forked from Archives/langchain
Mv plan and execute to experimental (#4459)
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commit
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@ -30,8 +30,8 @@
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.agents import PlanAndExecute, load_agent_executor, load_chat_planner\n",
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"from langchain import SerpAPIWrapper\n",
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"from langchain.agents.tools import Tool\n",
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"from langchain import LLMMathChain"
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@ -54,7 +54,6 @@
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"source": [
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"search = SerpAPIWrapper()\n",
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"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIWrapper()\n",
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"llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
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"tools = [\n",
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" Tool(\n",
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@ -355,7 +354,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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@ -26,11 +26,6 @@ from langchain.agents.initialize import initialize_agent
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from langchain.agents.load_tools import get_all_tool_names, load_tools
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from langchain.agents.loading import load_agent
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from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
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from langchain.agents.plan_and_execute.agent_executor import PlanAndExecute
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from langchain.agents.plan_and_execute.executors.agent_executor import (
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load_agent_executor,
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)
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from langchain.agents.plan_and_execute.planners.chat_planner import load_chat_planner
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from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
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from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
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from langchain.agents.structured_chat.base import StructuredChatAgent
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@ -68,7 +63,4 @@ __all__ = [
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"load_agent",
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"load_tools",
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"tool",
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"PlanAndExecute",
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"load_chat_planner",
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"load_agent_executor",
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]
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@ -1,7 +0,0 @@
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from langchain.agents.plan_and_execute.agent_executor import PlanAndExecute
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from langchain.agents.plan_and_execute.executors.agent_executor import (
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load_agent_executor,
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)
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from langchain.agents.plan_and_execute.planners.chat_planner import load_chat_planner
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__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]
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@ -2,5 +2,18 @@ from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain.experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain.experimental.generative_agents.generative_agent import GenerativeAgent
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from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
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from langchain.experimental.plan_and_execute import (
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PlanAndExecute,
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load_agent_executor,
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load_chat_planner,
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)
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__all__ = ["BabyAGI", "AutoGPT", "GenerativeAgent", "GenerativeAgentMemory"]
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__all__ = [
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"BabyAGI",
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"AutoGPT",
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"GenerativeAgent",
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"GenerativeAgentMemory",
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"PlanAndExecute",
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"load_agent_executor",
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"load_chat_planner",
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]
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9
langchain/experimental/plan_and_execute/__init__.py
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9
langchain/experimental/plan_and_execute/__init__.py
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@ -0,0 +1,9 @@
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from langchain.experimental.plan_and_execute.agent_executor import PlanAndExecute
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from langchain.experimental.plan_and_execute.executors.agent_executor import (
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load_agent_executor,
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)
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from langchain.experimental.plan_and_execute.planners.chat_planner import (
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load_chat_planner,
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)
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__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]
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@ -2,14 +2,14 @@ from typing import Any, Dict, List, Optional
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from pydantic import Field
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from langchain.agents.plan_and_execute.executors.base import BaseExecutor
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from langchain.agents.plan_and_execute.planners.base import BasePlanner
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from langchain.agents.plan_and_execute.schema import (
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.experimental.plan_and_execute.executors.base import BaseExecutor
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from langchain.experimental.plan_and_execute.planners.base import BasePlanner
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from langchain.experimental.plan_and_execute.schema import (
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BaseStepContainer,
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ListStepContainer,
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)
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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class PlanAndExecute(Chain):
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@ -1,9 +1,9 @@
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from typing import List
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.plan_and_execute.executors.base import ChainExecutor
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from langchain.agents.structured_chat.base import StructuredChatAgent
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from langchain.base_language import BaseLanguageModel
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from langchain.experimental.plan_and_execute.executors.base import ChainExecutor
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from langchain.tools import BaseTool
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HUMAN_MESSAGE_TEMPLATE = """Previous steps: {previous_steps}
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@ -3,9 +3,9 @@ from typing import Any
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from pydantic import BaseModel
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from langchain.agents.plan_and_execute.schema import StepResponse
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from langchain.callbacks.manager import Callbacks
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from langchain.chains.base import Chain
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from langchain.experimental.plan_and_execute.schema import StepResponse
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class BaseExecutor(BaseModel):
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@ -3,9 +3,9 @@ from typing import Any, List, Optional
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from pydantic import BaseModel
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from langchain.agents.plan_and_execute.schema import Plan, PlanOutputParser
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from langchain.callbacks.manager import Callbacks
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from langchain.chains.llm import LLMChain
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from langchain.experimental.plan_and_execute.schema import Plan, PlanOutputParser
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class BasePlanner(BaseModel):
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@ -1,9 +1,13 @@
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import re
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from langchain.agents.plan_and_execute.planners.base import LLMPlanner
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from langchain.agents.plan_and_execute.schema import Plan, PlanOutputParser, Step
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from langchain.base_language import BaseLanguageModel
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from langchain.chains import LLMChain
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from langchain.experimental.plan_and_execute.planners.base import LLMPlanner
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from langchain.experimental.plan_and_execute.schema import (
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Plan,
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PlanOutputParser,
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Step,
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)
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from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.schema import SystemMessage
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@ -32,9 +32,6 @@ _EXPECTED = [
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"load_agent",
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"load_tools",
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"tool",
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"PlanAndExecute",
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"load_chat_planner",
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"load_agent_executor",
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]
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