Added async support to PlanAndExecute Chain (#8239)

- Description: Adds async support to the PlanAndExecute Chain

Maintainer responsibilities:
  - Async: @agola11

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py

@@ -1,6 +1,9 @@
 from typing import Any, Dict, List, Optional
 
-from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
 from langchain.chains.base import Chain
 from pydantic import Field
 
@@ -58,3 +61,35 @@ class PlanAndExecute(Chain):
                 )
             self.step_container.add_step(step, response)
         return {self.output_key: self.step_container.get_final_response()}
+
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        plan = await self.planner.aplan(
+            inputs,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )
+        if run_manager:
+            await run_manager.on_text(str(plan), verbose=self.verbose)
+        for step in plan.steps:
+            _new_inputs = {
+                "previous_steps": self.step_container,
+                "current_step": step,
+                "objective": inputs[self.input_key],
+            }
+            new_inputs = {**_new_inputs, **inputs}
+            response = await self.executor.astep(
+                new_inputs,
+                callbacks=run_manager.get_child() if run_manager else None,
+            )
+            if run_manager:
+                await run_manager.on_text(
+                    f"*****\n\nStep: {step.value}", verbose=self.verbose
+                )
+                await run_manager.on_text(
+                    f"\n\nResponse: {response.response}", verbose=self.verbose
+                )
+            self.step_container.add_step(step, response)
+        return {self.output_key: self.step_container.get_final_response()}

libs/langchain/langchain/experimental/plan_and_execute/agent_executor.py

@@ -2,7 +2,10 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Field
 
-from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
 from langchain.chains.base import Chain
 from langchain.experimental.plan_and_execute.executors.base import BaseExecutor
 from langchain.experimental.plan_and_execute.planners.base import BasePlanner
@@ -63,3 +66,35 @@ class PlanAndExecute(Chain):
                 )
             self.step_container.add_step(step, response)
         return {self.output_key: self.step_container.get_final_response()}
+
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        plan = await self.planner.aplan(
+            inputs,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )
+        if run_manager:
+            await run_manager.on_text(str(plan), verbose=self.verbose)
+        for step in plan.steps:
+            _new_inputs = {
+                "previous_steps": self.step_container,
+                "current_step": step,
+                "objective": inputs[self.input_key],
+            }
+            new_inputs = {**_new_inputs, **inputs}
+            response = await self.executor.astep(
+                new_inputs,
+                callbacks=run_manager.get_child() if run_manager else None,
+            )
+            if run_manager:
+                await run_manager.on_text(
+                    f"*****\n\nStep: {step.value}", verbose=self.verbose
+                )
+                await run_manager.on_text(
+                    f"\n\nResponse: {response.response}", verbose=self.verbose
+                )
+            self.step_container.add_step(step, response)
+        return {self.output_key: self.step_container.get_final_response()}