Structured Output Extractor

Extract reliable, typed data from LLM responses.

Core Workflow

1. Define schema: Create data structure
2. Choose method: Function calling vs prompting
3. Generate response: Call LLM with structure
4. Validate output: Parse and verify
5. Handle errors: Retry or fallback

Methods Comparison

Method	Reliability	Flexibility	Best For
OpenAI JSON Mode	High	Medium	Simple JSON
Function Calling	Very High	High	Complex schemas
Instructor	Very High	High	Python/TS apps
Zod + Prompting	Medium	High	Custom parsing

OpenAI Structured Outputs

JSON Mode

// extractors/json-mode.ts
import OpenAI from 'openai';

const openai = new OpenAI();

interface ExtractedData {
  name: string;
  email: string;
  phone?: string;
  company?: string;
}

export async function extractContactInfo(text: string): Promise<ExtractedData> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    response_format: { type: 'json_object' },
    messages: [
      {
        role: 'system',
        content: `Extract contact information from text. Return JSON with:
{
  "name": "string",
  "email": "string",
  "phone": "string or null",
  "company": "string or null"
}`,
      },
      { role: 'user', content: text },
    ],
  });

  return JSON.parse(response.choices[0].message.content!);
}

Structured Outputs (Beta)

// extractors/structured.ts
import OpenAI from 'openai';
import { z } from 'zod';
import { zodResponseFormat } from 'openai/helpers/zod';

const ContactSchema = z.object({
  name: z.string().describe('Full name of the contact'),
  email: z.string().email().describe('Email address'),
  phone: z.string().nullable().describe('Phone number if available'),
  company: z.string().nullable().describe('Company name if mentioned'),
  role: z.string().nullable().describe('Job title or role'),
});

type Contact = z.infer<typeof ContactSchema>;

export async function extractContact(text: string): Promise<Contact> {
  const response = await openai.beta.chat.completions.parse({
    model: 'gpt-4o-2024-08-06',
    messages: [
      {
        role: 'system',
        content: 'Extract contact information from the provided text.',
      },
      { role: 'user', content: text },
    ],
    response_format: zodResponseFormat(ContactSchema, 'contact'),
  });

  return response.choices[0].message.parsed!;
}

Function Calling

Define Functions

// extractors/function-calling.ts
import OpenAI from 'openai';

const openai = new OpenAI();

const functions = [
  {
    name: 'extract_entities',
    description: 'Extract named entities from text',
    parameters: {
      type: 'object',
      properties: {
        people: {
          type: 'array',
          items: {
            type: 'object',
            properties: {
              name: { type: 'string' },
              role: { type: 'string' },
              organization: { type: 'string' },
            },
            required: ['name'],
          },
        },
        organizations: {
          type: 'array',
          items: {
            type: 'object',
            properties: {
              name: { type: 'string' },
              type: { type: 'string', enum: ['company', 'nonprofit', 'government', 'other'] },
            },
            required: ['name'],
          },
        },
        locations: {
          type: 'array',
          items: {
            type: 'object',
            properties: {
              name: { type: 'string' },
              type: { type: 'string', enum: ['city', 'country', 'address', 'other'] },
            },
            required: ['name'],
          },
        },
        dates: {
          type: 'array',
          items: {
            type: 'object',
            properties: {
              text: { type: 'string' },
              normalized: { type: 'string', format: 'date' },
            },
            required: ['text'],
          },
        },
      },
      required: ['people', 'organizations', 'locations', 'dates'],
    },
  },
];

export async function extractEntities(text: string) {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [
      {
        role: 'system',
        content: 'Extract all named entities from the provided text.',
      },
      { role: 'user', content: text },
    ],
    functions,
    function_call: { name: 'extract_entities' },
  });

  const functionCall = response.choices[0].message.function_call;
  return JSON.parse(functionCall!.arguments);
}

Tool Use Pattern

// extractors/tools.ts
import OpenAI from 'openai';

const tools: OpenAI.Chat.ChatCompletionTool[] = [
  {
    type: 'function',
    function: {
      name: 'classify_intent',
      description: 'Classify the user intent from their message',
      parameters: {
        type: 'object',
        properties: {
          intent: {
            type: 'string',
            enum: ['question', 'complaint', 'feedback', 'request', 'other'],
          },
          confidence: {
            type: 'number',
            minimum: 0,
            maximum: 1,
          },
          entities: {
            type: 'object',
            properties: {
              product: { type: 'string' },
              issue: { type: 'string' },
              sentiment: { type: 'string', enum: ['positive', 'negative', 'neutral'] },
            },
          },
          suggestedAction: {
            type: 'string',
          },
        },
        required: ['intent', 'confidence'],
      },
    },
  },
];

export async function classifyMessage(message: string) {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [{ role: 'user', content: message }],
    tools,
    tool_choice: { type: 'function', function: { name: 'classify_intent' } },
  });

  const toolCall = response.choices[0].message.tool_calls?.[0];
  return JSON.parse(toolCall!.function.arguments);
}

Zod Schema Validation

With Instructor-like Pattern

// extractors/zod-extractor.ts
import { z } from 'zod';
import OpenAI from 'openai';

const openai = new OpenAI();

// Define schema
const ProductReviewSchema = z.object({
  productName: z.string(),
  rating: z.number().min(1).max(5),
  pros: z.array(z.string()),
  cons: z.array(z.string()),
  summary: z.string().max(200),
  wouldRecommend: z.boolean(),
  sentiment: z.enum(['positive', 'negative', 'mixed', 'neutral']),
});

type ProductReview = z.infer<typeof ProductReviewSchema>;

// Generate JSON schema from Zod
function zodToJsonSchema(schema: z.ZodObject<any>) {
  // Simplified - use zod-to-json-schema in production
  const shape = schema.shape;
  const properties: Record<string, any> = {};
  const required: string[] = [];

  for (const [key, value] of Object.entries(shape)) {
    const zodType = value as z.ZodTypeAny;
    properties[key] = zodTypeToJson(zodType);
    if (!zodType.isOptional()) {
      required.push(key);
    }
  }

  return { type: 'object', properties, required };
}

export async function extractReview(reviewText: string): Promise<ProductReview> {
  const schema = zodToJsonSchema(ProductReviewSchema);

  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    response_format: { type: 'json_object' },
    messages: [
      {
        role: 'system',
        content: `Extract a structured product review. Return JSON matching this schema:
${JSON.stringify(schema, null, 2)}`,
      },
      { role: 'user', content: reviewText },
    ],
  });

  const data = JSON.parse(response.choices[0].message.content!);

  // Validate with Zod
  return ProductReviewSchema.parse(data);
}

With Retry on Validation Error

// extractors/retry.ts
export async function extractWithRetry<T>(
  schema: z.ZodSchema<T>,
  prompt: string,
  text: string,
  maxRetries = 3
): Promise<T> {
  let lastError: Error | null = null;

  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      const response = await openai.chat.completions.create({
        model: 'gpt-4-turbo-preview',
        response_format: { type: 'json_object' },
        messages: [
          { role: 'system', content: prompt },
          { role: 'user', content: text },
          // Add error context on retry
          ...(lastError
            ? [
                {
                  role: 'user' as const,
                  content: `Previous attempt failed validation: ${lastError.message}. Please fix.`,
                },
              ]
            : []),
        ],
      });

      const data = JSON.parse(response.choices[0].message.content!);
      return schema.parse(data);
    } catch (error) {
      if (error instanceof z.ZodError) {
        lastError = new Error(
          error.errors.map((e) => `${e.path.join('.')}: ${e.message}`).join(', ')
        );
      } else {
        throw error;
      }
    }
  }

  throw new Error(`Failed after ${maxRetries} attempts: ${lastError?.message}`);
}

Complex Extraction Patterns

Hierarchical Data

// extractors/hierarchical.ts
const DocumentSchema = z.object({
  title: z.string(),
  authors: z.array(
    z.object({
      name: z.string(),
      affiliation: z.string().optional(),
    })
  ),
  abstract: z.string(),
  sections: z.array(
    z.object({
      heading: z.string(),
      content: z.string(),
      subsections: z
        .array(
          z.object({
            heading: z.string(),
            content: z.string(),
          })
        )
        .optional(),
    })
  ),
  references: z.array(
    z.object({
      authors: z.array(z.string()),
      title: z.string(),
      year: z.number(),
      source: z.string().optional(),
    })
  ),
  keywords: z.array(z.string()),
});

Multi-step Extraction

// extractors/multistep.ts
export async function extractComplex(document: string) {
  // Step 1: Extract structure
  const structure = await extract(
    z.object({
      sections: z.array(z.string()),
      hasReferences: z.boolean(),
    }),
    'Identify the document structure',
    document
  );

  // Step 2: Extract each section
  const sections = await Promise.all(
    structure.sections.map((section) =>
      extract(
        z.object({
          heading: z.string(),
          summary: z.string(),
          keyPoints: z.array(z.string()),
        }),
        `Extract details from section: ${section}`,
        document
      )
    )
  );

  // Step 3: Extract references if present
  let references = [];
  if (structure.hasReferences) {
    references = await extract(
      z.array(
        z.object({
          authors: z.array(z.string()),
          title: z.string(),
          year: z.number(),
        })
      ),
      'Extract all references',
      document
    );
  }

  return { sections, references };
}

Streaming Structured Output

// extractors/streaming.ts
import { zodToJsonSchema } from 'zod-to-json-schema';

export async function* streamExtract<T>(
  schema: z.ZodSchema<T>,
  prompt: string,
  text: string
): AsyncGenerator<{ partial: any; complete: boolean }> {
  const jsonSchema = zodToJsonSchema(schema);

  const stream = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    response_format: { type: 'json_object' },
    messages: [
      {
        role: 'system',
        content: `${prompt}\n\nReturn JSON matching: ${JSON.stringify(jsonSchema)}`,
      },
      { role: 'user', content: text },
    ],
    stream: true,
  });

  let fullContent = '';

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    fullContent += content;

    // Try to parse partial JSON
    try {
      const partial = JSON.parse(fullContent);
      yield { partial, complete: false };
    } catch {
      // JSON not complete yet
    }
  }

  // Final parse and validate
  const final = JSON.parse(fullContent);
  const validated = schema.parse(final);
  yield { partial: validated, complete: true };
}

Error Handling

// extractors/error-handling.ts
export class ExtractionError extends Error {
  constructor(
    message: string,
    public readonly raw: string,
    public readonly validationErrors?: z.ZodError
  ) {
    super(message);
    this.name = 'ExtractionError';
  }
}

export async function safeExtract<T>(
  schema: z.ZodSchema<T>,
  prompt: string,
  text: string
): Promise<{ success: true; data: T } | { success: false; error: ExtractionError }> {
  try {
    const data = await extractWithRetry(schema, prompt, text);
    return { success: true, data };
  } catch (error) {
    if (error instanceof z.ZodError) {
      return {
        success: false,
        error: new ExtractionError('Validation failed', text, error),
      };
    }
    return {
      success: false,
      error: new ExtractionError(
        error instanceof Error ? error.message : 'Unknown error',
        text
      ),
    };
  }
}

Best Practices

1. Use structured outputs: OpenAI's native feature when available
2. Define clear schemas: Descriptive field names and descriptions
3. Validate with Zod: Type-safe runtime validation
4. Retry on failure: With error context
5. Use function calling: For complex schemas
6. Handle edge cases: Null values, empty arrays
7. Stream for UX: Partial results during extraction
8. Test with variations: Different input formats

Output Checklist

Every structured extraction should include:

• Zod schema with descriptions
• Appropriate extraction method (JSON/function/tools)
• Runtime validation
• Retry logic with error context
• Error handling and typing
• Streaming support (if needed)
• Edge case handling
• Type-safe return values
• Comprehensive tests