# wrangler.toml
[ai]
binding = "AI"

export default {
  async fetch(request: Request, env: Env) {
    const response = await env.AI.run(
      "@cf/meta/llama-3.1-8b-instruct",
      {
        messages: [
          { role: "system", content: "You are a helpful assistant." },
          { role: "user", content: "Hello!" },
        ],
      }
    );

    return Response.json(response);
  },
};

const response = await env.AI.run("@cf/meta/llama-3.1-8b-instruct", {
  messages: [
    { role: "system", content: "あなたは親切なアシスタントです。" },
    { role: "user", content: "東京の天気を教えて" },
  ],
  max_tokens: 256,
  temperature: 0.7,
});

console.log(response.response);

const stream = await env.AI.run("@cf/meta/llama-3.1-8b-instruct", {
  messages: [{ role: "user", content: "長い物語を書いて" }],
  stream: true,
});

return new Response(stream, {
  headers: { "Content-Type": "text/event-stream" },
});

const response = await env.AI.run(
  "@hf/nousresearch/hermes-2-pro-mistral-7b",
  {
    messages: [{ role: "user", content: "東京の天気は？" }],
    tools: [
      {
        type: "function",
        function: {
          name: "get_weather",
          description: "指定した場所の天気を取得",
          parameters: {
            type: "object",
            properties: {
              location: { type: "string", description: "場所" },
            },
            required: ["location"],
          },
        },
      },
    ],
  }
);

const response = await env.AI.run(
  "@cf/stabilityai/stable-diffusion-xl-base-1.0",
  {
    prompt: "A beautiful sunset over mountains, high quality, 4k",
    negative_prompt: "blurry, low quality",
    num_steps: 20,
    guidance: 7.5,
  }
);

return new Response(response, {
  headers: { "Content-Type": "image/png" },
});

const audioData = await request.arrayBuffer();

const response = await env.AI.run(
  "@cf/openai/whisper-large-v3-turbo",
  {
    audio: [...new Uint8Array(audioData)],
  }
);

return Response.json({
  text: response.text,
  language: response.detected_language,
});

const response = await env.AI.run("@cf/baai/bge-base-en-v1.5", {
  text: ["Hello, world!", "How are you?"],
});

// response.data = [
//   { embedding: [0.1, 0.2, ...], shape: [768] },
//   { embedding: [0.3, 0.4, ...], shape: [768] }
// ]

# プリセット使用
wrangler vectorize create my-index \
  --preset @cf/baai/bge-small-en-v1.5

# 手動設定
wrangler vectorize create my-index \
  --dimensions 768 \
  --metric cosine

# wrangler.toml
[[vectorize]]
binding = "VECTORIZE"
index_name = "my-index"

// 挿入
await env.VECTORIZE.upsert([
  {
    id: "doc-1",
    values: embedding,  // from Workers AI
    metadata: { title: "Document 1", category: "tech" },
  },
]);

// 検索
const results = await env.VECTORIZE.query(queryVector, {
  topK: 5,
  filter: { category: "tech" },
  returnMetadata: "all",
});

// 結果: results.matches = [
//   { id: "doc-1", score: 0.95, metadata: {...} },
//   ...
// ]

// 削除
await env.VECTORIZE.deleteByIds(["doc-1", "doc-2"]);

export default {
  async fetch(request: Request, env: Env) {
    const { query } = await request.json();

    // 1. クエリを埋め込みに変換
    const embedResponse = await env.AI.run("@cf/baai/bge-base-en-v1.5", {
      text: [query],
    });
    const queryVector = embedResponse.data[0].embedding;

    // 2. 類似ドキュメント検索
    const results = await env.VECTORIZE.query(queryVector, {
      topK: 3,
      returnMetadata: "all",
    });

    // 3. コンテキスト構築
    const context = results.matches
      .map((m) => m.metadata?.content)
      .join("\n\n");

    // 4. LLM で回答生成
    const response = await env.AI.run("@cf/meta/llama-3.1-8b-instruct", {
      messages: [
        {
          role: "system",
          content: `以下のコンテキストに基づいて回答してください:\n\n${context}`,
        },
        { role: "user", content: query },
      ],
    });

    return Response.json({ answer: response.response });
  },
};

// AI Gateway 経由で OpenAI を呼び出し
const response = await fetch(
  "https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/openai/chat/completions",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${env.OPENAI_KEY}`,
    },
    body: JSON.stringify({
      model: "gpt-4",
      messages: [{ role: "user", content: "Hello" }],
    }),
  }
);

curl https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/@cf/meta/llama-3.1-8b-instruct \
  -X POST \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hello"}
    ]
  }'

// レスポンスをキャッシュ
const cacheKey = `ai:${hash(prompt)}`;
let response = await env.KV.get(cacheKey, "json");

if (!response) {
  response = await env.AI.run("@cf/meta/llama-3.1-8b-instruct", {
    messages: [{ role: "user", content: prompt }],
  });
  await env.KV.put(cacheKey, JSON.stringify(response), {
    expirationTtl: 3600,
  });
}