refactor: 程式碼品質優化、LangChain 遷移與內容擴充

3.LLM應用工程/1.LLM 部署/examples/06_model_comparison.py

@@ -282,8 +282,8 @@ def demo_basic_comparison():
     try:
         ollama.list()
         available_models.extend(["llama3.1:8b", "mistral:7b"])
-    except:
-        console.print("[yellow]⚠️  Ollama 不可用[/yellow]")
+    except Exception as e:
+        console.print(f"[yellow]⚠️  Ollama 不可用: {e}[/yellow]")
 
     if not available_models:
         console.print("[red]❌ 沒有可用的模型[/red]\n")
@@ -324,7 +324,7 @@ def demo_performance_comparison():
     try:
         ollama.list()
         models.append("llama3.1:8b")
-    except:
+    except Exception:
         pass
 
     if len(models) < 2:
@@ -444,7 +444,7 @@ def demo_quality_comparison():
     try:
         ollama.list()
         models.append("llama3.1:8b")
-    except:
+    except Exception:
         pass
 
     if not models:
@@ -477,7 +477,7 @@ def demo_save_comparison():
     try:
         ollama.list()
         models.append("llama3.1:8b")
-    except:
+    except Exception:
         pass
 
     if not models:

3.LLM應用工程/1.LLM 部署/tools/benchmark.py

@@ -223,7 +223,7 @@ def main():
             ollama_models = ollama.list().get('models', [])
             if ollama_models:
                 default_models.append(ollama_models[0]['name'])
-        except:
+        except (ImportError, Exception):
             pass
 
         if default_models:

3.LLM應用工程/11.MCP協議與工具調用/README.md

@@ -335,8 +335,29 @@ async def calculate(expression: str) -> str:
     Returns:
         計算結果
     """
+    import ast
+    import operator
+
+    # 安全的運算符映射
+    ops = {
+        ast.Add: operator.add, ast.Sub: operator.sub,
+        ast.Mult: operator.mul, ast.Div: operator.truediv,
+        ast.Pow: operator.pow, ast.USub: operator.neg
+    }
+
+    def safe_eval(node):
+        if isinstance(node, ast.Num):
+            return node.n
+        elif isinstance(node, ast.BinOp):
+            return ops[type(node.op)](safe_eval(node.left), safe_eval(node.right))
+        elif isinstance(node, ast.UnaryOp):
+            return ops[type(node.op)](safe_eval(node.operand))
+        else:
+            raise ValueError(f"不支援的運算: {type(node)}")
+
     try:
-        result = eval(expression)  # 注意: 生產環境需要安全處理
+        tree = ast.parse(expression, mode='eval')
+        result = safe_eval(tree.body)
         return f"結果: {result}"
     except Exception as e:
         return f"計算錯誤: {str(e)}"

3.LLM應用工程/2.LLM as API/README.md

@@ -378,9 +378,8 @@ if __name__ == "__main__":
 **RAG 應用範例**：
 ```python
 import gradio as gr
-from langchain.vectorstores import Chroma
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.chat_models import ChatOpenAI
+from langchain_community.vectorstores import Chroma
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
 from langchain.chains import RetrievalQA
 
 # 初始化

3.LLM應用工程/2.LLM as API/examples/basic_apis/03_gemini_basic.py

@@ -36,7 +36,7 @@ def basic_chat():
         print(f"  - Prompt tokens: {response.usage_metadata.prompt_token_count}")
         print(f"  - Candidates tokens: {response.usage_metadata.candidates_token_count}")
         print(f"  - Total tokens: {response.usage_metadata.total_token_count}")
-    except:
+    except AttributeError:
         pass
 
     return response
@@ -196,7 +196,7 @@ def safety_settings_example():
     try:
         for rating in response.candidates[0].safety_ratings:
             print(f"  - {rating.category.name}: {rating.probability.name}")
-    except:
+    except (AttributeError, IndexError):
         print("  無安全評分資訊")
 
     return response

3.LLM應用工程/2.LLM as API/examples/basic_apis/04_api_comparison.py

@@ -82,7 +82,7 @@ def test_gemini(prompt: str, model_name: str = "gemini-1.5-pro") -> Dict:
         prompt_tokens = response.usage_metadata.prompt_token_count
         completion_tokens = response.usage_metadata.candidates_token_count
         total_tokens = response.usage_metadata.total_token_count
-    except:
+    except AttributeError:
         prompt_tokens = 0
         completion_tokens = 0
         total_tokens = 0

3.LLM應用工程/2.LLM as API/examples/frontend_integration/streamlit_rag_chat.py

@@ -17,9 +17,9 @@
 # 嘗試導入 LangChain（如果可用）
 try:
     from langchain.text_splitter import RecursiveCharacterTextSplitter
-    from langchain.embeddings import OpenAIEmbeddings
-    from langchain.vectorstores import FAISS
-    from langchain.document_loaders import (
+    from langchain_openai import OpenAIEmbeddings
+    from langchain_community.vectorstores import FAISS
+    from langchain_community.document_loaders import (
         TextLoader,
         PDFMinerLoader,
         UnstructuredMarkdownLoader

3.LLM應用工程/3.Agent/AI_Agents_與_Agentic_Workflows_2024-2025.md

@@ -170,7 +170,7 @@ plan = [
 **實作範例**：
 ```python
 from langchain.memory import ConversationBufferMemory, VectorStoreRetrieverMemory
-from langchain.vectorstores import Chroma
+from langchain_community.vectorstores import Chroma
 
 # 短期記憶
 short_term_memory = ConversationBufferMemory(
@@ -484,9 +484,28 @@ def search_tool(query: str) -> str:
     return mock_results.get(query, "未找到相關資訊")
 
 def calculator_tool(expression: str) -> str:
-    """計算器工具"""
+    """計算器工具（安全版本）"""
+    import ast
+    import operator
+
+    ops = {
+        ast.Add: operator.add, ast.Sub: operator.sub,
+        ast.Mult: operator.mul, ast.Div: operator.truediv,
+        ast.Pow: operator.pow, ast.USub: operator.neg
+    }
+
+    def safe_eval(node):
+        if isinstance(node, ast.Num):
+            return node.n
+        elif isinstance(node, ast.BinOp):
+            return ops[type(node.op)](safe_eval(node.left), safe_eval(node.right))
+        elif isinstance(node, ast.UnaryOp):
+            return ops[type(node.op)](safe_eval(node.operand))
+        raise ValueError("不支援的運算")
+
     try:
-        result = eval(expression)  # 注意：生產環境應使用安全的計算方法
+        tree = ast.parse(expression, mode='eval')
+        result = safe_eval(tree.body)
         return str(result)
     except Exception as e:
         return f"計算錯誤: {str(e)}"
@@ -508,8 +527,9 @@ print(f"答案：{answer}")
 
 ```python
 from langchain.agents import initialize_agent, Tool, AgentType
-from langchain.llms import OpenAI
-from langchain.utilities import GoogleSearchAPIWrapper, PythonREPL
+from langchain_openai import OpenAI
+from langchain_community.utilities import GoogleSearchAPIWrapper
+from langchain_experimental.utilities import PythonREPL
 
 # 初始化工具
 search = GoogleSearchAPIWrapper()
@@ -2016,13 +2036,13 @@ class AgentEvaluator:
 class CostTracker:
     """成本追蹤器"""
 
-    # 2024 價格（美元）
+    # 2025 價格（美元）
     PRICING = {
-        "gpt-4": {"input": 0.03 / 1000, "output": 0.06 / 1000},
+        "gpt-4o": {"input": 0.0025 / 1000, "output": 0.01 / 1000},
+        "gpt-4o-mini": {"input": 0.00015 / 1000, "output": 0.0006 / 1000},
         "gpt-4-turbo": {"input": 0.01 / 1000, "output": 0.03 / 1000},
-        "gpt-3.5-turbo": {"input": 0.0005 / 1000, "output": 0.0015 / 1000},
         "claude-3-opus": {"input": 0.015 / 1000, "output": 0.075 / 1000},
-        "claude-3-sonnet": {"input": 0.003 / 1000, "output": 0.015 / 1000},
+        "claude-3.5-sonnet": {"input": 0.003 / 1000, "output": 0.015 / 1000},
     }
 
     def __init__(self):

3.LLM應用工程/3.Agent/examples/01_react_agent/react_agent_basic.py

@@ -89,8 +89,27 @@ def _calculator_tool(self, expression: str) -> str:
         Returns:
             計算結果
         """
+        import ast
+        import operator
+
+        ops = {
+            ast.Add: operator.add, ast.Sub: operator.sub,
+            ast.Mult: operator.mul, ast.Div: operator.truediv,
+            ast.Pow: operator.pow, ast.USub: operator.neg
+        }
+
+        def safe_eval(node):
+            if isinstance(node, ast.Num):
+                return node.n
+            elif isinstance(node, ast.BinOp):
+                return ops[type(node.op)](safe_eval(node.left), safe_eval(node.right))
+            elif isinstance(node, ast.UnaryOp):
+                return ops[type(node.op)](safe_eval(node.operand))
+            raise ValueError("不支援的運算")
+
         try:
-            result = eval(expression)  # 注意：生產環境應使用安全的計算方法
+            tree = ast.parse(expression, mode='eval')
+            result = safe_eval(tree.body)
             return str(result)
         except Exception as e:
             return f"計算錯誤: {str(e)}"

3.LLM應用工程/3.Agent/examples/utils/agent_utils.py

@@ -46,7 +46,7 @@ def setup_environment() -> Dict[str, str]:
 
 
 def get_llm(
-    model: str = "gpt-4-turbo-preview",
+    model: str = "gpt-4o",
     temperature: float = 0.7,
     max_tokens: Optional[int] = None,
     streaming: bool = False,

3.LLM應用工程/3.Agent/examples/utils/evaluator.py

@@ -20,7 +20,7 @@ class AgentEvaluator:
     使用 LLM 評估 Agent 的輸出質量。
     """
 
-    def __init__(self, model: str = "gpt-4-turbo-preview"):
+    def __init__(self, model: str = "gpt-4o"):
         """
         初始化評估器
 

3.LLM應用工程/4.(RAG) 基礎/README.md

@@ -257,9 +257,9 @@ print(f"元數據: {chunks[0].metadata}")
 ### 4.4.3 使用 Chroma 建立向量資料庫
 
 ```python
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.document_loaders import TextLoader
+from langchain_community.vectorstores import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.document_loaders import TextLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 # 1. 準備數據
@@ -318,9 +318,9 @@ for doc, score in results_with_scores:
 ### 4.4.4 完整的 RAG 系統實現
 
 ```python
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import OpenAI
+from langchain_community.vectorstores import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_openai import OpenAI
 from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 
@@ -378,11 +378,11 @@ for i, doc in enumerate(result['source_documents'], 1):
 ### 4.4.5 使用 LangChain 的簡化 RAG
 
 ```python
-from langchain.document_loaders import TextLoader
+from langchain_community.document_loaders import TextLoader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chat_models import ChatOpenAI
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_openai import ChatOpenAI
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 

3.LLM應用工程/5.進階 RAG 與多元資料檢索/2025_RAG技術最新發展.md

@@ -165,7 +165,7 @@ index = VectorStoreIndex.from_documents(
 )
 
 # 多模態查詢
-multimodal_llm = OpenAIMultiModal(model="gpt-4-vision-preview")
+multimodal_llm = OpenAIMultiModal(model="gpt-4o")  # GPT-4o 支援多模態
 query_engine = index.as_query_engine(
     llm=multimodal_llm,
     image_similarity_top_k=5

3.LLM應用工程/5.進階 RAG 與多元資料檢索/RAG_2.0_與多模態RAG系統.md

@@ -349,7 +349,7 @@ class MultimodalRAG:
 
     def __init__(self):
         self.text_embeddings = OpenAIEmbeddings()
-        self.vision_model = ChatOpenAI(model="gpt-4-vision-preview")
+        self.vision_model = ChatOpenAI(model="gpt-4o")  # GPT-4o 具備視覺能力
 
     def process_document_with_images(self, pdf_path: str):
         """處理包含圖像的 PDF 文檔"""

3.LLM應用工程/5.進階 RAG 與多元資料檢索/examples/4_agent_collaboration.py

@@ -118,13 +118,27 @@ def _run(self, expression: str) -> str:
             # 清理輸入
             expression = expression.strip()
 
-            # 安全檢查：只允許數字和基本運算符
-            allowed_chars = set("0123456789+-*/().() ")
-            if not all(c in allowed_chars for c in expression):
-                return "錯誤：表達式包含不允許的字符"
+            # 使用 ast 模組進行安全計算
+            import ast
+            import operator
+
+            ops = {
+                ast.Add: operator.add, ast.Sub: operator.sub,
+                ast.Mult: operator.mul, ast.Div: operator.truediv,
+                ast.Pow: operator.pow, ast.USub: operator.neg
+            }
 
-            # 計算
-            result = eval(expression)
+            def safe_eval(node):
+                if isinstance(node, ast.Num):
+                    return node.n
+                elif isinstance(node, ast.BinOp):
+                    return ops[type(node.op)](safe_eval(node.left), safe_eval(node.right))
+                elif isinstance(node, ast.UnaryOp):
+                    return ops[type(node.op)](safe_eval(node.operand))
+                raise ValueError("不支援的運算")
+
+            tree = ast.parse(expression, mode='eval')
+            result = safe_eval(tree.body)
             return f"計算結果: {result}"
 
         except ZeroDivisionError:

3.LLM應用工程/6.推論優化/1.量化技術/05_quantization_comparison.py

@@ -530,7 +530,7 @@ def plot_results(self):
         # 顯示（如果在互動環境中）
         try:
             plt.show()
-        except:
+        except Exception:
             pass
 
 

3.LLM應用工程/6.推論優化/2.KV-Cache/01_kv_cache_basic.py

@@ -342,7 +342,7 @@ def _plot_comparison(self, results: Dict):
 
             try:
                 plt.show()
-            except:
+            except Exception:
                 pass
 
         except Exception as e:

3.LLM應用工程/9.實戰/9.1-RAG-Agent端到端實戰/src/rag_agent_system.py

@@ -4,7 +4,7 @@
 from typing import List, Dict, Any, Optional, Tuple
 import json
 
-from langchain.chat_models import ChatOpenAI
+from langchain_openai import ChatOpenAI
 from langchain.schema import HumanMessage, SystemMessage, AIMessage
 
 from src.vector_store import VectorStoreManager, HybridSearcher

3.LLM應用工程/9.實戰/9.2-LLM自動化工作流程/examples/sample_code.py

@@ -28,7 +28,7 @@ def process_user_data(data):
             if item['age'] > 18:
                 result.append(item)
         return result
-    except:
+    except (KeyError, TypeError):
         return []
 
 

5.AI研究前沿_2024-2025/1.大型語言模型(LLM)/examples/01_llama31_complete.py

@@ -215,7 +215,7 @@ def _parse_tool_call(self, text: str) -> Optional[Dict]:
                 tool_call = json.loads(json_str)
                 if 'tool' in tool_call:
                     return tool_call
-        except:
+        except (json.JSONDecodeError, ValueError):
             pass
         return None