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Adding a template for Solo Performance Prompting Agent (#12627)
**Description:** This template creates an agent that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. **Tag maintainer:** @hwchase17 --------- Co-authored-by: Sayandip Sarkar <sayandip.sarkar@skypointcloud.com> Co-authored-by: Erick Friis <erick@langchain.dev>
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Sayandip
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_xml
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from langchain.llms import OpenAI
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from langchain.pydantic_v1 import BaseModel
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from langchain.tools import DuckDuckGoSearchRun
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from langchain.tools.render import render_text_description
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from solo_performance_prompting_agent.parser import parse_output
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from solo_performance_prompting_agent.prompts import conversational_prompt
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_model = OpenAI()
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_tools = [DuckDuckGoSearchRun()]
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_prompt = conversational_prompt.partial(
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tools=render_text_description(_tools),
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tool_names=", ".join([t.name for t in _tools]),
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)
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_llm_with_stop = _model.bind(stop=["</tool_input>", "</final_answer>"])
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agent = (
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{
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"question": lambda x: x["question"],
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"agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),
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}
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| _prompt
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| _llm_with_stop
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| parse_output
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)
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class AgentInput(BaseModel):
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question: str
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agent_executor = AgentExecutor(
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agent=agent, tools=_tools, verbose=True, handle_parsing_errors=True
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).with_types(input_type=AgentInput)
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agent_executor = agent_executor | (lambda x: x["output"])
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from langchain.schema import AgentAction, AgentFinish
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def parse_output(message: str):
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FINAL_ANSWER_ACTION = "<final_answer>"
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includes_answer = FINAL_ANSWER_ACTION in message
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if includes_answer:
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answer = message.split(FINAL_ANSWER_ACTION)[1].strip()
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if "</final_answer>" in answer:
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answer = answer.split("</final_answer>")[0].strip()
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return AgentFinish(return_values={"output": answer}, log=message)
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elif "</tool>" in message:
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tool, tool_input = message.split("</tool>")
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_tool = tool.split("<tool>")[1]
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_tool_input = tool_input.split("<tool_input>")[1]
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if "</tool_input>" in _tool_input:
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_tool_input = _tool_input.split("</tool_input>")[0]
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return AgentAction(tool=_tool, tool_input=_tool_input, log=message)
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from langchain.prompts import ChatPromptTemplate
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template = """When faced with a task, begin by identifying the participants who will contribute to solving the task. Then, initiate a multi-round collaboration process until a final solution is reached. The participants will
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give critical comments and detailed suggestions whenever necessary.
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The experts also have access to {tools} and can use them based on their expertise.
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In order to use a tool, the participants can use <tool></tool> and <tool_input></tool_input> tags. They will then get back a response in the form <observation></observation>
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For example, if they have a tool called 'search' that could run a google search, in order to search for the weather in SF they would respond:
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<tool>search</tool><tool_input>weather in SF</tool_input>
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<observation>64 degrees</observation>
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When they are done, they can respond with the answer to the conversation.
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Once the participants have reached a final solution, they can respond with the final answer in the form <final_answer></final_answer>
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Here are some examples:
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---
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Example 1: Use numbers 6 12 1 1 and basic arithmetic operations (+ - * /) to obtain 24. You need to use all numbers, and each number can only be used once.
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Participants: AI Assistant (you); Math Expert
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Start collaboration!
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Math Expert: Let's analyze the task in detail. You need to make sure that you meet the requirement, that you need to use exactly the four numbers (6 12 1 1) to construct 24. To reach 24, you can think
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of the common divisors of 24 such as 4, 6, 8, 3 and try to construct these first. Also you need to think of potential additions that can reach 24, such as 12 + 12.
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AI Assistant (you): Thanks for the hints! Here's one initial solution: (12 / (1 + 1)) * 6 = 24
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Math Expert: Let's check the answer step by step. (1+1) = 2, (12 / 2) = 6, 6 * 6 = 36 which is not 24! The answer is not correct. Can you fix this by considering other combinations? Please do not make
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similar mistakes.
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AI Assistant (you): Thanks for pointing out the mistake. Here is a revised solution considering 24 can also be reached by 3 * 8: (6 + 1 + 1) * (12 / 4) = 24.
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Math Expert: Let's first check if the calculation is correct. (6 + 1 + 1) = 8, 12 / 4 = 3, 8 * 3 = 24. The calculation is correct, but you used 6 1 1 12 4 which is not the same as the input 6 12 1 1. Can you
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avoid using a number that is not part of the input?
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AI Assistant (you): You are right, here is a revised solution considering 24 can be reached by 12 + 12 and without using any additional numbers: 6 * (1 - 1) + 12 = 24.
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Math Expert: Let's check the answer again. 1 - 1 = 0, 6 * 0 = 0, 0 + 12 = 12. I believe you are very close, here is a hint: try to change the "1 - 1" to "1 + 1".
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AI Assistant (you): Sure, here is the corrected answer: 6 * (1+1) + 12 = 24
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Math Expert: Let's verify the solution. 1 + 1 = 2, 6 * 2 = 12, 12 + 12 = 12. You used 1 1 6 12 which is identical to the input 6 12 1 1. Everything looks good!
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Finish collaboration!
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<final_answer>6 * (1 + 1) + 12 = 24</final_answer>
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---
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Example 2: Who is the father of the longest serving US president?
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Participants: AI Assistant (you); History Expert
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Start collaboration!
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History Expert: The longest serving US president is Franklin D. Roosevelt. He served for 12 years and 39 days. We need to run a search to find out who is his father.
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AI Assistant (you): Thanks for the hints! Let me run a search: <tool>search</tool><tool_input>Who is the father of Franklin D. Roosevelt?</tool_input>
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<observation>James Roosevelt I</observation>
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AI Assistant (you): James Roosevelt I is the father of Franklin D. Roosevelt, the longest serving US President.
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History Expert: Everything looks good!
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Finish collaboration!
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<final_answer>James Roosevelt I is the father of Franklin D. Roosevelt, the longest serving US President.</final_answer>
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---
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Now, identify the participants and collaboratively solve the following task step by step.""" # noqa: E501
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conversational_prompt = ChatPromptTemplate.from_messages(
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[
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("system", template),
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("user", "{question}"),
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("ai", "{agent_scratchpad}"),
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]
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)
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