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

config/app_config.py

@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+应用配置中心 - 集中管理所有配置项
+"""
+
+import os
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+
+@dataclass
+class AppConfig:
+    """应用配置中心"""
+    
+    # 分析配置
+    max_rounds: int = field(default=20)
+    force_max_rounds: bool = field(default=False)
+    default_output_dir: str = field(default="outputs")
+    
+    # 数据处理配置
+    max_file_size_mb: int = field(default=500)  # 最大文件大小（MB）
+    chunk_size: int = field(default=100000)  # 分块读取大小
+    data_cache_enabled: bool = field(default=True)
+    cache_dir: str = field(default=".cache/data")
+    
+    # LLM配置
+    llm_cache_enabled: bool = field(default=True)
+    llm_cache_dir: str = field(default=".cache/llm")
+    llm_stream_enabled: bool = field(default=False)
+    
+    # 代码执行配置
+    code_timeout: int = field(default=300)  # 代码执行超时（秒）
+    allowed_imports: List[str] = field(default_factory=lambda: [
+        'pandas', 'numpy', 'matplotlib', 'seaborn', 'plotly',
+        'scipy', 'sklearn', 'duckdb', 'datetime', 'json',
+        'os', 're', 'pathlib', 'glob', 'typing', 'collections',
+        'itertools', 'functools', 'warnings'
+    ])
+    
+    # Web配置
+    web_host: str = field(default="0.0.0.0")
+    web_port: int = field(default=8000)
+    upload_dir: str = field(default="uploads")
+    
+    # 日志配置
+    log_filename: str = field(default="log.txt")
+    enable_code_logging: bool = field(default=False)  # 是否记录生成的代码
+    
+    @classmethod
+    def from_env(cls) -> 'AppConfig':
+        """从环境变量创建配置"""
+        config = cls()
+        
+        # 从环境变量覆盖配置
+        if max_rounds := os.getenv("APP_MAX_ROUNDS"):
+            config.max_rounds = int(max_rounds)
+        
+        if chunk_size := os.getenv("APP_CHUNK_SIZE"):
+            config.chunk_size = int(chunk_size)
+            
+        if cache_enabled := os.getenv("APP_CACHE_ENABLED"):
+            config.data_cache_enabled = cache_enabled.lower() == "true"
+            
+        return config
+    
+    def validate(self) -> bool:
+        """验证配置"""
+        if self.max_rounds <= 0:
+            raise ValueError("max_rounds must be positive")
+        
+        if self.chunk_size <= 0:
+            raise ValueError("chunk_size must be positive")
+            
+        if self.code_timeout <= 0:
+            raise ValueError("code_timeout must be positive")
+            
+        return True
+
+
+# 全局配置实例
+app_config = AppConfig.from_env()

config/llm_config copy.py

@@ -20,7 +20,7 @@ class LLMConfig:
     provider: str = os.environ.get("LLM_PROVIDER", "openai")  # openai, gemini, etc.
     api_key: str = os.environ.get("OPENAI_API_KEY", "sk-2187174de21548b0b8b0c92129700199")
     base_url: str = os.environ.get("OPENAI_BASE_URL", "http://127.0.0.1:9999/v1")
-    model: str = os.environ.get("OPENAI_MODEL", "gemini-3-flash")
+    model: str = os.environ.get("OPENAI_MODEL", "gemini--flash")
     temperature: float = 0.5
     max_tokens: int = 131072
 

config/llm_config.py

@@ -20,7 +20,7 @@ class LLMConfig:
     provider: str = os.environ.get("LLM_PROVIDER", "openai")  # openai, gemini, etc.
     api_key: str = os.environ.get("OPENAI_API_KEY", "sk-Gce85QLROESeOWf3icd2mQnYHOrmMYojwVPQ0AubMjGQ5ZE2")
     base_url: str = os.environ.get("OPENAI_BASE_URL", "https://gemini.jeason.online/v1")
-    model: str = os.environ.get("OPENAI_MODEL", "gemini-2.5-flash")
+    model: str = os.environ.get("OPENAI_MODEL", "gemini-2.5-pro")
     temperature: float = 0.5
     max_tokens: int = 131072