/ai-town

A deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.

Primary LanguageTypeScriptMIT LicenseMIT

AI Town 🏠💻💌

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Screen Shot 2023-08-14 at 10 01 00 AM

AI Town is a virtual town where AI characters live, chat and socialize.

This project is a deployable starter kit for easily building and customizing your own version of AI town. Inspired by the research paper Generative Agents: Interactive Simulacra of Human Behavior.

The primary goal of this project, beyond just being a lot of fun to work on, is to provide a platform with a strong foundation that is meant to be extended. The back-end natively supports shared global state, transactions, and a simulation engine and should be suitable from everything from a simple project to play around with to a scalable, multi-player game. A secondary goal is to make a JS/TS framework available as most simulators in this space (including the original paper above) are written in Python.

Overview

Stack

Installation

Clone repo and Install packages

git clone https://github.com/a16z-infra/ai-town.git
cd ai-town
npm install
npm run dev

npm run dev will fail asking for environment variables. Enter them in the environment variables on your Convex dashboard to proceed. You can get there via npx convex dashboard or https://dashboard.convex.dev See below on how to get the various environment variables.

a. Set up Clerk

  • Go to https://dashboard.clerk.com/ and click on "Add Application"
  • Name your application and select the sign-in providers you would like to offer users
  • Create Application
  • Add VITE_CLERK_PUBLISHABLE_KEY and CLERK_SECRET_KEY to .env.local
VITE_CLERK_PUBLISHABLE_KEY=pk_***
CLERK_SECRET_KEY=sk_***
  • Go to JWT Templates and create a new Convex Template.
  • Copy the JWKS endpoint URL for use below.

b. OpenAI API key

Visit https://platform.openai.com/account/api-keys to get your OpenAI API key and set the OPENAI_API_KEY environment variable in your Convex deployment (see below).

c. Replicate API key (Optional) For Daily background music generation, create a Replicate account and create a token in your Profile's API Token page. Add the token as REPLICATE_API_TOKEN in your Convex environment variables.

d. Add environment variables to the Convex backend

Environment variables for a Convex backend is configured through the dashboard:

npx convex dashboard

Go to "settings" and add the following environment variables. CLERK_ISSUER_URL should be the domain in the URL from the JWKS endpoint (e.g., https://your-issuer-url.clerk.accounts.dev/).

OPENAI_API_KEY  sk-*******
CLERK_ISSUER_URL  https://****
REPLICATE_API_TOKEN **** #optional

Run the code

To run both the front and and back end:

npm run dev

You can now visit http://localhost:5173.

If you'd rather run the frontend in a separate terminal from Convex (which syncs your backend functions as they're saved), you can run these two commands:

npm run dev:frontend
npm run dev:backend

See package.json for details, but dev:backend runs npx convex dev

Note: The simulation will pause after 5 minutes if the window is idle. Loading the page will unpause it. You can also manually freeze & unfreeze the world with a button in the UI. If you want to run the world without the browser, you can comment-out the "stop inactive worlds" cron in convex/crons.ts.

Various commands to run / test / debug

To stop the back end, in case of too much activity

This will stop running the engine and agents. You can still run queries and run functions to debug.

npx convex run testing:stop

To restart the back end after stopping it

npx convex run testing:resume

To kick the engine in case the game engine or agents aren't running

npx convex run testing:kick

To archive the world

If you'd like to reset the world and start from scratch, you can archive the current world:

npx convex run testing:archive

Then, you can still look at the world's data in the dashboard, but the engine and agents will no longer run.

You can then create a fresh world with init.

npx convex run init

To clear all databases

You can wipe all tables with the wipeAllTables testing function.

npx convex run testing:wipeAllTables

To pause your backend deployment

You can go to the dashboard to your deployment settings to pause and un-pause your deployment. This will stop all functions, whether invoked from the client, scheduled, or as a cron job. See this as a last resort, as there are gentler ways of stopping above. Once you

Deploy the app

Deploy to Vercel

  • Register an account on Vercel and then install the Vercel CLI.
  • If you are using Github Codespaces: You will need to install the Vercel CLI and authenticate from your codespaces cli by running vercel login.
  • Deploy the app to Vercel with vercel --prod.

Deploy Convex functions to prod environment

Before you can run the app, you will need to make sure the Convex functions are deployed to its production environment.

  1. Run npx convex deploy to deploy the convex functions to production
  2. Run npx convex run init --prod

If you have existing data you want to clear, you can run npx convex run testing:wipeAllTables --prod

Customize your own simulation

NOTE: every time you change character data, you should re-run npx convex run testing:wipeAllTables and then npm run dev to re-upload everything to Convex. This is because character data is sent to Convex on the initial load. However, beware that npx convex run testing:wipeAllTables WILL wipe all of your data.

  1. Create your own characters and stories: All characters and stories, as well as their spritesheet references are stored in characters.ts. You can start by changing character descriptions.

  2. Updating spritesheets: in data/characters.ts, you will see this code:

export const characters = [
  {
    name: 'f1',
    textureUrl: '/assets/32x32folk.png',
    spritesheetData: f1SpritesheetData,
    speed: 0.1,
  },
  ...
];

You should find a sprite sheet for your character, and define sprite motion / assets in the corresponding file (in the above example, f1SpritesheetData was defined in f1.ts)

  1. Update the background (environment): data/firstmap.ts is where the map gets loaded. The easiest way to export a tilemap is by using Tiled -- Tiled exports tilemaps as a CSV and you can convert CSV to a 2d array accepted by firstmap.ts
  2. Change the background music by modifying the prompt in convex/music.ts
  3. Change how often to generate new music at convex/crons.ts by modifying the generate new background music job

Setting up local inference

We support using Ollama for conversation generations, but don't yet support using a local model for generating embeddings.

Steps to switch to using Ollama:

  1. Install Ollama When Ollama runs on your laptop, it by default uses http://localhost:11434 as an endpoint for generation. Next, we need to set up a tunnelmole or ngrok tunnel so Convex can access it:

    Using Tunnelmole

    Tunnelmole is an open source tunneling tool.

    You can install Tunnelmole using one of the following options:

    • NPM: npm install -g tunnelmole
    • Linux: curl -s https://tunnelmole.com/sh/install-linux.sh | sudo bash
    • Mac: curl -s https://tunnelmole.com/sh/install-mac.sh --output install-mac.sh && sudo bash install-mac.sh
    • Windows: Install with NPM, or if you don't have NodeJS installed, download the exe file for Windows here and put it somewhere in your PATH.

    Once Tunnelmole is installed, run the following command:

    tmole 11434

    Tunnelmole should output a unique url once you run this command.

    Using Ngrok

    Ngrok is a popular closed source tunneling tool.

    Once ngrok is installed and authenticated, run the following command:

    ngrok http http://localhost:11434

    Ngrok should output a unique url once you run this command.

  2. Add Ollama endpoint to Convex

  • run npx convex dashboard to bring up the convex dashboard
  • Go to Settings > Environment Variables
  • Add OLLAMA_HOST = [your tunnelmole/ngrok unique url from the previous step]
  • You might also want to set: ACTION_TIMEOUT to 100000 or more, to give your model more time to run. NUM_MEMORIES_TO_SEARCH to 1, to reduce the size of conversation prompts.

By default, we use llama2-7b model on Ollama. If you want to customize which model to use, you can set OLLAMA_MODEL variable under Environment Variables. Ollama model options can be found here.

Credits