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Prad Nukala
6406208b35
- 16 cross-shell utilities converted to sh/bash chezmoi executables: ai c2p cp-appid extract_agents fab gdc gh-sonr-org-migrate ghg-clone ghn gho git-summary launch_omp_livediff_overlay lc-elir mdcp mdp pj (ghg-clone not named ghg: unrelated cargo binary owns that name) - cwd/history helpers stay per-shell: yy c cd clip-last + ghg/ghn/gho cd-wrappers in bash/functions/ and zsh/functions/, loaded by rc files - fish keeps only shell-state functions; converted ones now hit PATH - cp-appid ignore gating moved to .local/bin path - dot_agents/skills: 82 skills materialized from dotstate storage so dot_claude skill symlinks resolve on fresh bootstrap; dotstate profile/symlinks.json released .agents/skills .bashrc .zshrc
2.7 KiB
2.7 KiB
Workers AI API Reference
Core Method
const response = await env.AI.run(model, input);
Text Generation
const result = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [
{ role: 'system', content: 'You are helpful' },
{ role: 'user', content: 'Hello' }
],
temperature: 0.7, // 0-1
max_tokens: 100
});
console.log(result.response);
Streaming:
const stream = await env.AI.run(model, { messages, stream: true });
return new Response(stream, { headers: { 'Content-Type': 'text/event-stream' } });
Embeddings
const result = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
text: ['Query', 'Doc 1', 'Doc 2'] // Batch for efficiency
});
const [queryEmbed, doc1Embed, doc2Embed] = result.data; // 768-dim vectors
Function Calling
const tools = [{
type: 'function',
function: {
name: 'getWeather',
description: 'Get weather for location',
parameters: {
type: 'object',
properties: { location: { type: 'string' } },
required: ['location']
}
}
}];
const response = await env.AI.run(model, { messages, tools });
if (response.tool_calls) {
const args = JSON.parse(response.tool_calls[0].function.arguments);
// Execute function, send result back
}
Image Generation
const image = await env.AI.run('@cf/stabilityai/stable-diffusion-xl-base-1.0', {
prompt: 'Mountain sunset',
num_steps: 20, // 1-20
guidance: 7.5 // 1-20
});
return new Response(image, { headers: { 'Content-Type': 'image/png' } });
Speech Recognition
const audioArray = Array.from(new Uint8Array(await request.arrayBuffer()));
const result = await env.AI.run('@cf/openai/whisper', { audio: audioArray });
console.log(result.text);
Translation
const result = await env.AI.run('@cf/meta/m2m100-1.2b', {
text: 'Hello',
source_lang: 'en',
target_lang: 'es'
});
console.log(result.translated_text);
REST API
curl https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/@cf/meta/llama-3.1-8b-instruct \
-H "Authorization: Bearer $TOKEN" \
-d '{"messages":[{"role":"user","content":"Hello"}]}'
Error Codes
| Code | Meaning | Fix |
|---|---|---|
| 7502 | Model not found | Check spelling |
| 7504 | Validation failed | Verify input schema |
| 7505 | Rate limited | Reduce rate or upgrade |
| 7506 | Context exceeded | Reduce input size |
Performance Tips
- Batch embeddings - single request for multiple texts
- Stream long responses - reduce perceived latency
- Accept cold starts - first request ~1-3s, subsequent ~100-500ms