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113 lines
2.7 KiB
Markdown
113 lines
2.7 KiB
Markdown
# Workers AI API Reference
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## Core Method
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```typescript
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const response = await env.AI.run(model, input);
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```
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## Text Generation
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```typescript
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const result = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
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messages: [
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{ role: 'system', content: 'You are helpful' },
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{ role: 'user', content: 'Hello' }
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],
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temperature: 0.7, // 0-1
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max_tokens: 100
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});
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console.log(result.response);
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```
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**Streaming:**
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```typescript
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const stream = await env.AI.run(model, { messages, stream: true });
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return new Response(stream, { headers: { 'Content-Type': 'text/event-stream' } });
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```
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## Embeddings
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```typescript
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const result = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
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text: ['Query', 'Doc 1', 'Doc 2'] // Batch for efficiency
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});
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const [queryEmbed, doc1Embed, doc2Embed] = result.data; // 768-dim vectors
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```
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## Function Calling
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```typescript
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const tools = [{
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type: 'function',
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function: {
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name: 'getWeather',
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description: 'Get weather for location',
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parameters: {
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type: 'object',
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properties: { location: { type: 'string' } },
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required: ['location']
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}
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}
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}];
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const response = await env.AI.run(model, { messages, tools });
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if (response.tool_calls) {
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const args = JSON.parse(response.tool_calls[0].function.arguments);
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// Execute function, send result back
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}
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```
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## Image Generation
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```typescript
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const image = await env.AI.run('@cf/stabilityai/stable-diffusion-xl-base-1.0', {
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prompt: 'Mountain sunset',
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num_steps: 20, // 1-20
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guidance: 7.5 // 1-20
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});
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return new Response(image, { headers: { 'Content-Type': 'image/png' } });
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```
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## Speech Recognition
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```typescript
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const audioArray = Array.from(new Uint8Array(await request.arrayBuffer()));
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const result = await env.AI.run('@cf/openai/whisper', { audio: audioArray });
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console.log(result.text);
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```
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## Translation
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```typescript
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const result = await env.AI.run('@cf/meta/m2m100-1.2b', {
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text: 'Hello',
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source_lang: 'en',
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target_lang: 'es'
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});
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console.log(result.translated_text);
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```
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## REST API
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```bash
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curl https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/@cf/meta/llama-3.1-8b-instruct \
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-H "Authorization: Bearer $TOKEN" \
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-d '{"messages":[{"role":"user","content":"Hello"}]}'
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```
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## Error Codes
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| Code | Meaning | Fix |
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|------|---------|-----|
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| 7502 | Model not found | Check spelling |
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| 7504 | Validation failed | Verify input schema |
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| 7505 | Rate limited | Reduce rate or upgrade |
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| 7506 | Context exceeded | Reduce input size |
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## Performance Tips
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1. **Batch embeddings** - single request for multiple texts
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2. **Stream long responses** - reduce perceived latency
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3. **Accept cold starts** - first request ~1-3s, subsequent ~100-500ms
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