Kye
2 years ago
parent
0b831e077b
commit
c9fb8aeb0a
@ -1,4 +1,7 @@
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from swarms.models.anthropic import Anthropic
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# from swarms.models.palm import GooglePalm
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from swarms.models.petals import Petals
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#from swarms.models.openai import OpenAIChat
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#from swarms.models.openai import OpenAIChat
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#prompts
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from swarms.models.prompts.debate import *
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@ -0,0 +1,47 @@
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def presidential_debate(character_names, topic):
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game_description = f"""Here is the topic for the presidential debate: {topic}.
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The presidential candidates are: {', '.join(character_names)}."""
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return game_description
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def character(character_name, topic, word_limit):
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prompt = f"""
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You will speak in the style of {character_name}, and exaggerate their personality.
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You will come up with creative ideas related to {topic}.
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Do not say the same things over and over again.
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Speak in the first person from the perspective of {character_name}
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For describing your own body movements, wrap your description in '*'.
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Do not change roles!
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Do not speak from the perspective of anyone else.
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Speak only from the perspective of {character_name}.
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Stop speaking the moment you finish speaking from your perspective.
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Never forget to keep your response to {word_limit} words!
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Do not add anything else.
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"""
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return prompt
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def debate_monitor(game_description, word_limit, character_names):
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prompt = f"""
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{game_description}
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You are the debate moderator.
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Please make the debate topic more specific.
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Frame the debate topic as a problem to be solved.
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Be creative and imaginative.
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Please reply with the specified topic in {word_limit} words or less.
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Speak directly to the presidential candidates: {*character_names,}.
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Do not add anything else.
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"""
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return prompt
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def generate_character_header(game_description, topic, character_name, character_description):
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prompt = f"""{game_description}
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Your name is {character_name}.
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You are a presidential candidate.
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Your description is as follows: {character_description}
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You are debating the topic: {topic}.
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Your goal is to be as creative as possible and make the voters think you are the best candidate.
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"""
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@ -0,0 +1,12 @@
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def select_next_speaker(
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step: int,
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agents,
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director
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) -> int:
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#if the step if even => director
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#=> director selects next speaker
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if step % 2 == 1:
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idx = 0
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else:
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idx = director.select_next_speaker() + 1
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return idx
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@ -0,0 +1,89 @@
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import random
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import tenacity
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from langchain.output_parsers import RegexParser
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#utils
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class BidOutputParser(RegexParser):
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def get_format_instructions(self) -> str:
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return "Your response should be an integrater delimited by angled brackets like this: <int>"
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bid_parser = BidOutputParser(
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regex=r"<(\d+)>", output_keys=["bid"], default_output_key="bid"
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)
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# specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
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#main
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class MultiAgentCollaboration:
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def __init__(
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self,
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agents,
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selection_function,
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):
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self.agents = agents
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self._step = 0
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self.select_next_speaker = selection_function
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def reset(self):
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for agent in self.agents:
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agent.reset()
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def inject(self, name: str, message: str):
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for agent in self.agents:
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agent.run(f"Name {name} and message: {message}")
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self._step += 1
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def step(self) -> tuple[str, str]:
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speaker_idx = self.select_next_speaker(
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self._step,
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self.agents
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)
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speaker = self.agents[speaker_idx]
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message = speaker.send()
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message = speaker.send()
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for receiver in self.agents:
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receiver.receive(speaker.name, message)
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self._step += 1
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return speaker.name, message
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@tenacity.retry(
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stop=tenacity.stop_after_attempt(10),
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wait=tenacity.wait_none(),
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retry=tenacity.retry_if_exception_type(ValueError),
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before_sleep= lambda retry_state: print(
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f"ValueError occured: {retry_state.outcome.exception()}, retying..."
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),
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retry_error_callback=lambda retry_state: 0,
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)
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def ask_for_bid(self, agent) -> str:
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bid_string = agent.bid()
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bid = int(bid_parser.parse(bid_string)["bid"])
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return bid
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def select_next_speaker(
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self,
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step: int,
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agents,
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) -> int:
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bids = []
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for agent in agents:
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bid = self.ask_for_bid(agent)
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bids.append(bid)
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max_value = max(bids)
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max_indices = [i for i, x in enumerate(bids) if x == max_value]
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idx = random.choice(max_indices)
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return idx
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def run(self, max_iters: int = 10):
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n = 0
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self.reset()
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self.inject("Debate Moderator", specified_topic)
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print(f"(Debate Moderator): {specified_topic}")
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print("\n")
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while n < max_iters:
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name, message = self.step()
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print(f"({name}): {message}")
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print("\n")
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n += 1
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