update experimental (#8402)

some changes were made to experimental, porting them over

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

@@ -6,6 +6,8 @@ from pydantic import Field
 
 
 class AutoGPTMemory(BaseChatMemory):
+    """Memory for AutoGPT."""
+
     retriever: VectorStoreRetriever = Field(exclude=True)
     """VectorStoreRetriever object to connect to."""
 

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

@@ -7,20 +7,18 @@ from langchain.schema import BaseOutputParser
 
 
 class AutoGPTAction(NamedTuple):
-    """Action for AutoGPT."""
+    """Action returned by AutoGPTOutputParser."""
 
     name: str
-    """Name of the action."""
     args: Dict
-    """Arguments for the action."""
 
 
 class BaseAutoGPTOutputParser(BaseOutputParser):
-    """Base class for AutoGPT output parsers."""
+    """Base Output parser for AutoGPT."""
 
     @abstractmethod
     def parse(self, text: str) -> AutoGPTAction:
-        """Parse text and return AutoGPTAction"""
+        """Return AutoGPTAction"""
 
 
 def preprocess_json_input(input_str: str) -> str:

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

@@ -123,7 +123,7 @@ class PromptGenerator:
 
 
 def get_prompt(tools: List[BaseTool]) -> str:
-    """Generate a prompt string.
+    """Generates a prompt string.
 
     It includes various constraints, commands, resources, and performance evaluations.
 

libs/experimental/langchain_experimental/autonomous_agents/baby_agi/task_creation.py

@@ -4,7 +4,7 @@ from langchain.schema.language_model import BaseLanguageModel
 
 
 class TaskCreationChain(LLMChain):
-    """Chain to generates tasks."""
+    """Chain generating tasks."""
 
     @classmethod
     def from_llm(cls, llm: BaseLanguageModel, verbose: bool = True) -> LLMChain:

libs/experimental/langchain_experimental/generative_agents/generative_agent.py

@@ -11,11 +11,10 @@ from langchain_experimental.generative_agents.memory import GenerativeAgentMemor
 
 
 class GenerativeAgent(BaseModel):
-    """A character with memory and innate characteristics."""
+    """An Agent as a character with memory and innate characteristics."""
 
     name: str
     """The character's name."""
-
     age: Optional[int] = None
     """The optional age of the character."""
     traits: str = "N/A"
@@ -29,13 +28,10 @@ class GenerativeAgent(BaseModel):
     verbose: bool = False
     summary: str = ""  #: :meta private:
     """Stateful self-summary generated via reflection on the character's memory."""
-
     summary_refresh_seconds: int = 3600  #: :meta private:
     """How frequently to re-generate the summary."""
-
     last_refreshed: datetime = Field(default_factory=datetime.now)  # : :meta private:
     """The last time the character's summary was regenerated."""
-
     daily_summaries: List[str] = Field(default_factory=list)  # : :meta private:
     """Summary of the events in the plan that the agent took."""
 

libs/experimental/langchain_experimental/generative_agents/memory.py

@@ -14,24 +14,21 @@ logger = logging.getLogger(__name__)
 
 
 class GenerativeAgentMemory(BaseMemory):
+    """Memory for the generative agent."""
+
     llm: BaseLanguageModel
     """The core language model."""
-
     memory_retriever: TimeWeightedVectorStoreRetriever
     """The retriever to fetch related memories."""
     verbose: bool = False
-
     reflection_threshold: Optional[float] = None
     """When aggregate_importance exceeds reflection_threshold, stop to reflect."""
-
     current_plan: List[str] = []
     """The current plan of the agent."""
-
     # A weight of 0.15 makes this less important than it
     # would be otherwise, relative to salience and time
     importance_weight: float = 0.15
     """How much weight to assign the memory importance."""
-
     aggregate_importance: float = 0.0  # : :meta private:
     """Track the sum of the 'importance' of recent memories.
 

libs/experimental/langchain_experimental/llms/jsonformer_decoder.py

@@ -17,7 +17,7 @@ def import_jsonformer() -> jsonformer:
     try:
         import jsonformer
     except ImportError:
-        raise ValueError(
+        raise ImportError(
             "Could not import jsonformer python package. "
             "Please install it with `pip install jsonformer`."
         )
@@ -25,6 +25,11 @@ def import_jsonformer() -> jsonformer:
 
 
 class JsonFormer(HuggingFacePipeline):
+    """Jsonformer wrapped LLM using HuggingFace Pipeline API.
+
+    This pipeline is experimental and not yet stable.
+    """
+
     json_schema: dict = Field(..., description="The JSON Schema to complete.")
     max_new_tokens: int = Field(
         default=200, description="Maximum number of new tokens to generate."

libs/experimental/langchain_experimental/llms/rellm_decoder.py

@@ -23,7 +23,7 @@ def import_rellm() -> rellm:
     try:
         import rellm
     except ImportError:
-        raise ValueError(
+        raise ImportError(
             "Could not import rellm python package. "
             "Please install it with `pip install rellm`."
         )
@@ -31,6 +31,8 @@ def import_rellm() -> rellm:
 
 
 class RELLM(HuggingFacePipeline):
+    """RELLM wrapped LLM using HuggingFace Pipeline API."""
+
     regex: RegexPattern = Field(..., description="The structured format to complete.")
     max_new_tokens: int = Field(
         default=200, description="Maximum number of new tokens to generate."

libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py

@@ -16,9 +16,14 @@ from langchain_experimental.plan_and_execute.schema import (
 
 
 class PlanAndExecute(Chain):
+    """Plan and execute a chain of steps."""
+
     planner: BasePlanner
+    """The planner to use."""
     executor: BaseExecutor
+    """The executor to use."""
     step_container: BaseStepContainer = Field(default_factory=ListStepContainer)
+    """The step container to use."""
     input_key: str = "input"
     output_key: str = "output"
 

libs/experimental/langchain_experimental/plan_and_execute/executors/base.py

@@ -9,6 +9,8 @@ from langchain_experimental.plan_and_execute.schema import StepResponse
 
 
 class BaseExecutor(BaseModel):
+    """Base executor."""
+
     @abstractmethod
     def step(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
@@ -19,11 +21,14 @@ class BaseExecutor(BaseModel):
     async def astep(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
     ) -> StepResponse:
-        """Take step."""
+        """Take async step."""
 
 
 class ChainExecutor(BaseExecutor):
+    """Chain executor."""
+
     chain: Chain
+    """The chain to use."""
 
     def step(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any

libs/experimental/langchain_experimental/plan_and_execute/planners/base.py

@@ -9,6 +9,8 @@ from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParse
 
 
 class BasePlanner(BaseModel):
+    """Base planner."""
+
     @abstractmethod
     def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
         """Given input, decide what to do."""
@@ -17,13 +19,18 @@ class BasePlanner(BaseModel):
     async def aplan(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
     ) -> Plan:
-        """Given input, decide what to do."""
+        """Given input, asynchronously decide what to do."""
 
 
 class LLMPlanner(BasePlanner):
+    """LLM planner."""
+
     llm_chain: LLMChain
+    """The LLM chain to use."""
     output_parser: PlanOutputParser
+    """The output parser to use."""
     stop: Optional[List] = None
+    """The stop list to use."""
 
     def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
         """Given input, decide what to do."""
@@ -33,7 +40,7 @@ class LLMPlanner(BasePlanner):
     async def aplan(
         self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
     ) -> Plan:
-        """Given input, decide what to do."""
+        """Given input, asynchronously decide what to do."""
         llm_response = await self.llm_chain.arun(
             **inputs, stop=self.stop, callbacks=callbacks
         )

libs/experimental/langchain_experimental/plan_and_execute/planners/chat_planner.py

@@ -25,6 +25,8 @@ SYSTEM_PROMPT = (
 
 
 class PlanningOutputParser(PlanOutputParser):
+    """Planning output parser."""
+
     def parse(self, text: str) -> Plan:
         steps = [Step(value=v) for v in re.split("\n\s*\d+\. ", text)[1:]]
         return Plan(steps=steps)
@@ -35,6 +37,7 @@ def load_chat_planner(
 ) -> LLMPlanner:
     """
     Load a chat planner.
+
     Args:
         llm: Language model.
         system_prompt: System prompt.

libs/experimental/langchain_experimental/plan_and_execute/schema.py

@@ -6,18 +6,29 @@ from pydantic import BaseModel, Field
 
 
 class Step(BaseModel):
+    """Step."""
+
     value: str
+    """The value."""
 
 
 class Plan(BaseModel):
+    """Plan."""
+
     steps: List[Step]
+    """The steps."""
 
 
 class StepResponse(BaseModel):
+    """Step response."""
+
     response: str
+    """The response."""
 
 
 class BaseStepContainer(BaseModel):
+    """Base step container."""
+
     @abstractmethod
     def add_step(self, step: Step, step_response: StepResponse) -> None:
         """Add step and step response to the container."""
@@ -28,7 +39,10 @@ class BaseStepContainer(BaseModel):
 
 
 class ListStepContainer(BaseStepContainer):
+    """List step container."""
+
     steps: List[Tuple[Step, StepResponse]] = Field(default_factory=list)
+    """The steps."""
 
     def add_step(self, step: Step, step_response: StepResponse) -> None:
         self.steps.append((step, step_response))
@@ -41,6 +55,8 @@ class ListStepContainer(BaseStepContainer):
 
 
 class PlanOutputParser(BaseOutputParser):
+    """Plan output parser."""
+
     @abstractmethod
     def parse(self, text: str) -> Plan:
         """Parse into a plan."""