Harrison/official pre release (#8106)

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

@@ -0,0 +1,55 @@
+from typing import List
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.structured_chat.base import StructuredChatAgent
+from langchain.schema.language_model import BaseLanguageModel
+from langchain.tools import BaseTool
+
+from langchain_experimental.plan_and_execute.executors.base import ChainExecutor
+
+HUMAN_MESSAGE_TEMPLATE = """Previous steps: {previous_steps}
+
+Current objective: {current_step}
+
+{agent_scratchpad}"""
+
+TASK_PREFIX = """{objective}
+
+"""
+
+
+def load_agent_executor(
+    llm: BaseLanguageModel,
+    tools: List[BaseTool],
+    verbose: bool = False,
+    include_task_in_prompt: bool = False,
+) -> ChainExecutor:
+    """
+    Load an agent executor.
+
+    Args:
+        llm: BaseLanguageModel
+        tools: List[BaseTool]
+        verbose: bool. Defaults to False.
+        include_task_in_prompt: bool. Defaults to False.
+
+    Returns:
+        ChainExecutor
+    """
+    input_variables = ["previous_steps", "current_step", "agent_scratchpad"]
+    template = HUMAN_MESSAGE_TEMPLATE
+
+    if include_task_in_prompt:
+        input_variables.append("objective")
+        template = TASK_PREFIX + template
+
+    agent = StructuredChatAgent.from_llm_and_tools(
+        llm,
+        tools,
+        human_message_template=template,
+        input_variables=input_variables,
+    )
+    agent_executor = AgentExecutor.from_agent_and_tools(
+        agent=agent, tools=tools, verbose=verbose
+    )
+    return ChainExecutor(chain=agent_executor)

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

@@ -0,0 +1,40 @@
+from abc import abstractmethod
+from typing import Any
+
+from langchain.callbacks.manager import Callbacks
+from langchain.chains.base import Chain
+from pydantic import BaseModel
+
+from langchain_experimental.plan_and_execute.schema import StepResponse
+
+
+class BaseExecutor(BaseModel):
+    @abstractmethod
+    def step(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> StepResponse:
+        """Take step."""
+
+    @abstractmethod
+    async def astep(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> StepResponse:
+        """Take step."""
+
+
+class ChainExecutor(BaseExecutor):
+    chain: Chain
+
+    def step(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> StepResponse:
+        """Take step."""
+        response = self.chain.run(**inputs, callbacks=callbacks)
+        return StepResponse(response=response)
+
+    async def astep(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> StepResponse:
+        """Take step."""
+        response = await self.chain.arun(**inputs, callbacks=callbacks)
+        return StepResponse(response=response)