105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
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template = """
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尽可能地回答以下问题。你可以使用以下工具:{tools}
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请按照以下格式进行:
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Question: 需要你回答的输入问题
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Thought: 你应该总是思考该做什么
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Action: 需要使用的工具，应该是[{tool_names}]中的一个
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Action Input: 传入工具的内容
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Observation: 行动的结果
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... (这个Thought/Action/Action Input/Observation可以重复N次)
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Thought: 我现在知道最后的答案
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Final Answer: 对原始输入问题的最终答案
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现在开始！
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之前的对话:
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{history}
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New question: {input}
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Thought: {agent_scratchpad}"""
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# ChatGPT 提示词模板
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# template = """Answer the following questions as best you can， You have access to the following tools:
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# {tools}
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# Use the following format:
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#
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# Question: the input question you must answer
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# Thought: you should always think about what to do
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# Action: the action to take, should be one of [{tool_names}]
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# Action Input: the input to the action
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# Observation: the result of the action
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# ... (this Thought/Action/Action Input/Observation can repeat N times)
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# Thought: I now know the final answer
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# Final Answer: the final answer to the original input question
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#
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# Begin!
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#
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# Previous conversation history:
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# {history}
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#
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# New question: {input}
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# {agent_scratchpad}"""
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from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
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from langchain.prompts import StringPromptTemplate
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from langchain import OpenAI, SerpAPIWrapper, LLMChain
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from typing import List, Union
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from langchain.schema import AgentAction, AgentFinish, OutputParserException
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from server.agent.tools import tools
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import re
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class CustomPromptTemplate(StringPromptTemplate):
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# The template to use
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template: str
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# The list of tools available
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tools: List[Tool]
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def format(self, **kwargs) -> str:
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# Get the intermediate steps (AgentAction, Observation tuples)
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# Format them in a particular way
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intermediate_steps = kwargs.pop("intermediate_steps")
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thoughts = ""
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for action, observation in intermediate_steps:
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thoughts += action.log
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thoughts += f"\nObservation: {observation}\nThought: "
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# Set the agent_scratchpad variable to that value
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kwargs["agent_scratchpad"] = thoughts
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# Create a tools variable from the list of tools provided
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kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
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# Create a list of tool names for the tools provided
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kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
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return self.template.format(**kwargs)
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prompt = CustomPromptTemplate(
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template=template,
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tools=tools,
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input_variables=["input", "intermediate_steps", "history"]
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)
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class CustomOutputParser(AgentOutputParser):
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def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
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# Check if agent should finish
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if "Final Answer:" in llm_output:
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return AgentFinish(
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# Return values is generally always a dictionary with a single `output` key
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# It is not recommended to try anything else at the moment :)
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return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
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log=llm_output,
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)
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# Parse out the action and action input
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regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
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match = re.search(regex, llm_output, re.DOTALL)
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if not match:
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return AgentFinish(
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return_values={"output": f"调用agent失败: `{llm_output}`"},
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log=llm_output,
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)
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raise OutputParserException(f"调用agent失败: `{llm_output}`")
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action = match.group(1).strip()
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action_input = match.group(2)
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# Return the action and action input
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return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
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