feat: implement streaming support for chat and enhance safety review process

- Updated .env.example to include API key placeholder and configuration instructions.
- Refactored main.py to support streaming responses from the LLM, improving user experience during chat interactions.
- Enhanced LLMClient to include methods for streaming chat and collecting responses.
- Modified safety review process to pass static analysis warnings to the LLM for better code safety evaluation.
- Improved UI components in chat_view.py to handle streaming messages effectively.

safety/llm_reviewer.py

@@ -5,7 +5,7 @@ LLM 软规则审查器
 
 import os
 import json
-from typing import Optional
+from typing import Optional, List
 from dataclasses import dataclass
 from dotenv import load_dotenv
 
@@ -36,7 +36,8 @@ class LLMReviewer:
         self,
         user_input: str,
         execution_plan: str,
-        code: str
+        code: str,
+        warnings: Optional[List[str]] = None
     ) -> LLMReviewResult:
         """
         审查代码安全性
@@ -45,6 +46,7 @@ class LLMReviewer:
             user_input: 用户原始需求
             execution_plan: 执行计划
             code: 待审查的代码
+            warnings: 静态检查产生的警告列表
             
         Returns:
             LLMReviewResult: 审查结果
@@ -52,20 +54,26 @@ class LLMReviewer:
         try:
             client = get_client()
             
+            # 构建警告信息
+            warning_text = ""
+            if warnings and len(warnings) > 0:
+                warning_text = "\n\n【静态检查警告】请重点审查以下内容：\n" + "\n".join(f"- {w}" for w in warnings)
+            
             messages = [
                 {"role": "system", "content": SAFETY_REVIEW_SYSTEM},
                 {"role": "user", "content": SAFETY_REVIEW_USER.format(
                     user_input=user_input,
                     execution_plan=execution_plan,
                     code=code
-                )}
+                ) + warning_text}
             ]
             
             response = client.chat(
                 messages=messages,
                 model=self.model_name,
                 temperature=0.1,
-                max_tokens=512
+                max_tokens=512,
+                timeout=120
             )
             
             return self._parse_response(response)
@@ -124,9 +132,9 @@ class LLMReviewer:
 def review_code_safety(
     user_input: str,
     execution_plan: str,
-    code: str
+    code: str,
+    warnings: Optional[List[str]] = None
 ) -> LLMReviewResult:
     """便捷函数：审查代码安全性"""
     reviewer = LLMReviewer()
-    return reviewer.review(user_input, execution_plan, code)
-
+    return reviewer.review(user_input, execution_plan, code, warnings)