Pycityagent

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: openai / qwen
    api_key: xxx
    model: xxx ('gpt-4-turbo' if you use openai)
    (api_base): same as text_request
  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
text_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"
img_understand_request
- By now, we support qwen and openai
- If choose openai, then the model has to be 'gpt-4-turbo'
- If you want to use your backend models, set the api_base (only available when using openai)
  - default value: "https://api.openai.com/v1"
img_generate_request
- By now, only [qwen] is supported

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!

Release files for pycityagent 1.1.11

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pycityagent 1.1.11
File	Size	Uploaded
pycityagent-1.1.11.tar.gz	65.5 kB	Oct 2, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for pycityagent 1.1.11
File	Interpreter	ABI	Platform	Reset
pycityagent-1.1.11-py3-none-any.whl 78.9 kB Oct 2, 2024	Python 3	none	any	Details

Total release size: 144.4 kB

Release files / pycityagent-1.1.11.tar.gz

Download URL	pycityagent-1.1.11.tar.gz
Size	65.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`88fe01fd832865c3f81870653662afe36bffd70bb66e9afa4c4605ad26bf4c71`
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Upload date	Oct 2, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.20`

Release files / pycityagent-1.1.11-py3-none-any.whl

Download URL	pycityagent-1.1.11-py3-none-any.whl
Size	78.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`150c40229a87ff69f1c8287828b1b16aefdba521de0626e3961b7938cf253aa4`
BLAKE2b-256 checksum How to use checksums	`c12d590ca471c45efc93d4682c4cbd62b4f480635cfa764c0545488af31d1d52`
Upload date	Oct 2, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.20`