const chunkText = (text) => {
  const cleanText = text.replace(/(\r\n|\n|\r)+/g, " ").replace(/\s+/g, " ").trim();
  const maxSize = 1500; // 約等于500英文單詞或1000中文字符
  const regex = new RegExp(`.{1,${maxSize}}(\\s|$)`, "g");
  return cleanText.match(regex) || [];
};

const { GoogleGenAI } = require("@google/generative-ai");
const genAI = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const getEmbeddings = async (chunks) => {
  const embeddings = [];
  for (const chunk of chunks) {
    const response = await genAI.embedContent({
      model: "models/gemini-embedding-exp-03-07",
      content: chunk,
      taskType: "retrieval_document", // 明確任務(wù)類型為文檔檢索
    });
    embeddings.push(response.embedding);
  }
  return embeddings;
};

This is an airport scenario. Provide protocol steps for: "${user_query}". 
Context: ${retrieved_documents} 
Conversation History: ${last_12_messages} 
Guest Profile: ${profile}, Location: ${location}, Mood: ${mood} 
Response Requirements: 
1. Strictly based on provided context 
2. Use numbered list 
3. Under 300 words

This is an airport scenario. Help staff respond to: "${user_query}". 
Focus on improving guest experience for ${profile} at ${location}. 
Context: ${retrieved_documents} 
Conversation History: ${last_12_messages} 
Response Requirements: 
1. Friendly tone with emojis 
2. Highlight key actions in bold 
3. Under 100 words 
4. Use Markdown formatting

const { QdrantClient } = require("@qdrant/js-client-rest");
const client = new QdrantClient({ url: "http://localhost:6333" }); // 本地部署地址

// 確保集合存在，向量維度與Gemini輸出一致
const ensureCollectionExists = async () => {
  const exists = await client.collectionExists("airport-protocols");
  if (!exists) {
    await client.createCollection("airport-protocols", {
      vectors: { size: 3072, distance: "Cosine" }, // 使用余弦相似度
      sharding: { key: "document_id" }, // 按文檔ID分片，提升多文檔檢索效率
    });
  }
};

// 批量插入向量（支持一次處理多個文檔塊）
const upsertVectors = async (documentId, chunks, embeddings) => {
  await ensureCollectionExists();
  const points = chunks.map((chunk, index) => ({
    id: `${documentId}-${index}`, // 唯一標識符
    vector: embeddings[index],
    payload: { text: chunk, document_id: documentId, source: "official-sop" }, // 附加元數(shù)據(jù)
  }));
  await client.upsert("airport-protocols", { points, wait: true }); // wait=true確保操作完成
};

const fs = require("fs");
const pdf = require("pdf-parse");

// 解析PDF并生成向量存入Qdrant
const processPDF = async (filePath, documentId) => {
  // 1. 解析PDF文本
  const text = await extractTextFromPDF(filePath);
  // 2. 分塊處理
  const chunks = chunkText(text);
  // 3. 生成嵌入向量
  const embeddings = await getEmbeddings(chunks);
  // 4. 存入向量數(shù)據(jù)庫
  await upsertVectors(documentId, chunks, embeddings);
  console.log(`Processed ${chunks.length} chunks for document ${documentId}`);
};

const extractTextFromPDF = async (filePath) => {
  const data = fs.readFileSync(filePath);
  const pdfData = await pdf(data);
  if (!pdfData.text) throw new Error("Invalid PDF file");
  return pdfData.text;
};

const queryGemini = async (userQuery, context, interactionId) => {
  // 1. 生成查詢向量
  const queryEmbedding = (await getEmbeddings([userQuery]))[0];
  // 2. 向量檢索
  const results = await client.query("airport-protocols", {
    query: queryEmbedding,
    limit: 3,
    with_payload: true,
  });
  const relevantChunks = results.points.map(p => p.payload.text).join("\n\n");

  // 3. 獲取對話歷史（最多12輪）
  const history = await getConversationHistory(interactionId, 12);

  // 4. 生成雙模式提示
  const protocolPrompt = buildProtocolPrompt(userQuery, relevantChunks, context, history);
  const experiencePrompt = buildExperiencePrompt(userQuery, relevantChunks, context, history);

  // 5. 并行調(diào)用Gemini（提升效率）
  const [protocolResp, experienceResp] = await Promise.all([
    genAI.generateContent({
      model: "models/gemini-2.5-pro-preview",
      contents: [{ role: "user", parts: [{ text: protocolPrompt }] }],
      generationConfig: { temperature: 0.1 } // 低溫度確保輸出確定性
    }),
    genAI.generateContent({
      model: "models/gemini-2.5-pro-preview",
      contents: [{ role: "user", parts: [{ text: experiencePrompt }] }],
      generationConfig: { temperature: 0.7 } // 高溫度增加靈活性
    })
  ]);

  return {
    protocol: protocolResp.text.trim(),
    experience: experienceResp.text.trim(),
    sources: results.points.map(p => p.payload.document_id) // 返回引用文檔ID
  };
};