稠密向量与稀疏向量高阶混合检索

var (
    ctx                 = context.Background()
    database            = "go-sdk-test-db"
    collectionName      = "go-sdk-test-coll"
)
bm25, err := encoder.NewBM25Encoder(&encoder.BM25EncoderParams{Bm25Language: "zh"})
if err != nil {
    log.Fatalf(err.Error())
}
sparseVec, err := bm25.EncodeQuery("腾讯云向量数据库")
if err != nil {
    log.Fatalf(err.Error())
}
annSearch := &tcvectordb.AnnParam{
    FieldName: "vector",
    Data:      []float32{0.2123, 0.21, 0.213},
}
keywordSearch := &tcvectordb.MatchOption{
    FieldName: "sparse_vector",
    Data:      sparseVecs,
}

searchRes, err := client.HybridSearch(ctx, database, collectionName, tcvectordb.HybridSearchDocumentParams{
    AnnParams: []*tcvectordb.AnnParam{annSearch},
    Match:     []*tcvectordb.MatchOption{keywordSearch},
    // rerank也支持rrf，使用方式见下
    // Rerank: &tcvectordb.RerankOption{
    //  Method:    tcvectordb.RerankRrf,
    //  RrfK: 1,
    // },
    Rerank: &tcvectordb.RerankOption{
       Method:    "weighted",
       FieldList: []string{"vector", "sparse_vector"},
       Weight:    []float32{0.6, 0.4},
    },
    Limit:        &limit,
    OutputFields: []string{"id", "sparse_vector"},
})
if err != nil {
    return err
}

// 输出相似性检索结果，检索结果为二维数组，每一位为一组返回结果，分别对应search时指定的多个向量
for i, item := range searchRes.Documents {
    log.Printf("HybridSearchDocumentResult, index: %d ==================", i)
    for _, doc := range item {
       log.Printf("HybridSearchDocument: %+v", doc)
    }
}