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from typing import Any, List, Optional, Sequence, Tuple |
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from langchain_core._api import deprecated |
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from langchain_core.agents import AgentAction |
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from langchain_core.callbacks import BaseCallbackManager |
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from langchain_core.language_models import BaseLanguageModel |
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from langchain_core.prompts import BasePromptTemplate |
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from langchain_core.prompts.chat import ( |
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ChatPromptTemplate, |
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HumanMessagePromptTemplate, |
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SystemMessagePromptTemplate, |
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) |
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from langchain_core.tools import BaseTool |
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from pydantic import Field |
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from langchain._api.deprecation import AGENT_DEPRECATION_WARNING |
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from langchain.agents.agent import Agent, AgentOutputParser |
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from langchain.agents.chat.output_parser import ChatOutputParser |
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from langchain.agents.chat.prompt import ( |
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FORMAT_INSTRUCTIONS, |
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HUMAN_MESSAGE, |
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SYSTEM_MESSAGE_PREFIX, |
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SYSTEM_MESSAGE_SUFFIX, |
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) |
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from langchain.agents.utils import validate_tools_single_input |
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from langchain.chains.llm import LLMChain |
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@deprecated( |
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"0.1.0", |
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message=AGENT_DEPRECATION_WARNING, |
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removal="1.0", |
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) |
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class ChatAgent(Agent): |
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"""Chat Agent.""" |
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output_parser: AgentOutputParser = Field(default_factory=ChatOutputParser) |
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"""Output parser for the agent.""" |
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@property |
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def observation_prefix(self) -> str: |
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"""Prefix to append the observation with.""" |
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return "Observation: " |
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@property |
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def llm_prefix(self) -> str: |
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"""Prefix to append the llm call with.""" |
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return "Thought:" |
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def _construct_scratchpad( |
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self, intermediate_steps: List[Tuple[AgentAction, str]] |
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) -> str: |
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agent_scratchpad = super()._construct_scratchpad(intermediate_steps) |
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if not isinstance(agent_scratchpad, str): |
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raise ValueError("agent_scratchpad should be of type string.") |
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if agent_scratchpad: |
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return ( |
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f"This was your previous work " |
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f"(but I haven't seen any of it! I only see what " |
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f"you return as final answer):\n{agent_scratchpad}" |
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) |
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else: |
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return agent_scratchpad |
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@classmethod |
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def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: |
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return ChatOutputParser() |
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@classmethod |
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def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: |
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super()._validate_tools(tools) |
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validate_tools_single_input(class_name=cls.__name__, tools=tools) |
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@property |
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def _stop(self) -> List[str]: |
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return ["Observation:"] |
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@classmethod |
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def create_prompt( |
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cls, |
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tools: Sequence[BaseTool], |
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system_message_prefix: str = SYSTEM_MESSAGE_PREFIX, |
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system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX, |
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human_message: str = HUMAN_MESSAGE, |
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format_instructions: str = FORMAT_INSTRUCTIONS, |
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input_variables: Optional[List[str]] = None, |
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) -> BasePromptTemplate: |
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"""Create a prompt from a list of tools. |
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Args: |
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tools: A list of tools. |
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system_message_prefix: The system message prefix. |
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Default is SYSTEM_MESSAGE_PREFIX. |
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system_message_suffix: The system message suffix. |
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Default is SYSTEM_MESSAGE_SUFFIX. |
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human_message: The human message. Default is HUMAN_MESSAGE. |
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format_instructions: The format instructions. |
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Default is FORMAT_INSTRUCTIONS. |
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input_variables: The input variables. Default is None. |
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Returns: |
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A prompt template. |
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""" |
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tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools]) |
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tool_names = ", ".join([tool.name for tool in tools]) |
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format_instructions = format_instructions.format(tool_names=tool_names) |
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template = "\n\n".join( |
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[ |
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system_message_prefix, |
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tool_strings, |
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format_instructions, |
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system_message_suffix, |
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] |
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) |
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messages = [ |
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SystemMessagePromptTemplate.from_template(template), |
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HumanMessagePromptTemplate.from_template(human_message), |
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] |
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if input_variables is None: |
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input_variables = ["input", "agent_scratchpad"] |
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return ChatPromptTemplate(input_variables=input_variables, messages=messages) |
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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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output_parser: Optional[AgentOutputParser] = None, |
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system_message_prefix: str = SYSTEM_MESSAGE_PREFIX, |
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system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX, |
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human_message: str = HUMAN_MESSAGE, |
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format_instructions: str = FORMAT_INSTRUCTIONS, |
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input_variables: Optional[List[str]] = None, |
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**kwargs: Any, |
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) -> Agent: |
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"""Construct an agent from an LLM and tools. |
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Args: |
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llm: The language model. |
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tools: A list of tools. |
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callback_manager: The callback manager. Default is None. |
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output_parser: The output parser. Default is None. |
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system_message_prefix: The system message prefix. |
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Default is SYSTEM_MESSAGE_PREFIX. |
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system_message_suffix: The system message suffix. |
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Default is SYSTEM_MESSAGE_SUFFIX. |
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human_message: The human message. Default is HUMAN_MESSAGE. |
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format_instructions: The format instructions. |
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Default is FORMAT_INSTRUCTIONS. |
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input_variables: The input variables. Default is None. |
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kwargs: Additional keyword arguments. |
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Returns: |
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An agent. |
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""" |
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cls._validate_tools(tools) |
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prompt = cls.create_prompt( |
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tools, |
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system_message_prefix=system_message_prefix, |
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system_message_suffix=system_message_suffix, |
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human_message=human_message, |
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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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tool_names = [tool.name for tool in tools] |
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_output_parser = output_parser or cls._get_default_output_parser() |
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return cls( |
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llm_chain=llm_chain, |
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allowed_tools=tool_names, |
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output_parser=_output_parser, |
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**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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