experimental: docstrings update (#18048)

Added missed docstrings. Formatted docsctrings to the consistent format.

libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py

@@ -26,7 +26,7 @@
 
 
 class AutoGPT:
-    """Agent class for interacting with Auto-GPT."""
+    """Agent for interacting with AutoGPT."""
 
     def __init__(
         self,

libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt_generator.py

@@ -7,7 +7,7 @@
 
 
 class PromptGenerator:
-    """A class for generating custom prompt strings.
+    """Generator of custom prompt strings.
 
     Does this based on constraints, commands, resources, and performance evaluations.
     """

libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/hugginggpt.py

@@ -15,6 +15,8 @@
 
 
 class HuggingGPT:
+    """Agent for interacting with HuggingGPT."""
+
     def __init__(self, llm: BaseLanguageModel, tools: List[BaseTool]):
         self.llm = llm
         self.tools = tools

libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/repsonse_generator.py

@@ -25,6 +25,8 @@ def from_llm(cls, llm: BaseLanguageModel, verbose: bool = True) -> LLMChain:
 
 
 class ResponseGenerator:
+    """Generates a response based on the input."""
+
     def __init__(self, llm_chain: LLMChain, stop: Optional[List] = None):
         self.llm_chain = llm_chain
         self.stop = stop
@@ -36,6 +38,8 @@ def generate(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) ->
 
 
 def load_response_generator(llm: BaseLanguageModel) -> ResponseGenerator:
+    """Load the ResponseGenerator."""
+
     llm_chain = ResponseGenerationChain.from_llm(llm)
     return ResponseGenerator(
         llm_chain=llm_chain,

libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py

@@ -9,6 +9,8 @@
 
 
 class Task:
+    """Task to be executed."""
+
     def __init__(self, task: str, id: int, dep: List[int], args: Dict, tool: BaseTool):
         self.task = task
         self.id = id
@@ -74,7 +76,7 @@ def run(self) -> str:
 
 
 class TaskExecutor:
-    """Load tools to execute tasks."""
+    """Load tools and execute tasks."""
 
     def __init__(self, plan: Plan):
         self.plan = plan

libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_planner.py

@@ -76,6 +76,8 @@ def from_llm(
 
 
 class Step:
+    """A step in the plan."""
+
     def __init__(
         self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool
     ):
@@ -87,6 +89,8 @@ def __init__(
 
 
 class Plan:
+    """A plan to execute."""
+
     def __init__(self, steps: List[Step]):
         self.steps = steps
 
@@ -98,6 +102,8 @@ def __repr__(self) -> str:
 
 
 class BasePlanner(BaseModel):
+    """Base class for a planner."""
+
     @abstractmethod
     def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
         """Given input, decide what to do."""
@@ -106,11 +112,22 @@ def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan
     async def aplan(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
     ) -> Plan:
-        """Given input, decide what to do."""
+        """Asynchronous Given input, decide what to do."""
 
 
 class PlanningOutputParser(BaseModel):
+    """Parses the output of the planning stage."""
+
     def parse(self, text: str, hf_tools: List[BaseTool]) -> Plan:
+        """Parse the output of the planning stage.
+
+        Args:
+            text: The output of the planning stage.
+            hf_tools: The tools available.
+
+        Returns:
+            The plan.
+        """
         steps = []
         for v in json.loads(re.findall(r"\[.*\]", text)[0]):
             choose_tool = None
@@ -124,6 +141,8 @@ def parse(self, text: str, hf_tools: List[BaseTool]) -> Plan:
 
 
 class TaskPlanner(BasePlanner):
+    """Planner for tasks."""
+
     llm_chain: LLMChain
     output_parser: PlanningOutputParser
     stop: Optional[List] = None
@@ -139,7 +158,7 @@ def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan
     async def aplan(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
     ) -> Plan:
-        """Given input, decided what to do."""
+        """Asynchronous Given input, decided what to do."""
         inputs["hf_tools"] = [
             f"{tool.name}: {tool.description}" for tool in inputs["hf_tools"]
         ]
@@ -150,5 +169,7 @@ async def aplan(
 
 
 def load_chat_planner(llm: BaseLanguageModel) -> TaskPlanner:
+    """Load the chat planner."""
+
     llm_chain = TaskPlaningChain.from_llm(llm)
     return TaskPlanner(llm_chain=llm_chain, output_parser=PlanningOutputParser())

libs/experimental/langchain_experimental/chat_models/llm_wrapper.py

@@ -24,6 +24,8 @@
 
 
 class ChatWrapper(BaseChatModel):
+    """Wrapper for chat LLMs."""
+
     llm: LLM
     sys_beg: str
     sys_end: str
@@ -130,6 +132,8 @@ def _to_chat_result(llm_result: LLMResult) -> ChatResult:
 
 
 class Llama2Chat(ChatWrapper):
+    """Wrapper for Llama-2-chat model."""
+
     @property
     def _llm_type(self) -> str:
         return "llama-2-chat"
@@ -145,6 +149,8 @@ def _llm_type(self) -> str:
 
 
 class Orca(ChatWrapper):
+    """Wrapper for Orca-style models."""
+
     @property
     def _llm_type(self) -> str:
         return "orca-style"
@@ -158,6 +164,8 @@ def _llm_type(self) -> str:
 
 
 class Vicuna(ChatWrapper):
+    """Wrapper for Vicuna-style models."""
+
     @property
     def _llm_type(self) -> str:
         return "vicuna-style"

libs/experimental/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.py

@@ -14,7 +14,10 @@
 
 
 class AmazonComprehendModerationChain(Chain):
-    """A subclass of Chain, designed to apply moderation to LLMs."""
+    """Moderation Chain, based on `Amazon Comprehend` service.
+
+    See more at https://aws.amazon.com/comprehend/
+    """
 
     output_key: str = "output"  #: :meta private:
     """Key used to fetch/store the output in data containers. Defaults to `output`"""
@@ -54,7 +57,7 @@ class AmazonComprehendModerationChain(Chain):
     @root_validator(pre=True)
     def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """
-        Creates an Amazon Comprehend client
+        Creates an Amazon Comprehend client.
 
         Args:
             values (Dict[str, Any]): A dictionary containing configuration values.

libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py

@@ -13,6 +13,8 @@
 
 
 class BaseModeration:
+    """Base class for moderation."""
+
     def __init__(
         self,
         client: Any,
@@ -109,6 +111,8 @@ def _log_message_for_verbose(self, message: str) -> None:
             self.run_manager.on_text(message)
 
     def moderate(self, prompt: Any) -> str:
+        """Moderate the input prompt."""
+
         from langchain_experimental.comprehend_moderation.base_moderation_config import (  # noqa: E501
             ModerationPiiConfig,
             ModerationPromptSafetyConfig,

libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py

@@ -2,6 +2,8 @@
 
 
 class BaseModerationCallbackHandler:
+    """Base class for moderation callback handlers."""
+
     def __init__(self) -> None:
         if (
             self._is_method_unchanged(

libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py

@@ -4,6 +4,8 @@
 
 
 class ModerationPiiConfig(BaseModel):
+    """Configuration for PII moderation filter."""
+
     threshold: float = 0.5
     """Threshold for PII confidence score, defaults to 0.5 i.e. 50%"""
 
@@ -21,6 +23,8 @@ class ModerationPiiConfig(BaseModel):
 
 
 class ModerationToxicityConfig(BaseModel):
+    """Configuration for Toxicity moderation filter."""
+
     threshold: float = 0.5
     """Threshold for Toxic label confidence score, defaults to 0.5 i.e. 50%"""
 
@@ -29,6 +33,8 @@ class ModerationToxicityConfig(BaseModel):
 
 
 class ModerationPromptSafetyConfig(BaseModel):
+    """Configuration for Prompt Safety moderation filter."""
+
     threshold: float = 0.5
     """
     Threshold for Prompt Safety classification
@@ -37,6 +43,8 @@ class ModerationPromptSafetyConfig(BaseModel):
 
 
 class BaseModerationConfig(BaseModel):
+    """Base configuration settings for moderation."""
+
     filters: List[
         Union[
             ModerationPiiConfig, ModerationToxicityConfig, ModerationPromptSafetyConfig

libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_exceptions.py

@@ -27,7 +27,7 @@ def __init__(
 
 
 class ModerationPromptSafetyError(Exception):
-    """Exception raised if Intention entities are detected.
+    """Exception raised if Unsafe prompts are detected.
 
     Attributes:
         message -- explanation of the error

libs/experimental/langchain_experimental/comprehend_moderation/pii.py

@@ -7,6 +7,8 @@
 
 
 class ComprehendPII:
+    """Class to handle Personally Identifiable Information (PII) moderation."""
+
     def __init__(
         self,
         client: Any,

libs/experimental/langchain_experimental/comprehend_moderation/prompt_safety.py

@@ -7,6 +7,8 @@
 
 
 class ComprehendPromptSafety:
+    """Class to handle prompt safety moderation."""
+
     def __init__(
         self,
         client: Any,

libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py

@@ -8,6 +8,8 @@
 
 
 class ComprehendToxicity:
+    """Class to handle toxicity moderation."""
+
     def __init__(
         self,
         client: Any,

libs/experimental/langchain_experimental/cpal/base.py

@@ -105,7 +105,7 @@ def _call(
 
 
 class NarrativeChain(_BaseStoryElementChain):
-    """Decompose the narrative into its story elements
+    """Decompose the narrative into its story elements.
 
     - causal model
     - query

libs/experimental/langchain_experimental/cpal/models.py

@@ -17,7 +17,7 @@
 
 class NarrativeModel(BaseModel):
     """
-    Represent the narrative input as three story elements.
+    Narrative input as three story elements.
     """
 
     story_outcome_question: str
@@ -33,6 +33,8 @@ def empty_str_to_none(cls, v: str) -> Union[str, None]:
 
 
 class EntityModel(BaseModel):
+    """Entity in the story."""
+
     name: str = Field(description="entity name")
     code: str = Field(description="entity actions")
     value: float = Field(description="entity initial value")
@@ -51,14 +53,17 @@ def lower_case_name(cls, v: str) -> str:
 
 
 class CausalModel(BaseModel):
+    """Casual data."""
+
     attribute: str = Field(description="name of the attribute to be calculated")
     entities: List[EntityModel] = Field(description="entities in the story")
 
     # TODO: root validate each `entity.depends_on` using system's entity names
 
 
 class EntitySettingModel(BaseModel):
-    """
+    """Entity initial conditions.
+
     Initial conditions for an entity
 
     {"name": "bud", "attribute": "pet_count", "value": 12}
@@ -75,7 +80,8 @@ def lower_case_transform(cls, v: str) -> str:
 
 
 class SystemSettingModel(BaseModel):
-    """
+    """System initial conditions.
+
     Initial global conditions for the system.
 
     {"parameter": "interest_rate", "value": .05}
@@ -86,8 +92,7 @@ class SystemSettingModel(BaseModel):
 
 
 class InterventionModel(BaseModel):
-    """
-    aka initial conditions
+    """Intervention data of the story aka initial conditions.
 
     >>> intervention.dict()
     {
@@ -110,7 +115,9 @@ def lower_case_name(cls, v: str) -> Union[str, None]:
 
 
 class QueryModel(BaseModel):
-    """translate a question about the story outcome into a programmatic expression"""
+    """Query data of the story.
+
+    translate a question about the story outcome into a programmatic expression"""
 
     question: str = Field(alias=Constant.narrative_input.value)  # input
     expression: str  # output, part of llm completion
@@ -119,11 +126,15 @@ class QueryModel(BaseModel):
 
 
 class ResultModel(BaseModel):
+    """Result of the story query."""
+
     question: str = Field(alias=Constant.narrative_input.value)  # input
     _result_table: str = PrivateAttr()  # result of the executed query
 
 
 class StoryModel(BaseModel):
+    """Story data."""
+
     causal_operations: Any = Field(required=True)
     intervention: Any = Field(required=True)
     query: Any = Field(required=True)