base on A local-ready LLM-generated and LLM-driven virtual pet with thoughts and feelings. 100% Javascript. # AI Tamago 🥚🐣 A 100% local, LLM-generated and driven virtual pet with thoughts, feelings and feedback. Revive your fond memories of Tamagotchi! https://ai-tamago.fly.dev/ All ascii animations are generated using chatgpt (included prompts in the repo). Have questions? Join [AI Stack devs](https://discord.gg/TsWCNVvRP5) and find me in #ai-tamago channel. **Demo** 🪄 https://github.com/ykhli/AI-tamago/assets/3489963/8d7cb2ac-537a-45d4-98a5-1802b773e364 ## Stack ### Local Mode - 🦙 Inference: [Ollama](https://github.com/jmorganca/ollama), with options to use OpenAI or [Replicate](https://replicate.com/) - 🔔 Game state: [Inngest](https://www.inngest.com/) - 💻 Transactional & vector database: [Supabase pgvector](https://supabase.com/docs/guides/database/extensions/pgvector) - 🧠 LLM Orchestration: [Langchain.js](https://js.langchain.com/docs/) - 🖼️ App logic: [Next.js](https://nextjs.org/) - 🧮 Embeddings generation: [Transformer.js](https://github.com/xenova/transformers.js) and [ all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) - 🖌️ UI: [Magic Patterns](https://www.magicpatterns.com/) and [Vercel v0](https://v0.dev/) ### Prod Mode All of above, plus: - 🔐 Auth & User Management: [Clerk](https://clerk.com/) - ☁️ Hosting: [Fly](https://fly.io/) - 🥇 Rate Limiting: [Upstash](https://upstash.com/) ## Overview - 🚀 [Quickstart](#quickstart) - 💻 [Deployment Guide](#deployment-guide) ## Prerequisites - [Install Docker](https://www.docker.com/get-started) ## Quickstart ### 1. Fork and Clone repo Fork the repo to your Github account, then run the following command to clone the repo: ``` git clone [email protected]:[YOUR_GITHUB_ACCOUNT_NAME]/AI-tamago.git ``` ### 2. Install dependencies ``` cd ai-tamago npm install ``` All client side tamagotchi code is in Tamagotchi.tsx ### 3. Install Ollama Instructions are [here](https://github.com/jmorganca/ollama#macos). ### 4. Run Supabase locally 1. Install Supabase CLI ``` brew install supabase/tap/supabase ``` 2. Start Supabase Make sure you are under `/ai-tamago` directory and run: ``` supabase start ``` Tips: To run migrations or reset database -- seed.sql and migrations will run `supabase db reset` ### 5. Fill in secrets Note: The secrets here are for your **local** supabase instance ``` cp .env.local.example .env.local ``` Then get `SUPABASE_PRIVATE_KEY` by running ``` supabase status ``` Copy `service_role key` and save it as `SUPABASE_PRIVATE_KEY` in `.env.local` ### 6. Set up Inngest `npx inngest-cli@latest dev` Make sure your app is up and running -- Inngest functions (which are used to drive game state) should register automatically. ### 7. Run app locally Now you are ready to test out the app locally! To do this, simply run `npm run dev` under the project root and visit `http://localhost:3000`. ## Deployment Guide Now you have played with the AI tamago locally -- it's time to deploy it somewhere more permanent so you can access it anytime! **0. Choose which model you want to use in production** - If you want to test out using Chatgpt in prod, simply remove `LLM_MODEL=ollama` from `.env.local` and fill in `OPENAI_API_KEY` - If you want to try [Replicate](https://replicate.com/), set `LLM_MODEL=replicate_llama` and fill in `REPLICATE_API_TOKEN` - If you want to deploy Ollama yourself, you can follow this awesome guide -- [Scaling Large Language Models to zero with Ollama](https://fly.io/blog/scaling-llm-ollama/). It is possible to run Ollama on a `performance-4x` Fly VM (CPU) with `100gb` volume, but if you can get access to GPUs they are much faster. Join Fly's GPU waitlist [here](https://fly.io/gpu) if you don't yet have access! **1. Switch to `deploy` branch -- this branch includes everything you need to deploy an app like [this](https://ai-tamago.fly.dev/).** ```git co deploy``` This branch contains a multi-tenancy-ready (thanks to Clerk) app, which means every user can get their own AI-tamago, and has token limit built in -- you can set how many times a user can send requests in the app (see `ratelimit.ts`) **2. Move to Supabase Cloud:** - Create a Supabase project [here](https://supabase.com/), then go to Project Settings -> API. Fill out secrets in `.env.local` - `SUPABASE_URL` is the URL value under "Project URL" - `SUPABASE_PRIVATE_KEY` is the key starts with `ey` under Project API Keys - Copy Supabase project id, which you can find from the url https://supabase.com/dashboard/project/[project-id] From your Ai-tamago project root, run: ``` supabase link --project-ref [insert project-id] supabase migration up supabase db reset --linked ``` **3. Create Upstash Redis instance for rate limiting** This will make sure no one user calls any API too many times and taking up all the inference workloads. We are using Upstash's [awesome rate limiting SDK](https://upstash.com/blog/upstash-ratelimit) here. - Sign in to [Upstash](https://upstash.com/) - Under "Redis" on the top nav, click on "Create Database" - Give it a name, and then select regions and other options based on your preference. Click on "Create" - Scroll down to "REST API" section and click on ".env". Now you can copy paste both environment variables (`UPSTASH_REDIS_REST_URL` and `UPSTASH_REDIS_REST_TOKEN`) to your .env.local **4. Now you are ready to deploy everything on Fly.io!** - Register an account on fly.io and then [install flyctl](https://fly.io/docs/hands-on/install-flyctl/) - Run `fly launch` under project root. This will generate a `fly.toml` that includes all the configurations you will need - Run `fly scale memory 512` to scale up the fly vm memory for this app. - Run `fly deploy --ha=false` to deploy the app. The --ha flag makes sure fly only spins up one instance, which is included in the free plan. - For any other non-localhost environment, the existing Clerk development instance should continue to work. You can upload the secrets to Fly by running `cat .env.local | fly secrets import` - If you want to make this a real product, you should create a prod environment under the [current Clerk instance](https://dashboard.clerk.com/). For more details on deploying a production app with Clerk, check out their documentation [here](https://clerk.com/docs/deployments/overview). **Note that you will likely need to manage your own domain and do domain verification as part of the process.** - Create a new file `.env.prod` locally and fill in all the production-environment secrets. Remember to update `NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY` and `CLERK_SECRET_KEY` by copying secrets from Clerk's production instance -`cat .env.prod | fly secrets import` to upload secrets. If you have questions, join [AI Stack devs](https://discord.gg/TsWCNVvRP5) and find me in #ai-tamago channel. ## Other Resources - [Adding auth with Clerk](https://clerk.com/docs/quickstarts/nextjs) - takes < 5mins - [Inngest deployment guide](https://www.inngest.com/docs/deploy)https://www.inngest.com/docs/deploy - [Running Ollama on Fly.io](https://fly.io/blog/scaling-llm-ollama/)https://fly.io/blog/scaling-llm-ollama/ - [Run a next.js app on Fly.io](https://fly.io/docs/js/frameworks/nextjs/#:~:text=Deploy%20an%20existing%20NextJS%20app&text=First%2C%20install%20flyctl%2C%20your%20Fly,the%20root%20of%20your%20application.&text=Creating%20app%20in%20%2FUsers%2Fme,source%20code%20Detected%20a%20Next.) ", Assign "at most 3 tags" to the expected json: {"id":"5899","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"