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8e0d5813c2
Import from core instead. Ran: ```bash git grep -l 'from langchain.schema\.output_parser' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.output_parser/from\ langchain_core.output_parsers/g" git grep -l 'from langchain.schema\.messages' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.messages/from\ langchain_core.messages/g" git grep -l 'from langchain.schema\.document' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.document/from\ langchain_core.documents/g" git grep -l 'from langchain.schema\.runnable' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.runnable/from\ langchain_core.runnables/g" git grep -l 'from langchain.schema\.vectorstore' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.vectorstore/from\ langchain_core.vectorstores/g" git grep -l 'from langchain.schema\.language_model' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.language_model/from\ langchain_core.language_models/g" git grep -l 'from langchain.schema\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.embeddings/from\ langchain_core.embeddings/g" git grep -l 'from langchain.schema\.storage' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.storage/from\ langchain_core.stores/g" git checkout master libs/langchain/tests/unit_tests/schema/ make format cd libs/experimental make format cd ../langchain make format ```
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
"""Python agent."""
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from typing import Any, Dict, Optional
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from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
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from langchain.agents.types import AgentType
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains.llm import LLMChain
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.messages import SystemMessage
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from langchain_experimental.agents.agent_toolkits.python.prompt import PREFIX
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from langchain_experimental.tools.python.tool import PythonREPLTool
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def create_python_agent(
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llm: BaseLanguageModel,
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tool: PythonREPLTool,
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agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
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callback_manager: Optional[BaseCallbackManager] = None,
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verbose: bool = False,
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prefix: str = PREFIX,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Dict[str, Any],
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) -> AgentExecutor:
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"""Construct a python agent from an LLM and tool."""
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tools = [tool]
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agent: BaseSingleActionAgent
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if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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elif agent_type == AgentType.OPENAI_FUNCTIONS:
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system_message = SystemMessage(content=prefix)
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_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
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agent = OpenAIFunctionsAgent(
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llm=llm,
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prompt=_prompt,
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tools=tools,
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callback_manager=callback_manager,
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**kwargs,
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)
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else:
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raise ValueError(f"Agent type {agent_type} not supported at the moment.")
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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verbose=verbose,
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**(agent_executor_kwargs or {}),
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)
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