
-    name = "requests_patch"
-    description = REQUESTS_PATCH_TOOL_DESCRIPTION
+    """Requests PATCH tool with LLM-instructed extraction of truncated responses."""
 
+    name = "requests_patch"
+    """Tool name."""
+    description = REQUESTS_PATCH_TOOL_DESCRIPTION
+    """Tool description."""
     response_length: Optional[int] = MAX_RESPONSE_LENGTH
+    """Maximum length of the response to be returned."""
     llm_chain: LLMChain = Field(
         default_factory=_get_default_llm_chain_factory(PARSING_PATCH_PROMPT)
     )
+    """LLMChain used to extract the response."""
 
     def _run(self, text: str) -> str:
         try:
@@ -135,13 +152,19 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
 
 
 class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
+    """A tool that sends a DELETE request and parses the response."""
+
     name = "requests_delete"
+    """The name of the tool."""
     description = REQUESTS_DELETE_TOOL_DESCRIPTION
+    """The description of the tool."""
 
     response_length: Optional[int] = MAX_RESPONSE_LENGTH
+    """The maximum length of the response."""
     llm_chain: LLMChain = Field(
         default_factory=_get_default_llm_chain_factory(PARSING_DELETE_PROMPT)
     )
+    """The LLM chain used to parse the response."""
 
     def _run(self, text: str) -> str:
         try:
@@ -265,7 +288,7 @@ def create_openapi_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Instantiate API planner and controller for a given spec.
+    """Instantiate OpenAI API planner and controller for a given spec.
 
     Inject credentials via requests_wrapper.
 

langchain/agents/agent_toolkits/openapi/toolkit.py

@@ -23,7 +23,7 @@ from langchain.tools.requests.tool import (
 
 
 class RequestsToolkit(BaseToolkit):
-    """Toolkit for making requests."""
+    """Toolkit for making REST requests."""
 
     requests_wrapper: TextRequestsWrapper
 

langchain/agents/agent_toolkits/playwright/toolkit.py

@@ -32,7 +32,7 @@ else:
 
 
 class PlayWrightBrowserToolkit(BaseToolkit):
-    """Toolkit for web browser tools."""
+    """Toolkit for PlayWright browser tools."""
 
     sync_browser: Optional["SyncBrowser"] = None
     async_browser: Optional["AsyncBrowser"] = None

langchain/agents/agent_toolkits/powerbi/base.py

@@ -30,7 +30,7 @@ def create_pbi_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a pbi agent from an LLM and tools."""
+    """Construct a Power BI agent from an LLM and tools."""
     if toolkit is None:
         if powerbi is None:
             raise ValueError("Must provide either a toolkit or powerbi dataset")

langchain/agents/agent_toolkits/powerbi/toolkit.py

@@ -29,7 +29,7 @@ from langchain.utilities.powerbi import PowerBIDataset
 
 
 class PowerBIToolkit(BaseToolkit):
-    """Toolkit for interacting with PowerBI dataset."""
+    """Toolkit for interacting with Power BI dataset."""
 
     powerbi: PowerBIDataset = Field(exclude=True)
     llm: Union[BaseLanguageModel, BaseChatModel] = Field(exclude=True)

langchain/agents/agent_toolkits/spark/base.py

@@ -43,7 +43,7 @@ def create_spark_dataframe_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a spark agent from an LLM and dataframe."""
+    """Construct a Spark agent from an LLM and dataframe."""
 
     if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
         raise ValueError("Spark is not installed. run `pip install pyspark`.")

langchain/agents/agent_toolkits/spark_sql/base.py

@@ -27,7 +27,7 @@ def create_spark_sql_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a sql agent from an LLM and tools."""
+    """Construct a Spark SQL agent from an LLM and tools."""
     tools = toolkit.get_tools()
     prefix = prefix.format(top_k=top_k)
     prompt = ZeroShotAgent.create_prompt(

langchain/agents/agent_toolkits/sql/base.py

@@ -40,7 +40,7 @@ def create_sql_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a sql agent from an LLM and tools."""
+    """Construct an SQL agent from an LLM and tools."""
     tools = toolkit.get_tools()
     prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
     agent: BaseSingleActionAgent

langchain/agents/agent_toolkits/sql/toolkit.py

@@ -1,4 +1,4 @@
-"""Toolkit for interacting with a SQL database."""
+"""Toolkit for interacting with an SQL database."""
 from typing import List
 
 from pydantic import Field
@@ -23,7 +23,7 @@ class SQLDatabaseToolkit(BaseToolkit):
 
     @property
     def dialect(self) -> str:
-        """Return string representation of dialect to use."""
+        """Return string representation of SQL dialect to use."""
         return self.db.dialect
 
     class Config:

langchain/agents/agent_toolkits/vectorstore/base.py

@@ -22,7 +22,7 @@ def create_vectorstore_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a vectorstore agent from an LLM and tools."""
+    """Construct a VectorStore agent from an LLM and tools."""
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
     llm_chain = LLMChain(
@@ -50,7 +50,7 @@ def create_vectorstore_router_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a vectorstore router agent from an LLM and tools."""
+    """Construct a VectorStore router agent from an LLM and tools."""
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
     llm_chain = LLMChain(

langchain/agents/agent_toolkits/vectorstore/toolkit.py

@@ -15,7 +15,7 @@ from langchain.vectorstores.base import VectorStore
 
 
 class VectorStoreInfo(BaseModel):
-    """Information about a vectorstore."""
+    """Information about a VectorStore."""
 
     vectorstore: VectorStore = Field(exclude=True)
     name: str
@@ -28,7 +28,7 @@ class VectorStoreInfo(BaseModel):
 
 
 class VectorStoreToolkit(BaseToolkit):
-    """Toolkit for interacting with a vector store."""
+    """Toolkit for interacting with a Vector Store."""
 
     vectorstore_info: VectorStoreInfo = Field(exclude=True)
     llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
@@ -62,7 +62,7 @@ class VectorStoreToolkit(BaseToolkit):
 
 
 class VectorStoreRouterToolkit(BaseToolkit):
-    """Toolkit for routing between vector stores."""
+    """Toolkit for routing between Vector Stores."""
 
     vectorstores: List[VectorStoreInfo] = Field(exclude=True)
     llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))

langchain/agents/agent_toolkits/xorbits/base.py

@@ -35,7 +35,7 @@ def create_xorbits_agent(
         from xorbits import numpy as np
         from xorbits import pandas as pd
     except ImportError:
-        raise ValueError(
+        raise ImportError(
             "Xorbits package not installed, please install with `pip install xorbits`"
         )
 

langchain/agents/chat/base.py

@@ -24,7 +24,10 @@ from langchain.tools.base import BaseTool
 
 
 class ChatAgent(Agent):
+    """Chat Agent."""
+
     output_parser: AgentOutputParser = Field(default_factory=ChatOutputParser)
+    """Output parser for the agent."""
 
     @property
     def observation_prefix(self) -> str:

langchain/agents/chat/output_parser.py

@@ -10,7 +10,10 @@ FINAL_ANSWER_ACTION = "Final Answer:"
 
 
 class ChatOutputParser(AgentOutputParser):
+    """Output parser for the chat agent."""
+
     pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL)
+    """Regex pattern to parse the output."""
 
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS

langchain/agents/conversational/base.py

@@ -18,10 +18,12 @@ from langchain.tools.base import BaseTool
 
 
 class ConversationalAgent(Agent):
-    """An agent designed to hold a conversation in addition to using tools."""
+    """An agent that holds a conversation in addition to using tools."""
 
     ai_prefix: str = "AI"
+    """Prefix to use before AI output."""
     output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
+    """Output parser for the agent."""
 
     @classmethod
     def _get_default_output_parser(
@@ -55,7 +57,7 @@ class ConversationalAgent(Agent):
         human_prefix: str = "Human",
         input_variables: Optional[List[str]] = None,
     ) -> PromptTemplate:
-        """Create prompt in the style of the zero shot agent.
+        """Create prompt in the style of the zero-shot agent.
 
         Args:
             tools: List of tools the agent will have access to, used to format the

langchain/agents/conversational/output_parser.py

@@ -7,7 +7,10 @@ from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
 class ConvoOutputParser(AgentOutputParser):
+    """Output parser for the conversational agent."""
+
     ai_prefix: str = "AI"
+    """Prefix to use before AI output."""
 
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS

langchain/agents/conversational_chat/output_parser.py

@@ -9,6 +9,8 @@ from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
 class ConvoOutputParser(AgentOutputParser):
+    """Output parser for the conversational agent."""
+
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS
 

langchain/agents/load_tools.py

@@ -421,7 +421,7 @@ def load_tools(
 
     Args:
         tool_names: name of tools to load.
-        llm: Optional language model, may be needed to initialize certain tools.
+        llm: An optional language model, may be needed to initialize certain tools.
         callbacks: Optional callback manager or list of callback handlers.
             If not provided, default global callback manager will be used.
 

langchain/agents/loading.py

@@ -36,7 +36,17 @@ def load_agent_from_config(
     tools: Optional[List[Tool]] = None,
     **kwargs: Any,
 ) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
-    """Load agent from Config Dict."""
+    """Load agent from Config Dict.
+
+    Args:
+        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.
+
+    Returns:
+        An agent executor.
+    """
     if "_type" not in config:
         raise ValueError("Must specify an agent Type in config")
     load_from_tools = config.pop("load_from_llm_and_tools", False)
@@ -78,7 +88,15 @@ def load_agent_from_config(
 def load_agent(
     path: Union[str, Path], **kwargs: Any
 ) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
-    """Unified method for loading a agent from LangChainHub or local fs."""
+    """Unified method for loading an agent from LangChainHub or local fs.
+
+    Args:
+        path: Path to the agent file.
+        **kwargs: Additional key word arguments passed to the agent executor.
+
+    Returns:
+        An agent executor.
+    """
     if hub_result := try_load_from_hub(
         path, _load_agent_from_file, "agents", {"json", "yaml"}
     ):

langchain/agents/mrkl/output_parser.py

@@ -9,6 +9,8 @@ FINAL_ANSWER_ACTION = "Final Answer:"
 
 
 class MRKLOutputParser(AgentOutputParser):
+    """MRKL Output parser for the chat agent."""
+
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS
 

langchain/agents/react/base.py

@@ -28,7 +28,7 @@ class ReActDocstoreAgent(Agent):
 
     @property
     def _agent_type(self) -> str:
-        """Return Identifier of agent type."""
+        """Return Identifier of an agent type."""
         return AgentType.REACT_DOCSTORE
 
     @classmethod

langchain/agents/react/output_parser.py

@@ -6,6 +6,8 @@ from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
 class ReActOutputParser(AgentOutputParser):
+    """Output parser for the ReAct agent."""
+
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         action_prefix = "Action: "
         if not text.strip().split("\n")[-1].startswith(action_prefix):

langchain/agents/schema.py

@@ -5,6 +5,8 @@ from langchain.schema import AgentAction
 
 
 class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
+    """Chat prompt template for the agent scratchpad."""
+
     def _construct_agent_scratchpad(
         self, intermediate_steps: List[Tuple[AgentAction, str]]
     ) -> str:

langchain/agents/self_ask_with_search/base.py

@@ -27,7 +27,7 @@ class SelfAskWithSearchAgent(Agent):
 
     @property
     def _agent_type(self) -> str:
-        """Return Identifier of agent type."""
+        """Return Identifier of an agent type."""
         return AgentType.SELF_ASK_WITH_SEARCH
 
     @classmethod
@@ -75,7 +75,7 @@ class SelfAskWithSearchChain(AgentExecutor):
         search_chain: Union[GoogleSerperAPIWrapper, SerpAPIWrapper],
         **kwargs: Any,
     ):
-        """Initialize with just an LLM and a search chain."""
+        """Initialize only with an LLM and a search chain."""
         search_tool = Tool(
             name="Intermediate Answer",
             func=search_chain.run,

langchain/agents/self_ask_with_search/output_parser.py

@@ -5,6 +5,8 @@ from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
 class SelfAskOutputParser(AgentOutputParser):
+    """Output parser for the self-ask agent."""
+
     followups: Sequence[str] = ("Follow up:", "Followup:")
     finish_string: str = "So the final answer is: "
 

langchain/agents/structured_chat/base.py

@@ -23,9 +23,12 @@ HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
 
 
 class StructuredChatAgent(Agent):
+    """Structured Chat Agent."""
+
     output_parser: AgentOutputParser = Field(
         default_factory=StructuredChatOutputParserWithRetries
     )
+    """Output parser for the agent."""
 
     @property
     def observation_prefix(self) -> str:

langchain/agents/structured_chat/output_parser.py

@@ -17,6 +17,8 @@ logger = logging.getLogger(__name__)
 
 
 class StructuredChatOutputParser(AgentOutputParser):
+    """Output parser for the structured chat agent."""
+
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS
 
@@ -46,8 +48,12 @@ class StructuredChatOutputParser(AgentOutputParser):
 
 
 class StructuredChatOutputParserWithRetries(AgentOutputParser):
+    """Output parser with retries for the structured chat agent."""
+
     base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
+    """The base parser to use."""
     output_fixing_parser: Optional[OutputFixingParser] = None
+    """The output fixing parser to use."""
 
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS

langchain/agents/tools.py

@@ -12,7 +12,9 @@ class InvalidTool(BaseTool):
     """Tool that is run when invalid tool name is encountered by agent."""
 
     name = "invalid_tool"
+    """Name of the tool."""
     description = "Called when tool name is invalid."
+    """Description of the tool."""
 
     def _run(
         self, tool_name: str, run_manager: Optional[CallbackManagerForToolRun] = None

langchain/schema/output_parser.py

@@ -27,7 +27,7 @@ class BaseLLMOutputParser(Serializable, ABC, Generic[T]):
 
 
 class BaseOutputParser(BaseLLMOutputParser, ABC, Generic[T]):
-    """Class to parse the output of an LLM call.
+    """Base class to parse the output of an LLM call.
 
     Output parsers help structure language model responses.