新增folder功能，删除unique_id

fastapi_app.py

@@ -224,7 +224,10 @@ async def process_files_async_endpoint(request: QueueTaskRequest, authorization:
         
         # 估算处理时间（基于文件数量）
         estimated_time = 0
-        if request.files:
+        if request.upload_folder:
+            # 对于upload_folder，无法预先估算文件数量，使用默认时间
+            estimated_time = 120  # 默认2分钟
+        elif request.files:
             total_files = sum(len(file_list) for file_list in request.files.values())
             estimated_time = max(30, total_files * 10)  # 每个文件预估10秒，最少30秒
         
@@ -246,17 +249,25 @@ async def process_files_async_endpoint(request: QueueTaskRequest, authorization:
         
         # 提交异步任务
         task = process_files_async(
-            unique_id=dataset_id,
+            dataset_id=dataset_id,
             files=request.files,
-            system_prompt=request.system_prompt,
-            mcp_settings=request.mcp_settings,
+            upload_folder=request.upload_folder,
             task_id=task_id
         )
         
+        # 构建更详细的消息
+        message = f"文件处理任务已提交到队列，项目ID: {dataset_id}"
+        if request.upload_folder:
+            group_count = len(request.upload_folder)
+            message += f"，将从 {group_count} 个上传文件夹自动扫描文件"
+        elif request.files:
+            total_files = sum(len(file_list) for file_list in request.files.values())
+            message += f"，包含 {total_files} 个文件"
+        
         return QueueTaskResponse(
             success=True,
-            message=f"文件处理任务已提交到队列，项目ID: {dataset_id}",
-            unique_id=dataset_id,
+            message=message,
+            dataset_id=dataset_id,
             task_id=task_id,  # 使用我们自己的task_id
             task_status="pending",
             estimated_processing_time=estimated_time
@@ -319,7 +330,7 @@ async def process_files_incremental_endpoint(request: IncrementalTaskRequest, au
         return QueueTaskResponse(
             success=True,
             message=f"增量文件处理任务已提交到队列 - 添加 {total_add_files} 个文件，删除 {total_remove_files} 个文件，项目ID: {dataset_id}",
-            unique_id=dataset_id,
+            dataset_id=dataset_id,
             task_id=task_id,
             task_status="pending",
             estimated_processing_time=estimated_time
@@ -618,7 +629,7 @@ async def get_project_tasks(dataset_id: str):
 async def cleanup_project_async_endpoint(dataset_id: str, remove_all: bool = False):
     """异步清理项目文件"""
     try:
-        task = cleanup_project_async(unique_id=dataset_id, remove_all=remove_all)
+        task = cleanup_project_async(dataset_id=dataset_id, remove_all=remove_all)
         
         return {
             "success": True,
@@ -976,19 +987,33 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
 
 
 @app.post("/api/v1/upload")
-async def upload_file(file: UploadFile = File(...)):
+async def upload_file(file: UploadFile = File(...), folder: Optional[str] = None):
     """
-    文件上传API接口，上传文件到 ./projects/uploads 目录
+    文件上传API接口，上传文件到 ./projects/uploads/ 目录下
+    
+    可以指定自定义文件夹名，如果不指定则使用日期文件夹
 
     Args:
         file: 上传的文件
+        folder: 可选的自定义文件夹名
 
     Returns:
-        dict: 包含文件路径和文件名的响应
+        dict: 包含文件路径和文件夹信息的响应
     """
     try:
-        # 确保上传目录存在
-        upload_dir = os.path.join("projects", "uploads")
+        # 确定上传文件夹
+        if folder:
+            # 使用指定的自定义文件夹
+            target_folder = folder
+            # 安全性检查：防止路径遍历攻击
+            target_folder = os.path.basename(target_folder)
+        else:
+            # 获取当前日期并格式化为年月日
+            current_date = datetime.now()
+            target_folder = current_date.strftime("%Y%m%d")
+        
+        # 创建上传目录
+        upload_dir = os.path.join("projects", "uploads", target_folder)
         os.makedirs(upload_dir, exist_ok=True)
 
         # 生成唯一文件名
@@ -1003,9 +1028,8 @@ async def upload_file(file: UploadFile = File(...)):
         return {
             "success": True,
             "message": "文件上传成功",
-            "filename": unique_filename,
-            "original_filename": file.filename,
-            "file_path": file_path
+            "file_path": file_path,
+            "folder": target_folder
         }
 
     except Exception as e:

task_queue/integration_tasks.py

@@ -6,6 +6,8 @@ Queue tasks for file processing integration.
 import os
 import json
 import time
+import hashlib
+import shutil
 from typing import Dict, List, Optional, Any
 
 from task_queue.config import huey
@@ -15,45 +17,134 @@ from utils import download_dataset_files, save_processed_files_log, load_process
 from utils.dataset_manager import remove_dataset_directory_by_key
 
 
+def scan_upload_folder(upload_dir: str) -> List[str]:
+    """
+    扫描上传文件夹中的所有支持格式的文件
+    
+    Args:
+        upload_dir: 上传文件夹路径
+        
+    Returns:
+        List[str]: 支持的文件路径列表
+    """
+    supported_extensions = {
+        # 文本文件
+        '.txt', '.md', '.rtf',
+        # 文档文件
+        '.doc', '.docx', '.pdf', '.odt',
+        # 表格文件
+        '.xls', '.xlsx', '.csv', '.ods',
+        # 演示文件
+        '.ppt', '.pptx', '.odp',
+        # 电子书
+        '.epub', '.mobi',
+        # 网页文件
+        '.html', '.htm',
+        # 配置文件
+        '.json', '.xml', '.yaml', '.yml',
+        # 代码文件
+        '.py', '.js', '.java', '.cpp', '.c', '.go', '.rs',
+        # 压缩文件
+        '.zip', '.rar', '.7z', '.tar', '.gz'
+    }
+    
+    scanned_files = []
+    
+    if not os.path.exists(upload_dir):
+        return scanned_files
+    
+    for root, dirs, files in os.walk(upload_dir):
+        for file in files:
+            # 跳过隐藏文件和系统文件
+            if file.startswith('.') or file.startswith('~'):
+                continue
+            
+            file_path = os.path.join(root, file)
+            file_extension = os.path.splitext(file)[1].lower()
+            
+            # 检查文件扩展名是否支持
+            if file_extension in supported_extensions:
+                scanned_files.append(file_path)
+            else:
+                # 对于没有扩展名的文件，也尝试处理（可能是文本文件）
+                if not file_extension:
+                    try:
+                        # 尝试读取文件头部来判断是否为文本文件
+                        with open(file_path, 'r', encoding='utf-8') as f:
+                            f.read(1024)  # 读取前1KB
+                        scanned_files.append(file_path)
+                    except (UnicodeDecodeError, PermissionError):
+                        # 不是文本文件或无法读取，跳过
+                        pass
+    
+    return scanned_files
+
+
 @huey.task()
 def process_files_async(
-    unique_id: str,
+    dataset_id: str,
     files: Optional[Dict[str, List[str]]] = None,
-    system_prompt: Optional[str] = None,
-    mcp_settings: Optional[List[Dict]] = None,
+    upload_folder: Optional[Dict[str, str]] = None,
     task_id: Optional[str] = None
 ) -> Dict[str, Any]:
     """
     异步处理文件任务 - 与现有files/process API兼容
     
     Args:
-        unique_id: 项目唯一ID
+        dataset_id: 项目唯一ID
         files: 按key分组的文件路径字典
-        system_prompt: 系统提示词
-        mcp_settings: MCP设置
+        upload_folder: 上传文件夹字典，按组名组织文件夹，例如 {'group1': 'my_project1', 'group2': 'my_project2'}
         task_id: 任务ID（用于状态跟踪）
         
     Returns:
         处理结果字典
     """
     try:
-        print(f"开始异步处理文件任务，项目ID: {unique_id}")
+        print(f"开始异步处理文件任务，项目ID: {dataset_id}")
         
         # 如果有task_id，设置初始状态
         if task_id:
             task_status_store.set_status(
                 task_id=task_id,
-                unique_id=unique_id,
+                unique_id=dataset_id,
                 status="running"
             )
         
         # 确保项目目录存在
-        project_dir = os.path.join("projects", "data", unique_id)
+        project_dir = os.path.join("projects", "data", dataset_id)
         if not os.path.exists(project_dir):
             os.makedirs(project_dir, exist_ok=True)
         
         # 处理文件：使用按key分组格式
         processed_files_by_key = {}
+        
+        # 如果提供了upload_folder，扫描这些文件夹中的文件
+        if upload_folder and not files:
+            scanned_files_by_group = {}
+            total_scanned_files = 0
+            
+            for group_name, folder_name in upload_folder.items():
+                # 安全性检查：防止路径遍历攻击
+                safe_folder_name = os.path.basename(folder_name)
+                upload_dir = os.path.join("projects", "uploads", safe_folder_name)
+                
+                if os.path.exists(upload_dir):
+                    scanned_files = scan_upload_folder(upload_dir)
+                    if scanned_files:
+                        scanned_files_by_group[group_name] = scanned_files
+                        total_scanned_files += len(scanned_files)
+                        print(f"从上传文件夹 '{safe_folder_name}' (组: {group_name}) 扫描到 {len(scanned_files)} 个文件")
+                    else:
+                        print(f"上传文件夹 '{safe_folder_name}' (组: {group_name}) 中没有找到支持的文件")
+                else:
+                    print(f"上传文件夹不存在: {upload_dir} (组: {group_name})")
+            
+            if scanned_files_by_group:
+                files = scanned_files_by_group
+                print(f"总共从 {len(scanned_files_by_group)} 个组扫描到 {total_scanned_files} 个文件")
+            else:
+                print(f"所有上传文件夹中都没有找到支持的文件")
+        
         if files:
             # 使用请求中的文件（按key分组）
             # 由于这是异步任务，需要同步调用
@@ -64,11 +155,11 @@ def process_files_async(
                 loop = asyncio.new_event_loop()
                 asyncio.set_event_loop(loop)
             
-            processed_files_by_key = loop.run_until_complete(download_dataset_files(unique_id, files))
+            processed_files_by_key = loop.run_until_complete(download_dataset_files(dataset_id, files))
             total_files = sum(len(files_list) for files_list in processed_files_by_key.values())
-            print(f"异步处理了 {total_files} 个数据集文件，涉及 {len(processed_files_by_key)} 个key，项目ID: {unique_id}")
+            print(f"异步处理了 {total_files} 个数据集文件，涉及 {len(processed_files_by_key)} 个key，项目ID: {dataset_id}")
         else:
-            print(f"请求中未提供文件，项目ID: {unique_id}")
+            print(f"请求中未提供文件，项目ID: {dataset_id}")
         
         # 收集项目目录下所有的 document.txt 文件
         document_files = []
@@ -77,33 +168,20 @@ def process_files_async(
                 if file == "document.txt":
                     document_files.append(os.path.join(root, file))
         
-        # 保存system_prompt和mcp_settings到项目目录（如果提供）
-        if 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(system_prompt)
-            print(f"已保存system_prompt，项目ID: {unique_id}")
-        
-        if 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(mcp_settings, f, ensure_ascii=False, indent=2)
-            print(f"已保存mcp_settings，项目ID: {unique_id}")
-        
         # 生成项目README.md文件
         try:
             from utils.project_manager import save_project_readme
-            save_project_readme(unique_id)
-            print(f"已生成README.md文件，项目ID: {unique_id}")
+            save_project_readme(dataset_id)
+            print(f"已生成README.md文件，项目ID: {dataset_id}")
         except Exception as e:
-            print(f"生成README.md失败，项目ID: {unique_id}, 错误: {str(e)}")
+            print(f"生成README.md失败，项目ID: {dataset_id}, 错误: {str(e)}")
             # 不影响主要处理流程，继续执行
         
         # 构建结果文件列表
         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", "data", unique_id, "dataset", key, "document.txt")
+            document_path = os.path.join("projects", "data", dataset_id, "dataset", key, "document.txt")
             if os.path.exists(document_path):
                 result_files.append(document_path)
         
@@ -116,7 +194,7 @@ def process_files_async(
         result = {
             "status": "success",
             "message": f"成功异步处理了 {len(result_files)} 个文档文件，涉及 {len(processed_files_by_key)} 个key",
-            "unique_id": unique_id,
+            "dataset_id": dataset_id,
             "processed_files": result_files,
             "processed_files_by_key": processed_files_by_key,
             "document_files": document_files,
@@ -132,7 +210,7 @@ def process_files_async(
                 result=result
             )
         
-        print(f"异步文件处理任务完成: {unique_id}")
+        print(f"异步文件处理任务完成: {dataset_id}")
         return result
         
     except Exception as e:
@@ -150,7 +228,7 @@ def process_files_async(
         return {
             "status": "error",
             "message": error_msg,
-            "unique_id": unique_id,
+            "dataset_id": dataset_id,
             "error": str(e)
         }
 
@@ -220,8 +298,23 @@ def process_files_incremental_async(
                     # 删除特定文件
                     for file_path in file_list:
                         print(f"删除特定文件: {key}/{file_path}")
+                        
+                        # 实际删除文件
+                        filename = os.path.basename(file_path)
+                        
+                        # 删除原始文件
+                        source_file = os.path.join("projects", "data", dataset_id, "files", key, filename)
+                        if os.path.exists(source_file):
+                            os.remove(source_file)
+                            removed_files.append(f"file:{key}/{filename}")
+                        
+                        # 删除处理后的文件目录
+                        processed_dir = os.path.join("projects", "data", dataset_id, "processed", key, filename)
+                        if os.path.exists(processed_dir):
+                            shutil.rmtree(processed_dir)
+                            removed_files.append(f"processed:{key}/{filename}")
+                        
                         # 计算文件hash以在日志中查找
-                        import hashlib
                         file_hash = hashlib.md5(file_path.encode('utf-8')).hexdigest()
                         
                         # 从处理日志中移除
@@ -241,7 +334,7 @@ def process_files_incremental_async(
                 loop = asyncio.new_event_loop()
                 asyncio.set_event_loop(loop)
             
-            processed_files_by_key = loop.run_until_complete(download_dataset_files(dataset_id, files_to_add))
+            processed_files_by_key = loop.run_until_complete(download_dataset_files(dataset_id, files_to_add, incremental_mode=True))
             total_added_files = sum(len(files_list) for files_list in processed_files_by_key.values())
             print(f"异步处理了 {total_added_files} 个数据集文件，涉及 {len(processed_files_by_key)} 个key，项目ID: {dataset_id}")
             
@@ -347,23 +440,23 @@ def process_files_incremental_async(
 
 @huey.task()
 def cleanup_project_async(
-    unique_id: str,
+    dataset_id: str,
     remove_all: bool = False
 ) -> Dict[str, Any]:
     """
     异步清理项目文件
     
     Args:
-        unique_id: 项目唯一ID
+        dataset_id: 项目唯一ID
         remove_all: 是否删除整个项目目录
         
     Returns:
         清理结果字典
     """
     try:
-        print(f"开始异步清理项目，项目ID: {unique_id}")
+        print(f"开始异步清理项目，项目ID: {dataset_id}")
         
-        project_dir = os.path.join("projects", "data", unique_id)
+        project_dir = os.path.join("projects", "data", dataset_id)
         removed_items = []
         
         if remove_all and os.path.exists(project_dir):
@@ -373,7 +466,7 @@ def cleanup_project_async(
             result = {
                 "status": "success",
                 "message": f"已删除整个项目目录: {project_dir}",
-                "unique_id": unique_id,
+                "dataset_id": dataset_id,
                 "removed_items": removed_items,
                 "action": "remove_all"
             }
@@ -386,13 +479,13 @@ def cleanup_project_async(
             
             result = {
                 "status": "success",
-                "message": f"已清理项目处理日志，项目ID: {unique_id}",
-                "unique_id": unique_id,
+                "message": f"已清理项目处理日志，项目ID: {dataset_id}",
+                "dataset_id": dataset_id,
                 "removed_items": removed_items,
                 "action": "cleanup_logs"
             }
         
-        print(f"异步清理任务完成: {unique_id}")
+        print(f"异步清理任务完成: {dataset_id}")
         return result
         
     except Exception as e:
@@ -401,6 +494,6 @@ def cleanup_project_async(
         return {
             "status": "error",
             "message": error_msg,
-            "unique_id": unique_id,
+            "dataset_id": dataset_id,
             "error": str(e)
         }

utils/api_models.py

@@ -215,15 +215,31 @@ def create_error_response(message: str, error_type: str = "error", **kwargs) ->
 
 class QueueTaskRequest(BaseModel):
     """队列任务请求模型"""
-    unique_id: str
+    dataset_id: str
     files: Optional[Dict[str, List[str]]] = Field(default=None, description="Files organized by key groups. Each key maps to a list of file paths (supports zip files)")
-    system_prompt: Optional[str] = None
-    mcp_settings: Optional[List[Dict]] = None
+    upload_folder: Optional[Dict[str, str]] = Field(default=None, description="Upload folders organized by group names. Each key maps to a folder name. Example: {'group1': 'my_project1', 'group2': 'my_project2'}")
     priority: Optional[int] = Field(default=0, description="Task priority (higher number = higher priority)")
     delay: Optional[int] = Field(default=0, description="Delay execution by N seconds")
 
     model_config = ConfigDict(extra='allow')
     
+    @field_validator('upload_folder', mode='before')
+    @classmethod
+    def validate_upload_folder(cls, v):
+        """Validate upload_folder dict format"""
+        if v is None:
+            return None
+        if isinstance(v, dict):
+            # 验证字典格式
+            for key, value in v.items():
+                if not isinstance(key, str):
+                    raise ValueError(f"Key in upload_folder dict must be string, got {type(key)}")
+                if not isinstance(value, str):
+                    raise ValueError(f"Value in upload_folder dict must be string (folder name), got {type(value)} for key '{key}'")
+            return v
+        else:
+            raise ValueError(f"upload_folder must be a dict with group names as keys and folder names as values, got {type(v)}")
+    
     @field_validator('files', mode='before')
     @classmethod
     def validate_files(cls, v):
@@ -300,7 +316,7 @@ class QueueTaskResponse(BaseModel):
     """队列任务响应模型"""
     success: bool
     message: str
-    unique_id: str
+    dataset_id: str
     task_id: Optional[str] = None
     task_status: Optional[str] = None
     estimated_processing_time: Optional[int] = None  # seconds

utils/data_merger.py

@@ -193,8 +193,12 @@ def merge_embeddings_by_group(unique_id: str, group_name: str) -> Dict:
         
         # Load and merge all embedding data
         all_chunks = []
+        all_embeddings = []  # 修复：收集所有embeddings向量
         total_chunks = 0
         dimensions = 0
+        chunking_strategy = 'unknown'
+        chunking_params = {}
+        model_path = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
         
         for filename_stem, embedding_path in sorted(embedding_files):
             try:
@@ -204,35 +208,110 @@ def merge_embeddings_by_group(unique_id: str, group_name: str) -> Dict:
                 if isinstance(embedding_data, dict) and 'chunks' in embedding_data:
                     chunks = embedding_data['chunks']
                     
+                    # 获取embeddings向量（关键修复）
+                    if 'embeddings' in embedding_data:
+                        embeddings = embedding_data['embeddings']
+                        all_embeddings.append(embeddings)
+                        
+                        # 从第一个文件获取模型信息
+                        if 'model_path' in embedding_data:
+                            model_path = embedding_data['model_path']
+                        if 'chunking_strategy' in embedding_data:
+                            chunking_strategy = embedding_data['chunking_strategy']
+                        if 'chunking_params' in embedding_data:
+                            chunking_params = embedding_data['chunking_params']
+                    
                     # Add source file metadata to each chunk
                     for chunk in chunks:
                         if isinstance(chunk, dict):
                             chunk['source_file'] = filename_stem
                             chunk['source_group'] = group_name
+                        elif isinstance(chunk, str):
+                            # 如果chunk是字符串，保持原样
+                            pass
                     
                     all_chunks.extend(chunks)
                     total_chunks += len(chunks)
                     
-                    # Get dimensions from first chunk if available
-                    if dimensions == 0 and chunks and isinstance(chunks[0], dict):
-                        if 'embedding' in chunks[0] and hasattr(chunks[0]['embedding'], 'shape'):
-                            dimensions = chunks[0]['embedding'].shape[0]
-                    
                     result["source_files"].append(filename_stem)
                 
             except Exception as e:
                 print(f"Error loading embedding file {embedding_path}: {str(e)}")
                 continue
         
-        if all_chunks:
+        if all_chunks and all_embeddings:
+            # 合并所有embeddings向量
+            try:
+                # 尝试使用torch合并张量
+                import torch
+                if all(isinstance(emb, torch.Tensor) for emb in all_embeddings):
+                    merged_embeddings = torch.cat(all_embeddings, dim=0)
+                    dimensions = merged_embeddings.shape[1]
+                else:
+                    # 如果不是tensor类型，尝试转换为numpy
+                    import numpy as np
+                    if NUMPY_SUPPORT:
+                        np_embeddings = []
+                        for emb in all_embeddings:
+                            if hasattr(emb, 'numpy'):
+                                np_embeddings.append(emb.numpy())
+                            elif isinstance(emb, np.ndarray):
+                                np_embeddings.append(emb)
+                            else:
+                                # 如果无法转换，跳过这个文件
+                                print(f"Warning: Cannot convert embedding to numpy from file {filename_stem}")
+                                continue
+                        
+                        if np_embeddings:
+                            merged_embeddings = np.concatenate(np_embeddings, axis=0)
+                            dimensions = merged_embeddings.shape[1]
+                        else:
+                            result["error"] = "No valid embedding tensors could be merged"
+                            return result
+                    else:
+                        result["error"] = "NumPy not available for merging embeddings"
+                        return result
+                        
+            except ImportError:
+                # 如果没有torch，尝试使用numpy
+                if NUMPY_SUPPORT:
+                    import numpy as np
+                    np_embeddings = []
+                    for emb in all_embeddings:
+                        if hasattr(emb, 'numpy'):
+                            np_embeddings.append(emb.numpy())
+                        elif isinstance(emb, np.ndarray):
+                            np_embeddings.append(emb)
+                        else:
+                            print(f"Warning: Cannot convert embedding to numpy from file {filename_stem}")
+                            continue
+                    
+                    if np_embeddings:
+                        merged_embeddings = np.concatenate(np_embeddings, axis=0)
+                        dimensions = merged_embeddings.shape[1]
+                    else:
+                        result["error"] = "No valid embedding tensors could be merged"
+                        return result
+                else:
+                    result["error"] = "Neither torch nor numpy available for merging embeddings"
+                    return result
+            except Exception as e:
+                result["error"] = f"Failed to merge embedding tensors: {str(e)}"
+                print(f"Error merging embedding tensors: {str(e)}")
+                return result
+            
             # Create merged embedding data structure
             merged_embedding_data = {
                 'chunks': all_chunks,
+                'embeddings': merged_embeddings,  # 关键修复：添加embeddings键
                 'total_chunks': total_chunks,
                 'dimensions': dimensions,
                 'source_files': result["source_files"],
                 'group_name': group_name,
-                'merged_at': str(os.path.getmtime(merged_embedding_path) if os.path.exists(merged_embedding_path) else 0)
+                'merged_at': str(__import__('time').time()),
+                'chunking_strategy': chunking_strategy,
+                'chunking_params': chunking_params,
+                'model_path': model_path
             }
             
             # Save merged embeddings

utils/dataset_manager.py

@@ -21,34 +21,42 @@ from utils.data_merger import (
 )
 
 
-async def download_dataset_files(unique_id: str, files: Dict[str, List[str]]) -> Dict[str, List[str]]:
+async def download_dataset_files(unique_id: str, files: Dict[str, List[str]], incremental_mode: bool = False) -> Dict[str, List[str]]:
     """
     Process dataset files with new architecture:
     1. Sync files to group directories
     2. Process each file individually
     3. Merge results by group
-    4. Clean up orphaned files
+    4. Clean up orphaned files (only in non-incremental mode)
+    
+    Args:
+        unique_id: Project ID
+        files: Dictionary of files to process, grouped by key
+        incremental_mode: If True, preserve existing files and only process new ones
     """
     if not files:
         return {}
     
-    print(f"Starting new file processing for project: {unique_id}")
+    print(f"Starting {'incremental' if incremental_mode else 'full'} file processing for project: {unique_id}")
     
     # Ensure project directories exist
     ensure_directories(unique_id)
     
     # Step 1: Sync files to group directories
     print("Step 1: Syncing files to group directories...")
-    synced_files, failed_files = sync_files_to_group(unique_id, files)
+    synced_files, failed_files = sync_files_to_group(unique_id, files, incremental_mode)
     
-    # Step 2: Detect changes and cleanup orphaned files
+    # Step 2: Detect changes and cleanup orphaned files (only in non-incremental mode)
     from utils.file_manager import detect_file_changes
-    changes = detect_file_changes(unique_id, files)
+    changes = detect_file_changes(unique_id, files, incremental_mode)
     
-    if any(changes["removed"].values()):
+    # Only cleanup orphaned files in non-incremental mode or when files are explicitly removed
+    if not incremental_mode and any(changes["removed"].values()):
         print("Step 2: Cleaning up orphaned files...")
         removed_files = cleanup_orphaned_files(unique_id, changes)
         print(f"Removed orphaned files: {removed_files}")
+    elif incremental_mode:
+        print("Step 2: Skipping cleanup in incremental mode to preserve existing files")
     
     # Step 3: Process individual files
     print("Step 3: Processing individual files...")

utils/file_manager.py

@@ -30,8 +30,16 @@ def get_existing_files(unique_id: str) -> Dict[str, Set[str]]:
     return existing_files
 
 
-def detect_file_changes(unique_id: str, new_files: Dict[str, List[str]]) -> Dict:
-    """Detect file changes: added, removed, and existing files."""
+def detect_file_changes(unique_id: str, new_files: Dict[str, List[str]], incremental_mode: bool = False) -> Dict:
+    """
+    Detect file changes: added, removed, and existing files.
+    
+    Args:
+        unique_id: Project ID
+        new_files: Dictionary of files to process, grouped by key
+        incremental_mode: If True, only detect removed files when files_to_remove is explicitly provided
+                         This prevents accidental deletion of existing files during incremental additions
+    """
     existing_files = get_existing_files(unique_id)
     
     changes = {
@@ -63,6 +71,8 @@ def detect_file_changes(unique_id: str, new_files: Dict[str, List[str]]) -> Dict
             changes["removed"][group] = set()
     
     # Detect removed files (files that exist but not in new request)
+    # Skip this step in incremental mode to preserve existing files
+    if not incremental_mode:
         for group, existing_filenames in existing_files.items():
             if group in new_files_sets:
                 # Group exists in new request, check for individual file removals
@@ -76,10 +86,15 @@ def detect_file_changes(unique_id: str, new_files: Dict[str, List[str]]) -> Dict
     return changes
 
 
-def sync_files_to_group(unique_id: str, files: Dict[str, List[str]]) -> Tuple[Dict, Dict]:
+def sync_files_to_group(unique_id: str, files: Dict[str, List[str]], incremental_mode: bool = False) -> Tuple[Dict, Dict]:
     """
     Sync files to group directories and return sync results.
     
+    Args:
+        unique_id: Project ID
+        files: Dictionary of files to sync, grouped by key
+        incremental_mode: If True, preserve existing files and only add new ones
+    
     Returns:
         Tuple of (synced_files, failed_files)
     """
@@ -90,7 +105,7 @@ def sync_files_to_group(unique_id: str, files: Dict[str, List[str]]) -> Tuple[Di
     os.makedirs(files_dir, exist_ok=True)
     
     # Detect changes first
-    changes = detect_file_changes(unique_id, files)
+    changes = detect_file_changes(unique_id, files, incremental_mode)
     
     synced_files = {}
     failed_files = {}