支持表格

backend/api/kb_file_router.py

@@ -364,6 +364,17 @@ async def upload_file(
             except ValueError as e:
                 logger.warning(f"❌ 文本文件字数超限: {file.filename}, {e}")
                 raise BadRequestError(str(e)) from e
+
+        existing_file = await KnowledgeBaseFileService.get_active_file_by_name(
+            conn, kb_id, file.filename
+        )
+        if existing_file and existing_file.status in ("processing", "completed"):
+            raise BadRequestError(f"文件 '{file.filename}' 已存在于该知识库中")
+        retry_old_file_path = (
+            existing_file.file_path
+            if existing_file and existing_file.status == "failed"
+            else None
+        )
         
         # 生成唯一文件名
         timestamp = int(time.time() * 1000)
@@ -374,6 +385,7 @@ async def upload_file(
         oss_service = get_oss_service()
         file_path = None
         file_url = None
+        uploaded_oss_object_name = None
         
         logger.info(f"☁️  开始上传文件，OSS 状态: {'已启用' if oss_service.enabled else '未启用'}")
         
@@ -382,6 +394,7 @@ async def upload_file(
             file_url = oss_service.upload_file_from_bytes(content, oss_object_name, file.filename)
             if file_url:
                 file_path = file_url
+                uploaded_oss_object_name = oss_object_name
                 logger.info(f"✅ 文件已上传到 OSS: {file_url}")
                 
                 # 🔑 图片审核：在创建文件记录前进行审核
@@ -439,12 +452,25 @@ async def upload_file(
             file_path = str(local_path)
             logger.info(f"💾 文件已保存到本地: {file_path}")
         
-        # 创建文件记录
+        try:
-        logger.info(f"📝 创建文件记录: {file.filename}")
+            # 创建文件记录（处理失败的可复用原记录重试）
-        file_record = await KnowledgeBaseFileService.create_file_record(
+            logger.info(f"📝 创建文件记录: {file.filename}")
-            conn, kb_id, current_user.id, file.filename, file_path, file_size, file_type
+            file_record = await KnowledgeBaseFileService.create_file_record(
-        )
+                conn, kb_id, current_user.id, file.filename, file_path, file_size, file_type
-        logger.info(f"✅ 文件记录已创建: ID={file_record.id}, 状态={file_record.status}")
+            )
+            logger.info(f"✅ 文件记录已创建: ID={file_record.id}, 状态={file_record.status}")
+        except ValueError:
+            if uploaded_oss_object_name and oss_service.enabled:
+                oss_service.delete_file(uploaded_oss_object_name)
+            raise
+
+        if retry_old_file_path and retry_old_file_path != file_path and retry_old_file_path.startswith(
+            ("http://", "https://")
+        ):
+            old_oss_object = oss_service.extract_object_name_from_url(retry_old_file_path, kb_id)
+            if old_oss_object:
+                oss_service.delete_file(old_oss_object)
+                logger.info(f"🗑️  已清理失败重试前的 OSS 文件: {old_oss_object}")
 
         # 审计日志：上传
         await AuditService.write(
@@ -479,7 +505,6 @@ async def upload_file(
     except BadRequestError:
         raise
     except ValueError as e:
-        # 文件名重复等业务错误
         logger.warning(f"文件上传验证失败: {e}")
         raise BadRequestError(str(e))
     except Exception as e:

backend/core/llm_catalog.py

@@ -282,6 +282,8 @@ def build_chat_model(
         base_url = llm_env.resolved_deepseek_chat_base_url().strip().rstrip("/")
     else:
         raise ValueError(f"未知提供方: {provider}")
+
+    print("-----------------------------------走到这里了-----------------------------------",extra_kwargs)
     return ChatOpenAI(
         model=api_model,
         api_key=api_key,

backend/core/llm_env.py

@@ -56,3 +56,18 @@ def dashscope_native_http_api_base() -> str:
 def resolved_deepseek_chat_base_url() -> str:
     """DeepSeek OpenAI 兼容 base：仅从 ``DEEPSEEK_API_BASE`` 读取，无内置默认。"""
     return _getenv_nonempty("DEEPSEEK_API_BASE", "deepseek_api_base").strip().rstrip("/")
+
+
+def tongyi_embedding_api_key() -> str:
+    """通义 Embedding：优先 ``ZL_DASHSCOPE_API_KEY``，否则回退 ``DASHSCOPE_API_KEY``。"""
+    return _getenv_nonempty("ZL_DASHSCOPE_API_KEY", "zl_dashscope_api_key") or _getenv_nonempty(
+        "DASHSCOPE_API_KEY", "dashscope_api_key"
+    )
+
+
+def tongyi_embedding_api_base() -> str:
+    """通义 Embedding OpenAI 兼容 base：优先 ``ZL_DASHSCOPE_API_BASE``，否则 ``DASHSCOPE_API_BASE``。"""
+    zl = _getenv_nonempty("ZL_DASHSCOPE_API_BASE", "zl_dashscope_api_base").strip().rstrip("/")
+    if zl:
+        return zl
+    return tongyi_openai_compatible_base_url()

backend/pyproject.toml

@@ -43,7 +43,7 @@ dependencies = [
     "sse-starlette>=3.0.3",
     "streamlit>=1.52.0",
     "tavily-python>=0.7.13",
-    "unstructured[docx]>=0.18.21",
+    "unstructured[docx,xlsx]>=0.18.21",
     "uvicorn>=0.38.0",
     "redis>=5.0.0",
     "alibabacloud-dysmsapi20170525>=3.0.0",

backend/services/knowledge_base_file_service.py

@@ -17,6 +17,25 @@ logger = get_logger(__name__)
 class KnowledgeBaseFileService:
     """知识库文件服务类"""
 
+    @staticmethod
+    async def get_active_file_by_name(
+        conn: asyncpg.Connection,
+        knowledge_base_id: int,
+        file_name: str,
+    ) -> Optional[KnowledgeBaseFile]:
+        """按知识库 + 文件名查询未删除的文件记录。"""
+        row = await conn.fetchrow(
+            """
+            SELECT id, knowledge_base_id, user_id, file_name, file_path, file_size,
+                   file_type, status, chunk_count, created_at, updated_at, is_deleted, deleted_at
+            FROM knowledge_base_file
+            WHERE knowledge_base_id = $1 AND file_name = $2 AND is_deleted = FALSE
+            """,
+            knowledge_base_id,
+            file_name,
+        )
+        return KnowledgeBaseFile(**dict(row)) if row else None
+
     @staticmethod
     async def create_file_record(
         conn: asyncpg.Connection,
@@ -43,18 +62,35 @@ class KnowledgeBaseFileService:
             KnowledgeBaseFile: 创建的文件记录
         """
         try:
-            # 检查文件名是否已存在
+            existing = await KnowledgeBaseFileService.get_active_file_by_name(
-            existing = await conn.fetchrow(
+                conn, knowledge_base_id, file_name
-                """
-                SELECT id FROM knowledge_base_file 
-                WHERE knowledge_base_id = $1 AND file_name = $2 AND is_deleted = FALSE
-                """,
-                knowledge_base_id, file_name
             )
-            
+
             if existing:
+                if existing.status == "failed":
+                    row = await conn.fetchrow(
+                        """
+                        UPDATE knowledge_base_file
+                        SET file_path = $1, file_size = $2, file_type = $3,
+                            status = 'processing', chunk_count = 0,
+                            user_id = $4, updated_at = CURRENT_TIMESTAMP
+                        WHERE id = $5
+                        RETURNING id, knowledge_base_id, user_id, file_name, file_path, file_size,
+                                  file_type, status, chunk_count, created_at, updated_at, is_deleted, deleted_at
+                        """,
+                        file_path,
+                        file_size,
+                        file_type,
+                        user_id,
+                        existing.id,
+                    )
+                    logger.info(
+                        f"重试处理失败文件: {file_name}, 知识库 ID: {knowledge_base_id}, file_id={existing.id}"
+                    )
+                    return KnowledgeBaseFile(**dict(row))
+
                 raise ValueError(f"文件 '{file_name}' 已存在于该知识库中")
-            
+
             # 插入文件记录
             row = await conn.fetchrow(
                 """

backend/services/vector_service.py

@@ -71,17 +71,61 @@ except ImportError:
     fitz = None
 
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_ollama import OllamaEmbeddings
+from langchain_core.embeddings import Embeddings
 from langchain_openai import OpenAIEmbeddings
 from langchain_chroma import Chroma
 import bs4
 
 from logger.logging import get_logger
 from core.config import settings
+from core.llm_env import tongyi_embedding_api_base, tongyi_embedding_api_key
 from services.kb_text_limits import validate_kb_text_length, validate_chat_file_text_length
 
 logger = get_logger(__name__)
 
+# 通义 text-embedding-v4（OpenAI 兼容）单次请求最多 10 条，超出会 400/500
+_TONGYI_EMBEDDING_MAX_BATCH = 10
+
+
+class TongyiEmbeddings(Embeddings):
+    """通义千问 Embedding 封装：固定走 ZL/DashScope 网关，并按 API 上限分批请求。"""
+
+    def __init__(self, *, model: str, api_key: str, base_url: str, dimensions: int) -> None:
+        self._inner = OpenAIEmbeddings(
+            model=model,
+            api_key=api_key,
+            base_url=base_url,
+            check_embedding_ctx_length=False,
+            dimensions=dimensions,
+        )
+
+    def _batched(self, texts: List[str]) -> List[List[float]]:
+        if not texts:
+            return []
+        vectors: List[List[float]] = []
+        for start in range(0, len(texts), _TONGYI_EMBEDDING_MAX_BATCH):
+            batch = texts[start : start + _TONGYI_EMBEDDING_MAX_BATCH]
+            vectors.extend(self._inner.embed_documents(batch))
+        return vectors
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self._batched(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._inner.embed_query(text)
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        if not texts:
+            return []
+        vectors: List[List[float]] = []
+        for start in range(0, len(texts), _TONGYI_EMBEDDING_MAX_BATCH):
+            batch = texts[start : start + _TONGYI_EMBEDDING_MAX_BATCH]
+            vectors.extend(await self._inner.aembed_documents(batch))
+        return vectors
+
+    async def aembed_query(self, text: str) -> List[float]:
+        return await self._inner.aembed_query(text)
+
 
 @dataclass
 class ProcessResult:
@@ -125,16 +169,28 @@ class VectorService:
     
     def __init__(self):
         """初始化向量服务"""
-        # 初始化嵌入模型
+        embedding_api_key = tongyi_embedding_api_key()
-        # self.embedding = OllamaEmbeddings(model="nomic-embed-text")
+        embedding_api_base = tongyi_embedding_api_base()
-        # DashScope 兼容网关只接受字符串 input；默认 check_embedding_ctx_length=True
+        if not embedding_api_key:
-        # 会用 tiktoken 转成 token id 列表再请求，导致 400：contents is neither str nor list of str
+            raise ValueError(
-        print(settings.dashscope_api_key, settings.dashscope_api_base)
+                "未配置通义 Embedding API Key，请设置 ZL_DASHSCOPE_API_KEY 或 DASHSCOPE_API_KEY"
-        self.embedding = OpenAIEmbeddings(
+            )
-            model="text-embedding-v4",
+        if not embedding_api_base:
-            api_key=os.getenv("ZL_DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量，请在此处用您的API Key进行替换
+            raise ValueError(
-            base_url=os.getenv("ZL_DASHSCOPE_API_BASE"),
+                "未配置通义 Embedding API Base，请设置 ZL_DASHSCOPE_API_BASE 或 DASHSCOPE_API_BASE"
-            check_embedding_ctx_length=False,
+            )
+        # 通义 text-embedding-v4（OpenAI 兼容 /embeddings）；check_embedding_ctx_length=False
+        # 避免 tiktoken 将输入转成 token id 列表导致 DashScope 返回 400
+        self.embedding = TongyiEmbeddings(
+            model=settings.embedding_model,
+            api_key=embedding_api_key,
+            base_url=embedding_api_base,
+            dimensions=settings.embedding_dimension,
+        )
+        logger.info(
+            "通义 Embedding 已初始化: model={}, base_url={}",
+            settings.embedding_model,
+            embedding_api_base,
         )
         
         # 文本分割器配置（参考 server：增大 chunk_size 保留更多上下文）

backend/uv.lock

@@ -1124,7 +1124,7 @@ dependencies = [
     { name = "sse-starlette" },
     { name = "streamlit" },
     { name = "tavily-python" },
-    { name = "unstructured", extra = ["docx"] },
+    { name = "unstructured", extra = ["docx", "xlsx"] },
     { name = "uvicorn" },
 ]
 
@@ -1188,7 +1188,7 @@ requires-dist = [
     { name = "sse-starlette", specifier = ">=3.0.3" },
     { name = "streamlit", specifier = ">=1.52.0" },
     { name = "tavily-python", specifier = ">=0.7.13" },
-    { name = "unstructured", extras = ["docx"], specifier = ">=0.18.21" },
+    { name = "unstructured", extras = ["docx", "xlsx"], specifier = ">=0.18.21" },
     { name = "uvicorn", specifier = ">=0.38.0" },
 ]
 
@@ -1848,6 +1848,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" },
 ]
 
+[[package]]
+name = "msoffcrypto-tool"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "olefile" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a6/34/6250bdddaeaae24098e45449ea362fb3555a65fba30cad0ad5630ea48d1a/msoffcrypto_tool-6.0.0.tar.gz", hash = "sha256:9a5ebc4c0096b42e5d7ebc2350afdc92dc511061e935ca188468094fdd032bbe", size = 40593, upload-time = "2026-01-12T08:59:56.73Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3c/85/9e359fa9279e1d6861faaf9b6f037a3226374deb20a054c3937be6992013/msoffcrypto_tool-6.0.0-py3-none-any.whl", hash = "sha256:46c394ed5d9641e802fc79bf3fb0666a53748b23fa8c4aa634ae9d30d46fe397", size = 48791, upload-time = "2026-01-12T08:59:55.394Z" },
+]
+
 [[package]]
 name = "multidict"
 version = "6.7.1"
@@ -1905,6 +1918,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e6/cf/1c3795866cefaac6e648d4e98c373cafd97810f6e317c307371007ab4abb/neo4j-6.2.0-py3-none-any.whl", hash = "sha256:b87abdd13a5cc2e3bd51026926c2f20ac38fa3febe98c340520dce19e97388d0", size = 327824, upload-time = "2026-05-04T07:35:39.604Z" },
 ]
 
+[[package]]
+name = "networkx"
+version = "3.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" },
+]
+
 [[package]]
 name = "nltk"
 version = "3.9.4"
@@ -3353,6 +3375,13 @@ wheels = [
 docx = [
     { name = "python-docx" },
 ]
+xlsx = [
+    { name = "msoffcrypto-tool" },
+    { name = "networkx" },
+    { name = "openpyxl" },
+    { name = "pandas" },
+    { name = "xlrd" },
+]
 
 [[package]]
 name = "unstructured-client"
@@ -3594,6 +3623,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a4/f5/10b68b7b1544245097b2a1b8238f66f2fc6dcaeb24ba5d917f52bd2eed4f/wsproto-1.3.2-py3-none-any.whl", hash = "sha256:61eea322cdf56e8cc904bd3ad7573359a242ba65688716b0710a5eb12beab584", size = 24405, upload-time = "2025-11-20T18:18:00.454Z" },
 ]
 
+[[package]]
+name = "xlrd"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/07/5a/377161c2d3538d1990d7af382c79f3b2372e880b65de21b01b1a2b78691e/xlrd-2.0.2.tar.gz", hash = "sha256:08b5e25de58f21ce71dc7db3b3b8106c1fa776f3024c54e45b45b374e89234c9", size = 100167, upload-time = "2025-06-14T08:46:39.039Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/62/c8d562e7766786ba6587d09c5a8ba9f718ed3fa8af7f4553e8f91c36f302/xlrd-2.0.2-py2.py3-none-any.whl", hash = "sha256:ea762c3d29f4cca48d82df517b6d89fbce4db3107f9d78713e48cd321d5c9aa9", size = 96555, upload-time = "2025-06-14T08:46:37.766Z" },
+]
+
 [[package]]
 name = "xxhash"
 version = "3.7.0"