Work flow

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gpt prompt: Please guide me on structuring a secondary backend (Backend 2) that securely handles client requests with JWT tokens and interfaces with an existing LLM API (OpenAI). I want the clients to initiate requests from the front end (thingsboard client) to Backend 2. The client already have a jwt from a backend 1, and will send it to backend 2 to verify validity, and subsequently adapt and forward the main client querie to the LLM API. How should I design this intermediate Backend 2 effectively? Feel free to ask any clarifying questions to help me proceed.

ans: guidances on the concepts and artifacts involved DO: Comprehend ideas Basic

• Adaptation of user request to conform messages
• LLM forwarding to API
• Logging feat for server activities
• Error handling (invalidations, api errors, etc)
• Documentation: endpoints, auth, vendors Medium
• Auth validation for incoming msgs with JWT
• Security (origin check, token expiration, revocation)
• Testing: unit, integration, security, api (postman or insomnia) Advanced
• Scaling: load balancing and cache strategies Further sugestions
• Rate limiting of usage
• GraphQL for more felxibility
• Content security policy
• Easy to expand endpoints

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gpt prompt: Please create the files considering the following

• An express server to listen from a web client and redirect to OpenAI LLM API
• Only listen mesages from a client in a DOMAIN_URL env's variable
• Should validate an incoming string in the msg body, using it as bearer token to ask a BACKEND1 API
• Server should take a cameraData info in the body and build a prompt for an openai gpt-3.5 turbo model
• Send a query to the openai gpt-3.5 turbo model using the openai api
• return the response to the client as chatgptmsg property of the body

IMPORTANT: USE THE BEST PRACTICES TO ORGANICE THE PROJECT. SEPARATE CONCERNS IN A VERY SCALABLE WAY

ans: Guidance on how set up all DO: Comprehend procedure

• Init project: pnpm init
• Install dependencies: pnpn install express dotenv
• Implement custom middelwares
• Send query to openai
• Code basic server.js
• Join functions

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gpt prompt: Now please give the vanilla javascript code to build that server. Be organiced and separate concerns in files for further scalability

ans: folder structura and main files DO: build all proposed

• Folder structure: src(.env + index.js + server.js + /middleware + /routes + /test

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FEAT: openai gpt api test

• openaiTestScript.js
• Review chat api docu: https://platform.openai.com/docs/api-reference/chat/create
• Set up connection: OpenAI({key})
• call completion: openai.chat.completion.create(...args)
• Mock json data
• Create prompt
• Itereate prompt efficiency and effectiveness!!

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FEAT: Home page serving

• /public folder with html
• Indicate express page serving: app.use(express.static(./path/to/public))
• Learn: all nodejs path are from the root ./

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FEAT: Server activities logging

• Reseacrh logging
• Install and import morgan
• Add morgan as express middelware: app.use(morgan('tokenString'))
• Morgan bases on tokens: app.use(express.static("./src/public"));

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EXPERIMENT: Object analyzer function Please help me giving me a JavaScript function to describe a complex Json object in natural language, with enough detail to input the description into an LLM or ChatGPT in order it to later generate a function to traverse the object and apply basic data analytics to that described JSON object.

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DUTY: Tight WSL2 memory efficiency on my machine

• vmem was eating too much memory
• How to manage wsl memory
  • https://www.aleksandrhovhannisyan.com/blog/limiting-memory-usage-in-wsl-2/
• Key commands
  • On cmd
    • wsl
    • wsl --shutdown
  • On linux
    • free -h --giga
    • top -o %MEM
    • editor "$(wslpath "C:\Users\YourUsername\.wslconfig")"
    • write the file

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Set up Digital Ocean sever

• A cheap server: 1GB, 1core, 25GB

• as root

  • adduser {username} . Crear usuario
  • usermod -aG sudo {username} . Agregar user al group ENV INSTALLATION

• curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash . Install nvm (https://github.com/nvm-sh/nvm?tab=readme-ov-file#installing-and-updating)

• curl -fsSL https://get.pnpm.io/install.sh | sh - . Install pnpm (https://pnpm.io/installation) +

• nvm install --lts . Install Node LTS

• pnpm i -g pm2 . Install PM2 RUN PROJECT

  • git clone, pnpm i, node index.js, pm2 logs

• Some system check

  • top -o %MEM . Sort top by memory usage
  • ps -e . view all active processes
  • ps -e | grep node . check for NodeJS porcesses

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ADDED REACT VITE BUILD

• In other place, loaded created vite to see main files
• Brougth here the main files an lines
  • vite.config.js in root
  • .jsx files in /src
  • /assets dir in /src
  • .gitignore lines
  • .eslintrc.cjs in root
  • vite.svg file in public (build out)
  • inserted new dependencies in package.json
  • inserted script in package.json
  • run pnpm i
• Build app with pnpm run build
• Browser Error: 'Failed to load module script: Expected a JavaScript module script but the server responded with a MIME type of "text/jsx". Strict MIME type checking is enforced for module scripts per HTML spec.'
  • Set JS script path in build's index.html as relative (added '.' before '/')
• Set the build folder inside inner public
  • Build: { outDir : './path/to/build/dir' } in vite.config.js
• Error: vite.svg not found
  • Create an dummy/ugly outer public folder in root and added the svg file
  • Seems like docu said: vite consider links as relative, but images as globals
• Put all this test into a Git Branch

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Milestones

• voice2voice AI assistant beta

  • Capture voice in client
  • Post sound file to backend
  • Forward to whisper and fetch transcript
  • Forward transcript to gpt-turbo and fetch answer
  • Elaborate information corpus
  • Optimize prompting
  • Forward answer to whisper speech
  • Return speech answer to client
  • Play sound in client
  • Show transcript in client

• Make it pretty

  • Identify TB look and feel ->
  • Interact with gpt to clarify desing procedure
    • Long conversation
    • Main prompt: