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from typing import Any, List, Optional, Sequence, Tuple
from langchain_core.agents import AgentAction
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.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.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.tools.base import BaseTool
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:"]
@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)
@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
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