develop-ai-functions-example

oleh vercel

Skrip pengembangan dan validasi untuk fungsi AI SDK di berbagai penyedia dan kemampuan. Diatur berdasarkan kategori fungsi AI SDK (generasi teks, streaming, keluaran terstruktur, embeddings, generasi gambar, ucapan, transkripsi, peringkat ulang, dan agen). Konvensi penamaan file memetakan kombinasi penyedia dan fitur (misalnya, openai-tool-call.ts, amazon-bedrock-anthropic-cache-control.ts) untuk identifikasi cepat. Termasuk pembantu utilitas bersama untuk penanganan kesalahan, pemuatan lingkungan,...

npx skills add https://github.com/vercel/ai --skill develop-ai-functions-example

AI Functions Examples

The examples/ai-functions/ directory contains scripts for validating, testing, and iterating on AI SDK functions across providers.

Example Categories

Examples are organized by AI SDK function in examples/ai-functions/src/:

DirectoryPurpose
generate-text/Non-streaming text generation with generateText()
stream-text/Streaming text generation with streamText()
generate-object/Structured output generation with generateObject()
stream-object/Streaming structured output with streamObject()
agent/ToolLoopAgent examples for agentic workflows
embed/Single embedding generation with embed()
embed-many/Batch embedding generation with embedMany()
generate-image/Image generation with generateImage()
generate-speech/Text-to-speech with generateSpeech()
transcribe/Audio transcription with transcribe()
rerank/Document reranking with rerank()
middleware/Custom middleware implementations
registry/Provider registry setup and usage
telemetry/OpenTelemetry integration
complex/Multi-component examples (agents, routers)
lib/Shared utilities (not examples)
tools/Reusable tool definitions

File Naming Convention

Examples follow the pattern: {provider}-{feature}.ts

PatternExampleDescription
{provider}.tsopenai.tsBasic provider usage
{provider}-{feature}.tsopenai-tool-call.tsSpecific feature
{provider}-{sub-provider}.tsamazon-bedrock-anthropic.tsProvider with sub-provider
{provider}-{sub-provider}-{feature}.tsgoogle-vertex-anthropic-cache-control.tsSub-provider with feature

Example Structure

All examples use the run() wrapper from lib/run.ts which:

  • Loads environment variables from .env
  • Provides error handling with detailed API error logging

Basic Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText } from 'ai';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  console.log(result.text);
  console.log('Token usage:', result.usage);
  console.log('Finish reason:', result.finishReason);
});

Streaming Template

import { providerName } from '@ai-sdk/provider-name';
import { streamText } from 'ai';
import { printFullStream } from '../lib/print-full-stream';
import { run } from '../lib/run';

run(async () => {
  const result = streamText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  await printFullStream({ result });
});

Tool Calling Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText, tool } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    tools: {
      myTool: tool({
        description: 'Tool description',
        inputSchema: z.object({
          param: z.string().describe('Parameter description'),
        }),
        execute: async ({ param }) => {
          return { result: `Processed: ${param}` };
        },
      }),
    },
    prompt: 'Use the tool to...',
  });

  console.log(JSON.stringify(result, null, 2));
});

Structured Output Template

import { providerName } from '@ai-sdk/provider-name';
import { generateObject } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateObject({
    model: providerName('model-id'),
    schema: z.object({
      name: z.string(),
      items: z.array(z.string()),
    }),
    prompt: 'Generate a...',
  });

  console.log(JSON.stringify(result.object, null, 2));
  console.log('Token usage:', result.usage);
});

Running Examples

From the examples/ai-functions directory:

pnpm tsx src/generate-text/openai.ts
pnpm tsx src/stream-text/openai-tool-call.ts
pnpm tsx src/agent/openai-generate.ts

When to Write Examples

Write examples when:

  1. Adding a new provider: Create basic examples for each supported API (generateText, streamText, generateObject, etc.)

  2. Implementing a new feature: Demonstrate the feature with at least one provider example

  3. Reproducing a bug: Create an example that shows the issue for debugging

  4. Adding provider-specific options: Show how to use providerOptions for provider-specific settings

  5. Creating test fixtures: Use examples to generate API response fixtures (see capture-api-response-test-fixture skill)

Utility Helpers

The lib/ directory contains shared utilities:

FilePurpose
run.tsError-handling wrapper with .env loading
print.tsClean object printing (removes undefined values)
print-full-stream.tsColored streaming output for tool calls, reasoning, text
save-raw-chunks.tsSave streaming chunks for test fixtures
present-image.tsDisplay images in terminal
save-audio.tsSave audio files to disk

Using print utilities

import { print } from '../lib/print';

// Pretty print objects without undefined values
print('Result:', result);
print('Usage:', result.usage, { depth: 2 });

Using printFullStream

import { printFullStream } from '../lib/print-full-stream';

const result = streamText({ ... });
await printFullStream({ result }); // Colored output for text, tool calls, reasoning

Reusable Tools

The tools/ directory contains reusable tool definitions:

import { weatherTool } from '../tools/weather-tool';

const result = await generateText({
  model: openai('gpt-4o'),
  tools: { weather: weatherTool },
  prompt: 'What is the weather in San Francisco?',
});

Best Practices

  1. Keep examples focused: Each example should demonstrate one feature or use case

  2. Use descriptive prompts: Make it clear what the example is testing

  3. Handle errors gracefully: The run() wrapper handles this automatically

  4. Use realistic model IDs: Use actual model IDs that work with the provider

  5. Add comments for complex logic: Explain non-obvious code patterns

  6. Reuse tools when appropriate: Use weatherTool or create new reusable tools in tools/

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