Mv plan and execute to experimental (#4459)

docs/modules/agents/plan_and_execute.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.agents import PlanAndExecute, load_agent_executor, load_chat_planner\n",
     "from langchain import SerpAPIWrapper\n",
     "from langchain.agents.tools import Tool\n",
     "from langchain import LLMMathChain"
@@ -54,7 +54,6 @@
    "source": [
     "search = SerpAPIWrapper()\n",
     "llm = OpenAI(temperature=0)\n",
-    "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
@@ -355,7 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

langchain/agents/__init__.py

@@ -26,11 +26,6 @@ from langchain.agents.initialize import initialize_agent
 from langchain.agents.load_tools import get_all_tool_names, load_tools
 from langchain.agents.loading import load_agent
 from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
-from langchain.agents.plan_and_execute.agent_executor import PlanAndExecute
-from langchain.agents.plan_and_execute.executors.agent_executor import (
-    load_agent_executor,
-)
-from langchain.agents.plan_and_execute.planners.chat_planner import load_chat_planner
 from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
 from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
 from langchain.agents.structured_chat.base import StructuredChatAgent
@@ -68,7 +63,4 @@ __all__ = [
     "load_agent",
     "load_tools",
     "tool",
-    "PlanAndExecute",
-    "load_chat_planner",
-    "load_agent_executor",
 ]

langchain/agents/plan_and_execute/__init__.py

@@ -1,7 +0,0 @@
-from langchain.agents.plan_and_execute.agent_executor import PlanAndExecute
-from langchain.agents.plan_and_execute.executors.agent_executor import (
-    load_agent_executor,
-)
-from langchain.agents.plan_and_execute.planners.chat_planner import load_chat_planner
-
-__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]

langchain/experimental/__init__.py

@@ -2,5 +2,18 @@ from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
 from langchain.experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
 from langchain.experimental.generative_agents.generative_agent import GenerativeAgent
 from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
+from langchain.experimental.plan_and_execute import (
+    PlanAndExecute,
+    load_agent_executor,
+    load_chat_planner,
+)
 
-__all__ = ["BabyAGI", "AutoGPT", "GenerativeAgent", "GenerativeAgentMemory"]
+__all__ = [
+    "BabyAGI",
+    "AutoGPT",
+    "GenerativeAgent",
+    "GenerativeAgentMemory",
+    "PlanAndExecute",
+    "load_agent_executor",
+    "load_chat_planner",
+]

langchain/experimental/plan_and_execute/__init__.py

@@ -0,0 +1,9 @@
+from langchain.experimental.plan_and_execute.agent_executor import PlanAndExecute
+from langchain.experimental.plan_and_execute.executors.agent_executor import (
+    load_agent_executor,
+)
+from langchain.experimental.plan_and_execute.planners.chat_planner import (
+    load_chat_planner,
+)
+
+__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]

langchain/experimental/plan_and_execute/agent_executor.py

@@ -2,14 +2,14 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Field
 
-from langchain.agents.plan_and_execute.executors.base import BaseExecutor
-from langchain.agents.plan_and_execute.planners.base import BasePlanner
-from langchain.agents.plan_and_execute.schema import (
+from langchain.callbacks.manager import 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
+from langchain.experimental.plan_and_execute.schema import (
     BaseStepContainer,
     ListStepContainer,
 )
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain.chains.base import Chain
 
 
 class PlanAndExecute(Chain):

langchain/experimental/plan_and_execute/executors/agent_executor.py

@@ -1,9 +1,9 @@
 from typing import List
 
 from langchain.agents.agent import AgentExecutor
-from langchain.agents.plan_and_execute.executors.base import ChainExecutor
 from langchain.agents.structured_chat.base import StructuredChatAgent
 from langchain.base_language import BaseLanguageModel
+from langchain.experimental.plan_and_execute.executors.base import ChainExecutor
 from langchain.tools import BaseTool
 
 HUMAN_MESSAGE_TEMPLATE = """Previous steps: {previous_steps}

langchain/experimental/plan_and_execute/executors/base.py

@@ -3,9 +3,9 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from langchain.agents.plan_and_execute.schema import StepResponse
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.base import Chain
+from langchain.experimental.plan_and_execute.schema import StepResponse
 
 
 class BaseExecutor(BaseModel):

langchain/experimental/plan_and_execute/planners/base.py

@@ -3,9 +3,9 @@ from typing import Any, List, Optional
 
 from pydantic import BaseModel
 
-from langchain.agents.plan_and_execute.schema import Plan, PlanOutputParser
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
+from langchain.experimental.plan_and_execute.schema import Plan, PlanOutputParser
 
 
 class BasePlanner(BaseModel):

langchain/experimental/plan_and_execute/planners/chat_planner.py

@@ -1,9 +1,13 @@
 import re
 
-from langchain.agents.plan_and_execute.planners.base import LLMPlanner
-from langchain.agents.plan_and_execute.schema import Plan, PlanOutputParser, Step
 from langchain.base_language import BaseLanguageModel
 from langchain.chains import LLMChain
+from langchain.experimental.plan_and_execute.planners.base import LLMPlanner
+from langchain.experimental.plan_and_execute.schema import (
+    Plan,
+    PlanOutputParser,
+    Step,
+)
 from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
 from langchain.schema import SystemMessage
 

tests/unit_tests/agents/test_public_api.py

@@ -32,9 +32,6 @@ _EXPECTED = [
     "load_agent",
     "load_tools",
     "tool",
-    "PlanAndExecute",
-    "load_chat_planner",
-    "load_agent_executor",
 ]