Source code for langchain.agents.chat.base

from typing import Any, List, Optional, Sequence, Tuple

from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain_core.pydantic_v1 import Field
from langchain_core.tools import BaseTool

from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.chat.output_parser import ChatOutputParser
from langchain.agents.chat.prompt import (
    FORMAT_INSTRUCTIONS,
    HUMAN_MESSAGE,
    SYSTEM_MESSAGE_PREFIX,
    SYSTEM_MESSAGE_SUFFIX,
)
from langchain.agents.utils import validate_tools_single_input
from langchain.chains.llm import LLMChain


[docs]@deprecated("0.1.0", alternative="create_react_agent", removal="0.3.0") class ChatAgent(Agent): """Chat Agent.""" output_parser: AgentOutputParser = Field(default_factory=ChatOutputParser) """Output parser for the agent.""" @property def observation_prefix(self) -> str: """Prefix to append the observation with.""" return "Observation: " @property def llm_prefix(self) -> str: """Prefix to append the llm call with.""" return "Thought:" def _construct_scratchpad( self, intermediate_steps: List[Tuple[AgentAction, str]] ) -> str: agent_scratchpad = super()._construct_scratchpad(intermediate_steps) if not isinstance(agent_scratchpad, str): raise ValueError("agent_scratchpad should be of type string.") if agent_scratchpad: return ( f"This was your previous work " f"(but I haven't seen any of it! I only see what " f"you return as final answer):\n{agent_scratchpad}" ) else: return agent_scratchpad @classmethod def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: return ChatOutputParser() @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: super()._validate_tools(tools) validate_tools_single_input(class_name=cls.__name__, tools=tools) @property def _stop(self) -> List[str]: return ["Observation:"]
[docs] @classmethod def create_prompt( cls, tools: Sequence[BaseTool], system_message_prefix: str = SYSTEM_MESSAGE_PREFIX, system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX, human_message: str = HUMAN_MESSAGE, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, ) -> BasePromptTemplate: tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools]) tool_names = ", ".join([tool.name for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) template = "\n\n".join( [ system_message_prefix, tool_strings, format_instructions, system_message_suffix, ] ) messages = [ SystemMessagePromptTemplate.from_template(template), HumanMessagePromptTemplate.from_template(human_message), ] if input_variables is None: input_variables = ["input", "agent_scratchpad"] return ChatPromptTemplate(input_variables=input_variables, messages=messages) # type: ignore[arg-type]
[docs] @classmethod def from_llm_and_tools( cls, llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, system_message_prefix: str = SYSTEM_MESSAGE_PREFIX, system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX, human_message: str = HUMAN_MESSAGE, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) prompt = cls.create_prompt( tools, system_message_prefix=system_message_prefix, system_message_suffix=system_message_suffix, human_message=human_message, format_instructions=format_instructions, input_variables=input_variables, ) llm_chain = LLMChain( llm=llm, prompt=prompt, callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] _output_parser = output_parser or cls._get_default_output_parser() return cls( llm_chain=llm_chain, allowed_tools=tool_names, output_parser=_output_parser, **kwargs, )
@property def _agent_type(self) -> str: raise ValueError