ConfigGenerator

OpenAI Environment Generator

Generate safe environment variables and type-safe schemas for OpenAI API applications.

Do NOT enter your real key here! Use a placeholder like 'your_openai_api_key_here'.

Useful if you are part of multiple OpenAI organizations.

Used to restrict API keys and track costs at a project level.

Set this if you are using an OpenAI-compatible proxy, enterprise gateway, or local mock (e.g. LiteLLM, Cloudflare AI Gateway).

The default LLM to use (e.g., gpt-4o, gpt-4o-mini, gpt-3.5-turbo).

The default embedding model to use for RAG (e.g., text-embedding-3-small).

Request timeout in milliseconds. AI requests can take time; 30000ms is a safe default.

Number of times to retry failed requests (e.g., rate limits).

- Configure app to expect Server-Sent Events (SSE) streaming.
- Enable function/tool calling features.
- Enable cost and token usage tracking.
- Enable logging of requests/responses. Caution: May log sensitive user PII in production!
openai-environment-generator.yaml
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Overview

Enterprise OpenAI Environment Generator

The OpenAI Environment Generator creates production-ready environment files and typed configurations for connecting your applications to OpenAI's APIs. It enforces secure-by-default practices to prevent API key leaks and unexpected runtime errors.

Short Answer: Use this tool to generate .env.example, env.ts, and Docker configurations that handle OpenAI keys securely while setting up timeouts, retries, and models correctly.

How It Works

  1. Select your Framework: Choose between Next.js, Node.js, Python FastAPI, or Docker Compose.
  2. Configure Models: Set your default Chat (e.g. gpt-4o) and Embedding models.
  3. Enable Features: Toggle streaming, tool calling, or cost tracking logic.
  4. Generate: Get a validated, copy-paste ready environment configuration with setup guides.

Best Practices

  • Use @t3-oss/env-nextjs or Pydantic BaseSettings to validate env vars on boot.
  • Set a timeout (e.g. 30000ms) to prevent hanging connections.
  • Use separate API keys for Development and Production environments.
  • Track costs by setting up separate OpenAI Project IDs.

Common Mistakes

  • Prefixing keys with NEXT_PUBLIC_ or VITE_ (exposes them to the browser).
  • Committing your real .env file to Git.
  • Hardcoding the API key directly in your source code.
  • Not handling API rate limit (429) errors gracefully.

Security Recommendations

  • Use a Secret Manager (AWS Secrets Manager, Vercel Env Vars, HashiCorp Vault) in production.
  • Regularly rotate your API keys.
  • Set usage limits in your OpenAI dashboard to prevent billing attacks.
  • Route all AI requests through a secure backend server.

Frequently Asked Questions

Why shouldn't I use my OpenAI key on the frontend?
Frontend code is visible to anyone using the browser. If your API key is in the frontend, attackers can extract it and make requests at your expense, draining your credits.
What is an OpenAI Project ID?
A Project ID allows you to scope API keys to a specific project within your organization, making it easier to track costs, manage members, and set specific usage limits.