fix(embedding): use model-level batch_size for provider-specific limits

Root cause: DashScope embeddings accepts at most 10 inputs per request, but code used fixed batch_size=40, causing 400 Bad Request during indexing.

Changes:

- add batch_size to EmbedModelInfo with default 40

- set dashscope/text-embedding-v4 batch_size to 10

- persist batch_size in BaseEmbeddingModel and use it as default in batch/abatch encode

- update Milvus, evaluation, and upload-graph embedding paths to read model-configured batch_size

src/config/static/models.py

@@ -30,6 +30,7 @@ class EmbedModelInfo(BaseModel):
     base_url: str = Field(..., description="API 基础 URL")
     api_key: str = Field(..., description="API Key 或环境变量名")
     model_id: str | None = Field(None, description="可选的模型 ID")
+    batch_size: int = Field(40, description="批量向量化大小")
 
 
 class RerankerInfo(BaseModel):
@@ -203,6 +204,7 @@ DEFAULT_EMBED_MODELS: dict[str, EmbedModelInfo] = {
         dimension=1024,
         base_url="https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings",
         api_key="DASHSCOPE_API_KEY",
+        batch_size=10,
     ),
 }
 

src/knowledge/implementations/milvus.py

@@ -190,13 +190,14 @@ class MilvusKB(KnowledgeBase):
     def _get_async_embedding_function(self, embed_info: dict):
         """获取 embedding 函数"""
         embedding_model = self._get_async_embedding(embed_info)
-        return partial(embedding_model.abatch_encode, batch_size=40)
+        batch_size = int(getattr(embedding_model, "batch_size", 40) or 40)
+        return partial(embedding_model.abatch_encode, batch_size=batch_size)
 
     def _get_embedding_function(self, embed_info: dict):
         """获取 embedding 函数"""
         embedding_model = self._get_async_embedding(embed_info)
-
-        return partial(embedding_model.batch_encode, batch_size=40)
+        batch_size = int(getattr(embedding_model, "batch_size", 40) or 40)
+        return partial(embedding_model.batch_encode, batch_size=batch_size)
 
     async def _get_milvus_collection(self, db_id: str):
         """获取或创建 Milvus 集合"""

src/knowledge/services/upload_graph_service.py

@@ -488,17 +488,24 @@ class UploadGraphService:
             logger.error(f"加载图数据库信息失败：{e}")
             return False
 
-    async def aget_embedding(self, text, batch_size=40):
+    def _resolve_embedding_batch_size(self, batch_size=None):
+        if batch_size is not None:
+            return batch_size
+        return int(getattr(self.embed_model, "batch_size", 40) or 40)
+
+    async def aget_embedding(self, text, batch_size=None):
         if isinstance(text, list):
-            outputs = await self.embed_model.abatch_encode(text, batch_size=batch_size)
+            resolved_batch_size = self._resolve_embedding_batch_size(batch_size)
+            outputs = await self.embed_model.abatch_encode(text, batch_size=resolved_batch_size)
             return outputs
         else:
             outputs = await self.embed_model.aencode(text)
             return outputs
 
-    def get_embedding(self, text, batch_size=40):
+    def get_embedding(self, text, batch_size=None):
         if isinstance(text, list):
-            outputs = self.embed_model.batch_encode(text, batch_size=batch_size)
+            resolved_batch_size = self._resolve_embedding_batch_size(batch_size)
+            outputs = self.embed_model.batch_encode(text, batch_size=resolved_batch_size)
             return outputs
         else:
             outputs = self.embed_model.encode([text])[0]

src/models/embed.py

@@ -11,7 +11,17 @@ from src.utils import get_docker_safe_url, hashstr, logger
 
 
 class BaseEmbeddingModel(ABC):
-    def __init__(self, model=None, name=None, dimension=None, url=None, base_url=None, api_key=None, model_id=None):
+    def __init__(
+        self,
+        model=None,
+        name=None,
+        dimension=None,
+        url=None,
+        base_url=None,
+        api_key=None,
+        model_id=None,
+        batch_size=40,
+    ):
         """
         Args:
             model: 模型名称，冗余设计，同name
@@ -20,12 +30,14 @@ class BaseEmbeddingModel(ABC):
             url: 请求URL，冗余设计，同base_url
             base_url: 基础URL，请求URL，冗余设计，同url
             api_key: 请求API密钥
+            batch_size: 模型推荐的批量向量化大小
         """
         base_url = base_url or url
         self.model = model or name
         self.dimension = dimension
         self.base_url = get_docker_safe_url(base_url)
         self.api_key = os.getenv(api_key, api_key)
+        self.batch_size = int(batch_size or 40)
         self.embed_state = {}
 
     @abstractmethod
@@ -46,8 +58,9 @@ class BaseEmbeddingModel(ABC):
         """等同于aencode"""
         return await self.aencode(queries)
 
-    def batch_encode(self, messages: list[str], batch_size: int = 40) -> list[list[float]]:
+    def batch_encode(self, messages: list[str], batch_size: int | None = None) -> list[list[float]]:
         # logger.info(f"Batch encoding {len(messages)} messages")
+        batch_size = batch_size or self.batch_size
         data = []
         task_id = None
         if len(messages) > batch_size:
@@ -67,7 +80,8 @@ class BaseEmbeddingModel(ABC):
 
         return data
 
-    async def abatch_encode(self, messages: list[str], batch_size: int = 40) -> list[list[float]]:
+    async def abatch_encode(self, messages: list[str], batch_size: int | None = None) -> list[list[float]]:
+        batch_size = batch_size or self.batch_size
         data = []
         task_id = None
         if len(messages) > batch_size:

src/services/evaluation_service.py

@@ -5,6 +5,7 @@ import uuid
 from datetime import datetime
 from typing import Any
 
+from src import config
 from src.knowledge import knowledge_base
 from src.models import select_model
 from src.repositories.evaluation_repository import EvaluationRepository
@@ -345,9 +346,9 @@ class EvaluationService:
 
         await context.set_progress(15, "向量化")
 
+        db_meta = kb_instance.databases_meta.get(db_id, {})
+        embed_info = db_meta.get("embed_info", {})
         if not embedding_model_id:
-            db_meta = kb_instance.databases_meta.get(db_id, {})
-            embed_info = db_meta.get("embed_info", {})
             embedding_model_id = embed_info.get("name") or embed_info.get("model") or ""
         if not embedding_model_id:
             raise ValueError("Embedding model not specified")
@@ -355,11 +356,14 @@ class EvaluationService:
         from src.models import select_embedding_model, select_model
 
         embed_model = select_embedding_model(embedding_model_id)
+        batch_size = int(getattr(embed_model, "batch_size", 40) or 40)
+        if embedding_model_id in config.embed_model_names:
+            batch_size = config.embed_model_names[embedding_model_id].batch_size
         # TODO: Performance Optimization
         # Currently, we re-calculate embeddings for ALL chunks in the KB for every benchmark generation.
         # This is inefficient for large KBs (O(N) embedding calls).
         # Optimization: Reuse existing embeddings from Vector DB if embedding_model_id matches the KB's embedding model.
-        embeddings = await embed_model.abatch_encode(contents, batch_size=40)
+        embeddings = await embed_model.abatch_encode(contents, batch_size=batch_size)
         norms = [math.sqrt(sum(x * x for x in vec)) or 1.0 for vec in embeddings]
 
         def cosine(a, b, na, nb):