feat(retriever): configure Tencent VectorDB replicas via env

.env.example

@@ -337,6 +337,9 @@ CONCURRENCY_POOL_SIZE=5
 # Tencent VectorDB 集合名前缀（可选，默认 weknora_embeddings；实际集合会按向量维度追加后缀）
 # TENCENT_VECTORDB_COLLECTION=weknora_embeddings
 
+# Tencent VectorDB 集合副本数（可选，默认 1；单节点 QA 环境可设为 0）
+# TENCENT_VECTORDB_REPLICA_NUMBER=1
+
 # 如果使用MinIO作为文件存储，需要配置以下参数
 # MinIO访问端点（host:port），连接外部MinIO时需修改
 #

docs/api/vector-store.md

@@ -120,7 +120,7 @@ curl --location --request POST 'http://localhost:8080/api/v1/vector-stores/test'
 | connection_config | object | 是   | 连接配置（与所选引擎的 `connection_fields` 对应）                |
 | index_config      | object | 否   | 索引配置（与所选引擎的 `index_fields` 对应）                     |
 
-> Tencent VectorDB 使用 `engine_type: "tencent_vectordb"`。`connection_config` 中 `addr`、`username`、`api_key` 必填，`database` 可选；`index_config.collection_name` 表示集合名前缀，实际集合会按向量维度追加后缀（例如 `weknora_embeddings_768`）。该适配器同时支持向量检索和基于 BM25 sparse vector 的关键词检索；旧版本已创建且没有 `sparse_vector` 索引的集合需要重建并重新导入数据后才能启用关键词检索。
+> Tencent VectorDB 使用 `engine_type: "tencent_vectordb"`。`connection_config` 中 `addr`、`username`、`api_key` 必填，`database` 可选；`index_config.collection_name` 表示集合名前缀，实际集合会按向量维度追加后缀（例如 `weknora_embeddings_768`）；`index_config.replica_number` 表示创建集合时使用的副本数。该适配器同时支持向量检索和基于 BM25 sparse vector 的关键词检索；旧版本已创建且没有 `sparse_vector` 索引的集合需要重建并重新导入数据后才能启用关键词检索。
 
 **请求**:
 
@@ -158,7 +158,8 @@ curl --location 'http://localhost:8080/api/v1/vector-stores' \
         "database": "weknora"
     },
     "index_config": {
-        "collection_name": "weknora_embeddings"
+        "collection_name": "weknora_embeddings",
+        "replica_number": 1
     }
 }'
 ```
@@ -419,6 +420,8 @@ curl --location --request POST 'http://localhost:8080/api/v1/vector-stores/550e8
 - **可测试连通性**：`POST /vector-stores/:id/test` 正常工作
 - **被知识库绑定时**：未指定 `vector_store_id` 创建的知识库默认使用环境变量存储；这种知识库在响应中显示为 `vector_store_name="System default"` + `vector_store_source="env"`。
 
+Tencent VectorDB 环境变量存储可通过 `TENCENT_VECTORDB_REPLICA_NUMBER` 覆盖默认集合副本数。默认值为 `1`；单节点 QA 环境可设为 `0`，生产环境可按 Tencent VectorDB 集群规模调整。
+
 ## 错误码
 
 | HTTP 状态码 | code | 含义                                                |

docs/wiki/集成扩展/集成向量数据库.md

@@ -99,9 +99,11 @@ TENCENT_VECTORDB_USERNAME=root
 TENCENT_VECTORDB_API_KEY=your_tencent_vectordb_api_key
 TENCENT_VECTORDB_DATABASE=weknora
 TENCENT_VECTORDB_COLLECTION=weknora_embeddings
+TENCENT_VECTORDB_REPLICA_NUMBER=1
 ```
 
 `TENCENT_VECTORDB_COLLECTION` 是集合名前缀。WeKnora 会按向量维度创建实际集合，例如 `weknora_embeddings_768`。
+`TENCENT_VECTORDB_REPLICA_NUMBER` 是创建集合时使用的副本数，默认 `1`；单节点 QA 环境可设为 `0`，生产环境可按 Tencent VectorDB 集群规模调整。
 
 关键词检索依赖 Tencent VectorDB sparse vector 索引。新建集合会自动创建 `sparse_vector` 索引；旧版本已创建的向量集合如果没有该索引，需要重建集合并重新导入知识库数据后才能启用关键词检索。
 

docs/使用其他向量数据库.md

@@ -289,8 +289,10 @@ TENCENT_VECTORDB_USERNAME=root
 TENCENT_VECTORDB_API_KEY=your_tencent_vectordb_api_key
 TENCENT_VECTORDB_DATABASE=weknora
 TENCENT_VECTORDB_COLLECTION=weknora_embeddings
+TENCENT_VECTORDB_REPLICA_NUMBER=1
 ```
 
 `TENCENT_VECTORDB_COLLECTION` 是集合名前缀。WeKnora 会按向量维度创建实际集合，例如 `weknora_embeddings_768`，用于隔离不同 embedding 模型维度的数据。
+`TENCENT_VECTORDB_REPLICA_NUMBER` 是创建集合时使用的副本数，默认 `1`；单节点 QA 环境可设为 `0`，生产环境可按 Tencent VectorDB 集群规模调整。
 
 关键词检索依赖 Tencent VectorDB sparse vector 索引。新建集合会自动创建 `sparse_vector` 索引；旧版本已创建的向量集合如果没有该索引，需要重建集合并重新导入知识库数据后才能启用关键词检索。

internal/application/repository/retriever/tencentvectordb/repository.go

@@ -9,6 +9,7 @@ import (
 	"os"
 	"slices"
 	"sort"
+	"strconv"
 	"strings"
 	"unicode/utf8"
 
@@ -41,10 +42,23 @@ func NewTencentVectorDBRetrieveEngineRepository(
 		collectionBaseName: collectionBaseName,
 		useDimensionSuffix: shouldUseDimensionSuffix(indexCfg),
 		shardsNum:          defaultIfZero(indexCfg.GetShardsNum(1), 1),
-		replicasNum:        defaultIfZero(indexCfg.GetReplicaNumber(1), 1),
+		replicasNum:        resolveReplicaNumber(indexCfg),
 	}
 }
 
+func resolveReplicaNumber(indexCfg *types.IndexConfig) int {
+	if indexCfg != nil && indexCfg.ReplicaNumber > 0 {
+		return indexCfg.ReplicaNumber
+	}
+	if raw := strings.TrimSpace(os.Getenv(envTencentVectorDBReplicaNum)); raw != "" {
+		replicas, err := strconv.Atoi(raw)
+		if err == nil && replicas >= 0 {
+			return replicas
+		}
+	}
+	return defaultReplicaNumber
+}
+
 func (r *repository) EngineType() types.RetrieverEngineType {
 	return types.TencentVectorDBRetrieverEngineType
 }

internal/application/repository/retriever/tencentvectordb/repository_test.go

@@ -76,7 +76,22 @@ func TestTencentVectorDBDefaultsToReplicaNumberOne(t *testing.T) {
 	assert.Equal(t, 1, repo.replicasNum)
 }
 
+func TestTencentVectorDBUsesEnvReplicaNumber(t *testing.T) {
+	t.Setenv(envTencentVectorDBReplicaNum, "0")
+	repo := NewTencentVectorDBRetrieveEngineRepository(nil, "", nil).(*repository)
+
+	assert.Equal(t, 0, repo.replicasNum)
+}
+
+func TestTencentVectorDBUsesPositiveEnvReplicaNumber(t *testing.T) {
+	t.Setenv(envTencentVectorDBReplicaNum, "3")
+	repo := NewTencentVectorDBRetrieveEngineRepository(nil, "", nil).(*repository)
+
+	assert.Equal(t, 3, repo.replicasNum)
+}
+
 func TestTencentVectorDBUsesConfiguredReplicaNumber(t *testing.T) {
+	t.Setenv(envTencentVectorDBReplicaNum, "0")
 	repo := NewTencentVectorDBRetrieveEngineRepository(nil, "", &types.IndexConfig{
 		ReplicaNumber: 2,
 	}).(*repository)

internal/application/repository/retriever/tencentvectordb/structs.go

@@ -10,8 +10,10 @@ import (
 const (
 	envTencentVectorDBDatabase   = "TENCENT_VECTORDB_DATABASE"
 	envTencentVectorDBCollection = "TENCENT_VECTORDB_COLLECTION"
+	envTencentVectorDBReplicaNum = "TENCENT_VECTORDB_REPLICA_NUMBER"
 	defaultDatabaseName          = "weknora"
 	defaultCollectionName        = "weknora_embeddings"
+	defaultReplicaNumber         = 1
 
 	fieldID              = "id"
 	fieldVector          = "vector"

internal/types/vectorstore.go

@@ -19,6 +19,11 @@ import (
 // EnvStoreIDPrefix is the prefix for virtual env store IDs.
 const EnvStoreIDPrefix = "__env_"
 
+const (
+	envTencentVectorDBReplicaNumber     = "TENCENT_VECTORDB_REPLICA_NUMBER"
+	defaultTencentVectorDBReplicaNumber = 1
+)
+
 // IsEnvStoreID checks if the given ID is an env store virtual ID.
 func IsEnvStoreID(id string) bool {
 	return strings.HasPrefix(id, EnvStoreIDPrefix)
@@ -248,7 +253,7 @@ type IndexConfig struct {
 	ShardNumber       int `yaml:"shard_number" json:"shard_number,omitempty"`               // Qdrant: number of shards per collection
 	ReplicationFactor int `yaml:"replication_factor" json:"replication_factor,omitempty"`   // Qdrant, Weaviate: number of replicas
 	ShardsNum         int `yaml:"shards_num" json:"shards_num,omitempty"`                   // Milvus: number of shards per collection (CreateCollection)
-	ReplicaNumber     int `yaml:"replica_number" json:"replica_number,omitempty"`           // Milvus: in-memory replica count (LoadCollection)
+	ReplicaNumber     int `yaml:"replica_number" json:"replica_number,omitempty"`           // Milvus LoadCollection / Tencent VectorDB CreateCollection replicas
 	DesiredShardCount int `yaml:"desired_shard_count" json:"desired_shard_count,omitempty"` // Weaviate: number of shards per collection
 	BucketsNum        int `yaml:"buckets_num" json:"buckets_num,omitempty"`                 // Doris: number of buckets per table (DISTRIBUTED BY HASH ... BUCKETS N)
 	ReplicationNum    int `yaml:"replication_num" json:"replication_num,omitempty"`         // Doris: replication_num PROPERTIES
@@ -370,9 +375,9 @@ func (c *IndexConfig) GetShardsNum(def int) int {
 	return def
 }
 
-// GetReplicaNumber returns the configured replica_number (Milvus in-memory replicas), or def if unset/zero.
-// Milvus replicas are set at LoadCollection time, not CreateCollection.
-// They control how many query nodes hold the data in memory for read HA/throughput.
+// GetReplicaNumber returns the configured replica_number, or def if unset/zero.
+// Milvus applies it at LoadCollection time; Tencent VectorDB applies it at
+// CreateCollection time. It controls read HA/throughput replicas.
 func (c *IndexConfig) GetReplicaNumber(def int) int {
 	if c != nil && c.ReplicaNumber > 0 {
 		return c.ReplicaNumber
@@ -610,6 +615,21 @@ type VectorStoreTypeInfo struct {
 	IndexFields      []VectorStoreFieldInfo `json:"index_fields,omitempty"`
 }
 
+func resolveTencentVectorDBReplicaNumber(lookup EnvLookupFunc) int {
+	if lookup == nil {
+		lookup = os.Getenv
+	}
+	raw := strings.TrimSpace(lookup(envTencentVectorDBReplicaNumber))
+	if raw == "" {
+		return defaultTencentVectorDBReplicaNumber
+	}
+	replicas, err := strconv.Atoi(raw)
+	if err != nil || replicas < 0 {
+		return defaultTencentVectorDBReplicaNumber
+	}
+	return replicas
+}
+
 // VectorStoreFieldInfo describes a single configuration field exposed
 // by /api/v1/vector-stores/types for the registration UI. The optional
 // validation hints (`Immutable`, `Min`, `Max`, `Enum`) are used by both
@@ -644,6 +664,8 @@ type VectorStoreFieldInfo struct {
 
 // GetVectorStoreTypes returns metadata for all supported engine types.
 func GetVectorStoreTypes() []VectorStoreTypeInfo {
+	tencentVectorDBReplicaNumber := resolveTencentVectorDBReplicaNumber(os.Getenv)
+
 	return []VectorStoreTypeInfo{
 		{
 			Type:        "elasticsearch",
@@ -704,7 +726,7 @@ func GetVectorStoreTypes() []VectorStoreTypeInfo {
 			IndexFields: []VectorStoreFieldInfo{
 				{Name: "collection_name", Type: "string", Required: false, Description: "Collection Name", Default: "weknora_embeddings"},
 				{Name: "shards_num", Type: "number", Required: false, Description: "Shards", Default: 1},
-				{Name: "replica_number", Type: "number", Required: false, Description: "Replicas", Default: 1},
+				{Name: "replica_number", Type: "number", Required: false, Description: "Replicas", Default: tencentVectorDBReplicaNumber},
 			},
 		},
 		{

internal/types/vectorstore_test.go

@@ -344,6 +344,26 @@ func TestGetVectorStoreTypes(t *testing.T) {
 		assert.Equal(t, 1, seen["replica_number"].Default)
 	})
 
+	t.Run("tencent vectordb replica default follows env", func(t *testing.T) {
+		t.Setenv(envTencentVectorDBReplicaNumber, "0")
+		types := GetVectorStoreTypes()
+
+		var tencentType VectorStoreTypeInfo
+		for _, typ := range types {
+			if typ.Type == "tencent_vectordb" {
+				tencentType = typ
+				break
+			}
+		}
+		require.NotEmpty(t, tencentType.IndexFields)
+
+		seen := map[string]VectorStoreFieldInfo{}
+		for _, f := range tencentType.IndexFields {
+			seen[f.Name] = f
+		}
+		assert.Equal(t, 0, seen["replica_number"].Default)
+	})
+
 	t.Run("display names have no parenthetical suffix", func(t *testing.T) {
 		for _, typ := range types {
 			assert.NotContains(t, typ.DisplayName, "(", "display_name should not contain parenthetical suffix: %s", typ.DisplayName)