package com.ruoyi.ai.service.impl;

import com.ruoyi.ai.service.KnowledgeRagService;
import com.ruoyi.approve.pojo.KnowledgeBaseVector;
import com.ruoyi.approve.service.KnowledgeBaseVectorService;
import com.ruoyi.basic.pojo.StorageBlob;
import com.ruoyi.basic.service.StorageBlobService;
import com.ruoyi.common.config.FileProperties;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
import dev.langchain4j.store.embedding.EmbeddingSearchResult;
import dev.langchain4j.store.embedding.EmbeddingStore;
import io.pinecone.clients.Index;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.scheduling.annotation.Async;
import org.springframework.stereotype.Service;

import java.io.File;
import java.nio.charset.Charset;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

/**
 * 知识库RAG服务实现
 */
@Slf4j
@Service
public class KnowledgeRagServiceImpl implements KnowledgeRagService {

    private final KnowledgeBaseVectorService knowledgeBaseVectorService;
    private final StorageBlobService storageBlobService;
    private final EmbeddingModel embeddingModel;
    private final EmbeddingStore<TextSegment> embeddingStore;
    private final FileProperties fileProperties;
    private final Index pineconeIndex;

    @Value("${pinecone.namespace:knowledge-base}")
    private String namespace;

    public KnowledgeRagServiceImpl(
            KnowledgeBaseVectorService knowledgeBaseVectorService,
            StorageBlobService storageBlobService,
            EmbeddingModel embeddingModel,
            EmbeddingStore<TextSegment> embeddingStore,
            FileProperties fileProperties,
            Index pineconeIndex) {
        this.knowledgeBaseVectorService = knowledgeBaseVectorService;
        this.storageBlobService = storageBlobService;
        this.embeddingModel = embeddingModel;
        this.embeddingStore = embeddingStore;
        this.fileProperties = fileProperties;
        this.pineconeIndex = pineconeIndex;
    }

    private static final int CHUNK_SIZE = 500;
    private static final int CHUNK_OVERLAP = 100;
    private static final long CHUNK_THRESHOLD_BYTES = 80L * 1024 * 1024;
    private static final int EMBEDDING_MAX_LENGTH = 8000;

    @Override
    @Async("threadPoolTaskExecutor")
    public void processVectorAsync(Long vectorId) {
        log.info("开始异步向量化处理: vectorId={}, thread={}", vectorId, Thread.currentThread().getName());
        processVector(vectorId);
    }

    @Override
    public void processVector(Long vectorId) {
        log.info("开始处理向量化: vectorId={}", vectorId);
        KnowledgeBaseVector vector = knowledgeBaseVectorService.getById(vectorId);
        if (vector == null) {
            log.error("向量记录不存在: {}", vectorId);
            return;
        }

        try {
            knowledgeBaseVectorService.updateVectorStatus(vectorId,
                    KnowledgeBaseVector.STATUS_PROCESSING, null, null);

            StorageBlob blob = storageBlobService.getById(vector.getStorageBlobId());
            if (blob == null) {
                throw new RuntimeException("文件不存在: " + vector.getStorageBlobId());
            }

            File file = getFile(blob);
            log.info("文件路径: {}, 是否存在: {}", file.getAbsolutePath(), file.exists());
            long fileSize = file.length();

            String content = extractFileContent(file, vector.getFileName());
            log.info("文件内容长度: {}", content != null ? content.length() : 0);

            if (content == null || content.trim().isEmpty()) {
                throw new RuntimeException("文件内容为空");
            }

            List<TextSegment> chunks;
            boolean needChunk = fileSize > CHUNK_THRESHOLD_BYTES || content.length() > EMBEDDING_MAX_LENGTH;
            if (needChunk) {
                log.info("开始切片: fileSize={}, contentLength={}", fileSize, content.length());
                chunks = splitText(content, vector);
                log.info("切片完成，共 {} 个块", chunks.size());
            } else {
                log.info("文件较小，不进行切片");
                Map<String, Object> metadata = buildMetadata(vector);
                chunks = List.of(TextSegment.from(content, new dev.langchain4j.data.document.Metadata(metadata)));
            }

            int chunkCount = 0;
            for (TextSegment chunk : chunks) {
                Embedding embedding = embeddingModel.embed(chunk).content();
                embeddingStore.add(embedding, chunk);
                chunkCount++;
            }

            knowledgeBaseVectorService.updateVectorStatus(vectorId,
                    KnowledgeBaseVector.STATUS_COMPLETED, chunkCount, null);

            log.info("向量化处理完成: vectorId={}, chunkCount={}", vectorId, chunkCount);

        } catch (Exception e) {
            log.error("向量化处理失败: vectorId={}", vectorId, e);
            knowledgeBaseVectorService.updateVectorStatus(vectorId,
                    KnowledgeBaseVector.STATUS_FAILED, null, e.getMessage());
        }
    }

    @Override
    public List<String> searchRelevantContent(String namespace, String query, int maxResults) {
        try {
            Embedding queryEmbedding = embeddingModel.embed(query).content();

            EmbeddingSearchRequest searchRequest = EmbeddingSearchRequest.builder()
                    .queryEmbedding(queryEmbedding)
                    .maxResults(maxResults)
                    .minScore(0.7)
                    .build();

            EmbeddingSearchResult<TextSegment> searchResult = embeddingStore.search(searchRequest);

            return searchResult.matches().stream()
                    .map(match -> match.embedded().text())
                    .collect(Collectors.toList());

        } catch (Exception e) {
            log.error("向量检索失败: namespace={}", namespace, e);
            return new ArrayList<>();
        }
    }

    @Override
    public void deleteEmbeddings(String namespace, Long storageBlobId) {
        log.info("删除向量数据: namespace={}, storageBlobId={}", namespace, storageBlobId);
        try {
            Struct filter = Struct.newBuilder()
                    .putFields("storageBlobId", Value.newBuilder()
                            .setStructValue(Struct.newBuilder()
                                    .putFields("$eq", Value.newBuilder()
                                            .setNumberValue(storageBlobId.doubleValue())
                                            .build()))
                            .build())
                    .build();

            List<String> emptyIds = new ArrayList<>();
            pineconeIndex.delete(emptyIds, false, this.namespace, filter);
            log.info("向量删除完成: storageBlobId={}", storageBlobId);
        } catch (Exception e) {
            log.error("删除向量数据失败: namespace={}, storageBlobId={}", namespace, storageBlobId, e);
        }
    }

    private File getFile(StorageBlob blob) {
        String path = blob.getPath();
        if (path != null && !path.isEmpty()) {
            return new File(new File(fileProperties.getPath(), path), blob.getUidFilename());
        }
        return new File(fileProperties.getPath(), blob.getUidFilename());
    }

    private String extractFileContent(File file, String fileName) throws Exception {
        String ext = getFileExtension(fileName);

        if (isPlainText(ext)) {
            return readFileWithEncoding(file);
        }

        if ("docx".equals(ext)) {
            return extractDocx(file);
        }

        if ("xlsx".equals(ext)) {
            return extractXlsx(file);
        }

        if ("xls".equals(ext)) {
            return extractXls(file);
        }

        return readFileWithEncoding(file);
    }

    private String readFileWithEncoding(File file) throws Exception {
        byte[] bytes = Files.readAllBytes(file.toPath());

        String utf8Content = new String(bytes, StandardCharsets.UTF_8);
        if (isValidUtf8(utf8Content)) {
            log.debug("文件编码: UTF-8");
            return utf8Content;
        }

        try {
            Charset gbk = Charset.forName("GBK");
            String gbkContent = new String(bytes, gbk);
            log.debug("文件编码: GBK");
            return gbkContent;
        } catch (Exception e) {
            log.warn("编码检测失败，使用 UTF-8");
            return utf8Content;
        }
    }

    private boolean isValidUtf8(String decoded) {
        // 检查替换字符 U+FFFD (UTF-8 解码失败时出现)
        if (decoded.contains("�")) {
            return false;
        }
        int invalidCount = 0;
        int checkLen = Math.min(decoded.length(), 1000);
        for (int i = 0; i < checkLen; i++) {
            char c = decoded.charAt(i);
            // 检查私有使用区域 (U+E000-U+F8FF) 或异常控制字符
            if ((c >= '' && c <= '') || (c < ' ' && c != '\n' && c != '\r' && c != '\t')) {
                invalidCount++;
            }
        }
        return invalidCount < checkLen * 0.05;
    }

    private String getFileExtension(String fileName) {
        if (fileName == null || !fileName.contains(".")) {
            return "";
        }
        return fileName.substring(fileName.lastIndexOf('.') + 1).toLowerCase();
    }

    private boolean isPlainText(String ext) {
        return "txt".equals(ext) || "md".equals(ext) || "json".equals(ext)
                || "csv".equals(ext) || "xml".equals(ext) || "yaml".equals(ext)
                || "yml".equals(ext);
    }

    private String extractDocx(File file) throws Exception {
        try (var doc = new org.apache.poi.xwpf.usermodel.XWPFDocument(new java.io.FileInputStream(file));
             var extractor = new org.apache.poi.xwpf.extractor.XWPFWordExtractor(doc)) {
            return extractor.getText();
        }
    }

    private String extractXlsx(File file) throws Exception {
        try (var workbook = new org.apache.poi.xssf.usermodel.XSSFWorkbook(file)) {
            return extractWorkbook(workbook);
        }
    }

    private String extractXls(File file) throws Exception {
        try (var workbook = new org.apache.poi.hssf.usermodel.HSSFWorkbook(new java.io.FileInputStream(file))) {
            return extractWorkbook(workbook);
        }
    }

    private String extractWorkbook(org.apache.poi.ss.usermodel.Workbook workbook) {
        StringBuilder text = new StringBuilder();
        var formatter = new org.apache.poi.ss.usermodel.DataFormatter();
        for (int i = 0; i < workbook.getNumberOfSheets(); i++) {
            var sheet = workbook.getSheetAt(i);
            text.append("Sheet: ").append(sheet.getSheetName()).append("\n");
            for (var row : sheet) {
                for (var cell : row) {
                    text.append(formatter.formatCellValue(cell)).append("\t");
                }
                text.append("\n");
            }
        }
        return text.toString();
    }

    private List<TextSegment> splitText(String content, KnowledgeBaseVector vector) {
        List<TextSegment> chunks = new ArrayList<>();

        if (content.length() <= CHUNK_SIZE) {
            Map<String, Object> metadata = buildMetadata(vector);
            chunks.add(TextSegment.from(content, new dev.langchain4j.data.document.Metadata(metadata)));
            return chunks;
        }

        int start = 0;
        int chunkIndex = 0;
        while (start < content.length()) {
            int end = Math.min(start + CHUNK_SIZE, content.length());

            if (end < content.length()) {
                int lastPeriod = content.lastIndexOf('。', end);
                int lastNewline = content.lastIndexOf('\n', end);
                int boundary = Math.max(lastPeriod, lastNewline);
                if (boundary > start + CHUNK_SIZE / 2) {
                    end = boundary + 1;
                }
            }

            String chunkText = content.substring(start, end).trim();
            if (!chunkText.isEmpty()) {
                Map<String, Object> metadata = buildMetadata(vector);
                metadata.put("chunkIndex", chunkIndex);
                chunks.add(TextSegment.from(chunkText, new dev.langchain4j.data.document.Metadata(metadata)));
                chunkIndex++;
            }

            start = end - CHUNK_OVERLAP;
            if (start < 0) start = 0;
            if (start >= content.length() - CHUNK_OVERLAP) break;
        }

        return chunks;
    }

    private Map<String, Object> buildMetadata(KnowledgeBaseVector vector) {
        Map<String, Object> metadata = new HashMap<>();
        metadata.put("knowledgeBaseId", vector.getKnowledgeBaseId());
        metadata.put("storageBlobId", vector.getStorageBlobId());
        metadata.put("fileName", vector.getFileName());
        metadata.put("namespace", vector.getNamespace());
        return metadata;
    }
}