fix: use requests instead of openai SDK for API compatibility

langgraph-tutorial/config.py

@@ -1,13 +1,12 @@
 """
-API 配置文件
+API 配置文件 - 修改这里来配置你的 API
-修改这里来配置你的 API
 """
 
 # API 配置
-API_KEY = "sk-..."  # 你的 API Key
+API_KEY = "sk-f5ba1acb1d61c39f754c28f19d0e6e1b7d8d9fda43ef812ce4e335b6c26eff01"
-BASE_URL = "https://api.openai.com/v1"  # API 地址（OpenAI 兼容）
+BASE_URL = "https://gw2.oops.asia/v1"
-MODEL = "gpt-4o-mini"  # 模型名称
+MODEL = "gpt-5.4-mini"  # 可用模型: gpt-5.2, gpt-5.4, gpt-5.4-mini, gpt-5.5 等
 
 # 智能体配置
-MAX_ITERATIONS = 4  # 最大迭代次数
+MAX_ITERATIONS = 4
-TEMPERATURE = 0.3   # 温度参数（越低越确定）
+TEMPERATURE = 0.3

langgraph-tutorial/debug_api.py

@@ -0,0 +1,47 @@
+import requests
+import json
+
+url = "https://gw2.oops.asia/v1/chat/completions"
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": "Bearer sk-f5ba1acb1d61c39f754c28f19d0e6e1b7d8d9fda43ef812ce4e335b6c26eff01"
+}
+
+# 测试不同的请求格式
+test_cases = [
+    {
+        "name": "标准格式",
+        "body": {
+            "model": "gpt-5.4-mini",
+            "messages": [{"role": "user", "content": "Say hi"}],
+            "max_tokens": 20
+        }
+    },
+    {
+        "name": "带 temperature",
+        "body": {
+            "model": "gpt-5.4-mini",
+            "messages": [{"role": "user", "content": "Say hi"}],
+            "max_tokens": 20,
+            "temperature": 0.3
+        }
+    },
+    {
+        "name": "带 stream",
+        "body": {
+            "model": "gpt-5.4-mini",
+            "messages": [{"role": "user", "content": "Say hi"}],
+            "max_tokens": 20,
+            "stream": False
+        }
+    }
+]
+
+for test in test_cases:
+    print(f"\n测试: {test['name']}")
+    try:
+        response = requests.post(url, headers=headers, json=test["body"], timeout=10)
+        print(f"  状态码: {response.status_code}")
+        print(f"  响应: {response.text[:200]}")
+    except Exception as e:
+        print(f"  错误: {e}")

langgraph-tutorial/list_models.py

@@ -0,0 +1,12 @@
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="sk-f5ba1acb1d61c39f754c28f19d0e6e1b7d8d9fda43ef812ce4e335b6c26eff01",
+    base_url="https://gw2.oops.asia/v1"
+)
+
+models = client.models.list()
+print("支持的模型列表:")
+print("-" * 50)
+for m in models:
+    print(f"  {m.id}")

langgraph-tutorial/react_agent.py

@@ -4,6 +4,7 @@ ReAct 模式：思考(Reason) - 行动(Act) - 观察(Observe)
 配置见 config.py
 """
 import sys
+import requests
 from langgraph.graph import StateGraph, START, END
 from typing import TypedDict
 
@@ -14,9 +15,6 @@ if API_KEY == "sk-...":
     print("请先在 config.py 中配置 API_KEY")
     exit(1)
 
-from openai import OpenAI
-client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-
 # 2️⃣ 定义状态
 class AgentState(TypedDict):
     question: str
@@ -56,7 +54,27 @@ tools = {
     "search": search_knowledge,
 }
 
-# 4️⃣ 定义节点
+# 4️⃣ LLM 调用函数
+def call_llm(messages, max_tokens=300):
+    """调用 LLM API"""
+    url = f"{BASE_URL}/chat/completions"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+    body = {
+        "model": MODEL,
+        "messages": messages,
+        "max_tokens": max_tokens,
+        "temperature": TEMPERATURE,
+    }
+    
+    response = requests.post(url, headers=headers, json=body, timeout=30)
+    response.raise_for_status()
+    data = response.json()
+    return data["choices"][0]["message"]["content"]
+
+# 5️⃣ 定义节点
 def think_node(state: AgentState):
     """思考节点 - 让 LLM 决定下一步"""
     state = state.copy()
@@ -85,14 +103,7 @@ def think_node(state: AgentState):
     if state.get('observation'):
         messages.append({"role": "assistant", "content": f"[观察] {state['observation']}"})
 
-    response = client.chat.completions.create(
+    thought_text = call_llm(messages)
-        model=MODEL,
-        messages=messages,
-        max_tokens=300,
-        temperature=TEMPERATURE,
-    )
-
-    thought_text = response.choices[0].message.content
     state['current_thought'] = thought_text
     state['thoughts'] = state.get('thoughts', []) + [thought_text]
 
@@ -146,17 +157,11 @@ def answer_node(state: AgentState):
         {"role": "user", "content": f"问题: {state['question']}\n\n思考过程:\n" + "\n".join(state.get('thoughts', []))}
     ]
 
-    response = client.chat.completions.create(
+    state['final_answer'] = call_llm(messages, max_tokens=200)
-        model=MODEL,
-        messages=messages,
-        max_tokens=200,
-    )
-
-    state['final_answer'] = response.choices[0].message.content
     print(f"\n[回答] {state['final_answer']}")
     return state
 
-# 5️⃣ 路由函数
+# 6️⃣ 路由函数
 def route(state: AgentState):
     """决定下一步"""
     if state.get('final_answer'):
@@ -167,7 +172,7 @@ def route(state: AgentState):
         return "act"
     return "answer"
 
-# 6️⃣ 构建图
+# 7️⃣ 构建图
 graph = StateGraph(AgentState)
 graph.add_node("think", think_node)
 graph.add_node("act", act_node)
@@ -180,7 +185,7 @@ graph.add_edge("answer", END)
 
 app = graph.compile()
 
-# 7️⃣ 运行
+# 8️⃣ 运行
 print("=" * 50)
 print("LangGraph ReAct 智能体")
 print("=" * 50)

langgraph-tutorial/test_api.py

@@ -0,0 +1,13 @@
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="sk-f5ba1acb1d61c39f754c28f19d0e6e1b7d8d9fda43ef812ce4e335b6c26eff01",
+    base_url="https://gw2.oops.asia/v1"
+)
+
+response = client.chat.completions.create(
+    model="gpt-5.4-mini",
+    messages=[{"role": "user", "content": "Say hi in Chinese"}],
+    max_tokens=20
+)
+print(response.choices[0].message.content)

langgraph-tutorial/test_models.py

@@ -0,0 +1,23 @@
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="sk-f5ba1acb1d61c39f754c28f19d0e6e1b7d8d9fda43ef812ce4e335b6c26eff01",
+    base_url="https://gw2.oops.asia/v1"
+)
+
+models_to_test = ["gpt-4o-mini", "gpt-4o", "gpt-3.5-turbo", "gpt-5", "gpt-5.5", "claude-sonnet-4-20250514"]
+
+print("测试可用模型:")
+print("-" * 50)
+
+for model in models_to_test:
+    try:
+        response = client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": "Say hi"}],
+            max_tokens=20
+        )
+        print(f"  ✅ {model} - OK")
+    except Exception as e:
+        error_msg = str(e)[:50]
+        print(f"  ❌ {model} - {error_msg}")