AIyaodianzhushou/backend-java/src/main/java/com/pharmacopoeia/controller/ChatController.java

package com.pharmacopoeia.controller;

import com.pharmacopoeia.config.QwenProperties;
import com.pharmacopoeia.dto.ChatRequest;
import com.pharmacopoeia.dto.FeedbackRequest;
import com.pharmacopoeia.dto.ImageChatRequest;
import com.pharmacopoeia.dto.MultimodalChatRequest;
import com.pharmacopoeia.service.*;
import org.springframework.http.MediaType;
import org.springframework.http.ResponseEntity;
import org.springframework.http.codec.ServerSentEvent;
import org.springframework.web.bind.annotation.*;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Sinks;

import org.springframework.jdbc.core.JdbcTemplate;
import org.springframework.web.multipart.MultipartFile;

import java.util.*;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.stream.Collectors;

@RestController
@RequestMapping("/api/v1/chat")
public class ChatController {

    // 复用 PromptService.SECTION_DISPLAY 统一权威映射，避免两处重复定义导致不一致

    private final RetrieverService retrieverService;
    private final LLMService llmService;
    private final PromptService promptService;
    private final ChatPersistenceService persistenceService;
    private final RerankerService rerankerService;
    private final JdbcTemplate jdbc;
    private final QwenProperties props;

    public ChatController(RetrieverService rs, LLMService ls, PromptService ps,
                          ChatPersistenceService cps, RerankerService rrs,
                          JdbcTemplate jdbc, QwenProperties props) {
        this.retrieverService = rs;
        this.llmService = ls;
        this.promptService = ps;
        this.persistenceService = cps;
        this.rerankerService = rrs;
        this.jdbc = jdbc;
        this.props = props;
    }

    @PostMapping("/ask")
    public ResponseEntity<Map<String, Object>> chatAsk(@RequestBody ChatRequest request) {
        String query = request.getMessage();
        String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();

        String intent = retrieverService.classifyIntent(query);
        List<Map<String, Object>> docs = retrieverService.search(query, intent, 20);
        docs = rerankerService.rerank(docs, query, 5);

        // 统一：LLM 回答 + 原文对照
        List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
        String llmAnswer = cleanAnswer(llmService.chat(messages));
        String answer = llmAnswer;

        List<Map<String, Object>> sources = buildSources(docs);
        persistenceService.saveMessage(cid, "user", query, intent, null);
        persistenceService.saveMessage(cid, "assistant", answer, intent, sources);

        return ResponseEntity.ok(Map.of(
                "answer", answer,
                "sources", sources,
                "intent", intent,
                "conversation_id", cid
        ));
    }

    @PostMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ServerSentEvent<String>> chatStream(@RequestBody ChatRequest request) {
        String query = request.getMessage();
        final String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();

        final String intent = retrieverService.classifyIntent(query);
        final List<Map<String, Object>> docs = rerankerService.rerank(retrieverService.search(query, intent, 20), query, 5);

        Sinks.Many<ServerSentEvent<String>> sink = Sinks.many().unicast().onBackpressureBuffer();

        try {
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("intent").data(intent).build());
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("status").data("Retrieving...").build());
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("status").data("Matched " + docs.size() + " records, generating...").build());

            final List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
            StringBuilder fullAnswer = new StringBuilder();

            llmService.chatStream(messages)
                    .doOnNext(token -> {
                        fullAnswer.append(token);
                        sink.tryEmitNext(ServerSentEvent.<String>builder().data(token).build());
                    })
                    .doOnComplete(() -> {
                        final List<Map<String, Object>> sources = buildSources(docs);
                        try {
                            String meta = new com.fasterxml.jackson.databind.ObjectMapper().writeValueAsString(Map.of(
                                    "intent", intent,
                                    "sources", sources,
                                    "conversation_id", cid
                            ));
                            sink.tryEmitNext(ServerSentEvent.<String>builder().event("meta").data(meta).build());
                        } catch (Exception ignored) {}

                        String finalAnswer = cleanAnswer(fullAnswer.toString());
                        persistenceService.saveMessage(cid, "user", query, intent, null);
                        persistenceService.saveMessage(cid, "assistant", finalAnswer, intent, sources);
                        sink.tryEmitComplete();
                    })
                    .doOnError(e -> sink.tryEmitError(e))
                    .subscribe();
        } catch (Exception e) {
            sink.tryEmitError(e);
        }

        return sink.asFlux();
    }

    // ============================================================
    // 图片对话 API（Qwen VL 分析 + OCR → RAG 检索 → 联网搜索）
    // ============================================================

    @PostMapping("/ask-image")
    public ResponseEntity<Map<String, Object>> chatAskImage(@RequestBody ImageChatRequest request) {
        String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();

        // Step 1: Qwen VL 分析图片 + OCR 提取文字
        String ocrText = llmService.analyzeImage(
                request.getImageBase64(), request.getMimeType(),
                "请分析这张图片，提取其中所有文字信息（OCR），特别是药品名称、成分、用法用量等关键药学信息。简要输出即可。");

        // Step 2: 拼接查询 → RAG 检索
        String query = (!request.getMessage().isBlank())
                ? request.getMessage() + "\n\n（图片OCR提取内容：" + ocrText + "）"
                : ocrText;

        String intent = retrieverService.classifyIntent(query);
        List<Map<String, Object>> docs = retrieverService.search(query, intent, 20);
        docs = rerankerService.rerank(docs, query, 5);

        // Step 3: 构建 Prompt（含图片分析上下文）+ 联网搜索
        List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
        String imageContext = "\n\n【图片分析结果】\n" + ocrText + "\n";
        messages.get(0).put("content", messages.get(0).get("content") + imageContext);

        String llmAnswer = cleanAnswer(llmService.chat(messages, true));
        String answer = llmAnswer;

        List<Map<String, Object>> sources = buildSources(docs);
        persistenceService.saveMessage(cid, "user",
                request.getMessage().isBlank() ? "[图片]" : "[图片] " + request.getMessage(),
                intent, null);
        persistenceService.saveMessage(cid, "assistant", answer, intent, sources);

        return ResponseEntity.ok(Map.of(
                "answer", answer,
                "sources", sources,
                "intent", intent,
                "conversation_id", cid
        ));
    }

    @PostMapping(value = "/stream-image", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ServerSentEvent<String>> chatStreamImage(@RequestBody ImageChatRequest request) {
        final String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();

        Sinks.Many<ServerSentEvent<String>> sink = Sinks.many().unicast().onBackpressureBuffer();

        // 校验图片数据
        if (request.getImageBase64() == null || request.getImageBase64().isBlank()) {
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("status").data("图片数据为空，请重新上传").build());
            sink.tryEmitComplete();
            return sink.asFlux();
        }

        try {
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("status").data("正在分析图片（OCR 文字识别）...").build());

            // Step 1: Qwen VL 分析图片
            StringBuilder ocrBuilder = new StringBuilder();
            llmService.analyzeImageStream(request.getImageBase64(), request.getMimeType(),
                    "请分析这张图片，提取其中所有文字信息（OCR），特别是药品名称、成分、用法用量等。简要输出。")
                    .doOnNext(ocrBuilder::append)
                    .doOnComplete(() -> {
                        String ocrText = ocrBuilder.toString();
                        sink.tryEmitNext(ServerSentEvent.<String>builder().event("status").data("图片分析完成，正在检索药典知识库...").build());

                        // Step 2: 拼接查询 → RAG
                        String query = (!request.getMessage().isBlank())
                                ? request.getMessage() + "\n\n（图片OCR提取内容：" + ocrText + "）"
                                : ocrText;
                        final String intent = retrieverService.classifyIntent(query);
                        sink.tryEmitNext(ServerSentEvent.<String>builder().event("intent").data(intent).build());

                        final List<Map<String, Object>> docs = rerankerService.rerank(retrieverService.search(query, intent, 20), query, 5);
                        sink.tryEmitNext(ServerSentEvent.<String>builder().event("status")
                                .data("已匹配 " + docs.size() + " 条药典资料，生成回答中（已启用联网搜索）...").build());

                        // Step 3: 构建 Prompt + 联网搜索流式生成
                        final List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
                        String imageContext = "\n\n【图片分析结果】\n" + ocrText + "\n";
                        messages.get(0).put("content", messages.get(0).get("content") + imageContext);

                        StringBuilder fullAnswer = new StringBuilder();
                        llmService.chatStream(messages, true)
                                .doOnNext(token -> {
                                    fullAnswer.append(token);
                                    sink.tryEmitNext(ServerSentEvent.<String>builder().data(token).build());
                                })
                                .doOnComplete(() -> {
                                    
                                    final List<Map<String, Object>> sources = buildSources(docs);
                                    try {
                                        String meta = new com.fasterxml.jackson.databind.ObjectMapper().writeValueAsString(Map.of(
                                                "intent", intent,
                                                "sources", sources,
                                                "conversation_id", cid,
                                                "ocr_text", ocrText
                                        ));
                                        sink.tryEmitNext(ServerSentEvent.<String>builder().event("meta").data(meta).build());
                                    } catch (Exception ignored) {}

                                    String finalAnswer = cleanAnswer(fullAnswer.toString());
                                    persistenceService.saveMessage(cid, "user",
                                            request.getMessage().isBlank() ? "[图片]" : "[图片] " + request.getMessage(),
                                            intent, null);
                                    persistenceService.saveMessage(cid, "assistant", finalAnswer, intent, sources);
                                    sink.tryEmitComplete();
                                })
                                .doOnError(sink::tryEmitError)
                                .subscribe();
                    })
                    .doOnError(sink::tryEmitError)
                    .subscribe();
        } catch (Exception e) {
            sink.tryEmitError(e);
        }

        return sink.asFlux();
    }

    // ============================================================
    // 统一多模态对话 API（文本 + 图片 + 视频）
    // ============================================================

    @PostMapping("/ask-multimodal")
    public ResponseEntity<Map<String, Object>> chatAskMultimodal(@RequestBody MultimodalChatRequest request) {
        String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();

        // Step 1: 媒体分析（如果有附件）
        String ocrText = "";
        String mediaLabel = "";
        if (request.getMediaBase64() != null && !request.getMediaBase64().isBlank()
                && request.getMediaType() != null && !request.getMediaType().isBlank()) {
            mediaLabel = "video".equals(request.getMediaType()) ? "视频" : "图片";
            ocrText = llmService.analyzeMedia(
                    request.getMediaBase64(), request.getMediaType(),
                    request.getMediaMime(), "");
        }

        // Step 2: 拼接查询
        String query = request.getMessage() != null ? request.getMessage().trim() : "";
        if (!query.isEmpty() && !ocrText.isEmpty()) {
            query = query + "\n\n（" + mediaLabel + "OCR提取内容：" + ocrText + "）";
        } else if (!ocrText.isEmpty()) {
            query = ocrText;
        } else if (query.isEmpty()) {
            query = "请介绍一下自己";
        }

        // Step 3: RAG 检索
        String intent = retrieverService.classifyIntent(query);
        List<Map<String, Object>> docs = retrieverService.search(query, intent, 20);
        docs = rerankerService.rerank(docs, query, 5);

        // Step 4: 构建 Prompt + 联网搜索
        List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
        if (!ocrText.isEmpty()) {
            messages.get(0).put("content",
                    messages.get(0).get("content") + "\n\n【" + mediaLabel + "分析结果】\n" + ocrText + "\n");
        }

        boolean enableSearch = !ocrText.isEmpty() || props.isEnableWebSearch();
        String llmAnswer = cleanAnswer(llmService.chat(messages, enableSearch));
        String answer = llmAnswer;

        List<Map<String, Object>> sources = buildSources(docs);
        String userMsg = !request.getMessage().isBlank() ? request.getMessage()
                : !ocrText.isEmpty() ? "[" + mediaLabel + "]" : request.getMessage();
        persistenceService.saveMessage(cid, "user", userMsg, intent, null);
        persistenceService.saveMessage(cid, "assistant", answer, intent, sources);

        return ResponseEntity.ok(Map.of(
                "answer", answer,
                "sources", sources,
                "intent", intent,
                "conversation_id", cid
        ));
    }

    @PostMapping(value = "/stream-multimodal", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ServerSentEvent<String>> chatStreamMultimodal(@RequestBody MultimodalChatRequest request) {
        final String cid = request.getConversationId() != null && !request.getConversationId().isBlank()
                ? request.getConversationId()
                : UUID.randomUUID().toString();
        final boolean hasMedia = request.getMediaBase64() != null && !request.getMediaBase64().isBlank()
                && request.getMediaType() != null && !request.getMediaType().isBlank();
        final String mediaLabel = hasMedia && "video".equals(request.getMediaType()) ? "视频" : "图片";

        Sinks.Many<ServerSentEvent<String>> sink = Sinks.many().unicast().onBackpressureBuffer();

        try {
            if (hasMedia) {
                sink.tryEmitNext(ServerSentEvent.<String>builder().event("status")
                        .data("🔍 正在分析" + mediaLabel + "...").build());
                // 先发 OCR section header
                sink.tryEmitNext(ServerSentEvent.<String>builder()
                        .data("【📷 " + mediaLabel + "分析】\n\n").build());

                StringBuilder ocrBuilder = new StringBuilder();
                llmService.analyzeMediaStream(request.getMediaBase64(), request.getMediaType(),
                        request.getMediaMime(), "")
                        .doOnNext(token -> {
                            ocrBuilder.append(token);
                            sink.tryEmitNext(ServerSentEvent.<String>builder().data(token).build());
                        })
                        .doOnComplete(() -> {
                            sink.tryEmitNext(ServerSentEvent.<String>builder().data("\n\n").build());
                            doStreamAnswer(sink, cid, request, ocrBuilder.toString(), mediaLabel);
                        })
                        .doOnError(sink::tryEmitError)
                        .subscribe();
            } else {
                doStreamAnswer(sink, cid, request, "", "");
            }
        } catch (Exception e) {
            sink.tryEmitError(e);
        }

        return sink.asFlux();
    }

    // ============================================================
    // 文件上传 API（multipart → base64 → 复用已有对话管线）
    // ============================================================

    @PostMapping("/upload-image")
    public ResponseEntity<Map<String, Object>> uploadImage(
            @RequestParam("file") MultipartFile file,
            @RequestParam(defaultValue = "") String message,
            @RequestParam(defaultValue = "") String conversationId) {

        // 校验 MIME 类型
        Set<String> allowed = Set.of("image/jpeg", "image/png", "image/webp", "image/bmp");
        String contentType = file.getContentType();
        if (contentType == null || !allowed.contains(contentType)) {
            throw new IllegalArgumentException(
                    "不支持的图片格式: " + contentType + "，支持 jpg/png/webp/bmp");
        }

        // 校验大小 ≤ 10MB
        if (file.getSize() > 10 * 1024 * 1024) {
            throw new IllegalArgumentException("图片大小不能超过 10MB");
        }

        // 转 base64 → 委托给 ask-image
        String base64;
        try {
            base64 = Base64.getEncoder().encodeToString(file.getBytes());
        } catch (Exception e) {
            throw new RuntimeException("读取上传文件失败", e);
        }

        ImageChatRequest req = new ImageChatRequest();
        req.setImageBase64(base64);
        req.setMimeType(contentType);
        req.setMessage(message);
        req.setConversationId(
                conversationId.isBlank() ? UUID.randomUUID().toString() : conversationId);
        return chatAskImage(req);
    }

    @PostMapping("/upload-media")
    public ResponseEntity<Map<String, Object>> uploadMedia(
            @RequestParam("file") MultipartFile file,
            @RequestParam(defaultValue = "") String message,
            @RequestParam(defaultValue = "") String conversationId) {

        String contentType = file.getContentType();
        if (contentType == null) {
            throw new IllegalArgumentException("无法识别的媒体类型");
        }

        String mediaType;
        long maxSize;
        if (contentType.startsWith("image/")) {
            mediaType = "image";
            maxSize = 10 * 1024 * 1024;  // 10MB
        } else if (contentType.startsWith("video/")) {
            mediaType = "video";
            maxSize = 50 * 1024 * 1024;  // 50MB
        } else {
            throw new IllegalArgumentException(
                    "不支持的媒体格式: " + contentType + "，支持 jpg/png/webp/bmp/mp4/mov/avi/webm");
        }

        if (file.getSize() > maxSize) {
            throw new IllegalArgumentException(
                    "文件大小不能超过 " + (maxSize / 1024 / 1024) + "MB");
        }

        String base64;
        try {
            base64 = Base64.getEncoder().encodeToString(file.getBytes());
        } catch (Exception e) {
            throw new RuntimeException("读取上传文件失败", e);
        }

        MultimodalChatRequest req = new MultimodalChatRequest();
        req.setMessage(message);
        req.setMediaType(mediaType);
        req.setMediaBase64(base64);
        req.setMediaMime(contentType);
        req.setConversationId(
                conversationId.isBlank() ? UUID.randomUUID().toString() : conversationId);
        return chatAskMultimodal(req);
    }

    /** 流式多模态：OCR 完成后，走 RAG + 生成 */
    private void doStreamAnswer(Sinks.Many<ServerSentEvent<String>> sink, String cid,
                                MultimodalChatRequest request, String ocrText, String mediaLabel) {
        if (!ocrText.isEmpty()) {
            sink.tryEmitNext(ServerSentEvent.<String>builder().event("status")
                    .data("📚 检索药典知识库...").build());
        }

        String query = request.getMessage() != null ? request.getMessage().trim() : "";
        if (!query.isEmpty() && !ocrText.isEmpty()) {
            query = query + "\n\n（" + mediaLabel + "OCR提取内容：" + ocrText + "）";
        } else if (!ocrText.isEmpty()) {
            query = ocrText;
        } else if (query.isEmpty()) {
            query = "请介绍一下自己";
        }

        final String intent = retrieverService.classifyIntent(query);
        sink.tryEmitNext(ServerSentEvent.<String>builder().event("intent").data(intent).build());

        final List<Map<String, Object>> docs = rerankerService.rerank(retrieverService.search(query, intent, 20), query, 5);
        final boolean enableSearch = !ocrText.isEmpty() || props.isEnableWebSearch();
        sink.tryEmitNext(ServerSentEvent.<String>builder().event("status")
                .data("已匹配 " + docs.size() + " 条药典资料，生成回答中"
                        + (enableSearch ? "（已启用联网搜索）" : "") + "...").build());

        final List<Map<String, String>> messages = promptService.buildPrompt(query, docs, intent);
        if (!ocrText.isEmpty()) {
            messages.get(0).put("content",
                    messages.get(0).get("content") + "\n\n【" + mediaLabel + "分析结果】\n" + ocrText + "\n");
        }

        // 发送回答 section header
        sink.tryEmitNext(ServerSentEvent.<String>builder().data("\n【📚 药典参考回答】\n\n").build());

        StringBuilder fullAnswer = new StringBuilder();
        llmService.chatStream(messages, enableSearch)
                .doOnNext(token -> {
                    fullAnswer.append(token);
                    sink.tryEmitNext(ServerSentEvent.<String>builder().data(token).build());
                })
                .doOnComplete(() -> {
                    final List<Map<String, Object>> sources = buildSources(docs);
                    try {
                        String meta = new com.fasterxml.jackson.databind.ObjectMapper().writeValueAsString(Map.of(
                                "intent", intent, "sources", sources, "conversation_id", cid,
                                "ocr_text", ocrText));
                        sink.tryEmitNext(ServerSentEvent.<String>builder().event("meta").data(meta).build());
                    } catch (Exception ignored) {}
                    String finalAnswer = cleanAnswer(fullAnswer.toString());
                    String userMsg = !request.getMessage().isBlank() ? request.getMessage()
                            : !ocrText.isEmpty() ? "[" + mediaLabel + "]" : "";
                    persistenceService.saveMessage(cid, "user", userMsg, intent, null);
                    persistenceService.saveMessage(cid, "assistant", finalAnswer, intent, sources);
                    sink.tryEmitComplete();
                })
                .doOnError(sink::tryEmitError)
                .subscribe();
    }

    @GetMapping("/history")
    public ResponseEntity<Map<String, Object>> getHistory(
            @RequestParam(defaultValue = "1") int page,
            @RequestParam(defaultValue = "20") int pageSize) {
        // getHistory 现在直接返回包含 items/page/page_size/total/total_pages 的 Map
        var result = persistenceService.getHistory(page, pageSize);
        return ResponseEntity.ok(result);
    }

    @GetMapping("/history/{cid}")
    public ResponseEntity<Map<String, Object>> getConversationDetail(@PathVariable String cid) {
        var msgs = persistenceService.getConversationDetail(cid);
        return ResponseEntity.ok(Map.of("conversation_id", cid, "messages", msgs));
    }

    @PostMapping("/feedback")
    public ResponseEntity<Map<String, Object>> submitFeedback(@RequestBody FeedbackRequest request) {
        persistenceService.updateFeedback(request.getMessageId(), request.getFeedback());
        return ResponseEntity.ok(Map.of("status", "ok"));
    }

    @GetMapping("/admin/conversations")
    public ResponseEntity<Map<String, Object>> adminListConversations(
            @RequestParam(defaultValue = "1") int page,
            @RequestParam(defaultValue = "20") int pageSize,
            @RequestParam(required = false) String keyword) {
        int offset = (page - 1) * pageSize;

        StringBuilder sql = new StringBuilder("""
            SELECT DISTINCT ON (c.conversation_id)
                   c.conversation_id, c.title, c.created_at,
                   m.content AS last_msg, m.role
            FROM conversations c
            JOIN messages m ON m.conversation_id = c.conversation_id
            """);

        List<Object> params = new ArrayList<>();

        if (keyword != null && !keyword.isBlank()) {
            sql.append("WHERE m.content ILIKE ? ");
            params.add("%" + keyword + "%");
        }

        sql.append("""
            ORDER BY c.conversation_id, m.created_at DESC
            LIMIT ? OFFSET ?
            """);
        params.add(pageSize);
        params.add(offset);

        List<Map<String, Object>> items = jdbc.queryForList(
                sql.toString(), params.toArray());

        return ResponseEntity.ok(Map.of(
                "items", items,
                "page", page,
                "page_size", pageSize
        ));
    }

    private List<Map<String, Object>> buildSources(List<Map<String, Object>> docs) {
        Set<String> seen = new HashSet<>();
        return docs.stream()
                .filter(d -> {
                    String key = d.getOrDefault("name", "") + "|" + d.getOrDefault("section", "");
                    return seen.add(key);
                })
                .limit(8)  // 去重后最多 8 条，覆盖更多栏目
                .map(d -> {
                    String content = (String) d.getOrDefault("content", "");
                    String drugName = (String) d.getOrDefault("name", "");
                    String storedSection = (String) d.getOrDefault("section", "");
                    String sourceVersion = (String) d.getOrDefault("source_version", "");
                    String sourceVolume = (String) d.getOrDefault("source_volume", "");
                    String category = (String) d.getOrDefault("category", "");

                    // 优先用 DB 元数据，回退到内容解析
                    if (drugName == null || drugName.isEmpty()) {
                        drugName = extractDrugName(content);
                    }
                    String sectionDisplay = PromptService.SECTION_DISPLAY.getOrDefault(storedSection, storedSection);
                    if (sectionDisplay == null || sectionDisplay.isEmpty()) {
                        sectionDisplay = realSection(content, storedSection);
                    }

                    // 构建完整来源引用
                    StringBuilder sourceBuilder = new StringBuilder();
                    if (!sourceVersion.isEmpty()) sourceBuilder.append(sourceVersion);
                    if (!sourceVolume.isEmpty()) {
                        if (!sourceBuilder.isEmpty()) sourceBuilder.append(" ");
                        sourceBuilder.append(sourceVolume);
                    }
                    String fullSource = sourceBuilder.toString();

                    content = content.replaceAll("\\s*来源：.*$", "");
                    content = content.replaceAll("[\\r\\n]+", " ").trim();
                    String excerpt = content.length() > 500 ? content.substring(0, 500) + "…" : content;
                    return Map.<String, Object>of(
                            "drug_id", d.getOrDefault("drug_id", ""),
                            "name", drugName,
                            "section", sectionDisplay,
                            "category", category != null ? category : "",
                            "source", fullSource,
                            "excerpt", excerpt
                    );
                })
                .collect(Collectors.toList());
    }

    private String extractDrugName(String content) {
        if (content == null) return "";
        int start = content.indexOf("【");
        int end = content.indexOf(" - ");
        if (start >= 0 && end > start) {
            return content.substring(start + 1, end);
        }
        return content.length() > 20 ? content.substring(0, 20) : content;
    }

    /** 从 content 文本中提取真实 section（兜底"正文"） */
    private String realSection(String content, String storedSection) {
        if (!"正文".equals(storedSection) || content == null) return storedSection;
        int sep = content.indexOf(" - ");
        if (sep < 0) return storedSection;
        int end = content.indexOf("】", sep);
        if (end > sep) {
            return content.substring(sep + 3, end).trim();
        }
        return storedSection;
    }

    private String cleanAnswer(String text) {
        if (text == null) return "";
        // 去除多余空白行（保留单个换行），修复 Qwen 常见格式问题
        return text
                .replace("\r\n", "\n")
                .replaceAll("\\n{3,}", "\n\n")
                .trim();
    }
}