AstrBotDevs · zhangli091011 · Jun 20, 2026
diff --git a/astrbot/core/config/default.py b/astrbot/core/config/default.py
@@ -1838,6 +1838,7 @@
                         "embedding_api_base": "",
                         "embedding_model": "",
                         "embedding_dimensions": 1024,
+                        "max_batch_size": 100,
                         "timeout": 20,
                         "proxy": "",
                     },
@@ -1852,6 +1853,7 @@
                         "embedding_api_base": "",
                         "embedding_model": "gemini-embedding-exp-03-07",
                         "embedding_dimensions": 768,
+                        "max_batch_size": 100,
                         "timeout": 20,
                         "proxy": "",
                     },
@@ -1867,6 +1869,7 @@
                         "embedding_model": "nvidia/llama-nemotron-embed-1b-v2",
                         "input_type": "passage",
                         "embedding_dimensions": 1024,
+                        "max_batch_size": 100,
                         "timeout": 20,
                         "proxy": "",
                     },
@@ -1880,9 +1883,25 @@
                         "embedding_api_base": "http://localhost:11434",
                         "embedding_model": "nomic-embed-text",
                         "embedding_dimensions": 768,
+                        "max_batch_size": 100,
                         "timeout": 60,
                         "proxy": "",
                     },
+                    "阿里云百炼 Embedding": {
+                        "id": "bailian_embedding",
+                        "type": "openai_embedding",
+                        "provider": "bailian",
+                        "provider_type": "embedding",
+                        "hint": "provider_group.provider.bailian_embedding.hint",
+                        "enable": True,
+                        "embedding_api_key": "",
+                        "embedding_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+                        "embedding_model": "text-embedding-v4",
+                        "embedding_dimensions": 1024,
+                        "max_batch_size": 10,
+                        "timeout": 30,
+                        "proxy": "",
+                    },
                     "vLLM Rerank": {
                         "id": "vllm_rerank",
                         "type": "vllm_rerank",
@@ -2210,6 +2229,11 @@
                         "hint": "嵌入向量的维度。根据模型不同，可能需要调整，请参考具体模型的文档。此配置项请务必填写正确，否则将导致向量数据库无法正常工作。",
                         "_special": "get_embedding_dim",
                     },
+                    "max_batch_size": {
+                        "description": "最大批次大小",
+                        "type": "int",
+                        "hint": "每批向嵌入 API 发送的文本数量上限。各服务商限制不同：DashScope/阿里云百炼限制为 10；OpenAI 支持 2048；Ollama/Gemini/NVIDIA 通常可设为 100-2048。超过限制会触发 API 400 错误。",
+                    },
                     "embedding_model": {
                         "description": "嵌入模型",
                         "type": "string",

diff --git a/astrbot/core/knowledge_base/kb_helper.py b/astrbot/core/knowledge_base/kb_helper.py
@@ -365,7 +365,10 @@ async def upload_document(
             contents = []
             metadatas = []
             for idx, chunk_text in enumerate(chunks_text):
-                contents.append(chunk_text)
+                # Replace lone surrogates that break UTF-8 encoding (e.g., broken
+                # emoji codepoints from PDF text extraction).
+                sanitized = chunk_text.encode("utf-8", errors="replace").decode("utf-8")
+                contents.append(sanitized)
                 metadatas.append(
                     {
                         "kb_id": self.kb.kb_id,

diff --git a/astrbot/core/provider/provider.py b/astrbot/core/provider/provider.py
@@ -4,6 +4,7 @@
 from collections.abc import AsyncGenerator
 from typing import Literal, TypeAlias, Union
 
+from astrbot import logger
 from astrbot.core.agent.message import ContentPart, Message, is_checkpoint_message
 from astrbot.core.agent.tool import ToolSet
 from astrbot.core.provider.entities import (
@@ -338,6 +339,21 @@ def get_dim(self) -> int:
         """获取向量的维度"""
         ...
 
+    @property
+    def max_batch_size(self) -> int:
+        """Maximum batch size per single embedding API call.
+
+        Providers may set ``max_batch_size`` in their config to override this
+        value.  For example, DashScope / Alibaba Cloud Bailian requires 10.
+
+        The default of 100 is safe for most providers (OpenAI supports up to
+        2048, Ollama and Gemini also handle large batches without issues).
+
+        Returns:
+            The maximum number of texts per batch.
+        """
+        return int(self.provider_config.get("max_batch_size", 100))
+
     async def test(self) -> None:
         await self.get_embedding("astrbot")
 
@@ -362,6 +378,16 @@ async def get_embeddings_batch(
             向量列表
 
         """
+        # Respect the provider's maximum batch size limit.
+        if batch_size > self.max_batch_size:
+            logger.debug(
+                "Batch size %d exceeds provider limit %d, capping to %d.",
+                batch_size,
+                self.max_batch_size,
+                self.max_batch_size,
+            )
+            batch_size = self.max_batch_size
+
         semaphore = asyncio.Semaphore(tasks_limit)
         all_embeddings: list[list[float]] = []
         failed_batches: list[tuple[int, list[str]]] = []

diff --git a/dashboard/src/i18n/locales/en-US/features/config-metadata.json b/dashboard/src/i18n/locales/en-US/features/config-metadata.json
@@ -1386,6 +1386,9 @@
       "openai_embedding": {
         "hint": "If testing fails, try adding /v1 at the end for some OpenAI API versions."
       },
+      "bailian_embedding": {
+        "hint": "Alibaba Cloud Bailian Embedding uses OpenAI-compatible API format. Get your API Key from https://bailian.console.aliyun.com/?tab=model#/api-key. The recommended embedding model is text-embedding-v4 (1024 dimensions), with a batch limit of 10."
+      },
       "gemini_embedding": {
         "hint": "Gemini Embedding does not require manually adding /v1beta."
       },

diff --git a/dashboard/src/i18n/locales/zh-CN/features/config-metadata.json b/dashboard/src/i18n/locales/zh-CN/features/config-metadata.json
@@ -1388,6 +1388,9 @@
       "openai_embedding": {
         "hint": "如果测试不通过，可以尝试添加 /v1 在末尾以兼容部分 OpenAI API 版本。"
       },
+      "bailian_embedding": {
+        "hint": "阿里云百炼 Embedding 使用兼容 OpenAI 的 API 格式。API Key 从 https://bailian.console.aliyun.com/?tab=model#/api-key 获取。嵌入模型推荐 text-embedding-v4（1024 维），批次上限 10。"
+      },
       "gemini_embedding": {
         "hint": "Gemini Embedding 无需手动添加 /v1beta。"
       },

diff --git a/dashboard/src/views/knowledge-base/components/DocumentsTab.vue b/dashboard/src/views/knowledge-base/components/DocumentsTab.vue
@@ -173,7 +173,7 @@
             <h3 class="text-h6 mb-4">{{ t('upload.batchSettings') }}</h3>
             <v-row>
               <v-col cols="12" sm="4">
-                <v-text-field v-model.number="uploadSettings.batch_size" :label="t('upload.batchSize')" hint="每批处理的文本数量"
+                <v-text-field v-model.number="uploadSettings.batch_size" :label="t('upload.batchSize')" hint="每批处理的文本数量，实际值不超过嵌入提供方的 max_batch_size 限制"
                   persistent-hint type="number" variant="outlined" density="compact" />
               </v-col>
               <v-col cols="12" sm="4">