Harrison/official pre release (#8106)

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

@@ -0,0 +1,40 @@
+from abc import abstractmethod
+from typing import Any, List, Optional
+
+from langchain.callbacks.manager import Callbacks
+from langchain.chains.llm import LLMChain
+from pydantic import BaseModel
+
+from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
+
+
+class BasePlanner(BaseModel):
+    @abstractmethod
+    def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
+        """Given input, decide what to do."""
+
+    @abstractmethod
+    async def aplan(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> Plan:
+        """Given input, decide what to do."""
+
+
+class LLMPlanner(BasePlanner):
+    llm_chain: LLMChain
+    output_parser: PlanOutputParser
+    stop: Optional[List] = None
+
+    def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
+        """Given input, decide what to do."""
+        llm_response = self.llm_chain.run(**inputs, stop=self.stop, callbacks=callbacks)
+        return self.output_parser.parse(llm_response)
+
+    async def aplan(
+        self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
+    ) -> Plan:
+        """Given input, decide what to do."""
+        llm_response = await self.llm_chain.arun(
+            **inputs, stop=self.stop, callbacks=callbacks
+        )
+        return self.output_parser.parse(llm_response)