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perf: 收敛后端资源与健康检查开销

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- 缩小模块扫描范围并显式注册各业务模块自动配置

- 增加可配置线程池、MQ 连接池与消费线程池，降低默认资源占用

- 将 RAG 与分析库中间件探活下沉到健康检查并增加短缓存

- 补齐文档向量库生命周期释放与 SSE 断连清理
This commit is contained in:
陈子默
2026-05-28 11:22:14 +08:00
parent 72df00f25b
commit 11e595b088
43 changed files with 1343 additions and 288 deletions
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49
easyflow-api/easyflow-api-public/src/main/java/tech/easyflow/publicapi/controller/PublicKnowledgeShareController.java
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@@ -36,6 +36,7 @@ import tech.easyflow.ai.service.KnowledgeShareAuditService;
import tech.easyflow.ai.service.KnowledgeSharePermissionService;
import tech.easyflow.ai.service.ModelService;
import tech.easyflow.ai.service.impl.KnowledgeSharePermissionServiceImpl;
import tech.easyflow.ai.support.DocumentStoreLifecycleSupport;
import tech.easyflow.ai.vo.FaqImportResultVo;
import tech.easyflow.common.domain.Result;
import tech.easyflow.common.filestorage.FileStorageService;
@@ -342,18 +343,22 @@ public class PublicKnowledgeShareController {
if (documentStore == null) {
return Result.fail(2, "知识库没有配置向量库");
}
Model model = modelService.getModelInstance(knowledge.getVectorEmbedModelId());
if (model == null) {
return Result.fail(3, "知识库没有配置向量模型");
try {
Model model = modelService.getModelInstance(knowledge.getVectorEmbedModelId());
if (model == null) {
return Result.fail(3, "知识库没有配置向量模型");
}
EmbeddingModel embeddingModel = model.toEmbeddingModel();
documentStore.setEmbeddingModel(embeddingModel);
StoreOptions options = StoreOptions.ofCollectionName(knowledge.getVectorStoreCollection());
com.easyagents.core.document.Document doc = com.easyagents.core.document.Document.of(documentChunk.getContent());
doc.setId(current.getId());
StoreResult result = documentStore.update(doc, options);
audit(apiKey, "API更新文档 Chunk", "KNOWLEDGE_API_SHARE_WRITE", request.getRequestURI(), Map.of("knowledgeId", knowledgeId, "chunkId", documentChunk.getId()));
return Result.ok(result);
} finally {
DocumentStoreLifecycleSupport.closeQuietly(documentStore);
}
EmbeddingModel embeddingModel = model.toEmbeddingModel();
documentStore.setEmbeddingModel(embeddingModel);
StoreOptions options = StoreOptions.ofCollectionName(knowledge.getVectorStoreCollection());
com.easyagents.core.document.Document doc = com.easyagents.core.document.Document.of(documentChunk.getContent());
doc.setId(current.getId());
StoreResult result = documentStore.update(doc, options);
audit(apiKey, "API更新文档 Chunk", "KNOWLEDGE_API_SHARE_WRITE", request.getRequestURI(), Map.of("knowledgeId", knowledgeId, "chunkId", documentChunk.getId()));
return Result.ok(result);
}
return Result.ok(false);
}
@@ -376,16 +381,20 @@ public class PublicKnowledgeShareController {
if (documentStore == null) {
return Result.fail(2, "知识库没有配置向量库");
}
Model model = modelService.getModelInstance(knowledge.getVectorEmbedModelId());
if (model == null) {
return Result.fail(3, "知识库没有配置向量模型");
try {
Model model = modelService.getModelInstance(knowledge.getVectorEmbedModelId());
if (model == null) {
return Result.fail(3, "知识库没有配置向量模型");
}
documentStore.setEmbeddingModel(model.toEmbeddingModel());
StoreOptions options = StoreOptions.ofCollectionName(knowledge.getVectorStoreCollection());
documentStore.delete(Collections.singletonList(chunkId), options);
documentChunkService.removeById(chunkId);
audit(apiKey, "API删除文档 Chunk", "KNOWLEDGE_API_SHARE_WRITE", request.getRequestURI(), Map.of("knowledgeId", knowledgeId, "chunkId", chunkId));
return Result.ok(true);
} finally {
DocumentStoreLifecycleSupport.closeQuietly(documentStore);
}
documentStore.setEmbeddingModel(model.toEmbeddingModel());
StoreOptions options = StoreOptions.ofCollectionName(knowledge.getVectorStoreCollection());
documentStore.delete(Collections.singletonList(chunkId), options);
documentChunkService.removeById(chunkId);
audit(apiKey, "API删除文档 Chunk", "KNOWLEDGE_API_SHARE_WRITE", request.getRequestURI(), Map.of("knowledgeId", knowledgeId, "chunkId", chunkId));
return Result.ok(true);
}
/**