Orq.ai Documentation - AI Gateway & LLM Collaboration Platform

Use Cases

Extracting typed fields from unstructured text (invoices, emails, support tickets).
Pipelines where downstream code requires a guaranteed JSON schema.
Classification tasks with a controlled output set (enum values, boolean flags).
Generating config objects or API payloads directly from natural language.

Quick Start

Generate structured JSON responses with guaranteed schema compliance.

curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "input": "Extract info: John Doe, 30, john@example.com, knows Python and React",
    "text": {
      "format": {
        "type": "json_schema",
        "name": "user",
        "schema": {
          "type": "object",
          "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "email": {"type": "string"},
            "skills": {"type": "array", "items": {"type": "string"}}
          },
          "required": ["name", "age", "email", "skills"],
          "additionalProperties": false
        }
      }
    }
  }'

Configuration Options

Response Format Types

Type	Description
`"text"`	Default response format for text responses
`"json_object"`	Basic JSON output (no schema validation)
`"json_schema"`	Structured JSON with schema validation

Text Mode (Default)

Parameter	Type	Description
`type`	`"text"`	Default response format for text outputs

JSON Schema Mode

Parameter	Type	Description
`type`	`"json_schema"`	Enable schema validation
`json_schema.name`	string	Schema name/identifier
`json_schema.schema`	object	JSON Schema definition

Simple JSON Mode

Parameter	Type	Description
`type`	`"json_object"`	Basic JSON output (no schema)

Schema Examples

Simple Data Extraction

{
  "type": "json_schema",
  "json_schema": {
    "name": "contact_info",
    "schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "email": { "type": "string", "format": "email" },
        "phone": { "type": "string" },
        "company": { "type": "string" }
      },
      "required": ["name", "email"]
    }
  }
}

Complex Nested Structure

{
  "type": "json_schema",
  "json_schema": {
    "name": "product_analysis",
    "schema": {
      "type": "object",
      "properties": {
        "product": {
          "type": "object",
          "properties": {
            "name": { "type": "string" },
            "price": { "type": "number" },
            "category": { "type": "string" }
          }
        },
        "features": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": { "type": "string" },
              "importance": { "type": "integer", "minimum": 1, "maximum": 5 }
            }
          }
        },
        "summary": { "type": "string" }
      },
      "required": ["product", "features", "summary"]
    }
  }
}

Code examples

curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "input": "Extract user information from: John Doe, 30 years old, john@example.com, Software Engineer",
    "text": {
      "format": {
        "type": "json_schema",
        "name": "user_info",
        "schema": {
          "type": "object",
          "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "email": {"type": "string"},
            "occupation": {"type": "string"}
          },
          "required": ["name", "age", "email", "occupation"],
          "additionalProperties": false
        }
      }
    }
  }'

Common Use Cases

The examples in this section use the Chat Completions endpoint. Structured outputs work identically with the Responses API: use responses.create() with text: { format: { type: "json_schema", ... } } instead of response_format.

Data Extraction

from openai import OpenAI
from pydantic import BaseModel
from typing import List
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

class LineItem(BaseModel):
    description: str
    quantity: int
    unit_price: float
    total: float

class InvoiceData(BaseModel):
    invoice_number: str
    date: str
    vendor: str
    total_amount: float
    line_items: List[LineItem]

response = client.chat.completions.parse(
    model="openai/gpt-4o",
    messages=[{
        "role": "user",
        "content": "Extract invoice data from this text: [invoice text]"
    }],
    response_format=InvoiceData
)

Content Analysis

import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const SentimentSchema = z.object({
  sentiment: z.enum(["positive", "negative", "neutral"]),
  confidence: z.number().min(0).max(1),
  key_phrases: z.array(z.string()),
  summary: z.string(),
});

const response = await client.chat.completions.parse({
  model: "openai/gpt-4o",
  messages: [
    {
      role: "user",
      content: "Analyze sentiment of this review: [review text]",
    },
  ],
  response_format: zodResponseFormat(SentimentSchema, "sentiment_analysis"),
});

Form Generation

from openai import OpenAI
from pydantic import BaseModel, Field
from typing import List
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

class SurveyQuestion(BaseModel):
    question: str
    type: str
    options: List[str]

class SurveyForm(BaseModel):
    title: str
    description: str
    questions: List[SurveyQuestion] = Field(
        ...,
        description="List of survey questions with type and options"
    )
    estimated_duration: int = Field(description="Minutes to complete")

response = client.chat.completions.parse(
    model="openai/gpt-4o",
    messages=[{
        "role": "user",
        "content": "Create a customer satisfaction survey for a restaurant"
    }],
    response_format=SurveyForm
)

Advanced Patterns

Conditional Schemas

import { z } from "zod";

const ResponseSchema = z.discriminatedUnion("type", [
  z.object({
    type: z.literal("success"),
    data: z.object({
      result: z.string(),
      metadata: z.record(z.any()),
    }),
  }),
  z.object({
    type: z.literal("error"),
    error: z.object({
      code: z.string(),
      message: z.string(),
    }),
  }),
]);

Dynamic Schema Generation

from typing import List

def create_schema_for_fields(fields: List[str]):
    properties = {}
    for field in fields:
        properties[field] = {"type": "string"}

    return {
        "type": "object",
        "properties": properties,
        "required": fields
    }

# Generate schema based on user input
fields = ["name", "email", "department"]
schema = create_schema_for_fields(fields)

Validation and Error Handling

from openai import OpenAI
from pydantic import BaseModel
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

prompt = "Extract the following information: John Doe is 30 years old."

class MySchema(BaseModel):
    name: str
    age: int

try:
    response = client.chat.completions.parse(
        model="openai/gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        response_format=MySchema
    )

    if response.choices[0].message.parsed:
        data = response.choices[0].message.parsed
        # Process validated data
    else:
        # Handle parsing failure
        print("Failed to parse response")

except Exception as e:
    print(f"Error: {e}")
    # Fallback to regular completion

Best Practices

Schema design:

Use descriptive field names.
Add field descriptions for better results.
Mark essential fields as required.
Use appropriate data types and constraints. Error handling:

import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const parseWithFallback = async (prompt: string, schema: z.ZodTypeAny) => {
  try {
    const response = await client.chat.completions.parse({
      model: "openai/gpt-4o",
      messages: [{ role: "user", content: prompt }],
      response_format: zodResponseFormat(schema, "data"),
    });

    return response.choices[0].message.parsed;
  } catch (error) {
    console.warn("Structured parsing failed, trying JSON mode");

    // Fallback to basic JSON mode
    const fallback = await client.chat.completions.create({
      model: "openai/gpt-4o",
      messages: [
        {
          role: "user",
          content: `${prompt}\n\nRespond with valid JSON only.`,
        },
      ],
      response_format: { type: "json_object" },
    });

    const content = fallback.choices[0].message.content;
    if (!content) throw new Error("Empty fallback response");
    return JSON.parse(content);
  }
};

Performance optimization:

# Cache schemas for reuse
schema_cache = {}

def create_schema(schema_name): ...  # replace with your schema factory

def get_cached_schema(schema_name):
    if schema_name not in schema_cache:
        schema_cache[schema_name] = create_schema(schema_name)
    return schema_cache[schema_name]

Troubleshooting

Schema validation fails

Simplify complex nested structures.
Ensure required fields are clearly specified.
Check field types match expected data.
Add field descriptions for clarity. Inconsistent outputs
Use more specific prompts.
Add examples in the prompt.
Increase model temperature for creativity.
Switch to a more capable model. Performance issues
Reduce schema complexity.
Cache schema definitions.
Use appropriate models for task complexity.
Consider breaking large schemas into smaller ones.

Limitations

Limitation	Impact	Workaround
Schema complexity	Large schemas may fail	Break into smaller schemas
Model support	Not all models support schemas	Use JSON mode as fallback
Nested depth	Deep nesting may cause issues	Flatten structures when possible
Array validation	Complex array items challenging	Simplify item schemas
Performance cost	Schema validation adds latency	Cache and optimize schemas

Integration Examples

Database Integration

from openai import OpenAI
from pydantic import BaseModel
from typing import Optional
from datetime import datetime
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

class DatabaseRecord(BaseModel):
    id: Optional[int] = None
    name: str
    email: str
    created_at: Optional[datetime] = None

# Generate structured data
response = client.chat.completions.parse(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Create user record"}],
    response_format=DatabaseRecord
)

# Direct database insertion (example using SQLAlchemy)
record = response.choices[0].message.parsed
# session.add(record)
# session.commit()

API Integration

import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const ApiResponseSchema = z.object({
  status: z.enum(["success", "error"]),
  data: z.string(),
  message: z.string().nullable().optional(),
});

const generateApiResponse = async (query) => {
  const response = await client.chat.completions.parse({
    model: "openai/gpt-4o",
    messages: [{ role: "user", content: query }],
    response_format: zodResponseFormat(ApiResponseSchema, "api_response"),
  });

  // Return structured API response
  return response.choices[0].message.parsed;
};

​Quick Start

​Configuration Options

​Response Format Types

​Text Mode (Default)

​JSON Schema Mode

​Simple JSON Mode

​Schema Examples

​Simple Data Extraction

​Complex Nested Structure

​Code examples

​Common Use Cases

​Data Extraction

​Content Analysis

​Form Generation

​Advanced Patterns

​Conditional Schemas

​Dynamic Schema Generation

​Validation and Error Handling

​Best Practices

​Troubleshooting

​Limitations

​Integration Examples

​Database Integration

​API Integration

Quick Start

Configuration Options

Response Format Types

Text Mode (Default)

JSON Schema Mode

Simple JSON Mode

Schema Examples

Simple Data Extraction

Complex Nested Structure

Code examples

Common Use Cases

Data Extraction

Content Analysis

Form Generation

Advanced Patterns

Conditional Schemas

Dynamic Schema Generation

Validation and Error Handling

Best Practices

Troubleshooting

Limitations

Integration Examples

Database Integration

API Integration