支持相似图像查询

api/graphql.go

@@ -181,7 +181,8 @@ func NewSchema(config Config) (graphql.Schema, error) {
 				},
 			}),
 			Args: graphql.FieldConfigArgument{
-				"id":            &graphql.ArgumentConfig{Type: graphql.Int, Description: "筛选图像中指定ID的"},
+				"similar":       &graphql.ArgumentConfig{Type: graphql.Int, Description: "获取与指定ID图像相似的图像"},
+				"id":            &graphql.ArgumentConfig{Type: graphql.Int, Description: "获取指定ID的图像"},
 				"width":         &graphql.ArgumentConfig{Type: graphql.Int, Description: "筛选图像中指定宽度的"},
 				"height":        &graphql.ArgumentConfig{Type: graphql.Int, Description: "筛选图像中指定高度的"},
 				"content":       &graphql.ArgumentConfig{Type: graphql.String, Description: "筛选图像中含有指定内容的"},
@@ -205,15 +206,16 @@ func NewSchema(config Config) (graphql.Schema, error) {
 			Resolve: func(p graphql.ResolveParams) (interface{}, error) {
 				// 定义参数结构体
 				var args struct {
-					First  int
+					First   int
-					Last   int
+					Last    int
-					After  string
+					After   string
-					Before string
+					Before  string
-					Text   string
+					Text    string
+					Similar int
 				}
 				mapstructure.Decode(p.Args, &args)
 
-				// 返回字段
+				// 处理要求返回的字段
 				var fields []string
 				requestedFields := p.Info.FieldASTs[0].SelectionSet.Selections
 				for _, field := range requestedFields {
@@ -271,8 +273,20 @@ func NewSchema(config Config) (graphql.Schema, error) {
 					}
 				}
 
-				// 特殊处理 text 参数
 				var id_list []string
+				// 特殊处理 similar 参数
+				if args.Similar != 0 {
+					fmt.Println("similar:", args.Similar)
+					id_list := models.GetSimilarImagesIdList(args.Similar, 200)
+					fmt.Println("ids:", id_list)
+					ids_str := strings.Trim(strings.Join(strings.Fields(fmt.Sprint(id_list)), ","), "[]")
+					if ids_str == "" {
+						return map[string]interface{}{"list": []Image{}, "total": 0}, nil
+					}
+					where = append(where, fmt.Sprintf("id IN (%s) LIMIT %d", ids_str, len(id_list)))
+				}
+
+				// 特殊处理 text 参数
 				if args.Text != "" {
 					resp, err := models.ZincSearch(map[string]interface{}{
 						"query": map[string]interface{}{

models/milvus.go

@@ -2,11 +2,17 @@ package models
 
 import (
 	"context"
+	"encoding/json"
 	"fmt"
 	"log"
+	"net/http"
 	"os"
 
 	"github.com/milvus-io/milvus-sdk-go/v2/client"
+	"github.com/milvus-io/milvus-sdk-go/v2/entity"
+	"github.com/spf13/viper"
+
+	lru "github.com/hashicorp/golang-lru/v2"
 )
 
 type MilvusConnection struct {
@@ -32,3 +38,101 @@ func (m *MilvusConnection) Init() (err error) {
 	log.Println("Milvus connection success")
 	return
 }
+
+var milvusConnection MilvusConnection
+var lruCache, _ = lru.New[int, []int64](100000)
+
+// 获取图像向量(从指定API)
+func GetNetWorkEmbedding(id int) (embedding []float32) {
+	host := viper.GetString("embedding.host")
+	port := viper.GetInt("embedding.port")
+	httpClient := &http.Client{}
+	request, err := http.NewRequest("PUT", fmt.Sprintf("http://%s:%d/api/default/%d", host, port, id), nil)
+	if err != nil {
+		log.Println("请求失败1:", err)
+		return
+	}
+	response, err := httpClient.Do(request)
+	if err != nil {
+		log.Println("请求失败2:", err)
+		return
+	}
+	defer response.Body.Close()
+
+	var result struct {
+		Code    int       `json:"code"`
+		Message string    `json:"message"`
+		Feature []float32 `json:"feature"`
+	}
+	err = json.NewDecoder(response.Body).Decode(&result)
+	if err != nil {
+		log.Println("解析失败:", err)
+		return
+	}
+	if result.Code != 0 {
+		log.Println("请求失败3:", result.Message)
+		return
+	}
+	return result.Feature
+}
+
+// 获取相似图像ID列
+func GetSimilarImagesIdList(id int, topK int) (ids []int64) {
+	// 从缓存中获取
+	if cache, ok := lruCache.Get(id); ok {
+		return cache
+	}
+
+	// 从Milvus中获取向量
+	var collection_name = "default"
+	var embedding []float32
+	var ctx = context.Background()
+
+	// 先检查 milvusConnection 是否已经初始化
+	if milvusConnection.Client == nil {
+		err := milvusConnection.Init()
+		if err != nil {
+			log.Println("Milvus 初始化失败:", err)
+			return
+		}
+	}
+
+	result, err := milvusConnection.Client.Query(ctx, collection_name, nil, fmt.Sprintf("id in [%d]", id), []string{"embedding"})
+	if err != nil {
+		log.Println("查詢向量失敗:", err)
+		embedding = GetNetWorkEmbedding(id)
+	} else {
+		for _, item := range result {
+			if item.Name() == "embedding" {
+				embedding = item.FieldData().GetVectors().GetFloatVector().Data
+				continue
+			}
+		}
+	}
+
+	// 处理向量不存在的情况
+	if len(embedding) == 0 {
+		log.Println("向量不存在, 也未能重新生成")
+		return ids
+	}
+
+	// 用向量查询相似图片
+	topk := 200
+	sp, _ := entity.NewIndexIvfFlatSearchParam(64)
+	vectors := []entity.Vector{entity.FloatVector(embedding)}
+	resultx, err := milvusConnection.Client.Search(ctx, collection_name, nil, "", []string{"id", "article_id"}, vectors, "embedding", entity.L2, topk, sp)
+	if err != nil {
+		log.Println("搜索相似失敗:", err)
+		return ids
+	}
+
+	// 输出结果
+	for _, item := range resultx {
+		ids = item.IDs.FieldData().GetScalars().GetLongData().GetData()
+	}
+
+	// 将结果缓存到 LRU 中
+	lruCache.Add(id, ids)
+
+	return ids
+}