二开定制指南
大约 24 分钟knowledgecustomization
知识库二次开发定制指南
定制概述
BladeX AI知识库模块提供了完善的扩展机制，支持开发者自定义文档处理器、分段器和进度跟踪器。本指南详细介绍如何进行二次开发定制，包括自定义处理器开发、工厂集成、配置测试等完整流程。
一、开发环境准备
1.1 项目结构了解
项目结构
在开始自定义开发前，需要了解知识库模块的项目结构和核心组件。
├── handler/ # 文档处理器
│ ├── FileHandlerFactory.java # 处理器工厂
│ ├── FileHandler.java # 处理器接口
│ └── impl/ # 处理器实现
│ ├── PdfFileHandler.java
│ ├── WordFileHandler.java
│ ├── CsvFileHandler.java
│ └── DefaultFileHandler.java
├── segment/ # 分段处理器
│ ├── FileSegmentFactory.java # 分段器工厂
│ ├── FileSegment.java # 分段器接口
│ └── impl/ # 分段器实现
│ ├── SemanticSegment.java
│ ├── FixedLengthSegment.java
│ └── SymbolSegment.java
├── service/ # 核心服务
│ ├── KnowledgeFileService.java
│ └── KnowledgeSegmentService.java
├── progress/ # 进度跟踪
│ └── FileProgressTrack.java
├── config/ # 配置管理
├── constant/ # 常量定义
├── exception/ # 异常处理
└── tool/ # 工具类1.2 依赖配置
依赖管理
确保项目中包含必要的依赖配置，支持自定义处理器的开发和集成。
<dependencies>
<!-- Spring Boot Starter -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
</dependency>
<!-- BladeX Core -->
<dependency>
<groupId>org.springblade</groupId>
<artifactId>blade-core-tool</artifactId>
</dependency>
<!-- 文档处理依赖 -->
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi-ooxml</artifactId>
</dependency>
<!-- PDF处理依赖 -->
<dependency>
<groupId>org.apache.pdfbox</groupId>
<artifactId>pdfbox</artifactId>
</dependency>
<!-- Jackson JSON处理 -->
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
</dependency>
</dependencies>二、自定义文档处理器开发
2.1 文档处理器开发
开发规范
自定义文档处理器必须实现FileHandler接口，并提供对应文档格式的处理逻辑。
开发步骤：
- 实现接口：实现
FileHandler接口 - 定义支持格式：指定处理器支持的文件类型
- 实现处理逻辑：编写文档内容提取逻辑
- 进度跟踪：集成进度跟踪机制
- 异常处理：处理文档解析异常
@Component
@Slf4j
public class CustomFileHandler implements FileHandler {
/**
* 支持的文件类型
*/
private static final String[] SUPPORTED_TYPES = {"custom", "ext"};
@Override
public String[] getSupportedFileTypes() {
return SUPPORTED_TYPES;
}
@Override
public String readContent(InputStream inputStream, FileProgressTrack progressTrack) {
try {
return readContentInternal(inputStream, progressTrack);
} catch (Exception e) {
log.error("自定义文档处理失败", e);
throw new RuntimeException("自定义文档处理失败: " + e.getMessage());
}
}
private String readContentInternal(InputStream inputStream,
FileProgressTrack progressTrack) throws IOException {
StringBuilder content = new StringBuilder();
try (BufferedReader reader = new BufferedReader(
new InputStreamReader(inputStream, StandardCharsets.UTF_8))) {
String line;
int lineCount = 0;
long bytesRead = 0;
while ((line = reader.readLine()) != null) {
// 自定义处理逻辑
String processedLine = processCustomLine(line);
content.append(processedLine).append("\n");
lineCount++;
bytesRead += line.getBytes(StandardCharsets.UTF_8).length;
// 进度跟踪
if (progressTrack != null && lineCount % 100 == 0) {
progressTrack.onProgress(bytesRead, -1, lineCount);
}
}
// 完成回调
if (progressTrack != null) {
progressTrack.onComplete(bytesRead, lineCount);
}
}
return content.toString();
}
/**
* 自定义行处理逻辑
*/
private String processCustomLine(String line) {
// 实现自定义的行处理逻辑
// 例如：格式转换、内容清理、特殊字符处理等
return line.trim();
}
@Override
public String previewContent(InputStream inputStream, int maxLength) {
try {
String fullContent = readContent(inputStream, null);
if (fullContent.length() <= maxLength) {
return fullContent;
}
return fullContent.substring(0, maxLength) + "...";
} catch (Exception e) {
log.error("预览自定义文档失败", e);
return "预览失败: " + e.getMessage();
}
}
@Override
public boolean validateFormat(InputStream inputStream) {
try {
// 实现格式验证逻辑
// 例如：检查文件头、验证文件结构等
byte[] header = new byte[4];
inputStream.read(header);
// 自定义验证逻辑
return isValidCustomFormat(header);
} catch (Exception e) {
log.error("验证自定义文档格式失败", e);
return false;
}
}
private boolean isValidCustomFormat(byte[] header) {
// 实现具体的格式验证逻辑
return true;
}
}2.2 复杂文档处理器示例
复杂处理器
对于复杂的文档格式，可以使用第三方库进行解析，并集成到处理器中。
@Component
@Slf4j
public class AdvancedDocumentHandler implements FileHandler {
private static final String[] SUPPORTED_TYPES = {"docx", "xlsx", "pptx"};
@Override
public String[] getSupportedFileTypes() {
return SUPPORTED_TYPES;
}
@Override
public String readContent(InputStream inputStream, FileProgressTrack progressTrack) {
try {
// 检测文档类型
String fileType = detectFileType(inputStream);
switch (fileType.toLowerCase()) {
case "docx":
return extractWordContent(inputStream, progressTrack);
case "xlsx":
return extractExcelContent(inputStream, progressTrack);
case "pptx":
return extractPowerPointContent(inputStream, progressTrack);
default:
throw new UnsupportedOperationException("不支持的文档类型: " + fileType);
}
} catch (Exception e) {
log.error("高级文档处理失败", e);
throw new RuntimeException("高级文档处理失败: " + e.getMessage());
}
}
private String extractWordContent(InputStream inputStream,
FileProgressTrack progressTrack) throws IOException {
StringBuilder content = new StringBuilder();
try (XWPFDocument document = new XWPFDocument(inputStream)) {
List<XWPFParagraph> paragraphs = document.getParagraphs();
int totalParagraphs = paragraphs.size();
for (int i = 0; i < paragraphs.size(); i++) {
XWPFParagraph paragraph = paragraphs.get(i);
String text = paragraph.getText();
if (StringUtil.isNotBlank(text)) {
content.append(text).append("\n");
}
// 进度跟踪
if (progressTrack != null && i % 10 == 0) {
progressTrack.onProgress(i, totalParagraphs, i);
}
}
// 处理表格
List<XWPFTable> tables = document.getTables();
for (XWPFTable table : tables) {
content.append(extractTableContent(table));
}
if (progressTrack != null) {
progressTrack.onComplete(content.length(), totalParagraphs);
}
}
return content.toString();
}
private String extractTableContent(XWPFTable table) {
StringBuilder tableContent = new StringBuilder();
for (XWPFTableRow row : table.getRows()) {
for (XWPFTableCell cell : row.getTableCells()) {
String cellText = cell.getText();
if (StringUtil.isNotBlank(cellText)) {
tableContent.append(cellText).append("\t");
}
}
tableContent.append("\n");
}
return tableContent.toString();
}
private String detectFileType(InputStream inputStream) {
// 实现文件类型检测逻辑
// 可以通过文件头、扩展名等方式检测
return "docx"; // 示例返回
}
}三、自定义分段器开发
3.1 分段器开发
分段器开发
自定义分段器需要实现FileSegment接口，并提供特定的分段逻辑。
@Component
@RequiredArgsConstructor
@Slf4j
public class CustomSegment implements FileSegment {
private final SegmentProperties properties;
@Override
public SegmentType getType() {
return SegmentType.CUSTOM;
}
@Override
public List<String> segment(SegmentRequest request) {
if (request == null || StringUtil.isBlank(request.getContent())) {
return new ArrayList<>();
}
String content = request.getContent();
int segmentLength = request.getSegmentLength() != null ?
request.getSegmentLength() : properties.getSegmentLength();
List<String> segments = new ArrayList<>();
try {
// 自定义分段逻辑
segments = performCustomSegmentation(content, segmentLength, request);
if (properties.isLogEnabled()) {
log.debug("自定义分段完成: 原始长度={}, 分段数量={}",
content.length(), segments.size());
}
} catch (Exception e) {
log.error("自定义分段处理失败", e);
// 降级处理：返回原始内容
segments.add(content);
}
return segments;
}
private List<String> performCustomSegmentation(String content,
int segmentLength, SegmentRequest request) {
List<String> segments = new ArrayList<>();
// 实现自定义分段算法
// 例如：基于特定规则、机器学习模型等
// 示例：基于自定义标记分段
String customDelimiter = request.getSegmentSymbol();
if (StringUtil.isNotBlank(customDelimiter)) {
return segmentByCustomDelimiter(content, customDelimiter);
}
// 示例：基于语义相似度分段
return segmentBySemantic(content, segmentLength);
}
private List<String> segmentByCustomDelimiter(String content, String delimiter) {
List<String> segments = new ArrayList<>();
String[] parts = content.split(Pattern.quote(delimiter));
for (String part : parts) {
String trimmed = part.trim();
if (StringUtil.isNotBlank(trimmed)) {
segments.add(trimmed);
}
}
return segments;
}
private List<String> segmentBySemantic(String content, int maxLength) {
List<String> segments = new ArrayList<>();
// 实现基于语义的分段逻辑
// 这里可以集成NLP库或调用外部API
String[] sentences = content.split("[.!?]+");
StringBuilder currentSegment = new StringBuilder();
for (String sentence : sentences) {
sentence = sentence.trim();
if (StringUtil.isBlank(sentence)) {
continue;
}
if (currentSegment.length() + sentence.length() > maxLength) {
if (currentSegment.length() > 0) {
segments.add(currentSegment.toString().trim());
currentSegment = new StringBuilder();
}
}
currentSegment.append(sentence).append(". ");
}
if (currentSegment.length() > 0) {
segments.add(currentSegment.toString().trim());
}
return segments;
}
}3.2 智能分段器示例
智能分段
智能分段器可以集成机器学习模型或外部API，实现更精确的分段效果。
@Component
@RequiredArgsConstructor
@Slf4j
public class AISegment implements FileSegment {
private final SegmentProperties properties;
private final ChatService chatService; // 集成LLM服务
@Override
public SegmentType getType() {
return SegmentType.AI_SEMANTIC;
}
@Override
public List<String> segment(SegmentRequest request) {
if (request == null || StringUtil.isBlank(request.getContent())) {
return new ArrayList<>();
}
try {
// 使用AI进行智能分段
return performAISegmentation(request);
} catch (Exception e) {
log.error("AI分段处理失败，降级到默认分段", e);
// 降级到固定长度分段
return fallbackSegmentation(request);
}
}
private List<String> performAISegmentation(SegmentRequest request) {
String content = request.getContent();
// 构建AI分段提示词
String prompt = buildSegmentationPrompt(content, request);
// 调用LLM进行分段
BladeChatRequest chatRequest = BladeChatRequest.builder()
.model("gpt-3.5-turbo")
.messages(List.of(ChatMessage.ofUser(prompt)))
.temperature(0.1)
.build();
BladeChatResponse response = chatService.chat(chatRequest);
String segmentResult = response.getContent();
// 解析AI返回的分段结果
return parseAISegmentResult(segmentResult);
}
private String buildSegmentationPrompt(String content, SegmentRequest request) {
StringBuilder prompt = new StringBuilder();
prompt.append("请将以下文本按照语义边界进行分段，每段长度控制在")
.append(request.getSegmentLength())
.append("字符以内。要求：\n");
prompt.append("1. 保持语义完整性\n");
prompt.append("2. 避免在句子中间分段\n");
prompt.append("3. 每段用'---'分隔\n");
prompt.append("4. 不要添加额外说明\n\n");
prompt.append("文本内容：\n").append(content);
return prompt.toString();
}
private List<String> parseAISegmentResult(String result) {
List<String> segments = new ArrayList<>();
String[] parts = result.split("---");
for (String part : parts) {
String trimmed = part.trim();
if (StringUtil.isNotBlank(trimmed)) {
segments.add(trimmed);
}
}
return segments;
}
private List<String> fallbackSegmentation(SegmentRequest request) {
// 降级到固定长度分段
String content = request.getContent();
int segmentLength = request.getSegmentLength();
List<String> segments = new ArrayList<>();
int start = 0;
while (start < content.length()) {
int end = Math.min(start + segmentLength, content.length());
// 尝试在句子边界截断
if (end < content.length()) {
int lastPeriod = content.lastIndexOf('.', end);
if (lastPeriod > start) {
end = lastPeriod + 1;
}
}
String segment = content.substring(start, end).trim();
if (StringUtil.isNotBlank(segment)) {
segments.add(segment);
}
start = end;
}
return segments;
}
}四、工厂类集成
4.1 自动注册机制
自动注册
Spring Boot的自动配置机制会自动发现和注册自定义的处理器和分段器。
// 文档处理器自动注册
@Component
@RequiredArgsConstructor
public class FileHandlerFactory implements InitializingBean {
private final List<FileHandler> handlers;
private final Map<String, FileHandler> handlerMap = new ConcurrentHashMap<>();
@Override
public void afterPropertiesSet() {
initHandlers();
}
private void initHandlers() {
for (FileHandler handler : handlers) {
String[] supportedTypes = handler.getSupportedFileTypes();
if (supportedTypes != null) {
for (String type : supportedTypes) {
if (StringUtil.isNotBlank(type)) {
String fileType = type.toLowerCase();
handlerMap.put(fileType, handler);
log.info("注册文档处理器: {} -> {}",
fileType, handler.getClass().getSimpleName());
}
}
}
}
}
}
// 分段器自动注册
@Component
@RequiredArgsConstructor
public class FileSegmentFactory implements InitializingBean {
private final List<FileSegment> segments;
private final Map<SegmentType, FileSegment> segmentMap = new ConcurrentHashMap<>();
@Override
public void afterPropertiesSet() {
initSegment();
}
private void initSegment() {
for (FileSegment segment : segments) {
segmentMap.put(segment.getType(), segment);
log.info("注册分段器: {} -> {}",
segment.getType(), segment.getClass().getSimpleName());
}
}
}4.2 手动注册机制
手动注册
对于需要动态注册的处理器，可以提供手动注册接口。
@Component
public class CustomHandlerRegistry {
@Autowired
private FileHandlerFactory handlerFactory;
@Autowired
private FileSegmentFactory segmentFactory;
/**
* 动态注册文档处理器
*/
public void registerFileHandler(String fileType, FileHandler handler) {
try {
// 使用反射访问私有字段
Field handlerMapField = FileHandlerFactory.class.getDeclaredField("handlerMap");
handlerMapField.setAccessible(true);
@SuppressWarnings("unchecked")
Map<String, FileHandler> handlerMap =
(Map<String, FileHandler>) handlerMapField.get(handlerFactory);
handlerMap.put(fileType.toLowerCase(), handler);
log.info("动态注册文档处理器: {} -> {}", fileType, handler.getClass().getSimpleName());
} catch (Exception e) {
log.error("动态注册文档处理器失败", e);
throw new RuntimeException("动态注册失败", e);
}
}
/**
* 动态注册分段器
*/
public void registerSegment(SegmentType type, FileSegment segment) {
try {
Field segmentMapField = FileSegmentFactory.class.getDeclaredField("segmentMap");
segmentMapField.setAccessible(true);
@SuppressWarnings("unchecked")
Map<SegmentType, FileSegment> segmentMap =
(Map<SegmentType, FileSegment>) segmentMapField.get(segmentFactory);
segmentMap.put(type, segment);
log.info("动态注册分段器: {} -> {}", type, segment.getClass().getSimpleName());
} catch (Exception e) {
log.error("动态注册分段器失败", e);
throw new RuntimeException("动态注册失败", e);
}
}
}五、配置和测试
5.1 配置管理
配置管理
为自定义处理器和分段器提供灵活的配置管理，支持不同环境的配置需求。
# application.yml
bladex:
knowledge:
# 文档处理配置
file:
max-size: 100MB
timeout: 300000
temp-dir: /tmp/knowledge
# 分段配置
segment:
default-length: 500
default-symbol: "$$"
log-enabled: true
# 自定义处理器配置
custom:
enabled: true
handlers:
- type: "custom"
class: "com.example.CustomFileHandler"
config:
buffer-size: 8192
encoding: "UTF-8"
segments:
- type: "CUSTOM"
class: "com.example.CustomSegment"
config:
algorithm: "semantic"
threshold: 0.8// 配置类
@ConfigurationProperties(prefix = "bladex.knowledge.custom")
@Data
public class CustomKnowledgeProperties {
private boolean enabled = true;
private List<HandlerConfig> handlers = new ArrayList<>();
private List<SegmentConfig> segments = new ArrayList<>();
@Data
public static class HandlerConfig {
private String type;
private String className;
private Map<String, Object> config = new HashMap<>();
}
@Data
public static class SegmentConfig {
private String type;
private String className;
private Map<String, Object> config = new HashMap<>();
}
}5.2 单元测试
测试覆盖
为自定义处理器和分段器编写完整的单元测试，确保功能的正确性和稳定性。
@ExtendWith(MockitoExtension.class)
class CustomFileHandlerTest {
@InjectMocks
private CustomFileHandler customFileHandler;
@Mock
private FileProgressTrack progressTrack;
@Test
void testReadContent() throws IOException {
// Given
String testContent = "这是测试内容\n第二行内容\n第三行内容";
InputStream inputStream = new ByteArrayInputStream(
testContent.getBytes(StandardCharsets.UTF_8));
// When
String result = customFileHandler.readContent(inputStream, progressTrack);
// Then
assertThat(result).isNotNull();
assertThat(result).contains("这是测试内容");
assertThat(result).contains("第二行内容");
assertThat(result).contains("第三行内容");
// 验证进度跟踪调用
verify(progressTrack, atLeastOnce()).onProgress(anyLong(), anyLong(), anyInt());
verify(progressTrack, times(1)).onComplete(anyLong(), anyInt());
}
@Test
void testGetSupportedFileTypes() {
// When
String[] supportedTypes = customFileHandler.getSupportedFileTypes();
// Then
assertThat(supportedTypes).isNotNull();
assertThat(supportedTypes).contains("custom", "ext");
}
@Test
void testValidateFormat() throws IOException {
// Given
byte[] validHeader = {0x43, 0x55, 0x53, 0x54}; // "CUST"
InputStream inputStream = new ByteArrayInputStream(validHeader);
// When
boolean isValid = customFileHandler.validateFormat(inputStream);
// Then
assertThat(isValid).isTrue();
}
@Test
void testPreviewContent() throws IOException {
// Given
String longContent = "这是一个很长的测试内容".repeat(100);
InputStream inputStream = new ByteArrayInputStream(
longContent.getBytes(StandardCharsets.UTF_8));
// When
String preview = customFileHandler.previewContent(inputStream, 100);
// Then
assertThat(preview).isNotNull();
assertThat(preview.length()).isLessThanOrEqualTo(103); // 100 + "..."
assertThat(preview).endsWith("...");
}
}
@ExtendWith(MockitoExtension.class)
class CustomSegmentTest {
@Mock
private SegmentProperties properties;
@InjectMocks
private CustomSegment customSegment;
@BeforeEach
void setUp() {
when(properties.getSegmentLength()).thenReturn(500);
when(properties.isLogEnabled()).thenReturn(true);
}
@Test
void testSegmentByCustomDelimiter() {
// Given
String content = "第一段内容$$第二段内容$$第三段内容";
SegmentRequest request = SegmentRequest.builder()
.content(content)
.segmentSymbol("$$")
.segmentLength(500)
.build();
// When
List<String> segments = customSegment.segment(request);
// Then
assertThat(segments).hasSize(3);
assertThat(segments.get(0)).isEqualTo("第一段内容");
assertThat(segments.get(1)).isEqualTo("第二段内容");
assertThat(segments.get(2)).isEqualTo("第三段内容");
}
@Test
void testSegmentBySemantic() {
// Given
String content = "这是第一个句子。这是第二个句子！这是第三个句子？";
SegmentRequest request = SegmentRequest.builder()
.content(content)
.segmentLength(20)
.build();
// When
List<String> segments = customSegment.segment(request);
// Then
assertThat(segments).isNotEmpty();
for (String segment : segments) {
assertThat(segment.length()).isLessThanOrEqualTo(25); // 允许一定误差
}
}
@Test
void testGetType() {
// When
SegmentType type = customSegment.getType();
// Then
assertThat(type).isEqualTo(SegmentType.CUSTOM);
}
@Test
void testEmptyContent() {
// Given
SegmentRequest request = SegmentRequest.builder()
.content("")
.build();
// When
List<String> segments = customSegment.segment(request);
// Then
assertThat(segments).isEmpty();
}
}5.3 集成测试
集成验证
编写集成测试验证自定义处理器在完整知识库处理流程中的执行效果。
@SpringBootTest
@TestPropertySource(properties = {
"bladex.knowledge.custom.enabled=true"
})
class CustomKnowledgeIntegrationTest {
@Autowired
private KnowledgeFileService knowledgeFileService;
@Autowired
private KnowledgeSegmentService knowledgeSegmentService;
@Autowired
private FileHandlerFactory handlerFactory;
@Autowired
private FileSegmentFactory segmentFactory;
@Test
void testCustomFileHandlerIntegration() throws IOException {
// Given
String testContent = "自定义文档内容测试";
InputStream inputStream = new ByteArrayInputStream(
testContent.getBytes(StandardCharsets.UTF_8));
// When
FileHandler handler = handlerFactory.getHandler("custom");
String content = handler.readContent(inputStream, null);
// Then
assertThat(handler).isInstanceOf(CustomFileHandler.class);
assertThat(content).contains("自定义文档内容测试");
}
@Test
void testCustomSegmentIntegration() {
// Given
String content = "这是测试内容$$分段测试$$集成验证";
SegmentRequest request = SegmentRequest.builder()
.content(content)
.segmentType(SegmentType.CUSTOM)
.segmentSymbol("$$")
.segmentLength(500)
.build();
// When
List<String> segments = knowledgeSegmentService.segmentContent(request);
// Then
assertThat(segments).hasSize(3);
assertThat(segments).contains("这是测试内容", "分段测试", "集成验证");
}
@Test
void testEndToEndProcessing() {
// Given
String fileUrl = "http://example.com/test.custom";
String fileType = "custom";
// When
String content = knowledgeFileService.readFileContent(fileUrl, fileType);
SegmentRequest segmentRequest = SegmentRequest.builder()
.content(content)
.segmentType(SegmentType.CUSTOM)
.segmentLength(500)
.build();
List<String> segments = knowledgeSegmentService.segmentContent(segmentRequest);
// Then
assertThat(content).isNotNull();
assertThat(segments).isNotEmpty();
}
}六、高级定制功能
6.1 智能缓存管理
缓存架构
知识库模块采用多层缓存架构，通过本地缓存和Redis缓存的结合，显著提升文档处理和分段检索的性能。
多层缓存设计：
@Component
@RequiredArgsConstructor
public class KnowledgeAdvancedCache {
private final RedisTemplate<String, Object> redisTemplate;
// 本地缓存 - 热点数据
private final Cache<String, Object> localCache = Caffeine.newBuilder()
.maximumSize(1000)
.expireAfterWrite(Duration.ofMinutes(30))
.recordStats()
.build();
/**
* 智能缓存获取
*/
public <T> T getWithFallback(String key, Class<T> type, Supplier<T> dataLoader) {
// 1. 先查本地缓存
T value = (T) localCache.getIfPresent(key);
if (value != null) {
return value;
}
// 2. 再查Redis缓存
value = (T) redisTemplate.opsForValue().get(key);
if (value != null) {
localCache.put(key, value);
return value;
}
// 3. 最后调用数据加载器
value = dataLoader.get();
if (value != null) {
cacheWithStrategy(key, value);
}
return value;
}
/**
* 智能缓存策略
*/
private void cacheWithStrategy(String key, Object value) {
// 根据数据类型和大小选择不同的缓存策略
if (isHotData(key)) {
// 热点数据同时存储在本地和Redis
localCache.put(key, value);
redisTemplate.opsForValue().set(key, value, Duration.ofHours(2));
} else if (isLargeData(value)) {
// 大数据只存储在Redis
redisTemplate.opsForValue().set(key, value, Duration.ofMinutes(30));
} else {
// 普通数据存储在本地缓存
localCache.put(key, value);
}
}
/**
* 缓存预热机制
*/
@EventListener
@Async
public void warmupCache(KnowledgeWarmupEvent event) {
Long knowledgeId = event.getKnowledgeId();
// 预热知识库基础信息
String knowledgeKey = "knowledge:" + knowledgeId;
getWithFallback(knowledgeKey, AiKnowledge.class,
() -> knowledgeService.getById(knowledgeId));
// 预热常用分段配置
String segmentConfigKey = "segment:config:" + knowledgeId;
getWithFallback(segmentConfigKey, SegmentConfig.class,
() -> buildDefaultSegmentConfig(knowledgeId));
}
private boolean isHotData(String key) {
return key.startsWith("knowledge:") || key.startsWith("assets:frequent:");
}
private boolean isLargeData(Object value) {
return value.toString().length() > 10000; // 大于10KB认为是大数据
}
}缓存失效策略：
@Component
public class KnowledgeCacheEvictionStrategy {
@Autowired
private KnowledgeAdvancedCache cache;
/**
* 智能缓存失效
*/
@EventListener
public void handleKnowledgeUpdate(KnowledgeUpdateEvent event) {
Long knowledgeId = event.getKnowledgeId();
// 级联清除相关缓存
List<String> keysToEvict = Arrays.asList(
"knowledge:" + knowledgeId,
"assets:count:" + knowledgeId,
"segment:config:" + knowledgeId,
"vector:config:" + knowledgeId
);
keysToEvict.forEach(cache::evict);
}
/**
* 定时缓存清理
*/
@Scheduled(fixedRate = 300000) // 5分钟
public void cleanupExpiredCache() {
cache.cleanUp();
}
}6.2 向量化深度集成
向量化优化
通过与RAG模块的深度集成，实现智能向量化处理和增量更新机制，大幅提升检索精度和响应速度。
智能向量化处理器：
@Component
@RequiredArgsConstructor
public class AdvancedVectorizationProcessor {
private final RagEngineService ragEngineService;
private final KnowledgeAdvancedCache cache;
private final MeterRegistry meterRegistry;
/**
* 智能向量化处理
*/
public CompletableFuture<VectorizeResult> processAdvancedVectorization(
Long assetsId, List<AiKnowledgeAssetsSegment> segments, VectorizeConfig config) {
return CompletableFuture.supplyAsync(() -> {
Timer.Sample sample = Timer.start(meterRegistry);
try {
// 1. 预处理分段内容
List<ProcessedSegment> processedSegments = preprocessSegments(segments, config);
// 2. 批量向量化
List<VectorResult> vectorResults = batchVectorize(processedSegments, config);
// 3. 质量评估
QualityMetrics metrics = assessVectorQuality(vectorResults);
// 4. 优化存储
optimizeVectorStorage(vectorResults, config);
sample.stop(Timer.builder("knowledge.advanced.vectorization")
.tag("assets_id", String.valueOf(assetsId))
.register(meterRegistry));
return VectorizeResult.builder()
.processedCount(vectorResults.size())
.qualityMetrics(metrics)
.build();
} catch (Exception e) {
sample.stop(Timer.builder("knowledge.advanced.vectorization.error")
.register(meterRegistry));
throw new KnowledgeException("高级向量化处理失败: " + e.getMessage());
}
});
}
/**
* 增量向量化更新
*/
public void processIncrementalUpdate(Long segmentId, String newContent, String oldEmbeddingId) {
// 检查内容是否实质性变化
if (!isContentSignificantlyChanged(newContent, oldEmbeddingId)) {
return;
}
// 智能更新策略
if (oldEmbeddingId != null) {
// 更新现有向量
ragEngineService.updateEmbedding(oldEmbeddingId, newContent, buildMetadata(segmentId));
} else {
// 创建新向量
ragEngineService.addText(newContent, buildMetadata(segmentId));
}
// 更新缓存
cache.evict("segment:vector:" + segmentId);
}
private List<ProcessedSegment> preprocessSegments(List<AiKnowledgeAssetsSegment> segments,
VectorizeConfig config) {
return segments.parallelStream()
.map(segment -> {
String content = segment.getSegmentIndex();
// 内容清理和标准化
content = cleanAndNormalizeContent(content);
// 语义增强
if (config.isSemanticEnhancementEnabled()) {
content = enhanceSemanticContent(content);
}
return ProcessedSegment.builder()
.segmentId(segment.getId())
.originalContent(segment.getSegmentIndex())
.processedContent(content)
.metadata(buildEnhancedMetadata(segment))
.build();
})
.collect(Collectors.toList());
}
private String enhanceSemanticContent(String content) {
// 添加语义上下文信息
// 可以集成NLP服务进行关键词提取、实体识别等
return content;
}
}6.3 智能监控告警
监控体系
构建全方位的监控告警体系，实时监控知识库处理性能，提供异常检测和智能告警功能。
智能监控组件：
@Component
@RequiredArgsConstructor
public class KnowledgeIntelligentMonitor {
private final MeterRegistry meterRegistry;
private final NotificationService notificationService;
// 性能指标收集器
private final Timer documentProcessTimer;
private final Timer segmentProcessTimer;
private final Timer vectorizeTimer;
private final Counter errorCounter;
private final Gauge queueSizeGauge;
@PostConstruct
public void initMetrics() {
// 初始化各种监控指标
this.documentProcessTimer = Timer.builder("knowledge.document.process.time")
.description("文档处理耗时")
.register(meterRegistry);
this.errorCounter = Counter.builder("knowledge.process.errors")
.description("处理错误计数")
.register(meterRegistry);
this.queueSizeGauge = Gauge.builder("knowledge.async.queue.size")
.description("异步队列大小")
.register(meterRegistry, this, KnowledgeIntelligentMonitor::getCurrentQueueSize);
}
/**
* 记录文档处理指标
*/
public void recordDocumentProcessing(String fileType, Duration duration,
boolean success, int segmentCount) {
documentProcessTimer.record(duration);
// 记录处理结果
Counter.builder("knowledge.document.processed")
.tag("file_type", fileType)
.tag("status", success ? "success" : "failure")
.register(meterRegistry)
.increment();
// 记录分段数量
Gauge.builder("knowledge.segments.generated")
.tag("file_type", fileType)
.register(meterRegistry, () -> segmentCount);
// 异常检测
if (!success || duration.toMillis() > getProcessingTimeThreshold(fileType)) {
triggerPerformanceAlert(fileType, duration, success);
}
}
/**
* 智能异常检测
*/
@EventListener
public void detectAnomalies(KnowledgeProcessEvent event) {
// 检测处理时间异常
if (event.getProcessingTime() > calculateDynamicThreshold(event.getFileType())) {
sendAlert(AlertType.PERFORMANCE_DEGRADATION,
"文档处理时间异常: " + event.getProcessingTime() + "ms");
}
// 检测错误率异常
double errorRate = calculateRecentErrorRate();
if (errorRate > 0.1) { // 错误率超过10%
sendAlert(AlertType.HIGH_ERROR_RATE,
"错误率异常: " + String.format("%.2f%%", errorRate * 100));
}
// 检测队列积压
int queueSize = getCurrentQueueSize();
if (queueSize > 100) {
sendAlert(AlertType.QUEUE_BACKLOG,
"队列积压严重: " + queueSize + " 个任务待处理");
}
}
/**
* 系统健康检查
*/
@Scheduled(fixedRate = 60000) // 每分钟检查
public void healthCheck() {
HealthStatus status = HealthStatus.builder()
.processingPerformance(calculateProcessingPerformance())
.errorRate(calculateRecentErrorRate())
.queueHealth(assessQueueHealth())
.cacheHitRate(calculateCacheHitRate())
.build();
// 更新健康状态指标
Gauge.builder("knowledge.health.score")
.register(meterRegistry, () -> status.getOverallScore());
// 健康状态告警
if (status.getOverallScore() < 0.8) {
sendAlert(AlertType.SYSTEM_HEALTH_LOW,
"系统健康度较低: " + status.getOverallScore());
}
}
private void sendAlert(AlertType alertType, String message) {
Alert alert = Alert.builder()
.type(alertType)
.message(message)
.timestamp(System.currentTimeMillis())
.severity(determineSeverity(alertType))
.build();
notificationService.sendAlert(alert);
}
private long calculateDynamicThreshold(String fileType) {
// 基于历史数据动态计算阈值
return getHistoricalAverageTime(fileType) * 3;
}
private int getCurrentQueueSize() {
// 获取当前异步队列大小
return ForkJoinPool.commonPool().getQueuedSubmissionCount();
}
}6.4 智能化处理增强
智能增强
通过AI技术增强文档处理能力，提供智能分段优化、内容质量评估和查询扩展等高级功能。
智能文档分析器：
@Component
@RequiredArgsConstructor
public class IntelligentDocumentAnalyzer {
private final LlmService llmService;
private final KnowledgeAdvancedCache cache;
/**
* 智能文档结构分析
*/
public DocumentStructure analyzeDocumentStructure(String content, String fileType) {
String cacheKey = "doc:structure:" + DigestUtils.md5Hex(content);
return cache.getWithFallback(cacheKey, DocumentStructure.class, () -> {
return performStructureAnalysis(content, fileType);
});
}
private DocumentStructure performStructureAnalysis(String content, String fileType) {
// 使用规则和AI相结合的方式分析文档结构
DocumentStructure structure = new DocumentStructure();
// 1. 基于规则的基础分析
structure = analyzeWithRules(content, fileType);
// 2. AI增强分析（可选）
if (shouldUseAIAnalysis(content)) {
structure = enhanceWithAI(structure, content);
}
return structure;
}
/**
* 智能分段质量评估
*/
public SegmentQualityReport assessSegmentQuality(List<String> segments) {
SegmentQualityReport report = new SegmentQualityReport();
for (int i = 0; i < segments.size(); i++) {
String segment = segments.get(i);
SegmentQuality quality = evaluateSegmentQuality(segment, i, segments);
report.addSegmentQuality(i, quality);
}
// 生成整体质量评估
report.calculateOverallQuality();
return report;
}
private SegmentQuality evaluateSegmentQuality(String segment, int index, List<String> allSegments) {
SegmentQuality quality = new SegmentQuality();
// 长度适宜性评估
quality.setLengthScore(evaluateLengthSuitability(segment));
// 语义完整性评估
quality.setCompletenessScore(evaluateSemanticCompleteness(segment));
// 上下文连贯性评估
quality.setCoherenceScore(evaluateContextCoherence(segment, index, allSegments));
// 信息密度评估
quality.setInformationDensityScore(evaluateInformationDensity(segment));
return quality;
}
/**
* 智能分段优化建议
*/
public List<SegmentOptimizationSuggestion> generateOptimizationSuggestions(
List<String> segments, SegmentQualityReport qualityReport) {
List<SegmentOptimizationSuggestion> suggestions = new ArrayList<>();
for (int i = 0; i < segments.size(); i++) {
SegmentQuality quality = qualityReport.getSegmentQuality(i);
if (quality.getOverallScore() < 0.7) {
SegmentOptimizationSuggestion suggestion =
generateSuggestion(segments.get(i), quality, i, segments);
suggestions.add(suggestion);
}
}
return suggestions;
}
/**
* 智能内容增强
*/
public String enhanceContent(String originalContent, ContentEnhancementConfig config) {
StringBuilder enhancedContent = new StringBuilder(originalContent);
// 1. 关键词提取和标注
if (config.isKeywordExtractionEnabled()) {
List<String> keywords = extractKeywords(originalContent);
enhancedContent.append("\n[关键词]: ").append(String.join(", ", keywords));
}
// 2. 实体识别和链接
if (config.isEntityLinkingEnabled()) {
List<Entity> entities = recognizeEntities(originalContent);
if (!entities.isEmpty()) {
enhancedContent.append("\n[实体]: ").append(formatEntities(entities));
}
}
// 3. 摘要生成
if (config.isSummaryGenerationEnabled() && originalContent.length() > 1000) {
String summary = generateSummary(originalContent);
enhancedContent.insert(0, "[摘要]: " + summary + "\n\n");
}
return enhancedContent.toString();
}
/**
* 智能查询扩展
*/
public List<String> expandQuery(String originalQuery, Long knowledgeId) {
List<String> expandedQueries = new ArrayList<>();
expandedQueries.add(originalQuery);
// 1. 同义词扩展
List<String> synonyms = findSynonyms(originalQuery);
expandedQueries.addAll(synonyms);
// 2. 领域特定扩展
List<String> domainTerms = findDomainSpecificTerms(originalQuery, knowledgeId);
expandedQueries.addAll(domainTerms);
// 3. 上下文扩展
List<String> contextualTerms = findContextualTerms(originalQuery, knowledgeId);
expandedQueries.addAll(contextualTerms);
return expandedQueries.stream().distinct().collect(Collectors.toList());
}
private boolean shouldUseAIAnalysis(String content) {
return content.length() > 10000 && isComplexDocument(content);
}
private DocumentStructure enhanceWithAI(DocumentStructure basicStructure, String content) {
// 使用LLM进行文档结构的深度分析
String prompt = buildStructureAnalysisPrompt(content);
String aiAnalysis = llmService.generateText(prompt);
return mergeAnalysisResults(basicStructure, parseAIAnalysis(aiAnalysis));
}
}七、部署和运维
7.1 配置管理
配置策略
为自定义处理器提供灵活的配置管理，支持不同环境的配置需求。
# 生产环境配置
spring:
profiles: prod
bladex:
knowledge:
custom:
enabled: true
performance:
max-file-size: 500MB
timeout: 600000
thread-pool-size: 8
monitoring:
enabled: true
metrics-interval: 60
cache:
enabled: true
ttl: 3600
max-size: 10007.2 监控和日志
监控集成
为自定义处理器添加监控指标，支持性能分析和问题诊断。
@Component
public class CustomKnowledgeMonitor {
private final MeterRegistry meterRegistry;
public CustomKnowledgeMonitor(MeterRegistry meterRegistry) {
this.meterRegistry = meterRegistry;
}
public void recordProcessing(String handlerType, Duration duration, boolean success) {
Timer.builder("knowledge.custom.process.time")
.tag("handler", handlerType)
.tag("status", success ? "success" : "failure")
.register(meterRegistry)
.record(duration);
}
public void recordSegmentation(String segmentType, int segmentCount) {
Gauge.builder("knowledge.custom.segment.count")
.tag("type", segmentType)
.register(meterRegistry, () -> segmentCount);
}
}通过以上完整的二次开发定制指南，开发者可以轻松扩展BladeX AI知识库模块的功能，满足特定的文档处理和分段需求。
八、最佳实践
8.1 开发规范
开发建议
遵循以下开发规范，确保知识库自定义组件的质量和可维护性。
代码规范：
- 命名规范：使用清晰、有意义的类名和方法名，体现业务含义
- 注释完整：为关键方法和复杂逻辑添加详细注释，特别是文档解析和分段逻辑
- 异常处理：统一使用KnowledgeException处理业务异常，提供清晰的错误信息
- 参数验证：严格验证文档格式、分段参数等输入的有效性
- 资源管理：正确管理文件流、数据库连接等外部资源的生命周期
设计原则：
- 单一职责：每个处理器专注于特定文档格式或分段策略
- 开闭原则：通过接口扩展功能，避免修改现有代码
- 依赖注入：合理使用Spring的依赖注入机制
- 配置外置：将可变参数配置化，支持不同环境的灵活配置
8.2 性能优化
性能考虑
在开发知识库自定义组件时，需要重点考虑性能因素，确保大文档处理的高效性。
文档处理优化：
// 流式处理大文档，避免内存溢出
public class OptimizedFileHandler implements FileHandler {
private static final int BUFFER_SIZE = 8192;
private static final int MAX_CONTENT_SIZE = 50 * 1024 * 1024; // 50MB
private static final int PROGRESS_BATCH_SIZE = 1000;
@Override
public String readContent(InputStream inputStream, FileProgressTrack progressTrack) {
StringBuilder content = new StringBuilder();
try (BufferedReader reader = new BufferedReader(
new InputStreamReader(inputStream, StandardCharsets.UTF_8),
BUFFER_SIZE)) { // 使用合适的缓冲区大小
String line;
int lineCount = 0;
while ((line = reader.readLine()) != null) {
// 定期检查内存使用情况
if (content.length() > MAX_CONTENT_SIZE) {
throw new KnowledgeException("文档过大，超出处理限制");
}
content.append(processLine(line)).append("\n");
// 批量更新进度，减少频繁调用
if (++lineCount % PROGRESS_BATCH_SIZE == 0 && progressTrack != null) {
progressTrack.onProgress(content.length(), -1, lineCount);
}
}
} catch (IOException e) {
throw new KnowledgeException("文档读取失败: " + e.getMessage());
}
return content.toString();
}
private String processLine(String line) {
// 行级处理优化：去除多余空白、统一格式等
return line.trim().replaceAll("\\s+", " ");
}
}分段处理优化：
// 并行分段处理，提升大文档分段效率
@Component
public class ParallelSegmentProcessor {
private static final int PARALLEL_THRESHOLD = 100000; // 10万字符
private static final int CHUNK_SIZE = 50000; // 5万字符
private final ForkJoinPool customThreadPool = new ForkJoinPool(
Math.min(Runtime.getRuntime().availableProcessors(), 4));
public List<String> parallelSegment(String content, SegmentConfig config) {
if (content.length() < PARALLEL_THRESHOLD) {
return sequentialSegment(content, config);
}
try {
// 将大文档分块并行处理
List<String> chunks = splitIntoChunks(content, CHUNK_SIZE);
List<CompletableFuture<List<String>>> futures = chunks.stream()
.map(chunk -> CompletableFuture.supplyAsync(
() -> segmentChunk(chunk, config), customThreadPool))
.collect(Collectors.toList());
return futures.stream()
.map(CompletableFuture::join)
.flatMap(List::stream)
.collect(Collectors.toList());
} finally {
// 确保线程池正确关闭
if (!customThreadPool.isShutdown()) {
customThreadPool.shutdown();
}
}
}
private List<String> splitIntoChunks(String content, int chunkSize) {
List<String> chunks = new ArrayList<>();
for (int i = 0; i < content.length(); i += chunkSize) {
int end = Math.min(i + chunkSize, content.length());
chunks.add(content.substring(i, end));
}
return chunks;
}
}缓存优化策略：
- 分层缓存：本地缓存热点数据，Redis缓存共享数据
- 缓存预热：启动时预加载常用配置和模板
- 智能失效：基于内容变化智能清除相关缓存
- 压缩存储：大文档内容使用压缩算法存储
8.3 安全考虑
安全建议
在知识库自定义开发中，需要充分考虑安全因素，保护数据和系统安全。
文档安全处理：
@Component
public class SecureDocumentValidator {
private static final long MAX_FILE_SIZE = 100 * 1024 * 1024L; // 100MB
private static final Set<String> ALLOWED_EXTENSIONS = Set.of(
"pdf", "doc", "docx", "txt", "md", "csv", "xlsx");
/**
* 安全的文档格式验证
*/
public boolean validateDocumentSafety(InputStream inputStream, String fileType) {
try {
// 1. 文件大小检查
if (!validateFileSize(inputStream)) {
log.warn("文件大小超出限制");
return false;
}
// 2. 文件类型验证
if (!validateFileType(fileType)) {
log.warn("不支持的文件类型: {}", fileType);
return false;
}
// 3. 恶意内容检测
if (!scanForMaliciousContent(inputStream)) {
log.warn("检测到潜在恶意内容");
return false;
}
return true;
} catch (Exception e) {
log.error("文档安全验证失败", e);
return false;
}
}
private boolean validateFileSize(InputStream inputStream) throws IOException {
return inputStream.available() <= MAX_FILE_SIZE;
}
private boolean validateFileType(String fileType) {
return ALLOWED_EXTENSIONS.contains(fileType.toLowerCase());
}
private boolean scanForMaliciousContent(InputStream inputStream) {
// 检测潜在的恶意内容
// 如：脚本注入、XML外部实体攻击等
try (BufferedReader reader = new BufferedReader(
new InputStreamReader(inputStream, StandardCharsets.UTF_8))) {
String line;
while ((line = reader.readLine()) != null) {
if (containsMaliciousPattern(line)) {
return false;
}
}
} catch (IOException e) {
log.error("内容扫描失败", e);
return false;
}
return true;
}
private boolean containsMaliciousPattern(String content) {
// 检查常见的恶意模式
String[] maliciousPatterns = {
"<script", "javascript:", "vbscript:", "data:text/html",
"<!ENTITY", "file://", "ftp://", "jar:"
};
String lowerContent = content.toLowerCase();
return Arrays.stream(maliciousPatterns)
.anyMatch(lowerContent::contains);
}
}数据安全措施：
- 输入验证：严格验证文档格式、文件大小、内容类型
- 权限控制：基于角色的知识库访问控制
- 敏感数据：对包含敏感信息的文档进行加密存储
- 审计日志：记录文档处理、分段、向量化等关键操作
- 错误处理：避免在错误信息中泄露系统内部信息
8.4 监控运维
运维建议
建立完善的监控运维体系，确保知识库系统的稳定运行和问题快速定位。
关键监控指标：
@Component
@RequiredArgsConstructor
public class KnowledgeOperationsMetrics {
private final MeterRegistry meterRegistry;
private final AlertService alertService;
private static final double ERROR_THRESHOLD = 0.05; // 5%错误率阈值
private static final int QUEUE_THRESHOLD = 1000; // 队列积压阈值
/**
* 关键业务指标监控
*/
public void recordBusinessMetrics(ProcessingContext context) {
// 文档处理成功率
Counter.builder("knowledge.document.success.rate")
.tag("file_type", context.getFileType())
.register(meterRegistry)
.increment();
// 平均分段数量
Gauge.builder("knowledge.segments.avg.count")
.tag("strategy", context.getSegmentStrategy())
.register(meterRegistry, () -> context.getSegmentCount());
// 向量化延迟
Timer.builder("knowledge.vectorization.latency")
.tag("model", context.getVectorModel())
.register(meterRegistry)
.record(context.getVectorizationDuration());
// 缓存命中率
Gauge.builder("knowledge.cache.hit.rate")
.register(meterRegistry, this::calculateCacheHitRate);
}
/**
* 异常情况告警
*/
@EventListener
public void handleProcessingErrors(KnowledgeErrorEvent event) {
// 错误率告警
double errorRate = calculateErrorRate(event.getTimeWindow());
if (errorRate > ERROR_THRESHOLD) {
alertService.sendAlert("知识库处理错误率过高: " +
String.format("%.2f%%", errorRate * 100));
}
// 队列积压告警
int queueSize = getProcessingQueueSize();
if (queueSize > QUEUE_THRESHOLD) {
alertService.sendAlert("知识库处理队列积压: " + queueSize + " 个任务");
}
// 处理时间异常告警
Duration avgProcessingTime = calculateAverageProcessingTime();
if (avgProcessingTime.toMillis() > getProcessingTimeThreshold()) {
alertService.sendAlert("知识库处理耗时异常: " + avgProcessingTime.toMillis() + "ms");
}
}
/**
* 系统健康检查
*/
@Scheduled(fixedRate = 60000) // 每分钟检查
public void performHealthCheck() {
HealthStatus status = HealthStatus.builder()
.errorRate(calculateErrorRate(Duration.ofMinutes(5)))
.queueHealth(assessQueueHealth())
.cachePerformance(calculateCacheHitRate())
.systemLoad(getSystemLoadAverage())
.build();
// 更新健康指标
Gauge.builder("knowledge.system.health.score")
.register(meterRegistry, () -> status.getOverallScore());
// 健康状态告警
if (status.getOverallScore() < 0.8) {
alertService.sendAlert("知识库系统健康度低: " + status.getOverallScore());
}
}
private double calculateCacheHitRate() {
// 实现缓存命中率计算逻辑
return 0.85; // 示例值
}
private int getProcessingQueueSize() {
// 获取处理队列大小
return ForkJoinPool.commonPool().getQueuedSubmissionCount();
}
}运维最佳实践：
- 容量规划：根据业务量合理配置线程池和队列大小
- 降级策略：在系统负载过高时自动启用降级处理
- 故障恢复：支持处理任务的断点续传和失败重试
- 性能调优：定期分析性能瓶颈并进行优化
- 版本管理：保持向后兼容性，支持平滑升级
8.5 业务场景适配
场景优化
针对不同的业务场景，提供相应的优化策略和配置建议。
企业文档管理场景：
# 企业文档处理配置
bladex:
knowledge:
enterprise:
# 支持大文档处理
max-file-size: 100MB
chunk-size: 1MB
parallel-enabled: true
# 智能分段配置
segment:
strategy: semantic
max-length: 1000
overlap-ratio: 0.1
quality-threshold: 0.8
# 向量化优化
vectorization:
batch-size: 100
retry-times: 3
timeout: 300s
# 缓存策略
cache:
document-cache-ttl: 3600s
segment-cache-ttl: 1800s
preload-enabled: true知识问答场景：
# 知识问答优化配置
bladex:
knowledge:
qa-optimized:
# 语义分段配置
segment:
strategy: llm_semantic
context-aware: true
question-oriented: true
semantic-threshold: 0.75
# 向量检索优化
search:
top-k: 10
similarity-threshold: 0.75
rerank-enabled: true
query-expansion: true
# 质量控制
quality:
segment-min-length: 50
segment-max-length: 2000
completeness-check: true实时处理场景：
# 实时处理配置
bladex:
knowledge:
realtime:
# 流式处理
streaming: true
buffer-size: 8KB
batch-processing: false
# 缓存策略
cache:
preload: true
refresh-interval: 60s
local-cache-enabled: true
# 异步处理
async:
thread-pool-size: 16
queue-capacity: 1000
timeout: 30s
# 监控配置
monitoring:
metrics-interval: 10s
alert-threshold: 100ms扩展建议：
- 领域适配：根据特定领域的文档特点定制处理逻辑
- 多语言支持：针对不同语言的文档特点优化分段策略
- 格式扩展：支持新的文档格式和数据源
- AI增强：集成最新的AI技术提升处理效果
- 性能监控：建立持续的性能监控和优化机制
通过遵循以上最佳实践，开发者可以构建高质量、高性能、安全可靠的知识库定制解决方案，充分发挥BladeX AI知识库模块的强大能力。