feat: Introduce LLM response caching and streaming, add application configuration, and enhance session data with progress and history tracking.

utils/cache_manager.py

@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""
+缓存管理器 - 支持数据和LLM响应缓存
+"""
+
+import os
+import json
+import hashlib
+import pickle
+from pathlib import Path
+from typing import Any, Optional, Callable
+from functools import wraps
+
+
+class CacheManager:
+    """缓存管理器"""
+    
+    def __init__(self, cache_dir: str = ".cache", enabled: bool = True):
+        self.cache_dir = Path(cache_dir)
+        self.enabled = enabled
+        
+        if self.enabled:
+            self.cache_dir.mkdir(parents=True, exist_ok=True)
+    
+    def _get_cache_key(self, *args, **kwargs) -> str:
+        """生成缓存键"""
+        key_data = f"{args}_{kwargs}"
+        return hashlib.md5(key_data.encode()).hexdigest()
+    
+    def _get_cache_path(self, key: str) -> Path:
+        """获取缓存文件路径"""
+        return self.cache_dir / f"{key}.pkl"
+    
+    def get(self, key: str) -> Optional[Any]:
+        """获取缓存"""
+        if not self.enabled:
+            return None
+            
+        cache_path = self._get_cache_path(key)
+        if cache_path.exists():
+            try:
+                with open(cache_path, 'rb') as f:
+                    return pickle.load(f)
+            except Exception as e:
+                print(f"⚠️ 读取缓存失败: {e}")
+                return None
+        return None
+    
+    def set(self, key: str, value: Any) -> None:
+        """设置缓存"""
+        if not self.enabled:
+            return
+            
+        cache_path = self._get_cache_path(key)
+        try:
+            with open(cache_path, 'wb') as f:
+                pickle.dump(value, f)
+        except Exception as e:
+            print(f"⚠️ 写入缓存失败: {e}")
+    
+    def clear(self) -> None:
+        """清空所有缓存"""
+        if self.cache_dir.exists():
+            for cache_file in self.cache_dir.glob("*.pkl"):
+                cache_file.unlink()
+            print("✅ 缓存已清空")
+    
+    def cached(self, key_func: Optional[Callable] = None):
+        """缓存装饰器"""
+        def decorator(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                if not self.enabled:
+                    return func(*args, **kwargs)
+                
+                # 生成缓存键
+                if key_func:
+                    cache_key = key_func(*args, **kwargs)
+                else:
+                    cache_key = self._get_cache_key(*args, **kwargs)
+                
+                # 尝试从缓存获取
+                cached_value = self.get(cache_key)
+                if cached_value is not None:
+                    print(f"💾 使用缓存: {cache_key[:8]}...")
+                    return cached_value
+                
+                # 执行函数并缓存结果
+                result = func(*args, **kwargs)
+                self.set(cache_key, result)
+                return result
+            
+            return wrapper
+        return decorator
+
+
+class LLMCacheManager(CacheManager):
+    """LLM响应缓存管理器"""
+    
+    def get_cache_key_from_messages(self, messages: list, model: str = "") -> str:
+        """从消息列表生成缓存键"""
+        key_data = json.dumps(messages, sort_keys=True) + model
+        return hashlib.md5(key_data.encode()).hexdigest()