File size: 6,590 Bytes
f1e6b80 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
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.tools import BaseTool
from pydantic import Field
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
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
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
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:"]
@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:
"""Create a prompt from a list of tools.
Args:
tools: A list of tools.
system_message_prefix: The system message prefix.
Default is SYSTEM_MESSAGE_PREFIX.
system_message_suffix: The system message suffix.
Default is SYSTEM_MESSAGE_SUFFIX.
human_message: The human message. Default is HUMAN_MESSAGE.
format_instructions: The format instructions.
Default is FORMAT_INSTRUCTIONS.
input_variables: The input variables. Default is None.
Returns:
A prompt template.
"""
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]
@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.
Args:
llm: The language model.
tools: A list of tools.
callback_manager: The callback manager. Default is None.
output_parser: The output parser. Default is None.
system_message_prefix: The system message prefix.
Default is SYSTEM_MESSAGE_PREFIX.
system_message_suffix: The system message suffix.
Default is SYSTEM_MESSAGE_SUFFIX.
human_message: The human message. Default is HUMAN_MESSAGE.
format_instructions: The format instructions.
Default is FORMAT_INSTRUCTIONS.
input_variables: The input variables. Default is None.
kwargs: Additional keyword arguments.
Returns:
An agent.
"""
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
|