pycityagent 1.1.8

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: openai / qwen
    api_key: xxx
    model: xxx
    (api_base): xxx (this is an optional config, if you use opanai and want to use your own backend LLM model, default to "https://api.openai.com/v1")
  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
  route_request: 
    server: http://api-opencity-2x.fiblab.net:58082
  streetview_request:
    engine: baidumap / googlemap
    mapAK: baidumap api-key (if you use baidumap engine)
    proxy: googlemap proxy (if you use googlemap engine)

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 see, 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 model
• If you want to use your backend models, set the api_base (only available when using openai)
  • default value: "https://api.openai.com/v1"

CITYSIM_REQUEST

• Most of the configuration options in this part are determined, such as simulator.server, map_request.mongo_coll, route_request.server
• map_request.cache_dir: used for storing map data your machine, you can justify the target dir as you wish (create the dir first)
• streetview_request: used for obtaining streetview images, by now, we support baidumap engine and googlemap engine
  • if you choose baidumap engine, you need to get a baidumap api-key

    streetview_request:
      engine: baidumap
      mapAK: xxxx
    

  • if you choose googlemap engine, you need to provide your proxy address, for example:

    streetview_request:
      engine: googlemap
      proxy: 
        http: http://xxxx
        https: https://xxxx
    

APPHUB_REQUEST

• Used for creating the connection between backend server and client.
• Put your app_id and app_secret here.
  • Create your account and apply in Opencity website

Installation

PyPI Installation

• Install from pip easily.

pip install pycityagent

Install from source code

• Clone this repo
• Install required packages

  pip install -r requirements.txt
  

• Install libGL.so.1, if you ara using Linux with a suitable package manager: (apt for instance)

  apt-get install libgl1
  

CODE and RUN

• Check the example folder and copy files from it (Remember replace the config file)
• Look at the Demo: (A citizen Agent 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.GetCitizenAgent("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'])
    agent.Bind()

    # 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!