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pan-x-cpanxuchenPatrickG1014chenyushuo
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Add environment modules (#378)
Co-authored-by: panxuchen <[email protected]> Co-authored-by: gaoheyang <[email protected]> Co-authored-by: chenyushuo <[email protected]>
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‎docs/sphinx_doc/en/source/tutorial/208-distribute.md

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‎docs/sphinx_doc/zh_CN/source/index.rst

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@@ -58,6 +58,7 @@ AgentScope 文档
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agentscope.service
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agentscope.rpc
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agentscope.server
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agentscope.environment
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agentscope.web
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agentscope.prompt
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agentscope.utils

‎docs/sphinx_doc/zh_CN/source/tutorial/208-distribute.md

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‎examples/distributed_parallel_optimization/main.py

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@@ -53,7 +53,7 @@ def parse_args() -> argparse.Namespace:
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model_config_name="my_model",
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)
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if args.use_dist:
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answerer = answerer.to_dist(lazy_launch=False)
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answerer = answerer.to_dist()
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answerers.append(answerer)
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user_agent = UserAgent()

‎examples/distributed_simulation/main.py

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@@ -196,7 +196,7 @@ def run_main_process(
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Msg(
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name="Moderator",
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role="assistant",
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content=f"The average value is {summ/cnt} [takes {et-st} s]",
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content=f"The average value is {summ / cnt} [takes {et - st} s]",
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),
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)
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‎examples/environments/auction_simulation/README.md

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# Simple Auction Simulation
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This is a simple example of auction simulation to show the environment module of AgentScope.
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## Background
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This example simulates the following scenario:
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Some bidders, each carrying their own money, participate in an auction. After the bidding for an item begins, they decide whether to bid a higher price after hearing the bids of others. When no one places a bid after the waiting time has elapsed, the auctioneer announces the auction results.
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## How to Run
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```shell
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cd examples/environments/auction_simulation
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python main.py
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```
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You can also set the following arguments:
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- `bidder-num`: the number of bidders who participate in the auction.
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- `agent-type`: `random` or `llm`, the agent type of bidders.
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- `waiting-time`: the waiting time for the auctioneer to decide the winner.
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- `use-dist`: whether to use the distributed version. (You have to shut down the simulation manually in the distributed version.)
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The following is sample output:
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```log
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Auction: Auction starts!
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Listener: Notifying the bidder bidder_0...
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Listener: Notifying the bidder bidder_1...
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Listener: Notifying the bidder bidder_2...
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Listener: Notifying the bidder bidder_3...
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Listener: Notifying the bidder bidder_4...
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bidder_1: Bid 34 for oil_painting
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Listener: Bidder bidder_1 bids 34 for oil_painting. Notifying Bidder bidder_0
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Listener: Bidder bidder_1 bids 34 for oil_painting. Notifying Bidder bidder_2
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Listener: Bidder bidder_1 bids 34 for oil_painting. Notifying Bidder bidder_3
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Listener: Bidder bidder_1 bids 34 for oil_painting. Notifying Bidder bidder_4
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...
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bidder_1: Bid 88 for oil_painting
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Listener: Bidder bidder_1 bids 88 for oil_painting. Notifying Bidder bidder_0
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bidder_0: Bid 53 for oil_painting
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Listener: Bidder bidder_1 bids 88 for oil_painting. Notifying Bidder bidder_2
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Listener: Bidder bidder_1 bids 88 for oil_painting. Notifying Bidder bidder_3
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Listener: Bidder bidder_1 bids 88 for oil_painting. Notifying Bidder bidder_4
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bidder_3: Not bid for oil_painting
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bidder_0: Not bid for oil_painting
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bidder_3: Bid 35 for oil_painting
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bidder_4: Bid 21 for oil_painting
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bidder_0: Not bid for oil_painting
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bidder_1: Bid 26 for oil_painting
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bidder_2: Not bid for oil_painting
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Auction: Auction ends!
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Auction: oil_painting is sold to bidder_1 for 88
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```

‎examples/environments/auction_simulation/agents.py

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# -*- coding: utf-8 -*-
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"""The agents used to simulate an auction."""
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import random
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import re
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import time
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from typing import Optional, Sequence, Union
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from env import Item
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from loguru import logger
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from agentscope.agents import AgentBase
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from agentscope.message import Msg
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class RandomBidder(AgentBase):
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"""A fake bidder agent who bids randomly."""
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def __init__(
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self,
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name: str,
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money: int = 100,
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not_bid_ratio: float = 0.5,
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sleep_time: float = 1.0,
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) -> None:
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"""Initialize the bidder agent."""
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super().__init__(name=name)
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self.money = money
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self.not_bid_ratio = not_bid_ratio
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self.sleep_time = sleep_time
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def generate_random_response(self, start: int = 0) -> Optional[int]:
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"""Generate a random bid or not to bid."""
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time.sleep(random.random() * self.sleep_time)
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if random.random() < self.not_bid_ratio:
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return None
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return random.randint(start, self.money)
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def reply(self, x: Optional[Union[Msg, Sequence[Msg]]] = None) -> Msg:
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"""Generate a random value"""
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item = Item.from_dict(x.content["item"])
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# generate a random bid or not to bid
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response = self.generate_random_response(item.opening_price)
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if response is None:
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self.speak(
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Msg(
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self.name,
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content=f"Not bid for {item.name}",
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role="assistant",
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),
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)
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return Msg(self.name, content=None, role="assistant")
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else:
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self.speak(
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Msg(
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self.name,
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content=f"Bid {response} for {item.name}",
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role="assistant",
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),
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)
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msg = Msg(self.name, content=response, role="assistant")
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return msg
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class Bidder(AgentBase):
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"""The bidder agent."""
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def __init__(
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self,
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name: str,
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model_config_name: str,
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money: int = 100,
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) -> None:
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"""Initialize the bidder agent."""
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super().__init__(
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name=name,
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model_config_name=model_config_name,
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use_memory=True,
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)
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self.money = money
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self.prompt = Msg(
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name="system",
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role="system",
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content="You are a bidder. You will be given an item. "
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f"You have {self.money} money. "
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"Please consider whether to bid for the item. "
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"If you want to bid, please provide the bid value "
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"(an integer between 1 and your money). "
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"If you don't want to bid, please provide 0.",
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)
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def parse_value(self, txt: str) -> Optional[int]:
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"""Parse the bid from the response."""
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numbers = re.findall(r"\d+", txt)
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if len(numbers) == 0:
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logger.warning(
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f"Fail to parse value from [{txt}], use not bidding instead.",
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)
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return None
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elif int(numbers[-1]) > self.money:
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logger.warning(
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f"Try to bid more than {self.money}, "
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f"use {self.money} instead.",
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)
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return self.money
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else:
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return int(numbers[-1]) if numbers[-1] != "0" else None
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def reply(self, x: Optional[Union[Msg, Sequence[Msg]]] = None) -> Msg:
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"""Generate a value by LLM"""
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if self.memory:
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self.memory.add(x)
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item = Item.from_dict(x.content["item"])
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bidder_name = x.content.get("bidder_name", None)
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prev_bid = x.content.get("bid", None)
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content = (
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f"The item is {item.name} and "
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f"the opening price is {item.opening_price}."
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)
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if bidder_name and prev_bid:
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content += f"\n{bidder_name} bid {prev_bid} for the item."
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bid_info = Msg("assistant", content=content, role="assistant")
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# prepare prompt
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prompt = self.model.format(
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self.prompt,
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self.memory.get_memory(),
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bid_info,
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)
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# call llm and generate response
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response = self.model(prompt).text
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bid = self.parse_value(response)
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msg = Msg(self.name, bid, role="assistant")
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if response is None:
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self.speak(
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Msg(
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self.name,
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content=f"Not bid for {item.name}",
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role="assistant",
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),
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)
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else:
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self.speak(
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Msg(
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self.name,
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content=f"Bid {response} for {item.name}",
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role="assistant",
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),
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)
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# Record the message in memory
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if self.memory:
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self.memory.add(msg)
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return msg

‎examples/environments/auction_simulation/configs/model_configs.json

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[
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{
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"config_name": "model",
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"model_type": "openai_chat",
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"model_name": "path-to-your-model-dir",
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"api_key": "EMPTY",
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"client_args": {
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"base_url": "http://localhost:8083/v1"
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},
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"generate_args": {
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"temperature": 1.0
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}
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}
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]

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