remove process file

README.md

@@ -278,7 +278,6 @@ curl -X POST "http://localhost:8001/api/v1/tasks/cleanup?older_than_days=7"
 - `POST /api/v1/chat/completions` - OpenAI 兼容的聊天接口
 
 ### 文件处理接口
-- `POST /api/v1/files/process` - 同步文件处理
 - `POST /api/v1/files/process/async` - 异步文件处理
 - `GET /api/v1/files/{unique_id}/status` - 文件处理状态
 

fastapi_app.py

@@ -16,8 +16,7 @@ from pydantic import BaseModel, Field
 # Import utility modules
 from utils import (
     # Models
-    Message, DatasetRequest, ChatRequest, FileProcessRequest,
+    Message, DatasetRequest, ChatRequest, ChatResponse, QueueTaskRequest, QueueTaskResponse,
-    FileProcessResponse, ChatResponse, QueueTaskRequest, QueueTaskResponse,
     QueueStatusResponse, TaskStatusResponse,
     
     # File utilities
@@ -178,103 +177,6 @@ async def generate_stream_response(agent, messages, request) -> AsyncGenerator[s
 # Models are now imported from utils module
 
 
-@app.post("/api/v1/files/process")
-async def process_files(request: FileProcessRequest, authorization: Optional[str] = Header(None)):
-    """
-    Process dataset files for a given unique_id.
-    Files are organized by key groups, and each group is combined into a single document.txt file.
-    Supports zip files which will be extracted and their txt/md contents combined.
-    
-    Args:
-        request: FileProcessRequest containing unique_id, files (key-grouped dict), system_prompt, and mcp_settings
-        authorization: Authorization header containing API key (Bearer <API_KEY>)
-        
-    Returns:
-        FileProcessResponse: Processing result with file list
-    """
-    try:
-        unique_id = request.unique_id
-        if not unique_id:
-            raise HTTPException(status_code=400, detail="unique_id is required")
-        
-        # 处理文件：使用按key分组格式
-        processed_files_by_key = {}
-        if request.files:
-            # 使用请求中的文件（按key分组）
-            processed_files_by_key = await download_dataset_files(unique_id, request.files)
-            total_files = sum(len(files) for files in processed_files_by_key.values())
-            print(f"Processed {total_files} dataset files across {len(processed_files_by_key)} keys for unique_id: {unique_id}")
-        else:
-            print(f"No files provided in request for unique_id: {unique_id}")
-        
-        # 使用unique_id获取项目目录
-        project_dir = os.path.join("projects", unique_id)
-        if not os.path.exists(project_dir):
-            raise HTTPException(status_code=400, detail=f"Project directory not found for unique_id: {unique_id}")
-        
-        # 收集项目目录下所有的 document.txt 文件
-        document_files = []
-        for root, dirs, files in os.walk(project_dir):
-            for file in files:
-                if file == "document.txt":
-                    document_files.append(os.path.join(root, file))
-        
-        # 合并所有处理的文件（包含新按key分组的文件）
-        all_files = document_files.copy()
-        for key, files in processed_files_by_key.items():
-            all_files.extend(files)
-        
-        if not all_files:
-            print(f"警告: 项目目录 {project_dir} 中未找到任何 document.txt 文件")
-        
-        # 保存system_prompt和mcp_settings到项目目录（如果提供）
-        if request.system_prompt:
-            system_prompt_file = os.path.join(project_dir, "system_prompt.md")
-            with open(system_prompt_file, 'w', encoding='utf-8') as f:
-                f.write(request.system_prompt)
-            print(f"Saved system_prompt for unique_id: {unique_id}")
-        
-        if request.mcp_settings:
-            mcp_settings_file = os.path.join(project_dir, "mcp_settings.json")
-            with open(mcp_settings_file, 'w', encoding='utf-8') as f:
-                json.dump(request.mcp_settings, f, ensure_ascii=False, indent=2)
-            print(f"Saved mcp_settings for unique_id: {unique_id}")
-        
-        # 生成项目README.md文件
-        try:
-            save_project_readme(unique_id)
-            print(f"Generated README.md for unique_id: {unique_id}")
-        except Exception as e:
-            print(f"Failed to generate README.md for unique_id: {unique_id}, error: {str(e)}")
-            # 不影响主要处理流程，继续执行
-        
-        # 返回结果包含按key分组的文件信息
-        result_files = []
-        for key in processed_files_by_key.keys():
-            # 添加对应的dataset document.txt路径
-            document_path = os.path.join("projects", unique_id, "dataset", key, "document.txt")
-            if os.path.exists(document_path):
-                result_files.append(document_path)
-        
-        # 对于没有在processed_files_by_key中但存在的document.txt文件，也添加到结果中
-        existing_document_paths = set(result_files)  # 避免重复
-        for doc_file in document_files:
-            if doc_file not in existing_document_paths:
-                result_files.append(doc_file)
-        
-        return FileProcessResponse(
-            success=True,
-            message=f"Successfully processed {len(result_files)} document files across {len(processed_files_by_key)} keys",
-            unique_id=unique_id,
-            processed_files=result_files
-        )
-        
-    except HTTPException:
-        raise
-    except Exception as e:
-        print(f"Error processing files: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
-
 
 @app.post("/api/v1/files/process/async")
 async def process_files_async_endpoint(request: QueueTaskRequest, authorization: Optional[str] = Header(None)):

task_queue/tasks.py

@@ -303,7 +303,7 @@ def _process_single_file(
         file_ext = os.path.splitext(original_filename)[1].lower()
         
         # 根据文件类型进行不同处理
-        supported_extensions = ['.txt', '.md', '.pdf', '.doc', '.docx', '.zip']
+        supported_extensions = ['.txt', '.md', '.csv', '.xlsx', '.zip']
         
         if file_ext not in supported_extensions:
             return {
@@ -353,4 +353,4 @@ def daily_cleanup():
     """每日清理任务"""
     print("执行每日清理任务")
     # 这里可以添加清理逻辑
-    return {"status": "completed", "message": "每日清理任务完成"}
+    return {"status": "completed", "message": "每日清理任务完成"}

utils/__init__.py

@@ -56,11 +56,9 @@ from .api_models import (
     Message,
     DatasetRequest,
     ChatRequest,
-    FileProcessRequest,
     DatasetResponse,
     ChatCompletionResponse,
     ChatResponse,
-    FileProcessResponse,
     ErrorResponse,
     HealthCheckResponse,
     SystemStatusResponse,
@@ -131,11 +129,9 @@ __all__ = [
     'Message',
     'DatasetRequest',
     'ChatRequest', 
-    'FileProcessRequest',
     'DatasetResponse',
     'ChatCompletionResponse',
     'ChatResponse',
-    'FileProcessResponse',
     'ErrorResponse',
     'HealthCheckResponse',
     'SystemStatusResponse',