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Version: 1.0.5

Structured Data with ASI:One

ASI:One can return responses that conform to a JSON schema you define. This example shows how to extract structured information—an order_summary—directly from natural language using the response_format parameter with a JSON schema.

import os
import json
import requests
from typing import List
from openai import OpenAI


# ---- Config from environment ----
LLM_API_ENDPOINT = os.getenv("LLM_API_ENDPOINT", "https://api.asi1.ai/v1")
LLM_API_KEY = os.getenv("LLM_API_KEY", "your-api-key")
LLM_MODEL = os.getenv("LLM_MODEL", "asi1")

# Initialize OpenAI client with ASI1 API
client = OpenAI(
    api_key=LLM_API_KEY,
    base_url=LLM_API_ENDPOINT
)

headers = {
    "Authorization": f"Bearer {LLM_API_KEY}",
    "Content-Type": "application/json",
}

# ---- Target JSON schema (order summary) ----
response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "order_summary",
        "strict": True,
        "schema": {
            "type": "object",
            "additionalProperties": False,
            "properties": {
                "order_id": {"type": "string"},
                "total": {"type": "number"},
                "currency": {"type": "string"},
                "items": {
                    "type": "array",
                    "minItems": 1,
                    "items": {
                        "type": "object",
                        "additionalProperties": False,
                        "properties": {
                            "sku": {"type": "string"},
                            "name": {"type": "string"},
                            "qty": {"type": "integer", "minimum": 1},
                            "unit_price": {"type": "number"}
                        },
                        "required": ["sku", "name", "qty", "unit_price"]
                    }
                }
            },
            "required": ["order_id", "total", "currency", "items"]
        }
    }
}

prompt = (
    "Generate a realistic example order summary with 2–3 items. "
    "Use USD as ISO currency code. Fill every required field. "
    "The order id is 1234567890. "
    "The total is 100. "
    "The currency is USD. "
    "The items are 100. "
    "The unit price is 1."
)

# ---- Chat Completions payload ----
payload = {
    "model": LLM_MODEL,
    "messages": [
        {"role": "system", "content": "Return ONLY valid JSON matching the provided schema."},
        {"role": "user", "content": prompt},
    ],
    "response_format": response_format,
}

resp = requests.post(
    f"{LLM_API_ENDPOINT}/chat/completions",
    headers=headers,
    json=payload,
    timeout=60,
)

# Raw response
if not resp.ok:
    print(resp.status_code, resp.text)
    resp.raise_for_status()

data = resp.json()

# Simple parse of final JSON content
try:
    content = (
        (data.get("choices") or [{}])[0]
        .get("message", {})
        .get("content", "")
    )
    parsed = json.loads(content) if content else None
except Exception:
    parsed = None

print("\n=== Output ===")
print(json.dumps(parsed, indent=2) if parsed is not None else "None")

Expected Output​

The model returns a response that conforms to the JSON schema you defined. The response is automatically structured as an order_summary object:

=== Output ===
{
  "currency": "USD",
  "items": [
    {
      "name": "Wireless Bluetooth Earbuds",
      "qty": 40,
      "sku": "WBE-001",
      "unit_price": 1.0
    },
    {
      "name": "USB-C Charging Cable (6ft)",
      "qty": 35,
      "sku": "UCC-002",
      "unit_price": 1.0
    },
    {
      "name": "Phone Stand - Adjustable",
      "qty": 25,
      "sku": "PSA-003",
      "unit_price": 1.0
    }
  ],
  "order_id": "1234567890",
  "total": 100.0
}

You now have structured order data that conforms to your schema, ready for further processing such as saving to a database, generating invoices, triggering notifications, or integrating with payment systems.

Next Steps​