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AI Provider for the vercel AI SDK","maintainers":[{"name":"threepointone","email":"threepointone@gmail.com"}],"readme":"# workers-ai-provider\n\n[Workers AI](https://developers.cloudflare.com/workers-ai/) provider for the [AI SDK](https://sdk.vercel.ai/). Run Cloudflare's models for chat, embeddings, image generation, transcription, text-to-speech, reranking, and [AI Search](https://developers.cloudflare.com/ai-search/) — all from a single provider.\n\n## Quick Start\n\n```jsonc\n// wrangler.jsonc\n{\n\t\"ai\": { \"binding\": \"AI\" },\n}\n```\n\n```ts\nimport { createWorkersAI } from \"workers-ai-provider\";\nimport { streamText } from \"ai\";\n\nexport default {\n\tasync fetch(req: Request, env: { AI: Ai }) {\n\t\tconst workersai = createWorkersAI({ binding: env.AI });\n\n\t\tconst result = streamText({\n\t\t\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\t\t\tmessages: [{ role: \"user\", content: \"Write a haiku about Cloudflare\" }],\n\t\t});\n\n\t\treturn result.toTextStreamResponse();\n\t},\n};\n```\n\n```bash\nnpm install workers-ai-provider ai\n```\n\n## Configuration\n\n### Workers binding (recommended)\n\nInside a Cloudflare Worker, pass the `env.AI` binding directly. No API keys needed.\n\n```ts\nconst workersai = createWorkersAI({ binding: env.AI });\n```\n\n### REST API\n\nOutside of Workers (Node.js, Bun, etc.), use your Cloudflare credentials:\n\n```ts\nconst workersai = createWorkersAI({\n\taccountId: process.env.CLOUDFLARE_ACCOUNT_ID,\n\tapiKey: process.env.CLOUDFLARE_API_TOKEN,\n});\n```\n\n### AI Gateway\n\nRoute requests through [AI Gateway](https://developers.cloudflare.com/ai-gateway/) for caching, rate limiting, and observability:\n\n```ts\nconst workersai = createWorkersAI({\n\tbinding: env.AI,\n\tgateway: { id: \"my-gateway\" },\n});\n```\n\n## Models\n\nBrowse the full catalog at [developers.cloudflare.com/workers-ai/models](https://developers.cloudflare.com/workers-ai/models/).\n\nSome good defaults:\n\n| Task | Model | Notes |\n| -------------- | -------------------------------------- | ----------------------------------- |\n| Chat | `@cf/moonshotai/kimi-k2.5` | 256k ctx, tools, vision, reasoning |\n| Chat | `@cf/zai-org/glm-4.7-flash` | Fast, multilingual, 131k ctx |\n| Chat | `@cf/openai/gpt-oss-120b` | OpenAI open-weights, high reasoning |\n| Reasoning | `@cf/moonshotai/kimi-k2.5` | Configurable `reasoning_effort` |\n| Reasoning | `@cf/qwen/qwq-32b` | Emits `reasoning_content` |\n| Embeddings | `@cf/baai/bge-base-en-v1.5` | 768-dim, English |\n| Embeddings | `@cf/google/embeddinggemma-300m` | 100+ languages, by Google |\n| Images | `@cf/black-forest-labs/flux-1-schnell` | Fast, free-tier image generation |\n| Transcription | `@cf/openai/whisper-large-v3-turbo` | Best accuracy, multilingual |\n| Transcription | `@cf/deepgram/nova-3` | Fast, high accuracy |\n| Text-to-Speech | `@cf/deepgram/aura-2-en` | Context-aware, natural pacing |\n| Reranking | `@cf/baai/bge-reranker-base` | Fast document reranking |\n\n## Text Generation\n\n```ts\nimport { generateText } from \"ai\";\n\nconst { text } = await generateText({\n\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\tprompt: \"Explain Workers AI in one paragraph\",\n});\n```\n\nStreaming:\n\n```ts\nimport { streamText } from \"ai\";\n\nconst result = streamText({\n\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\tmessages: [{ role: \"user\", content: \"Write a short story\" }],\n});\n\nfor await (const chunk of result.textStream) {\n\tprocess.stdout.write(chunk);\n}\n```\n\n## Reasoning Controls\n\nReasoning-capable Workers AI models (GLM-4.7-flash, Kimi K2.5/K2.6, GPT-OSS, QwQ) accept `reasoning_effort` and `chat_template_kwargs` on their inputs. Either set them at model creation time as settings, or per-call via `providerOptions[\"workers-ai\"]` (per-call wins):\n\n```ts\n// Settings-level (applies to every request on this model instance)\nconst model = workersai(\"@cf/zai-org/glm-4.7-flash\", {\n\treasoning_effort: \"low\", // \"low\" | \"medium\" | \"high\" | null\n\tchat_template_kwargs: { enable_thinking: false },\n});\n\nawait generateText({ model, prompt: \"Summarize in one sentence.\" });\n```\n\n```ts\n// Per-call (overrides any settings-level value)\nconst model = workersai(\"@cf/zai-org/glm-4.7-flash\");\n\nawait generateText({\n\tmodel,\n\tprompt: \"Summarize in one sentence.\",\n\tproviderOptions: {\n\t\t\"workers-ai\": { reasoning_effort: \"low\" },\n\t},\n});\n```\n\n`reasoning_effort: null` is meaningful — it's the explicit \"disable reasoning\" signal for models that support it. Both fields land on the `inputs` object of `binding.run()` (and the JSON body of the REST request), matching the shape expected by Workers AI. See the [model catalog](https://developers.cloudflare.com/workers-ai/models/) for per-model reasoning capabilities.\n\n## Vision (Image Inputs)\n\nSend images to vision-capable models like Kimi K2.5:\n\n```ts\nimport { generateText } from \"ai\";\n\nconst { text } = await generateText({\n\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\tmessages: [\n\t\t{\n\t\t\trole: \"user\",\n\t\t\tcontent: [\n\t\t\t\t{ type: \"text\", text: \"What's in this image?\" },\n\t\t\t\t{ type: \"image\", image: imageUint8Array },\n\t\t\t],\n\t\t},\n\t],\n});\n```\n\nImages can be provided as `Uint8Array`, base64 strings, or data URLs. Multiple images per message are supported. Works with both the binding and REST API configurations.\n\n## Tool Calling\n\n```ts\nimport { generateText, stepCountIs } from \"ai\";\nimport { z } from \"zod\";\n\nconst { text } = await generateText({\n\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\tprompt: \"What's the weather in London?\",\n\ttools: {\n\t\tgetWeather: {\n\t\t\tdescription: \"Get the current weather for a city\",\n\t\t\tinputSchema: z.object({ city: z.string() }),\n\t\t\texecute: async ({ city }) => ({ city, temperature: 18, condition: \"Cloudy\" }),\n\t\t},\n\t},\n\tstopWhen: stepCountIs(2),\n});\n```\n\n## Structured Output\n\n```ts\nimport { generateText, Output } from \"ai\";\nimport { z } from \"zod\";\n\nconst { output } = await generateText({\n\tmodel: workersai(\"@cf/moonshotai/kimi-k2.5\"),\n\tprompt: \"Recipe for spaghetti bolognese\",\n\toutput: Output.object({\n\t\tschema: z.object({\n\t\t\tname: z.string(),\n\t\t\tingredients: z.array(z.object({ name: z.string(), amount: z.string() })),\n\t\t\tsteps: z.array(z.string()),\n\t\t}),\n\t}),\n});\n```\n\n## Embeddings\n\n```ts\nimport { embedMany } from \"ai\";\n\nconst { embeddings } = await embedMany({\n\tmodel: workersai.textEmbedding(\"@cf/baai/bge-base-en-v1.5\"),\n\tvalues: [\"sunny day at the beach\", \"rainy afternoon in the city\"],\n});\n```\n\n## Image Generation\n\n```ts\nimport { generateImage } from \"ai\";\n\nconst { images } = await generateImage({\n\tmodel: workersai.image(\"@cf/black-forest-labs/flux-1-schnell\"),\n\tprompt: \"A mountain landscape at sunset\",\n\tsize: \"1024x1024\",\n});\n\n// images[0].uint8Array contains the PNG bytes\n```\n\n## Transcription (Speech-to-Text)\n\nTranscribe audio using Whisper or Deepgram Nova-3 models.\n\n```ts\nimport { transcribe } from \"ai\";\nimport { readFile } from \"node:fs/promises\";\n\nconst { text, segments } = await transcribe({\n\tmodel: workersai.transcription(\"@cf/openai/whisper-large-v3-turbo\"),\n\taudio: await readFile(\"./audio.mp3\"),\n\tmediaType: \"audio/mpeg\",\n});\n```\n\nWith language hints (Whisper only):\n\n```ts\nconst { text } = await transcribe({\n\tmodel: workersai.transcription(\"@cf/openai/whisper-large-v3-turbo\", {\n\t\tlanguage: \"fr\",\n\t}),\n\taudio: audioBuffer,\n\tmediaType: \"audio/wav\",\n});\n```\n\nDeepgram Nova-3 is also supported and detects language automatically:\n\n```ts\nconst { text } = await transcribe({\n\tmodel: workersai.transcription(\"@cf/deepgram/nova-3\"),\n\taudio: audioBuffer,\n\tmediaType: \"audio/wav\",\n});\n```\n\n## Text-to-Speech\n\nGenerate spoken audio from text using Deepgram Aura-2.\n\n```ts\nimport { speech } from \"ai\";\n\nconst { audio } = await speech({\n\tmodel: workersai.speech(\"@cf/deepgram/aura-2-en\"),\n\ttext: \"Hello from Cloudflare Workers AI!\",\n\tvoice: \"asteria\",\n});\n\n// audio is a Uint8Array of MP3 bytes\n```\n\n## Reranking\n\nReorder documents by relevance to a query — useful for RAG pipelines.\n\n```ts\nimport { rerank } from \"ai\";\n\nconst { results } = await rerank({\n\tmodel: workersai.reranking(\"@cf/baai/bge-reranker-base\"),\n\tquery: \"What is Cloudflare Workers?\",\n\tdocuments: [\n\t\t\"Cloudflare Workers lets you run JavaScript at the edge.\",\n\t\t\"A cookie is a small piece of data stored in the browser.\",\n\t\t\"Workers AI runs inference on Cloudflare's global network.\",\n\t],\n\ttopN: 2,\n});\n\n// results is sorted by relevance score\n```\n\n## AI Search\n\n[AI Search](https://developers.cloudflare.com/ai-search/) is Cloudflare's managed RAG service. Connect your data and query it with natural language.\n\n```jsonc\n// wrangler.jsonc\n{\n\t\"ai_search\": [{ \"binding\": \"AI_SEARCH\", \"name\": \"my-search-index\" }],\n}\n```\n\n```ts\nimport { createAISearch } from \"workers-ai-provider\";\nimport { generateText } from \"ai\";\n\nconst aisearch = createAISearch({ binding: env.AI_SEARCH });\n\nconst { text } = await generateText({\n\tmodel: aisearch(),\n\tmessages: [{ role: \"user\", content: \"How do I setup AI Gateway?\" }],\n});\n```\n\nStreaming works the same way — use `streamText` instead of `generateText`.\n\n> `createAutoRAG` still works but is deprecated. Use `createAISearch` instead.\n\n## API Reference\n\n### `createWorkersAI(options)`\n\n| Option | Type | Description |\n| ----------- | ---------------- | ---------------------------------------------------------------------------- |\n| `binding` | `Ai` | Workers AI binding (`env.AI`). Use this OR credentials. |\n| `accountId` | `string` | Cloudflare account ID. Required with `apiKey`. |\n| `apiKey` | `string` | Cloudflare API token. Required with `accountId`. |\n| `gateway` | `GatewayOptions` | Optional [AI Gateway](https://developers.cloudflare.com/ai-gateway/) config. |\n\nReturns a provider with model factories. Each factory accepts an optional second argument for per-model settings:\n\n```ts\nworkersai(\"@cf/moonshotai/kimi-k2.5\", {\n\tsessionAffinity: \"my-unique-session-id\",\n});\n```\n\n| Setting | Type | Description |\n| ----------------- | --------- | -------------------------------------------------------------------------------------------- |\n| `safePrompt` | `boolean` | Inject a safety prompt before all conversations. |\n| `sessionAffinity` | `string` | Routes requests with the same key to the same backend replica for prefix-cache optimization. |\n\nModel factories:\n\n```ts\n// Chat — for generateText / streamText\nworkersai(modelId);\nworkersai.chat(modelId);\n\n// Embeddings — for embedMany / embed\nworkersai.textEmbedding(modelId);\n\n// Images — for generateImage\nworkersai.image(modelId);\n\n// Transcription — for transcribe\nworkersai.transcription(modelId, settings?);\n\n// Text-to-Speech — for speech\nworkersai.speech(modelId);\n\n// Reranking — for rerank\nworkersai.reranking(modelId);\n```\n\n### `createAISearch(options)`\n\n| Option | Type | Description |\n| --------- | --------- | ------------------------------------ |\n| `binding` | `AutoRAG` | AI Search binding (`env.AI_SEARCH`). |\n\nReturns a callable provider:\n\n```ts\naisearch(); // AI Search model (shorthand)\naisearch.chat(); // AI Search model\n```\n","readmeFilename":"README.md"}