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!20 【新增】AI 知识库: 段落召回、段落启禁用、配置自定义
Merge pull request !20 from 小新/master-jdk21-ai
This commit is contained in:
commit
0b1d9ce251
@ -25,4 +25,11 @@ public class AiKnowledgeCreateMyReqVO {
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@NotNull(message = "嵌入模型不能为空")
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private Long modelId;
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@Schema(description = "相似性阈值", requiredMode = Schema.RequiredMode.REQUIRED, example = "0.5")
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@NotNull(message = "相似性阈值不能为空")
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private Double similarityThreshold;
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@Schema(description = "topK", requiredMode = Schema.RequiredMode.REQUIRED, example = "3")
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@NotNull(message = "topK 不能为空")
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private Integer topK;
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}
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@ -23,4 +23,23 @@ public class AiKnowledgeDocumentCreateReqVO {
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@URL(message = "文档 URL 格式不正确")
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private String url;
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@Schema(description = "每个文本块的目标 token 数", requiredMode = Schema.RequiredMode.REQUIRED, example = "800")
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@NotNull(message = "每个文本块的目标 token 数不能为空")
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private Integer defaultChunkSize;
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@Schema(description = "每个文本块的最小字符数", requiredMode = Schema.RequiredMode.REQUIRED, example = "350")
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@NotNull(message = "每个文本块的最小字符数不能为空")
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private Integer minChunkSizeChars;
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@Schema(description = "丢弃阈值", requiredMode = Schema.RequiredMode.REQUIRED, example = "5")
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@NotNull(message = "丢弃阈值不能为空")
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private Integer minChunkLengthToEmbed;
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@Schema(description = "最大块数", requiredMode = Schema.RequiredMode.REQUIRED, example = "10000")
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@NotNull(message = "最大块数不能为空")
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private Integer maxNumChunks;
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@Schema(description = "分块是否保留分隔符", requiredMode = Schema.RequiredMode.REQUIRED, example = "true")
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@NotNull(message = "分块是否保留分隔符不能为空")
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private Boolean keepSeparator;
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}
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@ -0,0 +1,17 @@
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package cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment;
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import io.swagger.v3.oas.annotations.media.Schema;
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import lombok.Data;
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@Schema(description = "管理后台 - AI 知识库段落召回 Request VO")
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@Data
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public class AiKnowledgeSegmentSearchReqVO {
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@Schema(description = "知识库编号", requiredMode = Schema.RequiredMode.REQUIRED, example = "24790")
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private Long knowledgeId;
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@Schema(description = "内容", requiredMode = Schema.RequiredMode.REQUIRED, example = "Java 学习路线")
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private String content;
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}
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@ -52,6 +52,18 @@ public class AiKnowledgeDO extends BaseDO {
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* 模型标识
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*/
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private String model;
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/**
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* topK
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*/
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private Integer topK;
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/**
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* 相似度阈值
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*/
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private Double similarityThreshold;
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/**
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* 状态
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* <p>
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@ -23,7 +23,7 @@ public class AiKnowledgeDocumentDO extends BaseDO {
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private Long id;
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/**
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* 知识库编号
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*
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* <p>
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* 关联 {@link AiKnowledgeDO#getId()}
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*/
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private Long knowledgeId;
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@ -47,6 +47,26 @@ public class AiKnowledgeDocumentDO extends BaseDO {
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* 字符数
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*/
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private Integer wordCount;
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/**
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* 每个文本块的目标 token 数
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*/
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private Integer defaultChunkSize;
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/**
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* 每个文本块的最小字符数
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*/
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private Integer minChunkSizeChars;
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/**
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* 低于此值的块会被丢弃
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*/
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private Integer minChunkLengthToEmbed;
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/**
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* 最大块数
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*/
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private Integer maxNumChunks;
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/**
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* 分块是否保留分隔符
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*/
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private Boolean keepSeparator;
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/**
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* 切片状态
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* <p>
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@ -2,6 +2,8 @@ package cn.iocoder.yudao.module.ai.dal.dataobject.knowledge;
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import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
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import cn.iocoder.yudao.framework.mybatis.core.dataobject.BaseDO;
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import com.baomidou.mybatisplus.annotation.FieldStrategy;
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import com.baomidou.mybatisplus.annotation.TableField;
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import com.baomidou.mybatisplus.annotation.TableId;
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import com.baomidou.mybatisplus.annotation.TableName;
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import lombok.Data;
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@ -25,16 +27,17 @@ public class AiKnowledgeSegmentDO extends BaseDO {
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/**
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* 向量库的编号
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*/
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@TableField(updateStrategy = FieldStrategy.ALWAYS)
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private String vectorId;
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/**
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* 知识库编号
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*
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* <p>
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* 关联 {@link AiKnowledgeDO#getId()}
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*/
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private Long knowledgeId;
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/**
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* 文档编号
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*
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* <p>
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* 关联 {@link AiKnowledgeDocumentDO#getId()}
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*/
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private Long documentId;
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@ -52,7 +55,7 @@ public class AiKnowledgeSegmentDO extends BaseDO {
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private Integer tokens;
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/**
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* 状态
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*
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* <p>
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* 枚举 {@link CommonStatusEnum}
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*/
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private Integer status;
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@ -7,6 +7,8 @@ import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowle
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeSegmentDO;
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import org.apache.ibatis.annotations.Mapper;
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import java.util.List;
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/**
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* AI 知识库-分片 Mapper
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*
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@ -22,4 +24,10 @@ public interface AiKnowledgeSegmentMapper extends BaseMapperX<AiKnowledgeSegment
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.likeIfPresent(AiKnowledgeSegmentDO::getContent, reqVO.getKeyword())
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.orderByDesc(AiKnowledgeSegmentDO::getId));
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}
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default List<AiKnowledgeSegmentDO> selectList(List<String> vectorIdList) {
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return selectList(new LambdaQueryWrapperX<AiKnowledgeSegmentDO>()
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.in(AiKnowledgeSegmentDO::getVectorId, vectorIdList)
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.orderByDesc(AiKnowledgeSegmentDO::getId));
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}
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}
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@ -9,15 +9,11 @@ import cn.iocoder.yudao.framework.common.util.object.BeanUtils;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.document.AiKnowledgeDocumentPageReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.document.AiKnowledgeDocumentUpdateReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.knowledge.AiKnowledgeDocumentCreateReqVO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDocumentDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeSegmentDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiChatModelDO;
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import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeDocumentMapper;
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import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeSegmentMapper;
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import cn.iocoder.yudao.module.ai.enums.knowledge.AiKnowledgeDocumentStatusEnum;
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import cn.iocoder.yudao.module.ai.service.model.AiApiKeyService;
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import cn.iocoder.yudao.module.ai.service.model.AiChatModelService;
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import jakarta.annotation.Resource;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.document.Document;
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@ -48,24 +44,16 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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@Resource
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private AiKnowledgeSegmentMapper segmentMapper;
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@Resource
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private TokenTextSplitter tokenTextSplitter;
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@Resource
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private TokenCountEstimator tokenCountEstimator;
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@Resource
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private AiApiKeyService apiKeyService;
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@Resource
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private AiKnowledgeService knowledgeService;
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@Resource
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private AiChatModelService chatModelService;
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@Override
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@Transactional(rollbackFor = Exception.class)
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public Long createKnowledgeDocument(AiKnowledgeDocumentCreateReqVO createReqVO) {
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// 0. 校验
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(createReqVO.getKnowledgeId());
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AiChatModelDO model = chatModelService.validateChatModel(knowledge.getModelId());
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// 0. 校验并获取向量存储实例
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VectorStore vectorStore = knowledgeService.getVectorStoreById(createReqVO.getKnowledgeId());
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// 1.1 下载文档
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TikaDocumentReader loader = new TikaDocumentReader(downloadFile(createReqVO.getUrl()));
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@ -82,6 +70,9 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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return documentId;
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}
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// 2 构造文本分段器
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TokenTextSplitter tokenTextSplitter = new TokenTextSplitter(createReqVO.getDefaultChunkSize(), createReqVO.getMinChunkSizeChars(), createReqVO.getMinChunkLengthToEmbed(),
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createReqVO.getMaxNumChunks(), createReqVO.getKeepSeparator());
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// 2.1 文档分段
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List<Document> segments = tokenTextSplitter.apply(documents);
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// 2.2 分段内容入库
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@ -92,8 +83,6 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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.setStatus(CommonStatusEnum.ENABLE.getStatus()));
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segmentMapper.insertBatch(segmentDOList);
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// 3.1 获取向量存储实例
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VectorStore vectorStore = apiKeyService.getOrCreateVectorStore(model.getKeyId());
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// 3.2 向量化并存储
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segments.forEach(segment -> segment.getMetadata().put(AiKnowledgeSegmentDO.FIELD_KNOWLEDGE_ID, createReqVO.getKnowledgeId()));
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vectorStore.add(segments);
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@ -2,10 +2,13 @@ package cn.iocoder.yudao.module.ai.service.knowledge;
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import cn.iocoder.yudao.framework.common.pojo.PageResult;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentPageReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentSearchReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentUpdateReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentUpdateStatusReqVO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeSegmentDO;
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import java.util.List;
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/**
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* AI 知识库段落 Service 接口
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*
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@ -35,4 +38,13 @@ public interface AiKnowledgeSegmentService {
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*/
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void updateKnowledgeSegmentStatus(AiKnowledgeSegmentUpdateStatusReqVO reqVO);
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/**
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* 段落召回
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*
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* @param reqVO 召回请求信息
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* @return 召回的段落
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*/
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List<AiKnowledgeSegmentDO> similaritySearch(AiKnowledgeSegmentSearchReqVO reqVO);
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}
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@ -1,16 +1,34 @@
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package cn.iocoder.yudao.module.ai.service.knowledge;
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import cn.hutool.core.collection.CollUtil;
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import cn.hutool.core.collection.ListUtil;
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import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
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import cn.iocoder.yudao.framework.common.pojo.PageResult;
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import cn.iocoder.yudao.framework.common.util.object.BeanUtils;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentPageReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentSearchReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentUpdateReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentUpdateStatusReqVO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeSegmentDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiChatModelDO;
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import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeSegmentMapper;
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import cn.iocoder.yudao.module.ai.service.model.AiApiKeyService;
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import cn.iocoder.yudao.module.ai.service.model.AiChatModelService;
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import jakarta.annotation.Resource;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
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import org.springframework.stereotype.Service;
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import java.util.List;
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import java.util.Objects;
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import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;
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import static cn.iocoder.yudao.module.ai.enums.ErrorCodeConstants.KNOWLEDGE_SEGMENT_NOT_EXISTS;
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/**
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* AI 知识库分片 Service 实现类
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*
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@ -23,6 +41,13 @@ public class AiKnowledgeSegmentServiceImpl implements AiKnowledgeSegmentService
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@Resource
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private AiKnowledgeSegmentMapper segmentMapper;
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@Resource
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private AiKnowledgeService knowledgeService;
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@Resource
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private AiChatModelService chatModelService;
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@Resource
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private AiApiKeyService apiKeyService;
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@Override
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public PageResult<AiKnowledgeSegmentDO> getKnowledgeSegmentPage(AiKnowledgeSegmentPageReqVO pageReqVO) {
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return segmentMapper.selectPage(pageReqVO);
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@ -30,13 +55,80 @@ public class AiKnowledgeSegmentServiceImpl implements AiKnowledgeSegmentService
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@Override
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public void updateKnowledgeSegment(AiKnowledgeSegmentUpdateReqVO reqVO) {
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segmentMapper.updateById(BeanUtils.toBean(reqVO, AiKnowledgeSegmentDO.class));
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// TODO @xin 重新向量化
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// 0 校验
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AiKnowledgeSegmentDO oldKnowledgeSegment = validateKnowledgeSegmentExists(reqVO.getId());
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// 2.1 获取知识库向量实例
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VectorStore vectorStore = knowledgeService.getVectorStoreById(oldKnowledgeSegment.getKnowledgeId());
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// 2.2 删除原向量
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vectorStore.delete(List.of(oldKnowledgeSegment.getVectorId()));
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// 2.3 重新向量化
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Document document = new Document(reqVO.getContent());
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document.getMetadata().put(AiKnowledgeSegmentDO.FIELD_KNOWLEDGE_ID, oldKnowledgeSegment.getKnowledgeId());
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vectorStore.add(List.of(document));
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// 2.1 更新段落内容
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AiKnowledgeSegmentDO knowledgeSegment = BeanUtils.toBean(reqVO, AiKnowledgeSegmentDO.class);
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knowledgeSegment.setVectorId(document.getId());
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segmentMapper.updateById(knowledgeSegment);
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}
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@Override
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public void updateKnowledgeSegmentStatus(AiKnowledgeSegmentUpdateStatusReqVO reqVO) {
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segmentMapper.updateById(BeanUtils.toBean(reqVO, AiKnowledgeSegmentDO.class));
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// TODO @xin 1.禁用删除向量 2.启用重新向量化
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// 0 校验
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AiKnowledgeSegmentDO oldKnowledgeSegment = validateKnowledgeSegmentExists(reqVO.getId());
|
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// 1 获取知识库向量实例
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VectorStore vectorStore = knowledgeService.getVectorStoreById(oldKnowledgeSegment.getKnowledgeId());
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AiKnowledgeSegmentDO knowledgeSegment = BeanUtils.toBean(reqVO, AiKnowledgeSegmentDO.class);
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if (Objects.equals(reqVO.getStatus(), CommonStatusEnum.ENABLE.getStatus())) {
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// 2.1 启用重新向量化
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Document document = new Document(oldKnowledgeSegment.getContent());
|
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document.getMetadata().put(AiKnowledgeSegmentDO.FIELD_KNOWLEDGE_ID, oldKnowledgeSegment.getKnowledgeId());
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vectorStore.add(List.of(document));
|
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knowledgeSegment.setVectorId(document.getId());
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} else {
|
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// 2.2 禁用删除向量
|
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vectorStore.delete(List.of(oldKnowledgeSegment.getVectorId()));
|
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knowledgeSegment.setVectorId(null);
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}
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// 3 更新段落状态
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segmentMapper.updateById(knowledgeSegment);
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}
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||||
@Override
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public List<AiKnowledgeSegmentDO> similaritySearch(AiKnowledgeSegmentSearchReqVO reqVO) {
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// 0. 校验
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(reqVO.getKnowledgeId());
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AiChatModelDO model = chatModelService.validateChatModel(knowledge.getModelId());
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// 1.1 获取向量存储实例
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VectorStore vectorStore = apiKeyService.getOrCreateVectorStore(model.getKeyId());
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// 1.2 向量检索
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List<Document> documentList = vectorStore.similaritySearch(SearchRequest.query(reqVO.getContent())
|
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.withTopK(knowledge.getTopK())
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.withSimilarityThreshold(knowledge.getSimilarityThreshold())
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.withFilterExpression(new FilterExpressionBuilder().eq(AiKnowledgeSegmentDO.FIELD_KNOWLEDGE_ID, reqVO.getKnowledgeId()).build()));
|
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if (CollUtil.isEmpty(documentList)) {
|
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return ListUtil.empty();
|
||||
}
|
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// 2.1 段落召回
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return segmentMapper.selectList(CollUtil.getFieldValues(documentList, "id", String.class));
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}
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||||
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||||
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/**
|
||||
* 校验段落是否存在
|
||||
*
|
||||
* @param id 文档编号
|
||||
* @return 段落信息
|
||||
*/
|
||||
private AiKnowledgeSegmentDO validateKnowledgeSegmentExists(Long id) {
|
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AiKnowledgeSegmentDO knowledgeSegment = segmentMapper.selectById(id);
|
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if (knowledgeSegment == null) {
|
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throw exception(KNOWLEDGE_SEGMENT_NOT_EXISTS);
|
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}
|
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return knowledgeSegment;
|
||||
}
|
||||
}
|
||||
|
@ -5,6 +5,7 @@ import cn.iocoder.yudao.framework.common.pojo.PageResult;
|
||||
import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.knowledge.AiKnowledgeCreateMyReqVO;
|
||||
import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.knowledge.AiKnowledgeUpdateMyReqVO;
|
||||
import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
|
||||
/**
|
||||
* AI 知识库-基础信息 Service 接口
|
||||
@ -47,4 +48,12 @@ public interface AiKnowledgeService {
|
||||
* @return 知识库分页
|
||||
*/
|
||||
PageResult<AiKnowledgeDO> getKnowledgePageMy(Long userId, PageParam pageReqVO);
|
||||
|
||||
/**
|
||||
* 根据知识库编号获取向量存储实例
|
||||
*
|
||||
* @param knowledgeId 知识库编号
|
||||
* @return 向量存储实例
|
||||
*/
|
||||
VectorStore getVectorStoreById(Long knowledgeId);
|
||||
}
|
||||
|
@ -10,9 +10,11 @@ import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.knowledge.AiKnow
|
||||
import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
|
||||
import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiChatModelDO;
|
||||
import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeMapper;
|
||||
import cn.iocoder.yudao.module.ai.service.model.AiApiKeyService;
|
||||
import cn.iocoder.yudao.module.ai.service.model.AiChatModelService;
|
||||
import jakarta.annotation.Resource;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.stereotype.Service;
|
||||
|
||||
import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;
|
||||
@ -32,6 +34,10 @@ public class AiKnowledgeServiceImpl implements AiKnowledgeService {
|
||||
|
||||
@Resource
|
||||
private AiKnowledgeMapper knowledgeMapper;
|
||||
@Resource
|
||||
private AiChatModelService chatModelService;
|
||||
@Resource
|
||||
private AiApiKeyService apiKeyService;
|
||||
|
||||
@Override
|
||||
public Long createKnowledgeMy(AiKnowledgeCreateMyReqVO createReqVO, Long userId) {
|
||||
@ -75,4 +81,11 @@ public class AiKnowledgeServiceImpl implements AiKnowledgeService {
|
||||
return knowledgeMapper.selectPageByMy(userId, pageReqVO);
|
||||
}
|
||||
|
||||
@Override
|
||||
public VectorStore getVectorStoreById(Long knowledgeId) {
|
||||
AiKnowledgeDO knowledge = validateKnowledgeExists(knowledgeId);
|
||||
AiChatModelDO model = chatModelService.validateChatModel(knowledge.getModelId());
|
||||
return apiKeyService.getOrCreateVectorStore(model.getKeyId());
|
||||
}
|
||||
|
||||
}
|
||||
|
@ -2,7 +2,6 @@ package cn.iocoder.yudao.module.ai.service.model;
|
||||
|
||||
import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactory;
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactory;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
|
||||
import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
|
||||
@ -39,8 +38,6 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
|
||||
|
||||
@Resource
|
||||
private AiModelFactory modelFactory;
|
||||
@Resource
|
||||
private AiVectorStoreFactory vectorFactory;
|
||||
|
||||
@Override
|
||||
public Long createApiKey(AiApiKeySaveReqVO createReqVO) {
|
||||
@ -149,7 +146,7 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
|
||||
public VectorStore getOrCreateVectorStore(Long id) {
|
||||
AiApiKeyDO apiKey = validateApiKey(id);
|
||||
AiPlatformEnum platform = AiPlatformEnum.validatePlatform(apiKey.getPlatform());
|
||||
return vectorFactory.getOrCreateVectorStore(getEmbeddingModel(id), platform, apiKey.getApiKey(), apiKey.getUrl());
|
||||
return modelFactory.getOrCreateVectorStore(getEmbeddingModel(id), platform, apiKey.getApiKey(), apiKey.getUrl());
|
||||
}
|
||||
|
||||
}
|
@ -46,11 +46,13 @@
|
||||
</dependency>
|
||||
|
||||
<!-- 向量化,基于 Redis 存储,Tika 解析内容 -->
|
||||
<dependency>
|
||||
<groupId>${spring-ai.groupId}</groupId>
|
||||
<artifactId>spring-ai-transformers-spring-boot-starter</artifactId>
|
||||
<version>${spring-ai.version}</version>
|
||||
</dependency>
|
||||
|
||||
<!-- 暂不做经济型,先注释 -->
|
||||
<!-- <dependency>-->
|
||||
<!-- <groupId>${spring-ai.groupId}</groupId>-->
|
||||
<!-- <artifactId>spring-ai-transformers-spring-boot-starter</artifactId>-->
|
||||
<!-- <version>${spring-ai.version}</version>-->
|
||||
<!-- </dependency>-->
|
||||
<dependency>
|
||||
<groupId>${spring-ai.groupId}</groupId>
|
||||
<artifactId>spring-ai-tika-document-reader</artifactId>
|
||||
|
@ -2,8 +2,6 @@ package cn.iocoder.yudao.framework.ai.config;
|
||||
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactory;
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactoryImpl;
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactory;
|
||||
import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactoryImpl;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatModel;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatOptions;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
|
||||
@ -38,11 +36,6 @@ public class YudaoAiAutoConfiguration {
|
||||
return new AiModelFactoryImpl();
|
||||
}
|
||||
|
||||
@Bean
|
||||
public AiVectorStoreFactory aiVectorFactory() {
|
||||
return new AiVectorStoreFactoryImpl();
|
||||
}
|
||||
|
||||
|
||||
// ========== 各种 AI Client 创建 ==========
|
||||
|
||||
@ -89,7 +82,7 @@ public class YudaoAiAutoConfiguration {
|
||||
// TODO @xin 免费版本
|
||||
// @Bean
|
||||
// @Lazy // TODO 芋艿:临时注释,避免无法启动」
|
||||
// public EmbeddingModel transformersEmbeddingClient() {
|
||||
// public TransformersEmbeddingModel transformersEmbeddingClient() {
|
||||
// return new TransformersEmbeddingModel(MetadataMode.EMBED);
|
||||
// }
|
||||
|
||||
@ -98,23 +91,24 @@ public class YudaoAiAutoConfiguration {
|
||||
*/
|
||||
// @Bean
|
||||
// @Lazy // TODO 芋艿:临时注释,避免无法启动
|
||||
// public RedisVectorStore vectorStore(TongYiTextEmbeddingModel tongYiTextEmbeddingModel, RedisVectorStoreProperties properties,
|
||||
// public RedisVectorStore vectorStore(TransformersEmbeddingModel embeddingModel, RedisVectorStoreProperties properties,
|
||||
// RedisProperties redisProperties) {
|
||||
// var config = RedisVectorStore.RedisVectorStoreConfig.builder()
|
||||
// .withIndexName(properties.getIndex())
|
||||
// .withPrefix(properties.getPrefix())
|
||||
// .withMetadataFields(new RedisVectorStore.MetadataField("knowledgeId", Schema.FieldType.NUMERIC))
|
||||
// .build();
|
||||
//
|
||||
// RedisVectorStore redisVectorStore = new RedisVectorStore(config, tongYiTextEmbeddingModel,
|
||||
// RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
|
||||
// new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
|
||||
// properties.isInitializeSchema());
|
||||
// redisVectorStore.afterPropertiesSet();
|
||||
// return redisVectorStore;
|
||||
// }
|
||||
|
||||
@Bean
|
||||
@Lazy // TODO 芋艿:临时注释,避免无法启动
|
||||
public TokenTextSplitter tokenTextSplitter() {
|
||||
//TODO @xin 配置提取
|
||||
return new TokenTextSplitter(500, 100, 5, 10000, true);
|
||||
}
|
||||
|
||||
|
@ -6,6 +6,7 @@ import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
|
||||
import org.springframework.ai.chat.model.ChatModel;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.image.ImageModel;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
|
||||
/**
|
||||
* AI Model 模型工厂的接口类
|
||||
@ -92,4 +93,17 @@ public interface AiModelFactory {
|
||||
*/
|
||||
EmbeddingModel getOrCreateEmbeddingModel(AiPlatformEnum platform, String apiKey, String url);
|
||||
|
||||
/**
|
||||
* 基于指定配置,获得 VectorStore 对象
|
||||
* <p>
|
||||
* 如果不存在,则进行创建
|
||||
*
|
||||
* @param embeddingModel 嵌入模型
|
||||
* @param platform 平台
|
||||
* @param apiKey API KEY
|
||||
* @param url API URL
|
||||
* @return VectorStore 对象
|
||||
*/
|
||||
VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url);
|
||||
|
||||
}
|
||||
|
@ -13,6 +13,7 @@ import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatModel;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
|
||||
import cn.iocoder.yudao.framework.ai.core.model.xinghuo.XingHuoChatModel;
|
||||
import cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
|
||||
import com.alibaba.cloud.ai.tongyi.TongYiAutoConfiguration;
|
||||
import com.alibaba.cloud.ai.tongyi.TongYiConnectionProperties;
|
||||
import com.alibaba.cloud.ai.tongyi.chat.TongYiChatModel;
|
||||
@ -54,13 +55,18 @@ import org.springframework.ai.qianfan.api.QianFanApi;
|
||||
import org.springframework.ai.qianfan.api.QianFanImageApi;
|
||||
import org.springframework.ai.stabilityai.StabilityAiImageModel;
|
||||
import org.springframework.ai.stabilityai.api.StabilityAiApi;
|
||||
import org.springframework.ai.vectorstore.RedisVectorStore;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.ai.zhipuai.ZhiPuAiChatModel;
|
||||
import org.springframework.ai.zhipuai.ZhiPuAiImageModel;
|
||||
import org.springframework.ai.zhipuai.api.ZhiPuAiApi;
|
||||
import org.springframework.ai.zhipuai.api.ZhiPuAiImageApi;
|
||||
import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
|
||||
import org.springframework.retry.support.RetryTemplate;
|
||||
import org.springframework.web.client.ResponseErrorHandler;
|
||||
import org.springframework.web.client.RestClient;
|
||||
import redis.clients.jedis.JedisPooled;
|
||||
import redis.clients.jedis.search.Schema;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
@ -191,6 +197,25 @@ public class AiModelFactoryImpl implements AiModelFactory {
|
||||
});
|
||||
}
|
||||
|
||||
@Override
|
||||
public VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url) {
|
||||
String cacheKey = buildClientCacheKey(VectorStore.class, platform, apiKey, url);
|
||||
return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
|
||||
String prefix = StrUtil.format("{}#{}:", platform.getPlatform(), apiKey);
|
||||
var config = RedisVectorStore.RedisVectorStoreConfig.builder()
|
||||
.withIndexName(cacheKey)
|
||||
.withPrefix(prefix)
|
||||
.withMetadataFields(new RedisVectorStore.MetadataField("knowledgeId", Schema.FieldType.NUMERIC))
|
||||
.build();
|
||||
RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
|
||||
RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
|
||||
new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
|
||||
true);
|
||||
redisVectorStore.afterPropertiesSet();
|
||||
return redisVectorStore;
|
||||
});
|
||||
}
|
||||
|
||||
private static String buildClientCacheKey(Class<?> clazz, Object... params) {
|
||||
if (ArrayUtil.isEmpty(params)) {
|
||||
return clazz.getName();
|
||||
|
@ -1,28 +0,0 @@
|
||||
package cn.iocoder.yudao.framework.ai.core.factory;
|
||||
|
||||
import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
|
||||
// TODO @xin:也放到 AiModelFactory 里面好了,后续改成 AiFactory
|
||||
/**
|
||||
* AI Vector 模型工厂的接口类
|
||||
*
|
||||
* @author xiaoxin
|
||||
*/
|
||||
public interface AiVectorStoreFactory {
|
||||
|
||||
/**
|
||||
* 基于指定配置,获得 VectorStore 对象
|
||||
* <p>
|
||||
* 如果不存在,则进行创建
|
||||
*
|
||||
* @param embeddingModel 嵌入模型
|
||||
* @param platform 平台
|
||||
* @param apiKey API KEY
|
||||
* @param url API URL
|
||||
* @return VectorStore 对象
|
||||
*/
|
||||
VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url);
|
||||
|
||||
}
|
@ -1,52 +0,0 @@
|
||||
package cn.iocoder.yudao.framework.ai.core.factory;
|
||||
|
||||
import cn.hutool.core.lang.Singleton;
|
||||
import cn.hutool.core.lang.func.Func0;
|
||||
import cn.hutool.core.util.ArrayUtil;
|
||||
import cn.hutool.core.util.StrUtil;
|
||||
import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
|
||||
import cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.vectorstore.RedisVectorStore;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
|
||||
import redis.clients.jedis.JedisPooled;
|
||||
|
||||
/**
|
||||
* AI Vector 模型工厂的实现类
|
||||
* 使用 redisVectorStore 实现 VectorStore
|
||||
*
|
||||
* @author xiaoxin
|
||||
*/
|
||||
public class AiVectorStoreFactoryImpl implements AiVectorStoreFactory {
|
||||
|
||||
@Override
|
||||
public VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url) {
|
||||
String cacheKey = buildClientCacheKey(VectorStore.class, platform, apiKey, url);
|
||||
return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
|
||||
// TODO 芋艿 @xin 这两个配置取哪好呢
|
||||
// TODO 不同模型的向量维度可能会不一样,目前看貌似是以 index 来做区分的,维度不一样存不到一个 index 上
|
||||
// TODO 回复:好的哈
|
||||
String index = "default-index";
|
||||
String prefix = "default:";
|
||||
var config = RedisVectorStore.RedisVectorStoreConfig.builder()
|
||||
.withIndexName(index)
|
||||
.withPrefix(prefix)
|
||||
.build();
|
||||
RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
|
||||
RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
|
||||
new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
|
||||
true);
|
||||
redisVectorStore.afterPropertiesSet();
|
||||
return redisVectorStore;
|
||||
});
|
||||
}
|
||||
|
||||
private static String buildClientCacheKey(Class<?> clazz, Object... params) {
|
||||
if (ArrayUtil.isEmpty(params)) {
|
||||
return clazz.getName();
|
||||
}
|
||||
return StrUtil.format("{}#{}", clazz.getName(), ArrayUtil.join(params, "_"));
|
||||
}
|
||||
|
||||
}
|
Loading…
Reference in New Issue
Block a user