测试服务器IP配置变更

feat: test
feat: 支持多数据库配置与PGVector检索
2026-04-08 22:14:22 +08:00 · 2026-04-03 18:26:20 +08:00 · 2026-04-03 17:59:05 +08:00 · 2026-04-03 03:17:32 +00:00 · 2026-04-03 03:17:29 +00:00 · 2026-04-03 03:17:26 +00:00
33 changed files with 1641 additions and 238 deletions
--- a/24
+++ b/24
@@ -0,0 +1,24 @@
+# 最小化Docker镜像
+FROM busybox:uclibc
+
+WORKDIR /app
+
+# 复制时区数据
+COPY timezone/localtime /etc/localtime
+COPY timezone/timezone /etc/timezone
+COPY timezone/Shanghai /usr/share/zoneinfo/Asia/Shanghai
+
+# 复制预构建的二进制文件和配置文件
+COPY rag_binary ./main
+COPY config.yml ./
+
+# 创建日志目录
+RUN mkdir -p /logs /app/resource/log/run /app/resource/log/server
+
+# 添加执行权限
+RUN chmod +x /app/main
+
+EXPOSE 3008
+
+# 使用root用户运行
+CMD ["./main"]
--- a/common/eino/a.go
+++ b/common/eino/a.go
@@ -0,0 +1,166 @@
+package eino
+
+import (
+	"context"
+	"errors"
+	"fmt"
+	"io"
+	"log"
+	"os"
+
+	"github.com/cloudwego/eino/components/prompt"
+	"github.com/cloudwego/eino/components/retriever"
+	"github.com/cloudwego/eino/schema"
+	"github.com/cloudwego/eino-ext/components/model/ark"
+)
+
+func main() {
+	ctx := context.Background()
+
+	// ==========================================
+	// 1. 初始化三大组件
+	// ==========================================
+	// 1.1 向量检索（从知识库查客服知识）
+	ragRetriever := NewPGVectorRetriever()
+
+	// 1.2 提示词模板（客服角色 + 历史 + 知识库 + 用户问题）
+	chatTpl := newCustomerServiceTemplate()
+
+	// 1.3 大模型（ARK）
+	chatModel, err := ark.NewChatModel(ctx, &ark.ChatModelConfig{
+		APIKey: os.Getenv("ARK_API_KEY"),
+		Model:  os.Getenv("ARK_MODEL_ID"),
+	})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// ==========================================
+	// 2. 模拟会话：从 DB 读取历史对话
+	// ==========================================
+	sessionHistory := []*schema.Message{
+		{Role: schema.User, Content: "你们发什么快递？"},
+		{Role: schema.Assistant, Content: "默认发中通快递"},
+		{Role: schema.User, Content: "可以发顺丰吗？"},
+	}
+
+	// 当前用户问题
+	userQuery := "那顺丰需要加钱吗？"
+
+	// ==========================================
+	// 3. RAG 检索知识库
+	// ==========================================
+	docs, err := ragRetriever.Retrieve(ctx, userQuery)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 拼接参考知识
+	knowledge := ""
+	for i, doc := range docs {
+		knowledge += fmt.Sprintf("[参考%d] %s\n", i+1, doc.Content)
+	}
+
+	// ==========================================
+	// 4. 模板格式化：系统提示 + 历史 + 知识 + 当前问题
+	// ==========================================
+	msgs, err := chatTpl.Format(ctx, map[string]any{
+		"history":   sessionHistory,
+		"knowledge": knowledge,
+		"question":  userQuery,
+	})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// ==========================================
+	// 5. 流式调用大模型生成客服回答
+	// ==========================================
+	fmt.Println("\n=== 客服回复 ===")
+	stream, err := chatModel.Stream(ctx, msgs)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	fullReply := make([]*schema.Message, 0, 100)
+	for {
+		chunk, err := stream.Recv()
+		if errors.Is(err, io.EOF) {
+			break
+		}
+		if err != nil {
+			log.Fatal(err)
+		}
+		fmt.Print(chunk.Content)
+		fullReply = append(fullReply, chunk)
+	}
+
+	// ==========================================
+	// 6. 拼接完整回复，存入 DB 作为新历史
+	// ==========================================
+	replyMsg, _ := schema.ConcatMessages(fullReply)
+	sessionHistory = append(sessionHistory,
+		&schema.Message{Role: schema.User, Content: userQuery},
+		replyMsg,
+	)
+
+	// 接下来把 sessionHistory 存回你的 MySQL/Redis 即可
+}
+
+// ==========================================
+// 本地客服提示词模板（不需要 MCP）
+// ==========================================
+func newCustomerServiceTemplate() prompt.ChatTemplate {
+	// 系统提示 + 多轮对话 + 知识库 + 用户问题
+	return prompt.FromMessages(schema.Messages{
+		{
+			Role: schema.System,
+			Content: `你是电商智能客服，语气友好简洁。
+请严格根据参考知识回答，不知道就说“抱歉，这个问题我需要帮你转接人工”。
+
+参考知识：
+{{.knowledge}}`,
+		},
+		// 历史对话会自动渲染在这里
+		{{range .history}}{{.}},{{end}},
+		// 当前用户问题
+	{Role: schema.User, Content: "{{.question}}"},
+	})
+}
+
+// ==========================================
+// PGVector 检索器（简化可直接用）
+// ==========================================
+type PGVectorRetriever struct {
+	topK int
+}
+
+func NewPGVectorRetriever() retriever.Retriever {
+	return &PGVectorRetriever{topK: 3}
+}
+
+func (r *PGVectorRetriever) Retrieve(
+	ctx context.Context,
+	query string,
+	opts ...retriever.Option,
+) ([]*schema.Document, error) {
+
+	options := retriever.GetCommonOptions(nil, opts...)
+	topK := r.topK
+	if options.TopK != nil {
+		topK = *options.TopK
+	}
+
+	// ===== 这里替换成你真实的 PG 向量检索 SQL =====
+	// 模拟知识库
+	return []*schema.Document{
+		{
+			ID:      "1",
+			Content: "顺丰快递需要补10元运费差价",
+		},
+		{
+			ID:      "2",
+			Content: "订单满99元可免费升级顺丰",
+		},
+	}, nil
+}
--- a/common/eino/b.go
+++ b/common/eino/b.go
@@ -0,0 +1,107 @@
+package eino
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/cloudwego/eino/schema"
+	"github.com/elastic/go-elasticsearch/v8"
+
+	"github.com/cloudwego/eino-ext/components/indexer/es8"
+)
+
+const (
+	indexName          = "eino_example"
+	fieldContent       = "content"
+	fieldContentVector = "content_vector"
+	fieldExtraLocation = "location"
+	docExtraLocation   = "location"
+)
+
+func TestIndexer() {
+	ctx := context.Background()
+
+	// 1. 创建 ES 客户端
+	client, err := elasticsearch.NewClient(elasticsearch.Config{
+		Addresses: []string{"http://localhost:9200"},
+	})
+
+	if err != nil {
+		fmt.Printf("create client error: %v\n", err)
+		return
+	}
+
+	// 2. 定义 Index Spec（选填：如果索引不存在，将自动创建）
+	indexSpec := &es8.IndexSpec{
+		Settings: map[string]any{
+			"number_of_shards":   1,
+			"number_of_replicas": 0,
+		},
+		Mappings: map[string]any{
+			"properties": map[string]any{
+				fieldContentVector: map[string]any{
+					"type":       "dense_vector",
+					"dims":       1024,
+					"index":      true,
+					"similarity": "l2_norm",
+				},
+			},
+		},
+	}
+
+	// 4. 准备文档
+	// 文档通常包含 ID 和 Content
+	// 也可以包含额外的 Metadata 用于过滤或其他用途
+	docs := []*schema.Document{
+		{
+			ID:      "1",
+			Content: "Eiffel Tower: Located in Paris, France.",
+			MetaData: map[string]any{
+				docExtraLocation: "France",
+			},
+		},
+		{
+			ID:      "2",
+			Content: "The Great Wall: Located in China.",
+			MetaData: map[string]any{
+				docExtraLocation: "China",
+			},
+		},
+	}
+
+	// 5. 创建 ES 索引器组件
+	indexer, err := es8.NewIndexer(ctx, &es8.IndexerConfig{
+		Client:    client,
+		Index:     indexName,
+		IndexSpec: indexSpec, // 添加此项以启用自动索引创建
+		BatchSize: 10,
+		// DocumentToFields 指定如何将文档字段映射到 ES 字段
+		DocumentToFields: func(ctx context.Context, doc *schema.Document) (field2Value map[string]es8.FieldValue, err error) {
+			return map[string]es8.FieldValue{
+				fieldContent: {
+					Value:    doc.Content,
+					EmbedKey: fieldContentVector, // 对文档内容进行向量化并保存到 "content_vector" 字段
+				},
+				fieldExtraLocation: {
+					// 额外的 metadata 字段
+					Value: doc.MetaData[docExtraLocation],
+				},
+			}, nil
+		},
+		// 提供 embedding 组件用于向量化
+		Embedding: EmbedderDashscope,
+	})
+
+	if err != nil {
+		fmt.Printf("create indexer error: %v\n", err)
+		return
+	}
+
+	// 6. 索引文档
+	ids, err := indexer.Store(ctx, docs)
+	if err != nil {
+		fmt.Printf("index error: %v\n", err)
+		return
+	}
+	fmt.Println("indexed ids:", ids)
+}
--- a/common/eino/base_task.go
+++ b/common/eino/base_task.go
@@ -0,0 +1,49 @@
+package eino
+
+import (
+	"time"
+
+	"gitea.com/red-future/common/beans"
+)
+
+// BaseTask 任务基类 - MongoDB版本
+type BaseTask struct {
+	beans.MongoBaseDO `bson:",inline"`
+	// 任务信息
+	TaskType TaskType     `bson:"taskType" json:"taskType"`
+	Status   TaskStatus   `bson:"status" json:"status"`
+	Priority TaskPriority `bson:"priority,omitempty" json:"priority,omitempty"`
+	// 进度
+	TotalItems     int64   `bson:"totalItems" json:"totalItems"`
+	ProcessedItems int64   `bson:"processedItems" json:"processedItems"`
+	Progress       float64 `bson:"progress" json:"progress"`
+	// 结果
+	StartTime    *time.Time `bson:"startTime" json:"startTime"`
+	EndTime      *time.Time `bson:"endTime,omitempty" json:"endTime,omitempty"`
+	Duration     int64      `bson:"duration,omitempty" json:"duration,omitempty"`
+	SuccessCount int64      `bson:"successCount" json:"successCount"`
+	FailCount    int64      `bson:"failCount" json:"failCount"`
+	// 其他
+	Executor string `bson:"executor,omitempty" json:"executor,omitempty"`
+}
+
+// SQLBaseTask 任务基类 - SQL版本
+type SQLBaseTask struct {
+	beans.SQLBaseDO
+	// 任务信息
+	TaskType TaskType     `json:"taskType"`
+	Status   TaskStatus   `json:"status"`
+	Priority TaskPriority `json:"priority,omitempty"`
+	// 进度
+	TotalItems     int64   `json:"totalItems"`
+	ProcessedItems int64   `json:"processedItems"`
+	Progress       float64 `json:"progress"`
+	// 结果
+	StartTime    *time.Time `json:"startTime"`
+	EndTime      *time.Time `json:"endTime,omitempty"`
+	Duration     int64      `json:"duration,omitempty"`
+	SuccessCount int64      `json:"successCount"`
+	FailCount    int64      `json:"failCount"`
+	// 其他
+	Executor string `json:"executor,omitempty"`
+}
--- a/common/eino/c.go
+++ b/common/eino/c.go
@@ -0,0 +1,94 @@
+package eino
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+
+	"github.com/cloudwego/eino/schema"
+	"github.com/elastic/go-elasticsearch/v8"
+	"github.com/elastic/go-elasticsearch/v8/typedapi/types"
+
+	"github.com/cloudwego/eino-ext/components/retriever/es8"
+	"github.com/cloudwego/eino-ext/components/retriever/es8/search_mode"
+)
+
+func TestRetriever() {
+	ctx := context.Background()
+
+	client, _ := elasticsearch.NewClient(elasticsearch.Config{
+		Addresses: []string{"http://localhost:9200"},
+	})
+
+	// 创建 retriever 组件
+	retriever, _ := es8.NewRetriever(ctx, &es8.RetrieverConfig{
+		Client: client,
+		Index:  indexName,
+		TopK:   5,
+		SearchMode: search_mode.SearchModeApproximate(&search_mode.ApproximateConfig{
+			QueryFieldName:  fieldContent,
+			VectorFieldName: fieldContentVector,
+			Hybrid:          false,
+			// RRF 仅在特定许可证下可用
+			// 参见: https://www.elastic.co/subscriptions
+			RRF:             false,
+			RRFRankConstant: nil,
+			RRFWindowSize:   nil,
+		}),
+		ResultParser: func(ctx context.Context, hit types.Hit) (doc *schema.Document, err error) {
+			doc = &schema.Document{
+				ID:       *hit.Id_,
+				Content:  "",
+				MetaData: map[string]any{},
+			}
+
+			var src map[string]any
+			if err = json.Unmarshal(hit.Source_, &src); err != nil {
+				return nil, err
+			}
+
+			for field, val := range src {
+				switch field {
+				case fieldContent:
+					doc.Content = val.(string)
+				case fieldContentVector:
+					var v []float64
+					for _, item := range val.([]interface{}) {
+						v = append(v, item.(float64))
+					}
+					doc.WithDenseVector(v)
+				case fieldExtraLocation:
+					doc.MetaData[docExtraLocation] = val.(string)
+				}
+			}
+
+			if hit.Score_ != nil {
+				doc.WithScore(float64(*hit.Score_))
+			}
+
+			return doc, nil
+		},
+		Embedding: EmbedderDashscope,
+	})
+
+	// 不带过滤器的搜索
+	docs, _ := retriever.Retrieve(ctx, "tourist attraction")
+
+	// 带过滤器的搜索
+	docs, _ = retriever.Retrieve(ctx, "tourist attraction",
+		es8.WithFilters([]types.Query{{
+			Term: map[string]types.TermQuery{
+				fieldExtraLocation: {
+					CaseInsensitive: of(true),
+					Value:           "China",
+				},
+			},
+		}}),
+	)
+
+	fmt.Printf("retrieved docs: %+v\n", docs)
+}
+
+func of[T any](v T) *T {
+	return &v
+}
--- a/common/eino/consts.go
+++ b/common/eino/consts.go
@@ -0,0 +1,8 @@
+package eino
+
+const (
+	providerArk       = "ark"
+	providerOpenai    = "openai"
+	providerQianfan   = "qianfan"
+	providerDashscope = "dashscope"
+)
--- a/common/eino/document_loader.go
+++ b/common/eino/document_loader.go
@@ -0,0 +1,51 @@
+package eino
+
+import (
+	"context"
+	"fmt"
+
+	"gitea.com/red-future/common/utils"
+	"github.com/cloudwego/eino-ext/components/document/loader/url"
+	"github.com/cloudwego/eino-ext/components/document/parser/docx"
+	"github.com/cloudwego/eino-ext/components/document/parser/pdf"
+	"github.com/cloudwego/eino-ext/components/document/parser/xlsx"
+	"github.com/cloudwego/eino/components/document"
+	"github.com/cloudwego/eino/components/document/parser"
+	"github.com/cloudwego/eino/schema"
+)
+
+// LoadDocument 业务函数：加载文件
+func LoadDocument(ctx context.Context, filePath, fileFormat string) (docs []*schema.Document, err error) {
+	p, err := docsParser(ctx, fileFormat)
+	if err != nil {
+		return
+	}
+	loader, err := url.NewLoader(ctx, &url.LoaderConfig{
+		Parser: p,
+	})
+	imageUrl, err := utils.GetFileAddressPrefix(ctx)
+	if err != nil {
+		return
+	}
+	docs, err = loader.Load(context.Background(), document.Source{
+		URI: fmt.Sprintf("%s%s", imageUrl, filePath),
+	})
+	return
+}
+
+func docsParser(ctx context.Context, fileFormat string) (p parser.Parser, err error) {
+	switch fileFormat {
+	case "docx":
+		p, err = docx.NewDocxParser(ctx, &docx.Config{
+			ToSections:     true,
+			IncludeHeaders: true,
+			IncludeFooters: true,
+			IncludeTables:  true,
+		})
+	case "pdf":
+		p, err = pdf.NewPDFParser(ctx, &pdf.Config{})
+	case "xlsx":
+		p, err = xlsx.NewXlsxParser(ctx, &xlsx.Config{})
+	}
+	return
+}
--- a/common/eino/document_semantic.go
+++ b/common/eino/document_semantic.go
@@ -0,0 +1,64 @@
+package eino
+
+import (
+	"context"
+
+	"github.com/cloudwego/eino-ext/components/document/transformer/splitter/recursive"
+	"github.com/cloudwego/eino-ext/components/document/transformer/splitter/semantic"
+	"github.com/cloudwego/eino/schema"
+	"github.com/gogf/gf/v2/frame/g"
+)
+
+// SemanticSplitDocument 语义分割文档
+func SemanticSplitDocument(ctx context.Context, docs []*schema.Document) (res []*schema.Document, err error) {
+	// 默认分隔符（支持中英文）
+	separators := []string{"\n\n", "\n", "。", "！", "？", "；", ".", "!", "?", ";"}
+	// 读取配置，使用合理的默认值
+	bufferSize := g.Cfg().MustGet(ctx, "eino.splitter.bufferSize").Int()
+	minChunkSize := g.Cfg().MustGet(ctx, "eino.splitter.minChunkSize").Int()
+	percentile := g.Cfg().MustGet(ctx, "eino.splitter.percentile").Float64()
+	batchSize := g.Cfg().MustGet(ctx, "eino.splitter.batchSize").Int()
+	if batchSize <= 0 {
+		batchSize = 10 // doubao-embedding-vision 限制每批最多 10 个
+	}
+
+	// 使用批量包装器
+	var batchEmbedder *BatchEmbedder
+	provider := g.Cfg().MustGet(ctx, "eino.embedding.provider").String()
+	switch provider {
+	case providerArk:
+		batchEmbedder = NewBatchEmbedder(EmbedderArk, batchSize)
+	case providerOpenai:
+		batchEmbedder = NewBatchEmbedder(EmbedderOpenAI, batchSize)
+	case providerDashscope:
+		batchEmbedder = NewBatchEmbedder(EmbedderDashscope, batchSize)
+	}
+
+	splitter, err := semantic.NewSplitter(ctx, &semantic.Config{
+		Embedding:    batchEmbedder,
+		BufferSize:   bufferSize,
+		MinChunkSize: minChunkSize,
+		Percentile:   percentile,
+		Separators:   separators,
+	})
+	if err != nil {
+		return
+	}
+	return splitter.Transform(ctx, docs)
+}
+
+// RecursiveSplitDocument 递归分割文档
+func RecursiveSplitDocument(ctx context.Context, docs []*schema.Document) (res []*schema.Document, err error) {
+	// 默认分隔符（支持中英文）
+	separators := []string{"\n\n", "\n", "。", "！", "？", "；", ".", "!", "?", ";"}
+	splitter, err := recursive.NewSplitter(ctx, &recursive.Config{
+		ChunkSize:   512,
+		OverlapSize: 100,
+		KeepType:    recursive.KeepTypeNone,
+		Separators:  separators,
+	})
+	if err != nil {
+		return
+	}
+	return splitter.Transform(ctx, docs)
+}
--- a/common/eino/embedding.go
+++ b/common/eino/embedding.go
@@ -0,0 +1,69 @@
+package eino
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/cloudwego/eino-ext/components/embedding/ark"
+	"github.com/cloudwego/eino-ext/components/embedding/dashscope"
+	"github.com/cloudwego/eino-ext/components/embedding/openai"
+	"github.com/gogf/gf/v2/frame/g"
+	"github.com/golang/glog"
+)
+
+// 全局只初始化一次
+var (
+	EmbedderArk       *ark.Embedder
+	EmbedderDashscope *dashscope.Embedder
+	EmbedderOpenAI    *openai.Embedder
+)
+
+func init() {
+	ctx := context.Background()
+	if !g.Cfg().MustGet(ctx, "eino.embedding").IsEmpty() {
+		var err error
+		provider := g.Cfg().MustGet(ctx, "eino.embedding.provider").String()
+		switch provider {
+		case providerArk:
+			cfg := &ark.EmbeddingConfig{
+				APIKey: g.Cfg().MustGet(ctx, "eino.embedding.apiKey").String(),
+				Model:  g.Cfg().MustGet(ctx, "eino.embedding.model").String(),
+			}
+			if apiType := g.Cfg().MustGet(ctx, "eino.embedding.apiType").String(); apiType != "" {
+				apiTypeVal := ark.APIType(apiType)
+				cfg.APIType = &apiTypeVal
+			}
+			EmbedderArk, err = ark.NewEmbedder(ctx, cfg)
+		case providerOpenai:
+			chatModelConfig := &openai.EmbeddingConfig{
+				APIKey: g.Cfg().MustGet(ctx, "eino.embedding.apiKey").String(),
+				Model:  g.Cfg().MustGet(ctx, "eino.embedding.model").String(),
+			}
+			EmbedderOpenAI, err = openai.NewEmbedder(ctx, chatModelConfig)
+		case providerDashscope:
+			cfg := &dashscope.EmbeddingConfig{
+				APIKey: g.Cfg().MustGet(ctx, "eino.embedding.apiKey").String(),
+				Model:  g.Cfg().MustGet(ctx, "eino.embedding.model").String(),
+			}
+			EmbedderDashscope, err = dashscope.NewEmbedder(ctx, cfg)
+		}
+		if err != nil {
+			glog.Fatalf("NewEmbedder of %v error: %v", provider, err)
+		}
+	}
+
+	return
+}
+
+func EmbedStrings(ctx context.Context, texts []string) (embeddings [][]float64, err error) {
+	provider := g.Cfg().MustGet(ctx, "eino.embedding.provider").String()
+	switch provider {
+	case providerArk:
+		return EmbedderArk.EmbedStrings(ctx, texts)
+	case providerOpenai:
+		return EmbedderOpenAI.EmbedStrings(ctx, texts)
+	case providerDashscope:
+		return EmbedderDashscope.EmbedStrings(ctx, texts)
+	}
+	return nil, fmt.Errorf("unsupported provider: %v", provider)
+}
--- a/common/eino/embedding_batch.go
+++ b/common/eino/embedding_batch.go
@@ -0,0 +1,47 @@
+package eino
+
+import (
+	"context"
+
+	"github.com/cloudwego/eino/components/embedding"
+)
+
+// BatchEmbedder 包装器，支持批量限制
+type BatchEmbedder struct {
+	embedder  embedding.Embedder
+	batchSize int
+}
+
+// NewBatchEmbedder 创建支持批量限制的 embedding 包装器
+func NewBatchEmbedder(embedder embedding.Embedder, batchSize int) *BatchEmbedder {
+	if batchSize <= 0 {
+		batchSize = 10 // 默认每批 10 个
+	}
+	return &BatchEmbedder{
+		embedder:  embedder,
+		batchSize: batchSize,
+	}
+}
+
+// EmbedStrings 分批调用 embedding
+func (b *BatchEmbedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) ([][]float64, error) {
+	if len(texts) <= b.batchSize {
+		return b.embedder.EmbedStrings(ctx, texts, opts...)
+	}
+
+	var allEmbeddings [][]float64
+	for i := 0; i < len(texts); i += b.batchSize {
+		end := i + b.batchSize
+		if end > len(texts) {
+			end = len(texts)
+		}
+
+		batch := texts[i:end]
+		embeddings, err := b.embedder.EmbedStrings(ctx, batch, opts...)
+		if err != nil {
+			return nil, err
+		}
+		allEmbeddings = append(allEmbeddings, embeddings...)
+	}
+	return allEmbeddings, nil
+}
--- a/common/eino/embedding_qwen.go
+++ b/common/eino/embedding_qwen.go
@@ -0,0 +1,273 @@
+/*
+ * Copyright 2024 Red Future Authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package eino
+
+import (
+	"context"
+	"fmt"
+	"net/http"
+	"time"
+
+	"github.com/cloudwego/eino/callbacks"
+	"github.com/cloudwego/eino/components"
+	"github.com/cloudwego/eino/components/embedding"
+	"github.com/gogf/gf/v2/frame/g"
+	"github.com/gogf/gf/v2/net/gclient"
+	"github.com/gogf/gf/v2/util/gconv"
+)
+
+var (
+	// 千问API默认配置
+	defaultBaseURL    = "https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding"
+	defaultTimeout    = 10 * time.Minute
+	defaultRetryTimes = 2
+)
+
+type QwenEmbeddingConfig struct {
+	// Timeout specifies the maximum duration to wait for API responses
+	// Optional. Default: 10 minutes
+	Timeout *time.Duration `json:"timeout"`
+
+	// HTTPClient specifies the client to send HTTP requests.
+	// Optional. Default &http.Client{Timeout: Timeout}
+	HTTPClient *http.Client `json:"http_client"`
+
+	// RetryTimes specifies the number of retry attempts for failed API calls
+	// Optional. Default: 2
+	RetryTimes *int `json:"retry_times"`
+
+	// BaseURL specifies the base URL for Qwen DashScope service
+	// Optional. Default: "https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding"
+	BaseURL string `json:"base_url"`
+
+	// APIKey specifies the API Key for authentication
+	// Required
+	APIKey string `json:"api_key"`
+
+	// Model specifies the model name for Qwen embedding
+	// Required. Examples: "text-embedding-v2", "text-embedding-v3"
+	Model string `json:"model"`
+
+	// TextType specifies the type of text: "document" or "query"
+	// Optional. Default: "document"
+	TextType string `json:"text_type"`
+
+	// MaxConcurrentRequests specifies the maximum number of concurrent requests allowed
+	// Optional. Default: 5
+	MaxConcurrentRequests *int `json:"max_concurrent_requests"`
+}
+
+type QwenEmbedder struct {
+	client *gclient.Client
+	conf   *QwenEmbeddingConfig
+}
+
+// EmbeddingRequest 千问embedding请求结构
+type EmbeddingRequest struct {
+	Model string `json:"model"`
+	Input struct {
+		Texts []string `json:"texts"`
+	} `json:"input"`
+	Parameters struct {
+		TextType string `json:"text_type,omitempty"`
+	} `json:"parameters,omitempty"`
+}
+
+// EmbeddingResponse 千问embedding响应结构
+type EmbeddingResponse struct {
+	Output struct {
+		Embeddings []struct {
+			TextIndex int       `json:"text_index"`
+			Embedding []float64 `json:"embedding"`
+		} `json:"embeddings"`
+	} `json:"output"`
+	Usage struct {
+		TotalTokens int `json:"total_tokens"`
+	} `json:"usage"`
+	RequestID string `json:"request_id"`
+}
+
+type APIError struct {
+	Code      string `json:"code"`
+	Message   string `json:"message"`
+	RequestID string `json:"request_id"`
+}
+
+func (e *APIError) Error() string {
+	return fmt.Sprintf("API Error: %s - %s (RequestID: %s)", e.Code, e.Message, e.RequestID)
+}
+
+func buildQwenClient(config *QwenEmbeddingConfig) *gclient.Client {
+	if len(config.BaseURL) == 0 {
+		config.BaseURL = defaultBaseURL
+	}
+	if config.Timeout == nil {
+		config.Timeout = &defaultTimeout
+	}
+	if config.RetryTimes == nil {
+		defaultRetryTimes := 2
+		config.RetryTimes = &defaultRetryTimes
+	}
+	if len(config.TextType) == 0 {
+		config.TextType = "document"
+	}
+	if config.MaxConcurrentRequests == nil {
+		defaultMaxConcurrentRequests := 5
+		config.MaxConcurrentRequests = &defaultMaxConcurrentRequests
+	}
+
+	client := g.Client()
+	client.SetTimeout(*config.Timeout)
+
+	return client
+}
+
+func NewQwenEmbedder(ctx context.Context, config *QwenEmbeddingConfig) (*QwenEmbedder, error) {
+	if len(config.APIKey) == 0 {
+		return nil, fmt.Errorf("[Qwen] APIKey is required")
+	}
+	if len(config.Model) == 0 {
+		return nil, fmt.Errorf("[Qwen] Model is required")
+	}
+
+	client := buildQwenClient(config)
+
+	return &QwenEmbedder{
+		client: client,
+		conf:   config,
+	}, nil
+}
+
+func (e *QwenEmbedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) (
+	[][]float64, error) {
+
+	if len(texts) == 0 {
+		return nil, fmt.Errorf("[Qwen] texts cannot be empty")
+	}
+
+	options := embedding.GetCommonOptions(&embedding.Options{
+		Model: &e.conf.Model,
+	}, opts...)
+
+	conf := &embedding.Config{
+		Model: dereferenceOrZero(options.Model),
+	}
+
+	ctx = callbacks.EnsureRunInfo(ctx, e.GetType(), components.ComponentOfEmbedding)
+	ctx = callbacks.OnStart(ctx, &embedding.CallbackInput{
+		Texts:  texts,
+		Config: conf,
+	})
+	defer func() {
+		if err := recover(); err != nil {
+			callbacks.OnError(ctx, fmt.Errorf("[Qwen] panic: %v", err))
+		}
+	}()
+
+	var usage *embedding.TokenUsage
+	var embeddings [][]float64
+	var err error
+
+	// 调用千问API获取embedding
+	embeddings, usage, err = e.callEmbeddingAPI(ctx, texts)
+	if err != nil {
+		callbacks.OnError(ctx, err)
+		return nil, err
+	}
+
+	callbacks.OnEnd(ctx, &embedding.CallbackOutput{
+		Embeddings: embeddings,
+		Config:     conf,
+		TokenUsage: usage,
+	})
+
+	return embeddings, nil
+}
+
+func (e *QwenEmbedder) callEmbeddingAPI(ctx context.Context, texts []string) ([][]float64, *embedding.TokenUsage, error) {
+	// 构建请求
+	var req EmbeddingRequest
+	req.Model = e.conf.Model
+	req.Input.Texts = texts
+	req.Parameters.TextType = e.conf.TextType
+
+	// 调用API
+	client := e.client.Clone()
+	client.SetHeader("Authorization", "Bearer "+e.conf.APIKey)
+	client.SetHeader("Content-Type", "application/json")
+	client.SetTimeout(*e.conf.Timeout)
+
+	resp, err := client.Post(ctx, e.conf.BaseURL, req)
+	if err != nil {
+		return nil, nil, fmt.Errorf("[Qwen] HTTP request error: %w", err)
+	}
+
+	defer resp.Close()
+
+	// 检查状态码
+	if resp.StatusCode != http.StatusOK {
+		var errResp APIError
+		result := resp.ReadAll()
+		if err = gconv.Struct(result, &errResp); err == nil && errResp.Code != "" {
+			return nil, nil, &errResp
+		}
+		return nil, nil, fmt.Errorf("[Qwen] HTTP status error: %d", resp.StatusCode)
+	}
+
+	// 解析响应
+	var apiResp EmbeddingResponse
+	result := resp.ReadAll()
+	if err = gconv.Struct(result, &apiResp); err != nil {
+		return nil, nil, fmt.Errorf("[Qwen] parse response error: %w", err)
+	}
+
+	// 解析响应结果
+	embeddings := make([][]float64, len(texts))
+	for _, emb := range apiResp.Output.Embeddings {
+		if emb.TextIndex >= 0 && emb.TextIndex < len(embeddings) {
+			embeddings[emb.TextIndex] = emb.Embedding
+		}
+	}
+
+	usage := &embedding.TokenUsage{
+		TotalTokens: apiResp.Usage.TotalTokens,
+	}
+
+	g.Log().Debugf(ctx, "[Qwen] Embedding success: request_id=%s, total_tokens=%d", apiResp.RequestID, usage.TotalTokens)
+
+	return embeddings, usage, nil
+}
+
+func (e *QwenEmbedder) GetType() string {
+	return getType()
+}
+
+func (e *QwenEmbedder) IsCallbacksEnabled() bool {
+	return true
+}
+
+func getType() string {
+	return "Qwen"
+}
+
+func dereferenceOrZero[T any](v *T) T {
+	if v == nil {
+		var t T
+		return t
+	}
+	return *v
+}
--- a/common/eino/indexer.go
+++ b/common/eino/indexer.go
@@ -0,0 +1,177 @@
+package eino
+
+import (
+	"context"
+	"database/sql"
+	"errors"
+	"fmt"
+	"rag/dao"
+	"rag/model/dto"
+	"rag/model/entity"
+
+	"gitea.com/red-future/common/beans"
+	"github.com/cloudwego/eino/callbacks"
+	"github.com/cloudwego/eino/components/indexer"
+	"github.com/cloudwego/eino/schema"
+	"github.com/gogf/gf/v2/os/glog"
+	"github.com/gogf/gf/v2/util/gconv"
+	"github.com/pgvector/pgvector-go"
+)
+
+type PGVectorIndexerOptions struct {
+	BatchSize int // 每批处理多少条
+}
+
+type PGVectorIndexer struct {
+	opts *PGVectorIndexerOptions
+}
+
+func NewPGVectorIndexer(opts *PGVectorIndexerOptions) *PGVectorIndexer {
+	// 默认值
+	if opts.BatchSize <= 0 {
+		opts.BatchSize = 5
+	}
+	return &PGVectorIndexer{opts: opts}
+}
+
+func (i *PGVectorIndexer) Store(ctx context.Context, docs []*schema.Document, opts ...indexer.Option) (rows int64, err error) {
+	commonOpts := indexer.GetCommonOptions(&indexer.Options{}, opts...)
+
+	if commonOpts.Embedding == nil {
+		return 0, errors.New("embedding model not set")
+	}
+
+	// 回调
+	ctx = callbacks.OnStart(ctx, &indexer.CallbackInput{Docs: docs})
+
+	rows, err = i.bulkStore(ctx, docs, commonOpts)
+	if err != nil {
+		callbacks.OnError(ctx, err)
+		return
+	}
+
+	callbacks.OnEnd(ctx, &indexer.CallbackOutput{IDs: gconv.Strings(rows)})
+	return
+}
+
+func (i *PGVectorIndexer) bulkStore(ctx context.Context, docs []*schema.Document, opts *indexer.Options) (rows int64, err error) {
+	var batchDocs []*schema.Document
+
+	// 官方ES同款逻辑：满 BatchSize 就处理一批
+	for _, doc := range docs {
+		batchDocs = append(batchDocs, doc)
+
+		// 满了 → 处理
+		if len(batchDocs) >= i.opts.BatchSize {
+			var r int64
+			r, err = i.doStore(ctx, batchDocs, opts)
+			if err != nil {
+				return
+			}
+			rows = rows + r
+			batchDocs = nil
+		}
+	}
+
+	// 最后一批
+	if len(batchDocs) > 0 {
+		var r int64
+		r, err = i.doStore(ctx, batchDocs, opts)
+		if err != nil {
+			return
+		}
+		rows = rows + r
+	}
+
+	return
+}
+
+func (i *PGVectorIndexer) doStore(ctx context.Context, docs []*schema.Document, opts *indexer.Options) (rows int64, err error) {
+
+	texts := make([]string, len(docs))
+	for i, d := range docs {
+		texts[i] = d.Content
+	}
+
+	// 向量化（官方ES也没有重试！）
+	vectors, err := opts.Embedding.EmbedStrings(ctx, texts)
+	if err != nil {
+		return
+	}
+
+	// 转成业务实体
+	var chunks []*dto.VectorDocumentChunkMsg
+	for idx, doc := range docs {
+		ck := new(dto.VectorDocumentChunkMsg)
+		err = gconv.Struct(doc.MetaData, ck)
+		if err != nil {
+			glog.Errorf(ctx, "doStore err: %v", err)
+			continue
+		}
+		ck.Content = doc.Content
+		ck.Vector = pgvector.NewVector(gconv.Float32s(vectors[idx]))
+		ck.VectorStatus = gconv.PtrInt8(1)
+		ck.Status = gconv.PtrInt8(1)
+		chunks = append(chunks, ck)
+	}
+	if len(chunks) == 0 {
+		return
+	}
+	ctx = context.WithValue(ctx, "user", &beans.User{
+		TenantId: chunks[0].TenantId,
+		UserName: chunks[0].Creator,
+	})
+	// 创建索引
+	if err = i.createOrUpdateDatasetIndex(ctx, chunks[0].DatasetId, len(vectors[0]), int64(len(chunks))); err != nil {
+		return
+	}
+	// 入库
+	rows, err = dao.DocumentChunk.BatchInsert(ctx, chunks)
+
+	return
+}
+
+func (i *PGVectorIndexer) createOrUpdateDatasetIndex(ctx context.Context, datasetId int64, dimension int, vectorCount int64) error {
+	exist, err := dao.DatasetIndex.GetByDatasetId(ctx, datasetId)
+	if err != nil && !errors.Is(err, sql.ErrNoRows) {
+		return err
+	}
+	if exist != nil {
+		_ = dao.DatasetIndex.IncVectorCount(ctx, exist.Id, vectorCount)
+		return nil
+	}
+
+	indexName := fmt.Sprintf("idx_dataset_%d_vector", datasetId)
+	idx := &entity.DatasetIndex{
+		DatasetId:   datasetId,
+		Name:        indexName,
+		Dimension:   dimension,
+		FieldType:   "float",
+		MetricType:  "COSINE",
+		Status:      gconv.PtrInt8(1),
+		VectorCount: vectorCount,
+		Description: fmt.Sprintf("数据集%d向量索引", datasetId),
+	}
+	_, err = dao.DatasetIndex.Insert(ctx, idx)
+	if err != nil {
+		return err
+	}
+	return i.createRealPGVectorIndex(ctx, indexName)
+}
+
+func (i *PGVectorIndexer) createRealPGVectorIndex(ctx context.Context, indexName string) error {
+	if err := dao.DatasetIndex.InsertIndex(ctx, indexName); err != nil {
+		glog.Errorf(ctx, "create vector index failed: %v", err)
+		return err
+	}
+	glog.Infof(ctx, "created pgvector index: %s", indexName)
+	return nil
+}
+
+func (i *PGVectorIndexer) GetType() string {
+	return "pgvector_indexer"
+}
+
+func (i *PGVectorIndexer) IsCallbacksEnabled() bool {
+	return true
+}
--- a/common/eino/priority_enum.go
+++ b/common/eino/priority_enum.go
@@ -0,0 +1,11 @@
+package eino
+
+// TaskPriority 任务优先级
+type TaskPriority string
+
+const (
+	TaskPriorityLow    TaskPriority = "low"    // 低优先级
+	TaskPriorityMedium TaskPriority = "medium" // 中优先级
+	TaskPriorityHigh   TaskPriority = "high"   // 高优先级
+	TaskPriorityUrgent TaskPriority = "urgent" // 紧急
+)
--- a/common/eino/retriever.go
+++ b/common/eino/retriever.go
@@ -0,0 +1,117 @@
+package eino
+
+import (
+	"context"
+	"errors"
+
+	"github.com/cloudwego/eino/callbacks"
+	"github.com/cloudwego/eino/components/embedding"
+	"github.com/cloudwego/eino/components/retriever"
+	"github.com/cloudwego/eino/schema"
+	"github.com/gogf/gf/v2/util/gconv"
+	"github.com/pgvector/pgvector-go"
+)
+
+type PGVectorRetrieverConfig struct {
+	Embedder     embedding.Embedder
+	DefaultTopK  int
+	DefaultIndex string
+}
+
+type PGVectorRetriever struct {
+	embedder embedding.Embedder
+	topK     int
+	index    string
+}
+
+func NewPGVectorRetriever(config *PGVectorRetrieverConfig) (*PGVectorRetriever, error) {
+	if config.Embedder == nil {
+		return nil, errors.New("embedder is required")
+	}
+	if config.DefaultTopK <= 0 {
+		config.DefaultTopK = 5
+	}
+
+	return &PGVectorRetriever{
+		embedder: config.Embedder,
+		topK:     config.DefaultTopK,
+		index:    config.DefaultIndex,
+	}, nil
+}
+
+func (r *PGVectorRetriever) Retrieve(ctx context.Context, query string, opts ...retriever.Option) ([]*schema.Document, error) {
+
+	// 1. 处理公共 Option（官方标准写法）
+	options := &retriever.Options{
+		Index:     &r.index,
+		TopK:      &r.topK,
+		Embedding: r.embedder,
+	}
+	options = retriever.GetCommonOptions(options, opts...)
+
+	// 2. 回调（官方标准）
+	ctx = callbacks.OnStart(ctx, &retriever.CallbackInput{
+		Query: query,
+		TopK:  *options.TopK,
+	})
+
+	// 3. 执行检索
+	docs, err := r.doRetrieve(ctx, query, options)
+	if err != nil {
+		callbacks.OnError(ctx, err)
+		return nil, err
+	}
+
+	// 4. 完成回调
+	callbacks.OnEnd(ctx, &retriever.CallbackOutput{
+		Docs: docs,
+	})
+
+	return docs, nil
+}
+
+func (r *PGVectorRetriever) doRetrieve(ctx context.Context, query string, opts *retriever.Options) ([]*schema.Document, error) {
+
+	// 1. 生成向量
+	vectors, err := opts.Embedding.EmbedStrings(ctx, []string{query})
+	if err != nil {
+		return nil, err
+	}
+	if len(vectors) == 0 {
+		return nil, errors.New("empty query vector")
+	}
+
+	queryVec := pgvector.NewVector(vectors[0])
+	topK := *opts.TopK
+
+	// 2. PG 向量相似度检索 SQL
+	sql := `
+	SELECT id, content, dataset_id, document_id,
+	       vector <-> ? AS distance
+	FROM document_chunk
+	ORDER BY distance ASC
+	LIMIT ?
+`
+
+	// 3. 查询
+	rows, err := dao.DocumentChunk.GetDB().GetAll(ctx, sql, queryVec, topK)
+	if err != nil {
+		return nil, err
+	}
+
+	// 4. 转为 Eino Document
+	docs := make([]*schema.Document, 0, len(rows))
+	for _, row := range rows {
+		docs = append(docs, &schema.Document{
+			ID:      gconv.String(row["id"]),
+			Content: gconv.String(row["content"]),
+			Metadata: map[string]any{
+				"dataset_id":  row["dataset_id"],
+				"document_id": row["document_id"],
+				"distance":    row["distance"],
+			},
+		})
+	}
+
+	return docs, nil
+}
--- a/common/eino/status_enum.go
+++ b/common/eino/status_enum.go
@@ -0,0 +1,12 @@
+package eino
+
+// TaskStatus 任务状态
+type TaskStatus string
+
+const (
+	TaskStatusPending   TaskStatus = "pending"   // 待处理
+	TaskStatusRunning   TaskStatus = "running"   // 运行中
+	TaskStatusCompleted TaskStatus = "completed" // 已完成
+	TaskStatusFailed    TaskStatus = "failed"    // 失败
+	TaskStatusCancelled TaskStatus = "cancelled" // 已取消
+)
--- a/common/eino/task_type.go
+++ b/common/eino/task_type.go
@@ -0,0 +1,14 @@
+package eino
+
+// TaskType 任务类型
+type TaskType string
+
+const (
+	TaskTypeDocumentIngestion     TaskType = "document_ingestion"     // 文档摄入任务
+	TaskTypeVectorIngestion       TaskType = "vector_ingestion"       // 向量摄入任务
+	TaskTypeIndexCreation         TaskType = "index_creation"         // 索引创建任务
+	TaskTypeQAProcessing          TaskType = "qa_processing"          // 问答处理任务
+	TaskTypeKnowledgeConstruction TaskType = "knowledge_construction" // 知识库构建任务
+	TaskTypeGraphBuilding         TaskType = "graph_building"         // 图谱构建任务
+	TaskTypeKnowledgeSync         TaskType = "knowledge_sync"         // 知识同步任务
+)
--- a/common/gse/utils.go
+++ b/common/gse/utils.go
@@ -0,0 +1,114 @@
+package gse
+
+import (
+	"context"
+	"sort"
+
+	"github.com/go-ego/gse"
+	"github.com/go-ego/gse/hmm/extracker"
+	"github.com/go-ego/gse/hmm/segment"
+	"github.com/gogf/gf/v2/os/glog"
+)
+
+var GseTool *gseTool
+
+// 初始化函数：程序启动时执行一次
+func init() {
+	var err error
+	GseTool, err = newGseTool()
+	if err != nil {
+		glog.Error(context.Background(), err)
+	}
+}
+
+// gseTool 关键词提取工具（gse v1.0.2 标准）
+type gseTool struct {
+	seg   gse.Segmenter
+	tfidf *extracker.TagExtracter
+	tr    *extracker.TextRanker
+}
+
+// newGseTool 初始化工具（内置词典 + 停用词）
+func newGseTool() (tool *gseTool, err error) {
+	// 1. 初始化分词器
+	var seg gse.Segmenter
+	// 内置词典（无外部文件）
+	err = seg.LoadDictEmbed()
+	if err != nil {
+		return
+	}
+	// 内置停用词（v1.0.2 标准）
+	err = seg.LoadStopEmbed()
+	if err != nil {
+		return
+	}
+
+	// 2. 初始化 TF-IDF 提取器
+	tfidf := &extracker.TagExtracter{}
+	tfidf.WithGse(seg)
+	err = tfidf.LoadIdf()
+	if err != nil {
+		return
+	}
+
+	// 3. 初始化 TextRank 提取器
+	tr := &extracker.TextRanker{}
+	tr.WithGse(seg)
+
+	tool = &gseTool{
+		seg:   seg,
+		tfidf: tfidf,
+		tr:    tr,
+	}
+	return
+}
+
+// Cut 分词（关键词提取唯一正确模式：精确模式 + HMM）
+func (k *gseTool) Cut(text string) []string {
+	return k.seg.Cut(text, true)
+}
+
+// Keyword 最终输出：关键词 + 权重
+type Keyword struct {
+	Word  string  `json:"word"`
+	Score float64 `json:"score"`
+}
+
+func (k *gseTool) Extract(text string, topN int) []Keyword {
+	// 1. 提取 TF-IDF
+	tfTags := k.extractTFIDF(text, topN)
+
+	// 2. 提取 TextRank
+	trTags := k.extractTextRank(text, topN)
+
+	// 3. 合并成最终关键词（业务最常用）
+	scoreMap := make(map[string]float64)
+	for _, tag := range tfTags {
+		scoreMap[tag.Text] = tag.Weight
+	}
+	for _, tag := range trTags {
+		scoreMap[tag.Text] = tag.Weight
+	}
+
+	// 转成切片并排序（高分在前）
+	res := make([]Keyword, 0, len(scoreMap))
+	for word, score := range scoreMap {
+		res = append(res, Keyword{Word: word, Score: score})
+	}
+
+	sort.Slice(res, func(i, j int) bool {
+		return res[i].Score > res[j].Score
+	})
+
+	return res
+}
+
+// ExtractTFIDF TF-IDF 关键词（带权重）90% 业务：文章标签、搜索、关键词
+func (k *gseTool) extractTFIDF(text string, topN int) segment.Segments {
+	return k.tfidf.ExtractTags(text, topN)
+}
+
+// ExtractTextRank TextRank 关键词（带权重）长文本、摘要、语义理解
+func (k *gseTool) extractTextRank(text string, topN int) segment.Segments {
+	return k.tr.TextRank(text, topN)
+}
--- a/config.yml
+++ b/config.yml
@@ -12,8 +12,9 @@ database:
      user: "postgres"
      pass: "Bjang09@686^*^"
      name: "rag"
+      prefix: "rag_knowledge_"        # (可选)表名前缀
      role: "master"              # (可选)数据库主从角色(master/slave)，默认为master。如果不使用应用主从机制请不配置或留空即可。
-      debug: false                # (可选)开启调试模式
+      debug: true                # (可选)开启调试模式
      dryRun: false               # (可选)ORM空跑(只读不写)
      charset: "utf8"             # (可选)数据库编码(如: utf8mb4/utf8/gbk/gb2312)，一般设置为utf8mb4。默认为utf8。
      timezone: "Asia/Shanghai"           # (可选)时区配置，例如:Local
@@ -30,7 +31,8 @@ database:
      port: "15432"
      user: "postgres"
      pass: "Bjang09@686^*^"
-      name: "rag"
+      name: "tenant-1"
+      prefix: "rag_knowledge_"        # (可选)表名前缀
      role: "slave"               # (可选)数据库主从角色(master/slave)，默认为master。如果不使用应用主从机制请不配置或留空即可。
      debug: false                # (可选)开启调试模式
      dryRun: false               # (可选)ORM空跑(只读不写)
@@ -44,15 +46,36 @@ database:
      updatedAt: "updated_at"     # (可选)自动更新时间字段名称
      deletedAt: "deleted_at"     # (可选)软删除时间字段名称
      timeMaintainDisabled: false # (可选)是否完全关闭时间更新特性，为true时CreatedAt/UpdatedAt/DeletedAt都将失效
-  tenant-1:
+  rag_knowledge:
    - type: "pgsql"
      host: "localhost"
      port: "5432"
      user: "postgres"
      pass: "123456"
-      name: "tenant"
+      name: "tenant-1"
+      prefix: "rag_knowledge_"        # (可选)表名前缀
+      role: "master"
+      debug: true                # (可选)开启调试模式
+      dryRun: false               # (可选)ORM空跑(只读不写)
+      charset: "utf8"             # (可选)数据库编码(如: utf8mb4/utf8/gbk/gb2312)，一般设置为utf8mb4。默认为utf8。
+      timezone: "Asia/Shanghai"           # (可选)时区配置，例如:Local
+      maxIdle: 5                  # (可选)连接池最大闲置的连接数(默认10)
+      maxOpen: 20                 # (可选)连接池最大打开的连接数(默认无限制)
+      maxLifetime: "30s"          # (可选)连接对象可重复使用的时间长度(默认30秒)
+      maxIdleConnTime: "30s"      # (可选，v2.10新增)连接池中空闲连接的最大生存时间(默认30秒)。可以通过配置文件或SetConnMaxIdleTime方法设置，避免长时间空闲连接占用资源。
+      createdAt: "created_at"     # (可选)自动创建时间字段名称
+      updatedAt: "updated_at"     # (可选)自动更新时间字段名称
+      deletedAt: "deleted_at"     # (可选)软删除时间字段名称
+      timeMaintainDisabled: false # (可选)是否完全关闭时间更新特性，为true时CreatedAt/UpdatedAt/DeletedAt都将失效
+  rag_vector:
+    - type: "pgsql"
+      host: "localhost"
+      port: "5432"
+      user: "postgres"
+      pass: "123456"
+      name: "tenant-1"
+      prefix: "rag_vector_"        # (可选)表名前缀
      role: "master"
-      prefix: "rag_"        # (可选)表名前缀
      debug: true                # (可选)开启调试模式
      dryRun: false               # (可选)ORM空跑(只读不写)
      charset: "utf8"             # (可选)数据库编码(如: utf8mb4/utf8/gbk/gb2312)，一般设置为utf8mb4。默认为utf8。
--- a/consts/public/table_name.go
+++ b/consts/public/table_name.go
@@ -1,5 +1,10 @@
 package public

+const (
+	DbNameKnowledge = "rag_knowledge"
+	DbNameVector    = "rag_vector"
+)
+
 // sql 数据库表名
 const (
 	TableNameDocument      = "document"
--- a/controller/dataset_index.go
+++ b/controller/dataset_index.go
@@ -1,5 +0,0 @@
-package controller
-
-type datasetIndex struct{}
-
-var DatasetIndex = new(datasetIndex)
--- a/dao/dataset.go
+++ b/dao/dataset.go
@@ -22,7 +22,7 @@ func (d *datasetDao) Insert(ctx context.Context, req *dto.CreateDatasetReq) (id
 	if err = gconv.Struct(req, &res); err != nil {
 		return
 	}
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDataset).Data(&res).Insert()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDataset).Data(&res).Insert()
 	if err != nil {
 		return
 	}
@@ -31,7 +31,7 @@ func (d *datasetDao) Insert(ctx context.Context, req *dto.CreateDatasetReq) (id

 // Update 更新数据集
 func (d *datasetDao) Update(ctx context.Context, req *dto.UpdateDatasetReq) (rows int64, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDataset).OmitEmpty()
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDataset).OmitEmpty()
 	if !g.IsEmpty(req.DocumentCount) {
 		model.Data(entity.DatasetCol.DocumentCount, &gdb.Counter{
 			Field: entity.DatasetCol.DocumentCount,
@@ -53,7 +53,7 @@ func (d *datasetDao) Update(ctx context.Context, req *dto.UpdateDatasetReq) (row

 // Delete 删除数据集
 func (d *datasetDao) Delete(ctx context.Context, req *dto.DeleteDatasetReq) (rows int64, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDataset).Where(entity.DatasetCol.Id, req.Id).Delete()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDataset).Where(entity.DatasetCol.Id, req.Id).Delete()
 	if err != nil {
 		return
 	}
@@ -61,7 +61,7 @@ func (d *datasetDao) Delete(ctx context.Context, req *dto.DeleteDatasetReq) (row
 }

 func (d *datasetDao) GetByID(ctx context.Context, req *dto.GetDatasetReq, fields ...string) (res *entity.Dataset, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDataset).Where(entity.DatasetCol.Id, req.Id).Fields(fields).One()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDataset).Where(entity.DatasetCol.Id, req.Id).Fields(fields).One()
 	if err != nil {
 		return
 	}
@@ -71,7 +71,7 @@ func (d *datasetDao) GetByID(ctx context.Context, req *dto.GetDatasetReq, fields

 // List 获取数据集列表
 func (d *datasetDao) List(ctx context.Context, req *dto.ListDatasetReq, fields ...string) (res []*entity.Dataset, total int, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDataset).Fields(fields).OmitEmpty()
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDataset).Fields(fields).OmitEmpty()
 	if !g.IsEmpty(req.Keyword) {
 		model.WhereLike(entity.DatasetCol.Name, "%"+req.Keyword+"%")
 	}
--- a/dao/dataset_index.go
+++ b/dao/dataset_index.go
@@ -16,7 +16,7 @@ type datasetIndexDao struct{}

 // Insert 插入数据集索引
 func (d *datasetIndexDao) Insert(ctx context.Context, index *entity.DatasetIndex) (id int64, err error) {
-	_, err = gfdb.DB(ctx).Model(ctx, public.TableNameDatasetIndex).Data(index).Insert()
+	_, err = gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDatasetIndex).Data(index).Insert()
 	if err != nil {
 		return
 	}
@@ -25,7 +25,7 @@ func (d *datasetIndexDao) Insert(ctx context.Context, index *entity.DatasetIndex

 // GetByDatasetId 根据数据集ID获取索引
 func (d *datasetIndexDao) GetByDatasetId(ctx context.Context, datasetId int64) (result *entity.DatasetIndex, err error) {
-	err = gfdb.DB(ctx).Model(ctx, public.TableNameDatasetIndex).Where(entity.DatasetIndexCol.DatasetId, datasetId).Scan(&result)
+	err = gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDatasetIndex).Where(entity.DatasetIndexCol.DatasetId, datasetId).Scan(&result)
 	if err != nil {
 		if err == sql.ErrNoRows {
 			return nil, nil
@@ -37,23 +37,20 @@ func (d *datasetIndexDao) GetByDatasetId(ctx context.Context, datasetId int64) (

 // IncVectorCount 增加或减少向量数量
 func (d *datasetIndexDao) IncVectorCount(ctx context.Context, id int64, delta int64) (err error) {
-	_, err = gfdb.DB(ctx).Model(ctx, public.TableNameDatasetIndex).
+	_, err = gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDatasetIndex).
 		Where(entity.DatasetIndexCol.Id, id).
 		Increment(entity.DatasetIndexCol.VectorCount, delta)
 	return
 }

 func (d *datasetIndexDao) InsertIndex(ctx context.Context, indexName string) (err error) {
-	prefix, err := gfdb.GetTablePrefix(ctx)
-	if err != nil {
-		return
-	}
+	db := gfdb.DB(ctx, public.DbNameVector)
 	sqlStr := fmt.Sprintf(`
 		CREATE INDEX IF NOT EXISTS %s
 		ON %s
 		USING ivfflat (vector vector_cosine_ops)
 		WHERE vector IS NOT NULL;
-	`, indexName, prefix+public.TableNameDocumentChunk)
-	_, err = gfdb.DB(ctx).Exec(ctx, sqlStr)
+	`, indexName, gfdb.TablePrefix+public.TableNameDocumentChunk)
+	_, err = db.Exec(ctx, sqlStr)
 	return
 }
--- a/dao/document.go
+++ b/dao/document.go
@@ -22,7 +22,7 @@ func (d *documentDao) Insert(ctx context.Context, req *dto.CreateDocumentReq) (i
 	if err = gconv.Struct(req, &res); err != nil {
 		return
 	}
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDocument).Data(&res).Insert()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDocument).Data(&res).Insert()
 	if err != nil {
 		return
 	}
@@ -31,7 +31,7 @@ func (d *documentDao) Insert(ctx context.Context, req *dto.CreateDocumentReq) (i

 // Update 更新文件
 func (d *documentDao) Update(ctx context.Context, req *dto.UpdateDocumentReq) (rows int64, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDocument).OmitEmpty()
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDocument).OmitEmpty()
 	if !g.IsEmpty(req.ChunkCount) {
 		model.Data(entity.DocumentCol.ChunkCount, &gdb.Counter{
 			Field: entity.DocumentCol.ChunkCount,
@@ -48,7 +48,7 @@ func (d *documentDao) Update(ctx context.Context, req *dto.UpdateDocumentReq) (r

 // Delete 删除文件
 func (d *documentDao) Delete(ctx context.Context, req *dto.DeleteDocumentReq) (rows int64, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDocument).Where(entity.DocumentCol.Id, req.Id).Delete()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDocument).Where(entity.DocumentCol.Id, req.Id).Delete()
 	if err != nil {
 		return
 	}
@@ -57,7 +57,7 @@ func (d *documentDao) Delete(ctx context.Context, req *dto.DeleteDocumentReq) (r

 // GetByID 根据ID获取文件
 func (d *documentDao) GetByID(ctx context.Context, req *dto.GetDocumentReq, fields ...string) (res *entity.Document, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDocument).Where(entity.DocumentCol.Id, req.Id).Fields(fields).One()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDocument).Where(entity.DocumentCol.Id, req.Id).Fields(fields).One()
 	if err != nil {
 		return
 	}
@@ -67,7 +67,7 @@ func (d *documentDao) GetByID(ctx context.Context, req *dto.GetDocumentReq, fiel

 // List 获取文件列表
 func (d *documentDao) List(ctx context.Context, req *dto.ListDocumentReq, fields ...string) (res []*entity.Document, total int, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDocument).OmitEmpty()
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameDocument).OmitEmpty()
 	if !g.IsEmpty(req.Keyword) {
 		model.WhereLike(entity.DocumentCol.Title, "%"+req.Keyword+"%")
 	}
--- a/dao/document_chunk.go
+++ b/dao/document_chunk.go
@@ -20,7 +20,7 @@ func (d *documentChunkDao) BatchInsert(ctx context.Context, req []*dto.VectorDoc
 	if err = gconv.Structs(req, &res); err != nil {
 		return
 	}
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameDocumentChunk).Data(&res).Insert()
+	r, err := gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDocumentChunk).Data(&res).Insert()
 	if err != nil {
 		return
 	}
@@ -29,7 +29,7 @@ func (d *documentChunkDao) BatchInsert(ctx context.Context, req []*dto.VectorDoc

 // Update 更新文件块
 func (d *documentChunkDao) Update(ctx context.Context, req *dto.UpdateDocumentChunkReq) (rows int64, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDocumentChunk)
+	model := gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDocumentChunk)
 	r, err := model.Data(&req).Where(entity.DocumentChunkCol.Id, req.Id).Update()
 	if err != nil {
 		return
@@ -39,7 +39,7 @@ func (d *documentChunkDao) Update(ctx context.Context, req *dto.UpdateDocumentCh

 // List 文件块列表
 func (d *documentChunkDao) List(ctx context.Context, req *dto.ListDocumentChunkReq, fields ...string) (res []*entity.DocumentChunk, total int, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameDocumentChunk).Fields(fields).OmitEmpty().
+	model := gfdb.DB(ctx, public.DbNameVector).Model(ctx, public.TableNameDocumentChunk).Fields(fields).OmitEmpty().
 		Where(entity.DocumentChunkCol.DatasetId, req.DatasetId).
 		Where(entity.DocumentChunkCol.DocumentId, req.DocumentId).
 		Where(entity.DocumentChunkCol.Status, req.Status).
@@ -55,50 +55,3 @@ func (d *documentChunkDao) List(ctx context.Context, req *dto.ListDocumentChunkR
 	err = r.Structs(&res)
 	return
 }
-
-//// Insert 插入向量文档
-//func (d *vectorDocumentDao) Insert(ctx context.Context, docs []*entity.DocumentChunk) (ids []interface{}, err error) {
-//	if len(docs) == 0 {
-//		return
-//	}
-//	interfaces := make([]interface{}, len(docs))
-//	for i := range docs {
-//		interfaces[i] = docs[i]
-//	}
-//	return mongoDB.Insert(ctx, interfaces, CollectionVectorDoc)
-//}
-//
-//// DeleteByIDs 根据ID删除向量文档
-//func (d *vectorDocumentDao) DeleteByIDs(ctx context.Context, ids []string) (err error) {
-//	if len(ids) == 0 {
-//		return
-//	}
-//	objectIDs := make([]bson.ObjectID, len(ids))
-//	for i, id := range ids {
-//		objectIDs[i], err = bson.ObjectIDFromHex(id)
-//		if err != nil {
-//			return err
-//		}
-//	}
-//	filter := bson.M{"_id": bson.M{"$in": objectIDs}}
-//	_, err = mongoDB.Delete(ctx, filter, CollectionVectorDoc)
-//	return
-//}
-//
-//// GetByIndexID 根据索引ID获取向量文档
-//func (d *vectorDocumentDao) GetByIndexID(ctx context.Context, indexID string, limit int) (result []*entity.DocumentChunk, err error) {
-//	filter := bson.M{"indexId": indexID}
-//	page := &beans.Page{PageNum: 1, PageSize: int64(limit)}
-//	_, err = mongoDB.Find(ctx, filter, &result, CollectionVectorDoc, page, nil)
-//	return
-//}
-//
-//// GetByVectorIDs 根据向量ID获取向量文档
-//func (d *vectorDocumentDao) GetByVectorIDs(ctx context.Context, vectorIDs []string) (result []*entity.DocumentChunk, err error) {
-//	if len(vectorIDs) == 0 {
-//		return
-//	}
-//	filter := bson.M{"vectorId": bson.M{"$in": vectorIDs}}
-//	_, err = mongoDB.Find(ctx, filter, &result, CollectionVectorDoc, &beans.Page{PageSize: -1}, nil)
-//	return
-//}
--- a/dao/keyword.go
+++ b/dao/keyword.go
@@ -20,7 +20,7 @@ func (d *keywordDao) Insert(ctx context.Context, req *dto.CreateKeywordReq) (id
 	if err = gconv.Struct(req, &res); err != nil {
 		return
 	}
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).Data(&res).Insert()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).Data(&res).Insert()
 	if err != nil {
 		return
 	}
@@ -32,7 +32,7 @@ func (d *keywordDao) BatchSaveOrUpdate(ctx context.Context, req []*dto.CreateKey
 	if err = gconv.Structs(req, &res); err != nil {
 		return
 	}
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).Data(&res).OnConflict(
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).Data(&res).OnConflict(
 		entity.KeywordCol.TenantId,
 		entity.KeywordCol.DatasetId,
 		entity.KeywordCol.DocumentId,
@@ -44,7 +44,7 @@ func (d *keywordDao) BatchSaveOrUpdate(ctx context.Context, req []*dto.CreateKey
 }

 func (d *keywordDao) Update(ctx context.Context, req *dto.UpdateKeywordReq) (rows int64, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword)
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword)
 	r, err := model.Data(&req).Where(entity.KeywordCol.Id, req.Id).Update()
 	if err != nil {
 		return
@@ -53,7 +53,7 @@ func (d *keywordDao) Update(ctx context.Context, req *dto.UpdateKeywordReq) (row
 }

 func (d *keywordDao) Delete(ctx context.Context, req *dto.DeleteKeywordReq) (rows int64, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).Where(entity.KeywordCol.Id, req.Id).Delete()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).Where(entity.KeywordCol.Id, req.Id).Delete()
 	if err != nil {
 		return
 	}
@@ -61,7 +61,7 @@ func (d *keywordDao) Delete(ctx context.Context, req *dto.DeleteKeywordReq) (row
 }

 func (d *keywordDao) Count(ctx context.Context, req *dto.ListKeywordReq) (count int, err error) {
-	count, err = gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).OmitEmpty().
+	count, err = gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).OmitEmpty().
 		Where(entity.KeywordCol.DatasetId, req.DatasetId).
 		Where(entity.KeywordCol.DocumentId, req.DocumentId).
 		Where(entity.KeywordCol.Word, req.Word).Count()
@@ -69,7 +69,7 @@ func (d *keywordDao) Count(ctx context.Context, req *dto.ListKeywordReq) (count
 }

 func (d *keywordDao) GetByID(ctx context.Context, req *dto.GetKeywordReq, fields ...string) (res *entity.Document, err error) {
-	r, err := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).Where(entity.KeywordCol.Id, req.Id).Fields(fields).One()
+	r, err := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).Where(entity.KeywordCol.Id, req.Id).Fields(fields).One()
 	if err != nil {
 		return
 	}
@@ -78,7 +78,7 @@ func (d *keywordDao) GetByID(ctx context.Context, req *dto.GetKeywordReq, fields
 }

 func (d *keywordDao) List(ctx context.Context, req *dto.ListKeywordReq, fields ...string) (res []*entity.Keyword, total int, err error) {
-	model := gfdb.DB(ctx).Model(ctx, public.TableNameKeyword).Fields(fields).OmitEmpty()
+	model := gfdb.DB(ctx, public.DbNameKnowledge).Model(ctx, public.TableNameKeyword).Fields(fields).OmitEmpty()
 	if !g.IsEmpty(req.Keyword) {
 		model.WhereLike(entity.KeywordCol.Word, "%"+req.Keyword+"%")
 	}
--- a/go.mod
+++ b/go.mod
@@ -3,15 +3,30 @@ module rag
 go 1.26.0

 require (
-	gitea.com/red-future/common v0.0.6
+	gitea.com/red-future/common v0.0.11
 	github.com/bjang03/gmq v0.0.0-00010101000000-000000000000
 	github.com/cloudwego/eino v0.8.6
+	github.com/cloudwego/eino-ext/components/document/loader/url v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/document/parser/docx v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/document/parser/pdf v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/document/parser/xlsx v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/document/transformer/splitter/recursive v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/document/transformer/splitter/semantic v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/embedding/ark v0.1.1
+	github.com/cloudwego/eino-ext/components/embedding/dashscope v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/embedding/openai v0.0.0-20260323112355-f061db7e8419
+	github.com/cloudwego/eino-ext/components/indexer/es8 v0.0.0-20260331071634-4f359694d2d9
+	github.com/cloudwego/eino-ext/components/model/ark v0.1.65
+	github.com/cloudwego/eino-ext/components/retriever/es8 v0.0.0-20260331071634-4f359694d2d9
+	github.com/elastic/go-elasticsearch/v8 v8.16.0
+	github.com/go-ego/gse v1.0.2
 	github.com/gogf/gf/contrib/drivers/pgsql/v2 v2.10.0
 	github.com/gogf/gf/v2 v2.10.0
+	github.com/golang/glog v1.2.5
 	github.com/pgvector/pgvector-go v0.3.0
 )

-replace gitea.com/red-future/common v0.0.6 => ../common
+//replace gitea.com/red-future/common v0.0.11 => ../common

 replace github.com/bjang03/gmq => ../gmq

@@ -35,18 +50,7 @@ require (
 	github.com/clipperhouse/displaywidth v0.11.0 // indirect
 	github.com/clipperhouse/uax29/v2 v2.7.0 // indirect
 	github.com/cloudwego/base64x v0.1.6 // indirect
-	github.com/cloudwego/eino-ext/components/document/loader/url v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/document/parser/docx v0.0.0-20260323112355-f061db7e8419 // indirect
 	github.com/cloudwego/eino-ext/components/document/parser/html v0.0.0-20241224063832-9fbcc0e56c28 // indirect
-	github.com/cloudwego/eino-ext/components/document/parser/pdf v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/document/parser/xlsx v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/document/transformer/splitter/recursive v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/document/transformer/splitter/semantic v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/embedding/ark v0.1.1 // indirect
-	github.com/cloudwego/eino-ext/components/embedding/dashscope v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/embedding/openai v0.0.0-20260323112355-f061db7e8419 // indirect
-	github.com/cloudwego/eino-ext/components/indexer/es8 v0.0.0-20260331071634-4f359694d2d9 // indirect
-	github.com/cloudwego/eino-ext/components/retriever/es8 v0.0.0-20260331071634-4f359694d2d9 // indirect
 	github.com/cloudwego/eino-ext/libs/acl/openai v0.1.14 // indirect
 	github.com/dgraph-io/badger/v4 v4.2.0 // indirect
 	github.com/dgraph-io/ristretto v0.1.1 // indirect
@@ -56,13 +60,11 @@ require (
 	github.com/eino-contrib/docx2md v0.0.1 // indirect
 	github.com/eino-contrib/jsonschema v1.0.3 // indirect
 	github.com/elastic/elastic-transport-go/v8 v8.10.0 // indirect
-	github.com/elastic/go-elasticsearch/v8 v8.16.0 // indirect
 	github.com/emirpasic/gods/v2 v2.0.0-alpha // indirect
 	github.com/evanphx/json-patch v0.5.2 // indirect
 	github.com/fatih/color v1.19.0 // indirect
 	github.com/fsnotify/fsnotify v1.9.0 // indirect
 	github.com/gabriel-vasile/mimetype v1.4.13 // indirect
-	github.com/go-ego/gse v1.0.2 // indirect
 	github.com/go-logr/logr v1.4.3 // indirect
 	github.com/go-logr/stdr v1.2.2 // indirect
 	github.com/go-playground/locales v0.14.1 // indirect
@@ -74,7 +76,6 @@ require (
 	github.com/gogf/gf/contrib/trace/otlphttp/v2 v2.9.5 // indirect
 	github.com/gogo/protobuf v1.3.2 // indirect
 	github.com/golang-jwt/jwt/v5 v5.3.1 // indirect
-	github.com/golang/glog v1.2.5 // indirect
 	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
 	github.com/golang/protobuf v1.5.4 // indirect
 	github.com/golang/snappy v1.0.0 // indirect
@@ -134,7 +135,7 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/vcaesar/cedar v0.30.0 // indirect
 	github.com/volcengine/volc-sdk-golang v1.0.199 // indirect
-	github.com/volcengine/volcengine-go-sdk v1.0.181 // indirect
+	github.com/volcengine/volcengine-go-sdk v1.2.9 // indirect
 	github.com/wk8/go-ordered-map/v2 v2.1.8 // indirect
 	github.com/xuri/efp v0.0.0-20240408161823-9ad904a10d6d // indirect
 	github.com/xuri/excelize/v2 v2.9.0 // indirect
--- a/go.sum
+++ b/go.sum
@@ -33,6 +33,8 @@ cloud.google.com/go/storage v1.10.0/go.mod h1:FLPqc6j+Ki4BU591ie1oL6qBQGu2Bl/tZ9
 dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
 entgo.io/ent v0.14.3 h1:wokAV/kIlH9TeklJWGGS7AYJdVckr0DloWjIcO9iIIQ=
 entgo.io/ent v0.14.3/go.mod h1:aDPE/OziPEu8+OWbzy4UlvWmD2/kbRuWfK2A40hcxJM=
+gitea.com/red-future/common v0.0.11 h1:AV7W3G0uZ8aPpHHSHd4ZHmLWe5+2STPKe/AYPoPCWVc=
+gitea.com/red-future/common v0.0.11/go.mod h1:B8syUI4XbLCDQSeRHURYxEwnWw8mEFgmqCxjC+lM+NU=
 github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
 github.com/BurntSushi/toml v1.1.0/go.mod h1:CxXYINrC8qIiEnFrOxCa7Jy5BFHlXnUU2pbicEuybxQ=
 github.com/BurntSushi/toml v1.6.0 h1:dRaEfpa2VI55EwlIW72hMRHdWouJeRF7TPYhI+AUQjk=
@@ -156,6 +158,8 @@ github.com/cloudwego/eino-ext/components/embedding/openai v0.0.0-20260323112355-
 github.com/cloudwego/eino-ext/components/embedding/openai v0.0.0-20260323112355-f061db7e8419/go.mod h1:SajSFFRIXJXIbxadAAlSUIS5KTY8R/jzJg9RNSOXCCI=
 github.com/cloudwego/eino-ext/components/indexer/es8 v0.0.0-20260331071634-4f359694d2d9 h1:vZ3dL8xwo2sy73aBVKs4AJiO5OCHRxMOJUwIYkp0CWs=
 github.com/cloudwego/eino-ext/components/indexer/es8 v0.0.0-20260331071634-4f359694d2d9/go.mod h1:+oI0sr0rA0OHCxaQJ0rzMYld3LAODHhPKzBx5JYCya0=
+github.com/cloudwego/eino-ext/components/model/ark v0.1.65 h1:52ukXVU9ntToTa36SwI8be81qskGkpUEZraIFOf0wqk=
+github.com/cloudwego/eino-ext/components/model/ark v0.1.65/go.mod h1:aabMR15RTXBSi9Eu13CWavzE+no5BQO4FJUEEdqImbg=
 github.com/cloudwego/eino-ext/components/retriever/es8 v0.0.0-20260331071634-4f359694d2d9 h1:Sl6giB1SJlA+ZlO0gzPH05IsUORtdYYPN6GiyH1B9MA=
 github.com/cloudwego/eino-ext/components/retriever/es8 v0.0.0-20260331071634-4f359694d2d9/go.mod h1:H4kNmiTe2irnvipVNIP4q8yqXf2fZ6v24krvQYBtYb8=
 github.com/cloudwego/eino-ext/libs/acl/openai v0.1.14 h1:yOZII6VYaL00CVZYba+HUixFygsW0Xz/1QjQ5htj1Ls=
@@ -733,8 +737,8 @@ github.com/vmihailenco/tagparser/v2 v2.0.0/go.mod h1:Wri+At7QHww0WTrCBeu4J6bNtoV
 github.com/volcengine/volc-sdk-golang v1.0.23/go.mod h1:AfG/PZRUkHJ9inETvbjNifTDgut25Wbkm2QoYBTbvyU=
 github.com/volcengine/volc-sdk-golang v1.0.199 h1:zv9QOqTl/IsLwtfC37GlJtcz6vMAHi+pjq8ILWjLYUc=
 github.com/volcengine/volc-sdk-golang v1.0.199/go.mod h1:stZX+EPgv1vF4nZwOlEe8iGcriUPRBKX8zA19gXycOQ=
-github.com/volcengine/volcengine-go-sdk v1.0.181 h1:/3PB4M1N4fjMqiSKTJwX43EZ5Nn1HUOtQrSCk+22+wI=
-github.com/volcengine/volcengine-go-sdk v1.0.181/go.mod h1:gfEDc1s7SYaGoY+WH2dRrS3qiuDJMkwqyfXWCa7+7oA=
+github.com/volcengine/volcengine-go-sdk v1.2.9 h1:du2gnImtyWXKkQFnJW/GXCs+UBibGGOXIbP1Ams2pB8=
+github.com/volcengine/volcengine-go-sdk v1.2.9/go.mod h1:oxoVo+A17kvkwPkIeIHPVLjSw7EQAm+l/Vau1YGHN+A=
 github.com/wk8/go-ordered-map/v2 v2.1.8 h1:5h/BUHu93oj4gIdvHHHGsScSTMijfx5PeYkE/fJgbpc=
 github.com/wk8/go-ordered-map/v2 v2.1.8/go.mod h1:5nJHM5DyteebpVlHnWMV0rPz6Zp7+xBAnxjb1X5vnTw=
 github.com/x-cray/logrus-prefixed-formatter v0.5.2 h1:00txxvfBM9muc0jiLIEAkAcIMJzfthRT6usrui8uGmg=
@@ -1191,6 +1195,7 @@ google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp0
 google.golang.org/protobuf v1.26.0/go.mod h1:9q0QmTI4eRPtz6boOQmLYwt+qCgq0jsYwAQnmE0givc=
 google.golang.org/protobuf v1.27.1/go.mod h1:9q0QmTI4eRPtz6boOQmLYwt+qCgq0jsYwAQnmE0givc=
 google.golang.org/protobuf v1.28.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
+google.golang.org/protobuf v1.31.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
 google.golang.org/protobuf v1.36.11 h1:fV6ZwhNocDyBLK0dj+fg8ektcVegBBuEolpbTQyBNVE=
 google.golang.org/protobuf v1.36.11/go.mod h1:HTf+CrKn2C3g5S8VImy6tdcUvCska2kB7j23XfzDpco=
 gopkg.in/alecthomas/kingpin.v2 v2.2.6/go.mod h1:FMv+mEhP44yOT+4EoQTLFTRgOQ1FBLkstjWtayDeSgw=
--- a/model/dto/dataset_index.go
+++ b/model/dto/dataset_index.go
@@ -1 +0,0 @@
-package dto
--- a/service/dataset_index.go
+++ b/service/dataset_index.go
@@ -1,5 +0,0 @@
-package service
-
-var DatasetIndex = new(datasetIndexService)
-
-type datasetIndexService struct{}
--- a/service/document.go
+++ b/service/document.go
@@ -2,7 +2,10 @@ package service

 import (
 	"context"
+	"errors"
 	"fmt"
+	"rag/common/eino"
+	"rag/common/gse"
 	"rag/consts/document"
 	"rag/consts/public"
 	"rag/dao"
@@ -16,8 +19,6 @@ import (
 	"gitea.com/red-future/common/db/gfdb"
 	"gitea.com/red-future/common/full-text-search/meilisearch"
 	"gitea.com/red-future/common/http"
-	"gitea.com/red-future/common/rag/eino"
-	"gitea.com/red-future/common/rag/gse"
 	"gitea.com/red-future/common/utils"
 	gmq "github.com/bjang03/gmq/core/gmq"
 	"github.com/bjang03/gmq/mq"
@@ -123,6 +124,9 @@ func (s *documentService) Process(ctx context.Context, req *dto.ProcessDocumentR
 	if err != nil {
 		return nil, err
 	}
+	if g.IsEmpty(doc) {
+		return nil, errors.New("document not found")
+	}

 	// 2. 使用eino框架进行文件切分（并发执行）
 	var vectorDocsCount, chunks int64
@@ -251,7 +255,7 @@ func (s *documentService) sqlSplitDocument(ctx context.Context, doc *entity.Docu
 		return
 	}
 	// 3. 组装向量文档
-	var vectorDocs = make([]dto.VectorDocumentChunkMsg, 0)
+	var docsChunk = make([]*schema.Document, 0)
 	for i, t := range docsSplit {
 		contentHash := gmd5.MustEncryptString(t.Content)
 		// 检查是否重复
@@ -263,27 +267,26 @@ func (s *documentService) sqlSplitDocument(ctx context.Context, doc *entity.Docu
 		if !success {
 			continue
 		}
-		vectorDocs = append(vectorDocs, dto.VectorDocumentChunkMsg{
-			TenantId:    doc.TenantId,
-			Creator:     doc.Creator,
-			DatasetId:   doc.DatasetId,
-			DocumentId:  doc.Id,
-			Content:     t.Content,
-			ContentHash: contentHash,
-			ChunkIndex:  gconv.Int64(i),
-		})
-
+		var metaData = make(map[string]any)
+		metaData[entity.DocumentCol.TenantId] = doc.TenantId
+		metaData[entity.DocumentCol.Creator] = doc.Creator
+		metaData[entity.DocumentCol.DatasetId] = doc.DatasetId
+		metaData[entity.DocumentChunkCol.DocumentId] = doc.Id
+		metaData[entity.DocumentChunkCol.ContentHash] = contentHash
+		metaData[entity.DocumentChunkCol.ChunkIndex] = gconv.Int64(i)
+		t.MetaData = metaData
+		docsChunk = append(docsChunk, t)
 	}
 	// 4. 发送消息到队列
-	if len(vectorDocs) > 0 {
+	if len(docsChunk) > 0 {
 		err = gmq.GetGmq("primary").GmqPublish(ctx, &mq.RedisPubMessage{
 			PubMessage: types.PubMessage{
 				Topic: public.KnowledgeDocumentChunkTopic,
-				Data:  vectorDocs,
+				Data:  docsChunk,
 			},
 		})
 	}
-	vectorDocsCount = gconv.Int64(len(vectorDocs))
+	vectorDocsCount = gconv.Int64(len(docsChunk))
 	return
 }

@@ -318,12 +321,12 @@ func (s *documentService) esSplitDocument(ctx context.Context, doc *entity.Docum
 		}
 		// 构建Meilisearch文档
 		meiliDocs = append(meiliDocs, map[string]interface{}{
-			"id":          contentHash,
-			"datasetId":   doc.DatasetId,
-			"documentId":  doc.Id,
-			"content":     t.Content,
-			"contentHash": contentHash,
-			"chunkIndex":  i,
+			entity.DocumentChunkCol.Id:          contentHash,
+			entity.DocumentChunkCol.DatasetId:   doc.DatasetId,
+			entity.DocumentChunkCol.DocumentId:  doc.Id,
+			entity.DocumentChunkCol.Content:     t.Content,
+			entity.DocumentChunkCol.ContentHash: contentHash,
+			entity.DocumentChunkCol.ChunkIndex:  i,
 		})
 	}
 	// 4. 写入到meilisearch数据库中
--- a/service/document_chunk.go
+++ b/service/document_chunk.go
@@ -2,23 +2,20 @@ package service

 import (
 	"context"
-	"database/sql"
-	"errors"
-	"fmt"
+	"rag/common/eino"
 	"rag/consts/document"
 	"rag/consts/public"
 	"rag/dao"
 	"rag/model/dto"
 	"rag/model/entity"

-	"gitea.com/red-future/common/beans"
-	"gitea.com/red-future/common/rag/eino"
 	gmq "github.com/bjang03/gmq/core/gmq"
 	"github.com/bjang03/gmq/mq"
 	"github.com/bjang03/gmq/types"
+	"github.com/cloudwego/eino/components/indexer"
+	"github.com/cloudwego/eino/schema"
 	"github.com/gogf/gf/v2/frame/g"
 	"github.com/gogf/gf/v2/util/gconv"
-	"github.com/pgvector/pgvector-go"
 )

 var DocumentChunk = new(documentChunkService)
@@ -49,115 +46,33 @@ func (s *documentChunkService) List(ctx context.Context, req *dto.ListDocumentCh
 }

 func (s *documentChunkService) DocsChunkMsg(ctx context.Context, msg any) (err error) {
-	var req = make([]*dto.VectorDocumentChunkMsg, 0)
+	var docs = make([]*schema.Document, 0)
 	msgMap := gconv.Map(msg)
-	if err = gconv.Structs(msgMap["data"], &req); err != nil {
+	if err = gconv.Structs(msgMap["data"], &docs); err != nil {
 		g.Log().Error(ctx, "DocsChunkMsg err:", err)
 		return
 	}
-	if len(req) == 0 {
+	if len(docs) == 0 {
 		g.Log().Error(ctx, "DocsChunkMsg err:", "msg is empty")
 		return
 	}

-	ctx = context.WithValue(ctx, "user", &beans.User{
-		TenantId: req[0].TenantId,
-		UserName: req[0].Creator,
+	idx := eino.NewPGVectorIndexer(&eino.PGVectorIndexerOptions{
+		BatchSize: 10,
 	})
-
-	// 调用eino接口获取向量
-	var vectorDocsStr = make([]string, 0, len(req))
-	for _, t := range req {
-		vectorDocsStr = append(vectorDocsStr, t.Content)
-	}
-	embeddings, err := eino.EmbedStrings(ctx, vectorDocsStr)
-	if err != nil {
-		g.Log().Error(ctx, "DocsChunkMsg err:", err)
-		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
+	rows, err := idx.Store(ctx, docs, indexer.WithEmbedding(eino.EmbedderDashscope))
+	if err != nil || rows == 0 {
+		g.Log().Error(ctx, "DocsChunkMsg rows: , err:", rows, err)
 		return
 	}
-
-	// 获取向量维度
-	dimension := 0
-	if len(embeddings) > 0 {
-		dimension = len(embeddings[0])
-	}
-
-	// 创建或更新DatasetIndex
-	err = s.createOrUpdateDatasetIndex(ctx, req[0].DatasetId, dimension, int64(len(req)))
-	if err != nil {
-		g.Log().Error(ctx, "CreateOrUpdateDatasetIndex err:", err)
-		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
-		return
-	}
-
-	// 更新向量文档
-	for i, embedding := range embeddings {
-		req[i].Vector = pgvector.NewVector(gconv.Float32s(embedding))
-		req[i].VectorStatus = document.VectorStatusCompleted.Code()
-		req[i].Status = document.StatusEnable.Code()
-	}
-	_, err = dao.DocumentChunk.BatchInsert(ctx, req)
-	if err != nil {
-		g.Log().Error(ctx, "DocsChunkMsg err:", err)
-		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
-		return
-	}
-
-	err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusCompleted.Code())
+	tenantId := gconv.Uint64(docs[0].MetaData[entity.DocumentChunkCol.TenantId])
+	creator := gconv.String(docs[0].MetaData[entity.DocumentChunkCol.Creator])
+	documentId := gconv.Int64(docs[0].MetaData[entity.DocumentChunkCol.DocumentId])
+	err = s.publishKnowledgeDocumentMsg(ctx, tenantId, creator, documentId, document.VectorStatusCompleted.Code())

 	return
 }

-// createOrUpdateDatasetIndex 创建或更新数据集索引
-func (s *documentChunkService) createOrUpdateDatasetIndex(ctx context.Context, datasetId int64, dimension int, vectorCount int64) (err error) {
-	// 查询数据集是否已有索引
-	existIndex, err := dao.DatasetIndex.GetByDatasetId(ctx, datasetId)
-	if err != nil && !errors.Is(err, sql.ErrNoRows) {
-		return err
-	}
-
-	// 已有索引 → 只更新数量
-	if existIndex != nil {
-		_ = dao.DatasetIndex.IncVectorCount(ctx, existIndex.Id, vectorCount)
-		return nil
-	}
-
-	// ====================== 创建新索引 ======================
-	indexName := fmt.Sprintf("idx_dataset_%d_vector", datasetId) // 真实PG索引名
-	// 1. 插入索引配置
-	index := &entity.DatasetIndex{
-		DatasetId:   datasetId,
-		Name:        indexName,
-		Dimension:   dimension,
-		FieldType:   "float",
-		MetricType:  "COSINE",
-		Status:      gconv.PtrInt8(1),
-		VectorCount: vectorCount,
-		Description: fmt.Sprintf("数据集%d向量索引", datasetId),
-	}
-	_, err = dao.DatasetIndex.Insert(ctx, index)
-	if err != nil {
-		return err
-	}
-
-	// 2. 真正创建 PGVector 索引（唯一真实索引！）
-	err = s.createRealPGVectorIndex(ctx, indexName)
-	return err
-}
-
-// createRealPGVectorIndex 真正在PostgreSQL创建向量索引（真实可用）
-func (s *documentChunkService) createRealPGVectorIndex(ctx context.Context, indexName string) error {
-	// 执行真实建索引语句
-	err := dao.DatasetIndex.InsertIndex(ctx, indexName)
-	if err != nil {
-		g.Log().Error(ctx, "创建向量索引失败：", err)
-		return err
-	}
-	g.Log().Info(ctx, "PGVector真实索引创建成功："+indexName)
-	return nil
-}
-
 // publishKnowledgeDocumentMsg 发布消息
 func (s *documentChunkService) publishKnowledgeDocumentMsg(ctx context.Context, tenantId uint64, creator string, documentId int64, vectorStatus document.VectorStatus) (err error) {
 	knowledgeDocumentMsg := dto.KnowledgeDocumentMsg{
--- a/update.sql
+++ b/update.sql
@@ -134,9 +134,9 @@ CREATE TABLE IF NOT EXISTS rag_knowledge_keyword (
 );

 -- 唯一索引：保证 租户 + 数据集 + 文档 + 关键词 全局唯一
-CREATE UNIQUE INDEX uk_rag_knowledge_keyword_tenant_dataset_doc_word
-    ON rag_knowledge_keyword(tenant_id, dataset_id, document_id, word)
-    WHERE deleted_at IS NULL;
+-- CREATE UNIQUE INDEX uk_rag_knowledge_keyword_tenant_dataset_doc_word
+--     ON rag_knowledge_keyword(tenant_id, dataset_id, document_id, word)
+--     WHERE deleted_at IS NULL;

 -- 索引（按业务高频查询）
 CREATE INDEX idx_keyword_tenant_id ON rag_knowledge_keyword(tenant_id);
@@ -160,3 +160,118 @@ COMMENT ON COLUMN rag_knowledge_keyword.word IS '关键词';
 COMMENT ON COLUMN rag_knowledge_keyword.weight IS '权重';

 --------------------pgsql创建rag_knowledge_keyword表语句---------------------------
+
+
+
+--------------------pgsql创建rag_vector_dataset_index表语句---------------------------
+-- 向量数据集索引表
+CREATE TABLE IF NOT EXISTS rag_vector_dataset_index (
+    -- 基础字段
+                                                        id BIGINT PRIMARY KEY, -- 主键ID（非自增）
+                                                        tenant_id BIGINT NOT NULL DEFAULT 0, -- 租户ID int8
+                                                        creator VARCHAR(64) NOT NULL,
+    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    updater VARCHAR(64) NOT NULL,
+    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    deleted_at timestamp(6),
+
+    -- 核心字段
+    dataset_id INT8 NOT NULL,
+    name VARCHAR(255) NOT NULL,
+    collection VARCHAR(255) NOT NULL,
+    dimension INT NOT NULL,
+    field_type VARCHAR(50) NOT NULL,
+    metric_type VARCHAR(50) NOT NULL,
+    status SMALLINT NOT NULL DEFAULT 1, -- 状态：1启用/0停用
+    vector_count INT8 NOT NULL DEFAULT 0,
+    description TEXT
+    );
+
+-- 唯一约束
+ALTER TABLE rag_vector_dataset_index ADD CONSTRAINT uk_dataset_id_name UNIQUE (dataset_id, name);
+
+-- 索引
+CREATE INDEX idx_dataset_index_tenant_id ON rag_vector_dataset_index(tenant_id);
+CREATE INDEX idx_dataset_index_dataset_id ON rag_vector_dataset_index(dataset_id);
+CREATE INDEX idx_dataset_index_status ON rag_vector_dataset_index(status);
+
+-- 注释
+COMMENT ON TABLE rag_vector_dataset_index IS '向量数据集索引表';
+COMMENT ON COLUMN rag_vector_dataset_index.id IS '主键ID（非自增）';
+COMMENT ON COLUMN rag_vector_dataset_index.tenant_id IS '租户ID';
+COMMENT ON COLUMN rag_vector_dataset_index.creator IS '创建人';
+COMMENT ON COLUMN rag_vector_dataset_index.created_at IS '创建时间';
+COMMENT ON COLUMN rag_vector_dataset_index.updater IS '更新人';
+COMMENT ON COLUMN rag_vector_dataset_index.updated_at IS '更新时间';
+COMMENT ON COLUMN rag_vector_dataset_index.deleted_at IS '删除时间（软删）';
+COMMENT ON COLUMN rag_vector_dataset_index.dataset_id IS '数据集ID';
+COMMENT ON COLUMN rag_vector_dataset_index.name IS '索引名称';
+COMMENT ON COLUMN rag_vector_dataset_index.collection IS '向量集合名称';
+COMMENT ON COLUMN rag_vector_dataset_index.dimension IS '向量维度';
+COMMENT ON COLUMN rag_vector_dataset_index.field_type IS '字段类型';
+COMMENT ON COLUMN rag_vector_dataset_index.metric_type IS '度量类型';
+COMMENT ON COLUMN rag_vector_dataset_index.status IS '状态';
+COMMENT ON COLUMN rag_vector_dataset_index.vector_count IS '向量数量';
+COMMENT ON COLUMN rag_vector_dataset_index.description IS '描述';
+
+--------------------pgsql创建rag_vector_dataset_index表语句---------------------------
+
+--------------------pgsql创建rag_vector_document_chunk表语句---------------------------
+
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- 文档分块向量表
+CREATE TABLE IF NOT EXISTS rag_vector_document_chunk (
+    -- 基础字段
+                                                         id BIGINT PRIMARY KEY, -- 主键ID（非自增）
+                                                         tenant_id BIGINT NOT NULL DEFAULT 0, -- 租户ID int8
+                                                         creator VARCHAR(64) NOT NULL,
+    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    updater VARCHAR(64) NOT NULL,
+    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    deleted_at timestamp(6),
+
+    -- 核心字段
+    status SMALLINT NOT NULL DEFAULT 1, -- 状态：1启用/0停用
+    vector_status SMALLINT NOT NULL DEFAULT 1, -- 向量化状态: 1pending, 2processing, 3completed, 4failed,5partCompleted
+    dataset_id INT8 NOT NULL,
+    document_id INT8 NOT NULL,
+    content TEXT NOT NULL,
+    content_hash VARCHAR(128) NOT NULL,
+    chunk_index INT8 NOT NULL,
+
+    -- 向量字段（pgvector）
+    vector vector(1024) NOT NULL,
+
+    -- 扩展信息
+    metadata JSONB
+    );
+
+-- 索引
+CREATE INDEX idx_chunk_tenant_id ON rag_vector_document_chunk(tenant_id);
+CREATE INDEX idx_chunk_dataset_id ON rag_vector_document_chunk(dataset_id);
+CREATE INDEX idx_chunk_document_id ON rag_vector_document_chunk(document_id);
+CREATE INDEX idx_chunk_content_hash ON rag_vector_document_chunk(content_hash);
+CREATE INDEX idx_chunk_status ON rag_vector_document_chunk(status);
+CREATE INDEX idx_chunk_vector_status ON rag_vector_document_chunk(vector_status);
+
+-- 注释
+COMMENT ON TABLE rag_vector_document_chunk IS '文档分块向量表';
+COMMENT ON COLUMN rag_vector_document_chunk.id IS '主键ID（非自增）';
+COMMENT ON COLUMN rag_vector_document_chunk.tenant_id IS '租户ID';
+COMMENT ON COLUMN rag_vector_document_chunk.creator IS '创建人';
+COMMENT ON COLUMN rag_vector_document_chunk.created_at IS '创建时间';
+COMMENT ON COLUMN rag_vector_document_chunk.updater IS '更新人';
+COMMENT ON COLUMN rag_vector_document_chunk.updated_at IS '更新时间';
+COMMENT ON COLUMN rag_vector_document_chunk.deleted_at IS '删除时间（软删）';
+COMMENT ON COLUMN rag_vector_document_chunk.status IS '状态';
+COMMENT ON COLUMN rag_vector_document_chunk.vector_status IS '向量生成状态';
+COMMENT ON COLUMN rag_vector_document_chunk.dataset_id IS '数据集ID';
+COMMENT ON COLUMN rag_vector_document_chunk.document_id IS '文档ID';
+COMMENT ON COLUMN rag_vector_document_chunk.content IS '分块内容';
+COMMENT ON COLUMN rag_vector_document_chunk.content_hash IS '内容哈希';
+COMMENT ON COLUMN rag_vector_document_chunk.chunk_index IS '分块序号';
+COMMENT ON COLUMN rag_vector_document_chunk.vector IS '向量数据';
+COMMENT ON COLUMN rag_vector_document_chunk.metadata IS '扩展元数据';
+
+--------------------pgsql创建rag_vector_document_chunk表语句---------------------------
Author	SHA1	Message	Date
张斌	e5a27c00ed	测试服务器IP配置变更	2026-04-08 22:14:22 +08:00
qhd	b6896f3fb4	feat: test	2026-04-03 18:26:20 +08:00
qhd	026beea4d9	feat: 支持多数据库配置与PGVector检索	2026-04-03 17:59:05 +08:00
admin	86c2b7d66e	删除 .idea/vcs.xml	2026-04-03 03:17:32 +00:00
admin	722fbe0cc3	删除 .idea/rag.iml	2026-04-03 03:17:29 +00:00
admin	6d68b468a6	删除 .idea/modules.xml	2026-04-03 03:17:26 +00:00
admin	b33d50944a	删除 .idea/.gitignore	2026-04-03 03:17:22 +00:00
张斌	6f2df61bc5	1	2026-04-03 11:17:08 +08:00
qhd	b00d544fb7	feat: rag初始版	2026-04-03 11:14:44 +08:00