pycityagent 1.0.0

LLM-based城市模拟器agent构建库

Pycityagent

Table of Contents

• Introduction
  • The Whole Framework of CityAgent
  • The Workflow of CityAgent
• Hands On - By An Easy Demo

Introduction

Framework of CityAgent

• 

Workflow of CityAgent

• 

Hands On - By An Easy Demo

Apply for your App

• You first need to register your account in the Opencity website
• Login to the console, create your own app.
• Get your app_id and app_secret
  • 

Get your Config

• There are three parts of a config file: llm_request, citysim_request and apphub_request

llm_request:
  text_request:
    request_type: qwen
    api_key: xxx
    model: xxx
  img_understand_request:
    request_type: qwen
    api_key: xxx
    model: xxx
  img_generate_request:
    request_type: qwen
    api_key: xxx
    model: xxx

citysim_request:
  simulator: 
    server: https://api-opencity-2x.fiblab.net:58081
  map_request:
    mongo_coll: map_beijing_extend_20240205
    cache_dir: ./cache
  streetview_request:
    engine: baidumap / googlemap
    mapAK: your baidumap AK (if baidumap)
    proxy: your googlemap proxy (if googlemap, optional)

apphub_request:
  hub_url: https://api-opencity-2x.fiblab.net:58080
  app_id: your APP ID
  app_secret: your APP Secret
  profile_image: the profile image of your agent

• Forget about citysim_request, let's focus on the other two.

LLM_REQUEST

• As you can tell, the whole CityAgent is based on the LLM, by now, there are three different parts of config items: text_request, img_understand_request and img_generate_request
• By now, we support qwen and openai
  • Notice: Our environments are basically conducted with qwen. If you prefer to use openai, then you may encounter hardships. AND fell free to issue us.
• Get your api_key and chooce your models

CITYSIM_REQUEST

• There are no need to change the 'simulator' and 'map_request' config
• 'streetview_request': this is the config used for streetview
  • By now, we support 'baidumap' and 'googlemap'
  • If you use 'baidumap', then you need to apply for a mapAK
  • If you use 'googlemap', then you can config your own proxy by 'proxy' attribute

APPHUB_REQUEST

• This is basically used to connect with the backend.
• Put your app_id and app_secret here.

Installation

• Install from pip easily.

pip install pycityagent

CODE and RUN

• Check the example folder and copy files from it (Remember replace the config file)
• Look at the Demo:

import yaml
from pycityagent.simulator import Simulator
from pycityagent.urbanllm import LLMConfig, UrbanLLM
import asyncio
import time

async def main():
    # load your config
    with open('config_template.yaml', 'r') as file:
        config = yaml.safe_load(file)
    
    # get the simulator object
    smi = Simulator(config['citysim_request'])
    
    # get the person by person_id, return agent
    agent = await smi.GetAgent("name_of_agent", 8)

    # Help you build unique agent by scratch/profile
    agent.Image.load_scratch('scratch_template.json')

    # Load Memory and assist the agent to understand "Opencity"
    agent.Brain.Memory.Spatial.MemoryLoad('spatial_knowledge_template.json')
    agent.Brain.Memory.Social.MemoryLoad('social_background_template.json')

    # Connect to apphub so you can interact with your agent in front end
    agent.ConnectToHub(config['apphub_request'])

    # Creat the soul (a LLM processor actually)
    llmConfig = LLMConfig(config['llm_request'])
    soul = UrbanLLM(llmConfig)

    # Add the soul to your agent
    agent.add_soul(soul)
    
    # Start and have fun with it!!!
    while True:
        await agent.Run()
        time.sleep(1)

if __name__ == '__main__':
    asyncio.run(main())

Congratulations

• Following this "Hands On" guide, you have easily created an agent by your hand!
• You can observe your AGENT in your console or in the Opencity website.
• HAVE FUN WITH IT!