ask2api 1.1.2

A minimal CLI tool that turns natural language into instant APIs.

ask2api

ask2api is a minimal Python CLI tool that turns natural language prompts into structured API-style JSON responses using LLM.

It allows you to define a JSON Schema and force the model to answer strictly in that format.

Why ask2api?

Because LLMs are no longer just chatbots, they are also programmable API engines.

ask2api lets you use them that way. 🚀

Key features:

• Minimal dependencies
• CLI first
• Prompt → API behavior
• No markdown, no explanations, only valid JSON
• Vision modality support
• Designed for automation pipelines and AI-driven backend workflows

Installation

pip install ask2api

Set your API key:

export ASK2API_API_KEY="your_api_key"
# Or you can pass OpenAI key
# export OPENAI_API_KEY="your_api_key"

Usage

Text-only prompts

Instead of asking:

"Where is the capital of France?"

and receiving free-form text, you can do this:

ask2api -p "Where is the capital of France?" -sf schema.json

Or pass an example directly without a schema file:

ask2api -p "Where is the capital of France?" -e '{"country": "string", "city": "string"}'

And get a structured API response:

{
  "country": "France",
  "city": "Paris"
}

For more complex structures with different data types:

ask2api -p "Analyze carbon element" -e '{
  "symbol": "element symbol",
  "atomic_number": 1234,
  "atomic_weight": 12.34,
  "is_metal": true,
  "isotopes": ["name of the isotope"],
  "properties": {
    "melting_point": 1234.5,
    "boiling_point": 2345.6,
    "magnetic": true
  }
}'

Output:

{
  "symbol": "C",
  "atomic_number": 6,
  "atomic_weight": 12.011,
  "is_metal": false,
  "isotopes": [
    "C-12",
    "C-13",
    "C-14"
  ],
  "properties": {
    "melting_point": 3550,
    "boiling_point": 4827,
    "magnetic": false
  }
}

Vision modality

You can also analyze images and get structured JSON responses:

ask2api -p "Where is this place?" -sf schema.json -i https://upload.wikimedia.org/wikipedia/commons/6/64/Lesdeuxmagots.jpg

How it works

1. You define the desired output structure using a JSON Schema.
2. The schema is passed to the model using OpenAI's json_schema structured output format.
3. The system prompt enforces strict JSON-only responses.
4. For vision tasks, images are automatically encoded (base64 for local files) or passed as URLs.
5. The CLI prints the API-ready JSON output.

The model is treated as a deterministic API function.

Example schema

Create a file named schema.json:

{
  "type": "object",
  "properties": {
    "country": { "type": "string" },
    "city": { "type": "string" }
  },
  "required": ["country", "city"]
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT