LLM Structured Outputs & Strict JSON Schema Studio

Generate OpenAI strict json_schema, Anthropic tool schemas, and Gemini response schemas from JSON or interfaces

Structured Output Presets

Load real-world extraction payloads and strict schema definitions

Sample Payload / Schema Input

Sample JSON or Schema

Paste sample JSON or a draft schema to enforce strict compliance

Schema Name (Alphanumeric)

Instruction / Description

JSON Structure

Strict Mode Constraints Enforced

• additionalProperties: false added to every object layer

• required: [...] populated with 100% of object property keys

• Nested child objects and arrays are recursively normalized

Generated Strict Schemas & Client SDKs

strict: true

{
  "type": "object",
  "properties": {
    "vendorName": {
      "type": "string"
    },
    "invoiceNumber": {
      "type": "string"
    },
    "date": {
      "type": "string"
    },
    "totalAmount": {
      "type": "number"
    },
    "currency": {
      "type": "string"
    },
    "lineItems": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "description": {
            "type": "string"
          },
          "quantity": {
            "type": "integer"
          },
          "unitPrice": {
            "type": "integer"
          }
        },
        "required": [
          "description",
          "quantity",
          "unitPrice"
        ],
        "additionalProperties": false
      }
    },
    "isPaid": {
      "type": "boolean"
    }
  },
  "required": [
    "vendorName",
    "invoiceNumber",
    "date",
    "totalAmount",
    "currency",
    "lineItems",
    "isPaid"
  ],
  "additionalProperties": false
}

import OpenAI from 'openai';
const client = new OpenAI();

// Schema definition with strict: true
const InvoiceExtractionSchema = {
  name: 'InvoiceExtraction',
  strict: true,
  schema: {
    "type": "object",
    "properties": {
      "vendorName": {
        "type": "string"
      },
      "invoiceNumber": {
        "type": "string"
      },
      "date": {
        "type": "string"
      },
      "totalAmount": {
        "type": "number"
      },
      "currency": {
        "type": "string"
      },
      "lineItems": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "description": {
              "type": "string"
            },
            "quantity": {
              "type": "integer"
            },
            "unitPrice": {
              "type": "integer"
            }
          },
          "required": [
            "description",
            "quantity",
            "unitPrice"
          ],
          "additionalProperties": false
        }
      },
      "isPaid": {
        "type": "boolean"
      }
    },
    "required": [
      "vendorName",
      "invoiceNumber",
      "date",
      "totalAmount",
      "currency",
      "lineItems",
      "isPaid"
    ],
    "additionalProperties": false
  }
};

async function main() {
  const response = await client.chat.completions.create({
    model: 'gpt-4o-2024-08-06',
    messages: [
      { role: 'system', content: 'Extract structured information.' },
      { role: 'user', content: 'Process input...' }
    ],
    response_format: {
      type: 'json_schema',
      json_schema: InvoiceExtractionSchema
    }
  });

  const parsed = JSON.parse(response.choices[0].message.content || '{}');
  console.log(parsed);
}
main();

import json
from openai import OpenAI
client = OpenAI()

schema = {
    "name": "InvoiceExtraction",
    "strict": True,
    "schema": {
  "type": "object",
  "properties": {
    "vendorName": {
      "type": "string"
    },
    "invoiceNumber": {
      "type": "string"
    },
    "date": {
      "type": "string"
    },
    "totalAmount": {
      "type": "number"
    },
    "currency": {
      "type": "string"
    },
    "lineItems": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "description": {
            "type": "string"
          },
          "quantity": {
            "type": "integer"
          },
          "unitPrice": {
            "type": "integer"
          }
        },
        "required": [
          "description",
          "quantity",
          "unitPrice"
        ],
        "additionalProperties": False
      }
    },
    "isPaid": {
      "type": "boolean"
    }
  },
  "required": [
    "vendorName",
    "invoiceNumber",
    "date",
    "totalAmount",
    "currency",
    "lineItems",
    "isPaid"
  ],
  "additionalProperties": False
}
}

response = client.chat.completions.create(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "Extract structured information."},
        {"role": "user", "content": "Process input..."}
    ],
    response_format={"type": "json_schema", "json_schema": schema}
)

data = json.loads(response.choices[0].message.content)
print(data)

import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic();

const response = await anthropic.messages.create({
  model: 'claude-3-5-sonnet-20241022',
  max_tokens: 1024,
  tools: [
    {
      name: 'InvoiceExtraction',
      description: 'Strict structured output definition',
      input_schema: {
        "type": "object",
        "properties": {
          "vendorName": {
            "type": "string"
          },
          "invoiceNumber": {
            "type": "string"
          },
          "date": {
            "type": "string"
          },
          "totalAmount": {
            "type": "number"
          },
          "currency": {
            "type": "string"
          },
          "lineItems": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "description": {
                  "type": "string"
                },
                "quantity": {
                  "type": "integer"
                },
                "unitPrice": {
                  "type": "integer"
                }
              },
              "required": [
                "description",
                "quantity",
                "unitPrice"
              ],
              "additionalProperties": false
            }
          },
          "isPaid": {
            "type": "boolean"
          }
        },
        "required": [
          "vendorName",
          "invoiceNumber",
          "date",
          "totalAmount",
          "currency",
          "lineItems",
          "isPaid"
        ],
        "additionalProperties": false
      }
    }
  ],
  tool_choice: { type: 'tool', name: 'InvoiceExtraction' },
  messages: [{ role: 'user', content: 'Process input...' }]
});

const toolUse = response.content.find(block => block.type === 'tool_use');
console.log(toolUse?.input);

import { GoogleGenerativeAI, SchemaType } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);
const model = genAI.getGenerativeModel({
  model: 'gemini-1.5-pro',
  generationConfig: {
    responseMimeType: 'application/json',
    responseSchema: {
      "type": "object",
      "properties": {
        "vendorName": {
          "type": "string"
        },
        "invoiceNumber": {
          "type": "string"
        },
        "date": {
          "type": "string"
        },
        "totalAmount": {
          "type": "number"
        },
        "currency": {
          "type": "string"
        },
        "lineItems": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "description": {
                "type": "string"
              },
              "quantity": {
                "type": "integer"
              },
              "unitPrice": {
                "type": "integer"
              }
            },
            "required": [
              "description",
              "quantity",
              "unitPrice"
            ],
            "additionalProperties": false
          }
        },
        "isPaid": {
          "type": "boolean"
        }
      },
      "required": [
        "vendorName",
        "invoiceNumber",
        "date",
        "totalAmount",
        "currency",
        "lineItems",
        "isPaid"
      ],
      "additionalProperties": false
    }
  }
});

const result = await model.generateContent('Process input...');
console.log(JSON.parse(result.response.text()));

{ "type": "object", "properties": { "vendorName": { "type": "string" }, "invoiceNumber": { "type": "string" }, "date": { "type": "string" }, "totalAmount": { "type": "number" }, "currency": { "type": "string" }, "lineItems": { "type": "array", "items": { "type": "object", "properties": { "description": { "type": "string" }, "quantity": { "type": "integer" }, "unitPrice": { "type": "integer" } }, "required": [ "description", "quantity", "unitPrice" ], "additionalProperties": false } }, "isPaid": { "type": "boolean" } }, "required": [ "vendorName", "invoiceNumber", "date", "totalAmount", "currency", "lineItems", "isPaid" ], "additionalProperties": false }

import OpenAI from 'openai'; const client = new OpenAI(); // Schema definition with strict: true const InvoiceExtractionSchema = { name: 'InvoiceExtraction', strict: true, schema: { "type": "object", "properties": { "vendorName": { "type": "string" }, "invoiceNumber": { "type": "string" }, "date": { "type": "string" }, "totalAmount": { "type": "number" }, "currency": { "type": "string" }, "lineItems": { "type": "array", "items": { "type": "object", "properties": { "description": { "type": "string" }, "quantity": { "type": "integer" }, "unitPrice": { "type": "integer" } }, "required": [ "description", "quantity", "unitPrice" ], "additionalProperties": false } }, "isPaid": { "type": "boolean" } }, "required": [ "vendorName", "invoiceNumber", "date", "totalAmount", "currency", "lineItems", "isPaid" ], "additionalProperties": false } }; async function main() { const response = await client.chat.completions.create({ model: 'gpt-4o-2024-08-06', messages: [ { role: 'system', content: 'Extract structured information.' }, { role: 'user', content: 'Process input...' } ], response_format: { type: 'json_schema', json_schema: InvoiceExtractionSchema } }); const parsed = JSON.parse(response.choices[0].message.content || '{}'); console.log(parsed); } main();

import json from openai import OpenAI client = OpenAI() schema = { "name": "InvoiceExtraction", "strict": True, "schema": { "type": "object", "properties": { "vendorName": { "type": "string" }, "invoiceNumber": { "type": "string" }, "date": { "type": "string" }, "totalAmount": { "type": "number" }, "currency": { "type": "string" }, "lineItems": { "type": "array", "items": { "type": "object", "properties": { "description": { "type": "string" }, "quantity": { "type": "integer" }, "unitPrice": { "type": "integer" } }, "required": [ "description", "quantity", "unitPrice" ], "additionalProperties": False } }, "isPaid": { "type": "boolean" } }, "required": [ "vendorName", "invoiceNumber", "date", "totalAmount", "currency", "lineItems", "isPaid" ], "additionalProperties": False } } response = client.chat.completions.create( model="gpt-4o-2024-08-06", messages=[ {"role": "system", "content": "Extract structured information."}, {"role": "user", "content": "Process input..."} ], response_format={"type": "json_schema", "json_schema": schema} ) data = json.loads(response.choices[0].message.content) print(data)

import Anthropic from '@anthropic-ai/sdk'; const anthropic = new Anthropic(); const response = await anthropic.messages.create({ model: 'claude-3-5-sonnet-20241022', max_tokens: 1024, tools: [ { name: 'InvoiceExtraction', description: 'Strict structured output definition', input_schema: { "type": "object", "properties": { "vendorName": { "type": "string" }, "invoiceNumber": { "type": "string" }, "date": { "type": "string" }, "totalAmount": { "type": "number" }, "currency": { "type": "string" }, "lineItems": { "type": "array", "items": { "type": "object", "properties": { "description": { "type": "string" }, "quantity": { "type": "integer" }, "unitPrice": { "type": "integer" } }, "required": [ "description", "quantity", "unitPrice" ], "additionalProperties": false } }, "isPaid": { "type": "boolean" } }, "required": [ "vendorName", "invoiceNumber", "date", "totalAmount", "currency", "lineItems", "isPaid" ], "additionalProperties": false } } ], tool_choice: { type: 'tool', name: 'InvoiceExtraction' }, messages: [{ role: 'user', content: 'Process input...' }] }); const toolUse = response.content.find(block => block.type === 'tool_use'); console.log(toolUse?.input);

import { GoogleGenerativeAI, SchemaType } from '@google/generative-ai'; const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!); const model = genAI.getGenerativeModel({ model: 'gemini-1.5-pro', generationConfig: { responseMimeType: 'application/json', responseSchema: { "type": "object", "properties": { "vendorName": { "type": "string" }, "invoiceNumber": { "type": "string" }, "date": { "type": "string" }, "totalAmount": { "type": "number" }, "currency": { "type": "string" }, "lineItems": { "type": "array", "items": { "type": "object", "properties": { "description": { "type": "string" }, "quantity": { "type": "integer" }, "unitPrice": { "type": "integer" } }, "required": [ "description", "quantity", "unitPrice" ], "additionalProperties": false } }, "isPaid": { "type": "boolean" } }, "required": [ "vendorName", "invoiceNumber", "date", "totalAmount", "currency", "lineItems", "isPaid" ], "additionalProperties": false } } }); const result = await model.generateContent('Process input...'); console.log(JSON.parse(result.response.text()));