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54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from typing import Any, List, Optional, Sequence
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from langchain.agents.agent import AgentOutputParser, LLMSingleActionAgent
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from langchain.agents.chat_v2.prompt import (
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FORMAT_INSTRUCTIONS,
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PREFIX,
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SUFFIX,
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ChatOutputParser,
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create_prompt,
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)
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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.schema import BaseLanguageModel
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from langchain.tools import BaseTool
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class ChatAgentV2(LLMSingleActionAgent):
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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format_instructions: str = FORMAT_INSTRUCTIONS,
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input_variables: Optional[List[str]] = None,
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output_parser: Optional[AgentOutputParser] = None,
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stop: Optional[List[str]] = None,
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**kwargs: Any,
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) -> LLMSingleActionAgent:
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"""Construct an agent from an LLM and tools."""
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_stop = stop or ["Observation:"]
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_output_parser = output_parser or ChatOutputParser()
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prompt = create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix,
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format_instructions=format_instructions,
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input_variables=input_variables,
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)
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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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return cls(
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llm_chain=llm_chain, output_parser=_output_parser, stop=_stop, **kwargs
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)
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@property
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def _agent_type(self) -> str:
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raise ValueError
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