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feature: 对接deer-flow (#54)

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feature: 对接deer-flow
This commit is contained in:
hhhhsc701
2025-11-04 20:30:40 +08:00
committed by GitHub
parent dc30b0d892
commit f3958f08d9
710 changed files with 112812 additions and 52 deletions
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5
backend/services/main-application/src/main/resources/application.yml
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@@ -164,8 +164,3 @@ datamate:
quality-control:
enabled: ${QC_ENABLED:true}
threshold: ${QC_THRESHOLD:0.8}
# RAG配置
rag:
milvus-host: ${MILVUS_HOST:milvus-standalone}
milvus-port: ${MILVUS_PORT:19530}

2
backend/services/operator-market-service/src/main/java/com/datamate/operator/application/OperatorService.java
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@@ -32,7 +32,7 @@ public class OperatorService {
private final FileService fileService;
@Value("${operator.base.path:/operator}")
@Value("${operator.base.path:/operators}")
private String operatorBasePath;
public List<OperatorDto> getOperators(Integer page, Integer size, List<String> categories,

10
backend/services/rag-indexer-service/src/main/java/com/datamate/rag/indexer/infrastructure/event/RagEtlService.java
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@@ -51,9 +51,9 @@ import java.util.concurrent.Semaphore;
public class RagEtlService {
private static final Semaphore SEMAPHORE = new Semaphore(10);
@Value("${datamate.rag.milvus-host}")
@Value("${datamate.rag.milvus-host:-milvus-standalone}")
private String milvusHost;
@Value("${datamate.rag.milvus-port}")
@Value("${datamate.rag.milvus-port:-19530}")
private int milvusPort;
private final RagFileRepository ragFileRepository;
@@ -122,7 +122,7 @@ public class RagEtlService {
// 调用嵌入模型获取嵌入向量
List<Embedding> content = embeddingModel.embedAll(split).content();
// 存储嵌入向量到 Milvus
embeddingStore(embeddingModel, ragFile.getKnowledgeBaseId()).addAll(content, split);
embeddingStore(embeddingModel, event.knowledgeBase().getName()).addAll(content, split);
}
/**
@@ -152,11 +152,11 @@ public class RagEtlService {
};
}
public EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel, String knowledgeBaseId) {
public EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel, String knowledgeBaseName) {
return MilvusEmbeddingStore.builder()
.host(milvusHost)
.port(milvusPort)
.collectionName("datamate_" + knowledgeBaseId)
.collectionName(knowledgeBaseName)
.dimension(embeddingModel.dimension())
.build();
}