feat: global exception handler, concurrent VLM, and docker security

- Add global FastAPI exception handler for unhandled errors
- Add get_current_user_obj() dependency for cleaner auth patterns
- Switch VLM image description from serial to asyncio.gather concurrency
- Extract score conversion to shared score_utils module
- Docker: use env vars for passwords, remove hardcoded API key default
- Add .gitattributes and update .gitignore for tar.gz and tsbuildinfo

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

.gitattributes

@@ -0,0 +1,37 @@
+# Auto detect text files and perform LF normalization
+* text=auto
+
+# Source code - enforce LF line endings
+*.py text eol=lf
+*.ts text eol=lf
+*.tsx text eol=lf
+*.js text eol=lf
+*.jsx text eol=lf
+*.json text eol=lf
+*.css text eol=lf
+*.scss text eol=lf
+*.html text eol=lf
+*.md text eol=lf
+*.yml text eol=lf
+*.yaml text eol=lf
+*.toml text eol=lf
+*.sql text eol=lf
+*.sh text eol=lf
+*.bat text eol=lf
+*.conf text eol=lf
+*.cfg text eol=lf
+*.txt text eol=lf
+*.env text eol=lf
+
+# Binary files - don't touch
+*.png binary
+*.jpg binary
+*.jpeg binary
+*.gif binary
+*.ico binary
+*.pdf binary
+*.zip binary
+*.woff binary
+*.woff2 binary
+*.ttf binary
+*.eot binary

.gitignore

@@ -64,6 +64,7 @@ Desktop.ini
 # 其他
 *.tmp
 *.temp
+*.tar.gz
 .cache/
 *.coverage
 .coverage

backend/main.py

@@ -4,14 +4,17 @@ FastAPI应用入口
 """
 import os
 import sys
+import logging
 from pathlib import Path
 from datetime import datetime
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
+from fastapi.responses import FileResponse, JSONResponse
 import uvicorn
 
+logger = logging.getLogger(__name__)
+
 # 设置系统时区为北京时间
 os.environ['TZ'] = 'Asia/Shanghai'
 
@@ -52,9 +55,9 @@ def startup_knowledge_base():
     try:
         from src.services.file_watcher_service import start_file_watcher
         start_file_watcher()
-        print("知识库文件监控服务启动成功")
+        logger.info("知识库文件监控服务启动成功")
     except Exception as e:
-        print(f"启动知识库文件监控服务失败: {str(e)}")
+        logger.error(f"启动知识库文件监控服务失败: {str(e)}")
 
 
 def seed_forum_categories():
@@ -72,17 +75,17 @@ def seed_forum_categories():
     try:
         existing_count = db.query(ForumCategory).count()
         if existing_count > 0:
-            print(f"论坛分类已存在（{existing_count} 个），跳过初始化")
+            logger.info(f"论坛分类已存在（{existing_count} 个），跳过初始化")
             return
 
         for cat_data in DEFAULT_CATEGORIES:
             category = ForumCategory(**cat_data)
             db.add(category)
         db.commit()
-        print(f"已创建 {len(DEFAULT_CATEGORIES)} 个论坛分类")
+        logger.info(f"已创建 {len(DEFAULT_CATEGORIES)} 个论坛分类")
     except Exception as e:
         db.rollback()
-        print(f"初始化论坛分类失败: {e}")
+        logger.error(f"初始化论坛分类失败: {e}")
     finally:
         db.close()
 
@@ -94,10 +97,9 @@ async def startup_event():
     import asyncio
     import sys
     
-    print("=" * 50, file=sys.stderr)
-    print("应用启动事件开始", file=sys.stderr)
-    print("=" * 50, file=sys.stderr)
-    sys.stderr.flush()
+    logger.info("=" * 50)
+    logger.info("应用启动事件开始")
+    logger.info("=" * 50)
     
     # 等待数据库可用（重试机制，处理 DNS 解析延迟）
     max_retries = 10
@@ -105,28 +107,23 @@ async def startup_event():
     
     for attempt in range(max_retries):
         try:
-            print(f"尝试连接数据库（尝试 {attempt + 1}/{max_retries}）...", file=sys.stderr)
-            sys.stderr.flush()
+            logger.info(f"尝试连接数据库（尝试 {attempt + 1}/{max_retries}）...")
             # 尝试创建数据库表
             create_tables()
-            print("数据库表创建成功", file=sys.stderr)
-            sys.stderr.flush()
+            logger.info("数据库表创建成功")
             break
         except Exception as e:
             if attempt < max_retries - 1:
-                print(f"数据库连接失败（尝试 {attempt + 1}/{max_retries}）: {e}", file=sys.stderr)
-                print(f"等待 {retry_delay} 秒后重试...", file=sys.stderr)
-                sys.stderr.flush()
+                logger.warning(f"数据库连接失败（尝试 {attempt + 1}/{max_retries}）: {e}")
                 await asyncio.sleep(retry_delay)
             else:
-                print(f"数据库连接失败，已达到最大重试次数: {e}", file=sys.stderr)
-                sys.stderr.flush()
+                logger.error(f"数据库连接失败，已达到最大重试次数: {e}")
                 # 不抛出异常，让应用继续启动，但数据库操作会失败
     
     try:
         startup_knowledge_base()
     except Exception as e:
-        print(f"启动知识库服务失败: {e}", file=sys.stderr)
+        logger.error(f"启动知识库服务失败: {e}")
 
     # 确保系统知识库与目录同步
     try:
@@ -137,17 +134,15 @@ async def startup_event():
         kb_service.ensure_system_knowledge_bases()
         db.close()
     except Exception as e:
-        print(f"同步系统知识库失败: {e}", file=sys.stderr)
+        logger.error(f"同步系统知识库失败: {e}")
 
     # 初始化论坛分类
     try:
         seed_forum_categories()
     except Exception as e:
-        print(f"初始化论坛分类失败: {e}", file=sys.stderr)
-        sys.stderr.flush()
+        logger.error(f"初始化论坛分类失败: {e}")
 
-    print("应用启动事件完成", file=sys.stderr)
-    sys.stderr.flush()
+    logger.info("应用启动事件完成")
 
 # 应用关闭事件
 @app.on_event("shutdown")
@@ -156,9 +151,18 @@ async def shutdown_event():
     try:
         from src.services.file_watcher_service import stop_file_watcher
         stop_file_watcher()
-        print("知识库文件监控服务已停止")
+        logger.info("知识库文件监控服务已停止")
     except Exception as e:
-        print(f"停止知识库文件监控服务失败: {str(e)}")
+        logger.error(f"停止知识库文件监控服务失败: {str(e)}")
+
+# 全局异常处理器
+@app.exception_handler(Exception)
+async def generic_exception_handler(request: Request, exc: Exception):
+    logger.error(f"未处理的异常: {exc}", exc_info=True)
+    return JSONResponse(
+        status_code=500,
+        content={"detail": "服务器内部错误，请稍后重试"}
+    )
 
 # 注册路由
 app.include_router(auth.router)
@@ -315,11 +319,8 @@ async def list_services():
 
 if __name__ == "__main__":
     import sys
-    print("=" * 50, file=sys.stderr)
-    print("启动 Uvicorn 服务器...", file=sys.stderr)
-    print(f"Host: {settings.host}, Port: {settings.port}", file=sys.stderr)
-    print("=" * 50, file=sys.stderr)
-    sys.stderr.flush()
+    logger.info("启动 Uvicorn 服务器...")
+    logger.info(f"Host: {settings.host}, Port: {settings.port}")
     
     uvicorn.run(
         "main:app",

backend/src/api/document.py

@@ -1,6 +1,7 @@
 """
 文档管理API
 """
+import logging
 import asyncio
 import os
 import uuid
@@ -15,6 +16,8 @@ from ..core.security import get_current_user
 from ..models.document import Document, DocumentChunk
 from ..services.document_service import DocumentService
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter(prefix="/documents", tags=["文档管理"])
 
 
@@ -186,32 +189,30 @@ async def delete_document(
         
         # 1. 先删除向量数据和文档块
         try:
-            print(f"开始删除文档向量数据: {document.filename} (ID: {document.id})")
+            logger.info(f"开始删除文档向量数据: {document.filename} (ID: {document.id})")
             document_service = DocumentService(db)
             vector_deleted = document_service.delete_document_chunks(document.id)
             if vector_deleted:
-                print(f"成功删除文档向量数据: {document.filename}")
+                logger.info(f"成功删除文档向量数据: {document.filename}")
             else:
-                print(f"警告：删除文档向量数据失败: {document.filename}")
+                logger.warning(f"警告：删除文档向量数据失败: {document.filename}")
         except Exception as e:
-            print(f"删除向量数据时发生错误: {str(e)}")
-            import traceback
-            print(f"详细错误信息: {traceback.format_exc()}")
+            logger.error(f"删除向量数据时发生错误: {str(e)}", exc_info=True)
         
         # 2. 删除物理文件
         try:
             if os.path.exists(document.file_path):
                 os.remove(document.file_path)
-                print(f"成功删除物理文件: {document.file_path}")
+                logger.info(f"成功删除物理文件: {document.file_path}")
             else:
-                print(f"物理文件不存在: {document.file_path}")
+                logger.info(f"物理文件不存在: {document.file_path}")
         except Exception as e:
-            print(f"删除物理文件时发生错误: {str(e)}")
+            logger.error(f"删除物理文件时发生错误: {str(e)}")
         
         # 3. 删除数据库记录
         db.delete(document)
         db.commit()
-        print(f"成功删除文档数据库记录: {document.filename}")
+        logger.info(f"成功删除文档数据库记录: {document.filename}")
         
         return {"message": "文档删除成功"}
         

backend/src/api/knowledge_base.py

@@ -1,6 +1,7 @@
 """
 知识库CRUD API
 """
+import logging
 import os
 import uuid
 from pathlib import Path
@@ -18,6 +19,8 @@ from ..models.document import Document
 from ..models.user import User
 from ..services.document_service import DocumentService
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter(prefix="/knowledge-bases", tags=["知识库管理"])
 
 
@@ -64,7 +67,7 @@ async def get_knowledge_bases(
     db: Session = Depends(get_db)
 ):
     """获取用户的所有知识库"""
-    print(f"DEBUG: get_knowledge_bases called for user: {current_user}")
+    logger.debug(f"get_knowledge_bases called for user: {current_user}")
     try:
         # 获取用户ID
         user = db.query(User).filter(User.username == current_user).first()
@@ -118,7 +121,7 @@ async def create_knowledge_base(
     db: Session = Depends(get_db)
 ):
     """创建新知识库"""
-    print(f"DEBUG: create_knowledge_base called for user: {current_user}, data: {data}")
+    logger.debug(f"create_knowledge_base called for user: {current_user}, data: {data}")
     try:
         # 获取用户ID
         user = db.query(User).filter(User.username == current_user).first()
@@ -368,7 +371,7 @@ async def delete_knowledge_base(
             Document.knowledge_base_id == knowledge_base_id
         ).all()
         
-        print(f"开始删除知识库 '{knowledge_base.name}'，包含 {len(documents)} 个文档")
+        logger.info(f"开始删除知识库 '{knowledge_base.name}'，包含 {len(documents)} 个文档")
         
         # 2. 逐个删除文档的向量数据和物理文件
         document_service = DocumentService(db)
@@ -380,33 +383,31 @@ async def delete_knowledge_base(
                 # 删除向量数据
                 vector_deleted = document_service.delete_document_chunks(document.id)
                 if vector_deleted:
-                    print(f"成功删除文档向量数据: {document.filename}")
+                    logger.info(f"成功删除文档向量数据: {document.filename}")
                 else:
-                    print(f"警告：删除文档向量数据失败: {document.filename}")
+                    logger.warning(f"警告：删除文档向量数据失败: {document.filename}")
                     error_count += 1
                 
                 # 删除物理文件
                 if os.path.exists(document.file_path):
                     os.remove(document.file_path)
-                    print(f"成功删除物理文件: {document.file_path}")
+                    logger.info(f"成功删除物理文件: {document.file_path}")
                 else:
-                    print(f"物理文件不存在: {document.file_path}")
+                    logger.info(f"物理文件不存在: {document.file_path}")
                 
                 success_count += 1
                 
             except Exception as e:
-                print(f"删除文档 {document.filename} 的资源时出错: {str(e)}")
-                import traceback
-                print(f"详细错误信息: {traceback.format_exc()}")
+                logger.error(f"删除文档 {document.filename} 的资源时出错: {str(e)}", exc_info=True)
                 error_count += 1
                 # 继续处理其他文档
         
-        print(f"文档清理完成: 成功 {success_count} 个，失败 {error_count} 个")
+        logger.info(f"文档清理完成: 成功 {success_count} 个，失败 {error_count} 个")
         
         # 3. 删除知识库（级联删除文档记录）
         db.delete(knowledge_base)
         db.commit()
-        print(f"成功删除知识库数据库记录: {knowledge_base.name}")
+        logger.info(f"成功删除知识库数据库记录: {knowledge_base.name}")
         
         return {"message": "知识库删除成功"}
         
@@ -528,20 +529,18 @@ async def upload_document_to_knowledge_base(
         
         # 自动处理文档向量化
         try:
-            print(f"开始处理文档向量化: {document.filename} (ID: {document.id})")
+            logger.info(f"开始处理文档向量化: {document.filename} (ID: {document.id})")
             document_service = DocumentService(db)
             success = await document_service.process_document(document.id)
 
             if success:
-                print(f"文档向量化处理成功: {document.filename}")
+                logger.info(f"文档向量化处理成功: {document.filename}")
                 message = "文档上传并处理成功"
             else:
-                print(f"文档向量化处理失败: {document.filename}")
+                logger.warning(f"文档向量化处理失败: {document.filename}")
                 message = "文档上传成功，但向量化处理失败"
         except Exception as e:
-            print(f"文档向量化处理异常: {document.filename}, 错误: {str(e)}")
-            import traceback
-            print(f"详细错误信息: {traceback.format_exc()}")
+            logger.error(f"文档向量化处理异常: {document.filename}, 错误: {str(e)}", exc_info=True)
             message = "文档上传成功，但向量化处理失败"
         
         return DocumentUploadResponse(

backend/src/core/security.py

@@ -8,6 +8,7 @@ from passlib.context import CryptContext
 from fastapi import HTTPException, status, Depends
 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
 from pydantic import BaseModel
+from sqlalchemy.orm import Session
 
 from .config import get_settings
 
@@ -82,3 +83,24 @@ async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(s
         return username
     except JWTError:
         raise credentials_exception
+
+
+async def get_current_user_obj(current_user: str = Depends(get_current_user)):
+    """获取当前用户的完整User对象
+
+    用法: current_user: User = Depends(get_current_user_obj)
+    替代: current_user: str = Depends(get_current_user) + 手动 db.query(User)
+    """
+    from ..models.user import User
+    from .database import get_db
+    db = next(get_db())
+    try:
+        user = db.query(User).filter(User.username == current_user).first()
+        if not user:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="用户不存在"
+            )
+        return user
+    finally:
+        db.close()

backend/src/models/chat.py

@@ -13,7 +13,7 @@ class ChatSession(Base):
     __tablename__ = "chat_sessions"
     
     id = Column(Integer, primary_key=True, index=True)
-    user_id = Column(Integer, ForeignKey("users.id"), nullable=False)
+    user_id = Column(Integer, ForeignKey("users.id"), nullable=False, index=True)
     title = Column(String(200), nullable=True)
     is_active = Column(Boolean, default=True)
     created_at = Column(DateTime(timezone=True), server_default=func.now())
@@ -32,7 +32,7 @@ class ChatMessage(Base):
     __tablename__ = "chat_messages"
     
     id = Column(Integer, primary_key=True, index=True)
-    session_id = Column(Integer, ForeignKey("chat_sessions.id"), nullable=False)
+    session_id = Column(Integer, ForeignKey("chat_sessions.id"), nullable=False, index=True)
     role = Column(String(20), nullable=False)  # user, assistant, system
     content = Column(Text, nullable=False)
     message_metadata = Column(Text, nullable=True)  # JSON格式的元数据

backend/src/models/document.py

@@ -14,7 +14,7 @@ class Document(Base):
     
     id = Column(Integer, primary_key=True, index=True)
     user_id = Column(Integer, ForeignKey("users.id"), nullable=True)  # None表示系统文档
-    knowledge_base_id = Column(Integer, ForeignKey("knowledge_bases.id"), nullable=True)  # 所属知识库
+    knowledge_base_id = Column(Integer, ForeignKey("knowledge_bases.id"), nullable=True, index=True)
     filename = Column(String(255), nullable=False)
     original_filename = Column(String(255), nullable=False)
     file_path = Column(String(500), nullable=False)
@@ -47,7 +47,7 @@ class DocumentChunk(Base):
     __tablename__ = "document_chunks"
     
     id = Column(Integer, primary_key=True, index=True)
-    document_id = Column(Integer, ForeignKey("documents.id"), nullable=False)
+    document_id = Column(Integer, ForeignKey("documents.id"), nullable=False, index=True)
     chunk_index = Column(Integer, nullable=False)
     content = Column(Text, nullable=False)
     content_hash = Column(String(64), nullable=False)  # 内容哈希

backend/src/rag/score_utils.py

@@ -0,0 +1,13 @@
+"""
+分数转换工具
+"""
+import math
+
+
+def convert_distance_to_score(distance: float) -> float:
+    """将ChromaDB的distance转换为0-1范围的相似度分数"""
+    if distance < 0:
+        return (1 + distance) / 2
+    if distance > 100:
+        return 1 / (1 + math.log(distance))
+    return 1 / (1 + distance)

backend/src/services/document_service.py

@@ -3,15 +3,19 @@
 """
 import os
 import asyncio
+import logging
 from pathlib import Path
 from typing import List, Dict, Any, Optional
 from sqlalchemy.orm import Session
 from langchain_core.documents import Document as LangChainDocument
 
 from ..models.document import Document, DocumentChunk
+
+logger = logging.getLogger(__name__)
 from ..rag.vector_store import get_vector_store
 from ..rag.document_loaders import DocumentLoaderFactory, PDFImageExtractor
 from ..rag.text_splitters import get_text_splitter
+from ..rag.score_utils import convert_distance_to_score
 from ..llm.siliconflow import get_llm_client
 
 IMAGES_DIR = Path(__file__).parent.parent.parent.parent / "data" / "images"
@@ -65,14 +69,14 @@ class DocumentService:
             if success:
                 document.is_processed = True
                 self.db.commit()
-                print(f"[DocumentService] 文档 {document.filename} 处理完成: "
+                logger.info(f"[DocumentService] 文档 {document.filename} 处理完成: "
                       f"{len(splits)} 个文本块, {len(image_chunks)} 个图片描述块")
                 return True
 
             return False
 
         except Exception as e:
-            print(f"处理文档失败: {str(e)}")
+            logger.error(f"处理文档失败: {str(e)}")
             self.db.rollback()
             return False
 
@@ -94,56 +98,58 @@ class DocumentService:
             images = PDFImageExtractor.extract_images(str(file_path), str(img_output_dir))
 
             if not images:
-                print(f"[DocumentService] 未发现可提取的图片: {filename}")
+                logger.info(f"[DocumentService] 未发现可提取的图片: {filename}")
                 return []
 
-            print(f"[DocumentService] 提取到 {len(images)} 张图片, 开始VLM描述生成...")
+            logger.info(f"[DocumentService] 提取到 {len(images)} 张图片, 开始VLM描述生成...")
 
-            # 批量调用VLM生成描述
+            # 并发调用VLM生成描述（限制并发度为5）
             llm_client = get_llm_client()
-            for idx, img in enumerate(images):
-                try:
-                    description = await llm_client.describe_image(
-                        img["path"],
-                        img.get("context_text", "")
-                    )
+            semaphore = asyncio.Semaphore(5)
 
-                    if description:
-                        # 相对路径用于URL访问
-                        rel_path = f"{document_id}/{img['filename']}"
-                        image_url = f"/images/{rel_path}"
-
-                        # 构建图片描述文本块（URL写入内容，LLM可直接引用）
-                        chunk_content = (
-                            f"[图片描述 - 第{img['page']}页]\n"
-                            f"图片URL: {image_url}\n"
-                            f"图片内容：{description}"
+            async def describe_single_image(idx, img):
+                async with semaphore:
+                    try:
+                        description = await llm_client.describe_image(
+                            img["path"],
+                            img.get("context_text", "")
                         )
+                        if description:
+                            rel_path = f"{document_id}/{img['filename']}"
+                            image_url = f"/images/{rel_path}"
+                            chunk_content = (
+                                f"[图片描述 - 第{img['page']}页]\n"
+                                f"图片URL: {image_url}\n"
+                                f"图片内容：{description}"
+                            )
+                            chunk = LangChainDocument(
+                                page_content=chunk_content,
+                                metadata={
+                                    "document_id": document_id,
+                                    "knowledge_base_id": knowledge_base_id,
+                                    "title": title,
+                                    "filename": filename,
+                                    "source_type": "image",
+                                    "image_path": str(img["path"]),
+                                    "image_url": image_url,
+                                    "page": img["page"],
+                                }
+                            )
+                            logger.info(f"[DocumentService] 图片描述成功 {idx+1}/{len(images)}: {img['filename']}")
+                            return chunk
+                        else:
+                            logger.warning(f"[DocumentService] 图片描述为空 {idx+1}/{len(images)}: {img['filename']}")
+                            return None
+                    except Exception as e:
+                        logger.error(f"[DocumentService] 图片处理失败 {img['filename']}: {e}")
+                        return None
 
-                        chunk = LangChainDocument(
-                            page_content=chunk_content,
-                            metadata={
-                                "document_id": document_id,
-                                "knowledge_base_id": knowledge_base_id,
-                                "title": title,
-                                "filename": filename,
-                                "source_type": "image",
-                                "image_path": str(img["path"]),
-                                "image_url": image_url,
-                                "page": img["page"],
-                            }
-                        )
-                        image_chunks.append(chunk)
-                        print(f"[DocumentService] 图片描述成功 {idx+1}/{len(images)}: {img['filename']}")
-                    else:
-                        print(f"[DocumentService] 图片描述为空 {idx+1}/{len(images)}: {img['filename']}")
-
-                except Exception as e:
-                    print(f"[DocumentService] 图片处理失败 {img['filename']}: {e}")
-                    continue
+            tasks = [describe_single_image(idx, img) for idx, img in enumerate(images)]
+            results = await asyncio.gather(*tasks)
+            image_chunks = [r for r in results if r is not None]
 
         except Exception as e:
-            print(f"[DocumentService] PDF图片处理失败: {e}")
+            logger.error(f"[DocumentService] PDF图片处理失败: {e}")
 
         return image_chunks
     
@@ -166,7 +172,7 @@ class DocumentService:
             search_results = []
             for doc, distance in results:
                 metadata = doc.metadata if hasattr(doc, 'metadata') else {}
-                score = self._convert_distance_to_score(distance)
+                score = convert_distance_to_score(distance)
                 
                 search_results.append({
                     "content": doc.page_content,
@@ -178,24 +184,9 @@ class DocumentService:
             return search_results
             
         except Exception as e:
-            print(f"搜索文档失败: {str(e)}")
+            logger.error(f"搜索文档失败: {str(e)}")
             return []
     
-    def _convert_distance_to_score(self, distance: float) -> float:
-        """将ChromaDB的distance转换为0-1范围的相似度分数"""
-        import math
-        
-        # 内积距离（负值）
-        if distance < 0:
-            return (1 + distance) / 2
-        
-        # 大距离使用对数缩放
-        if distance > 100:
-            return 1 / (1 + math.log(distance))
-        
-        # 标准距离转换
-        return 1 / (1 + distance)
-    
     def get_document_chunks(self, document_id: int) -> List[DocumentChunk]:
         """获取文档的所有块"""
         return self.db.query(DocumentChunk).filter(
@@ -214,6 +205,6 @@ class DocumentService:
             self.db.commit()
             return True
         except Exception as e:
-            print(f"删除文档块失败: {str(e)}")
+            logger.error(f"删除文档块失败: {str(e)}")
             self.db.rollback()
             return False

docker-compose.yml

@@ -6,7 +6,7 @@ services:
     environment:
       POSTGRES_DB: course_agent_db
       POSTGRES_USER: user
-      POSTGRES_PASSWORD: password
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-password}
       POSTGRES_INITDB_ARGS: "--encoding=UTF8 --locale=C"
     volumes:
       - postgres_data:/var/lib/postgresql/data
@@ -35,7 +35,7 @@ services:
       - "8001:8001"  # 前端应用
     environment:
       # 数据库配置
-      DATABASE_URL: postgresql+psycopg://user:password@db:5432/course_agent_db
+      DATABASE_URL: postgresql+psycopg://user:${POSTGRES_PASSWORD:-password}@db:5432/course_agent_db
       
       # JWT配置
       SECRET_KEY: ${SECRET_KEY:-your-super-secret-key-change-in-production}
@@ -43,7 +43,7 @@ services:
       ACCESS_TOKEN_EXPIRE_MINUTES: 30
       
       # 硅基流动API配置
-      SILICONFLOW_API_KEY: ${SILICONFLOW_API_KEY:-sk-pvvtosiglncktlucwarxilvsypqcttqizgpcfdvodgcuaezn}
+      SILICONFLOW_API_KEY: ${SILICONFLOW_API_KEY:?请设置 SILICONFLOW_API_KEY 环境变量}
       SILICONFLOW_BASE_URL: ${SILICONFLOW_BASE_URL:-https://api.siliconflow.cn/v1}
       SILICONFLOW_MODEL: ${SILICONFLOW_MODEL:-Qwen/Qwen3-30B-A3B-Thinking-2507}