feat: Add review events persistence, version display, and auto-versioning system

2025-10-13 14:18:37 +03:00
parent cfba28f913
commit 2db1225618
56 changed files with 750 additions and 436 deletions
@@ -0,0 +1,137 @@
+"""
+Тест стриминга LLM messages от LangGraph
+"""
+
+import asyncio
+from langgraph.graph import StateGraph, END
+from typing import TypedDict, Annotated
+import operator
+from langchain_ollama import OllamaLLM
+
+
+class TestState(TypedDict):
+    messages: Annotated[list, operator.add]
+    result: str
+
+
+async def llm_node(state: TestState) -> TestState:
+    """Нода с LLM вызовом"""
+    print("  [LLM NODE] Вызов LLM...")
+    
+    llm = OllamaLLM(
+        model="qwen2.5-coder:3b",
+        base_url="http://localhost:11434",
+        temperature=0.7
+    )
+    
+    # Простой промпт для быстрого ответа
+    prompt = "Напиши короткую проверку кода на Python (не более 100 символов)"
+    
+    response = await llm.ainvoke(prompt)
+    
+    print(f"  [LLM NODE] Ответ получен: {response[:50]}...")
+    
+    return {
+        "messages": [{"role": "ai", "content": response}],
+        "result": response
+    }
+
+
+def create_test_graph():
+    """Создает тестовый граф с LLM"""
+    workflow = StateGraph(TestState)
+    
+    workflow.add_node("llm_call", llm_node)
+    
+    workflow.set_entry_point("llm_call")
+    workflow.add_edge("llm_call", END)
+    
+    return workflow.compile()
+
+
+async def test_with_llm():
+    """Тест стриминга с LLM"""
+    print("\n" + "="*80)
+    print("ТЕСТ СТРИМИНГА LLM MESSAGES")
+    print("="*80)
+    
+    graph = create_test_graph()
+    
+    initial_state: TestState = {
+        "messages": [],
+        "result": ""
+    }
+    
+    # Тест: updates + messages
+    print(f"\n🔍 Тест: stream_mode=['updates', 'messages']")
+    print("-" * 80)
+    
+    event_count = 0
+    messages_count = 0
+    
+    async for event in graph.astream(initial_state, stream_mode=["updates", "messages"]):
+        event_count += 1
+        
+        if isinstance(event, tuple) and len(event) >= 2:
+            event_type, event_data = event[0], event[1]
+            
+            print(f"\n📨 Event #{event_count}")
+            print(f"   Type: {event_type}")
+            print(f"   Data type: {type(event_data)}")
+            
+            if event_type == 'updates':
+                print(f"   ✅ Node update")
+                if isinstance(event_data, dict):
+                    for node_name in event_data.keys():
+                        print(f"      Node: {node_name}")
+            
+            elif event_type == 'messages':
+                messages_count += 1
+                print(f"   💬 LLM Messages (#{messages_count})")
+                
+                if isinstance(event_data, (list, tuple)):
+                    for i, msg in enumerate(event_data):
+                        print(f"      Message {i+1}:")
+                        
+                        # Извлекаем контент
+                        if hasattr(msg, 'content'):
+                            content = msg.content
+                            print(f"         Content: {content[:100]}...")
+                        elif isinstance(msg, dict):
+                            print(f"         Dict: {msg}")
+                        else:
+                            print(f"         Type: {type(msg)}")
+                            print(f"         Str: {str(msg)[:100]}...")
+    
+    print(f"\n" + "="*80)
+    print(f"✅ Всего событий: {event_count}")
+    print(f"✅ Messages событий: {messages_count}")
+    print("="*80)
+
+
+async def main():
+    print("\n" + "="*80)
+    print("ТЕСТИРОВАНИЕ LLM STREAMING В LANGGRAPH")
+    print("="*80)
+    print("\nПроверка Ollama...")
+    
+    try:
+        # Проверяем что Ollama доступен
+        from langchain_ollama import OllamaLLM
+        test_llm = OllamaLLM(model="qwen2.5-coder:3b", base_url="http://localhost:11434")
+        result = await test_llm.ainvoke("test")
+        print("✅ Ollama работает!")
+    except Exception as e:
+        print(f"❌ Ошибка подключения к Ollama: {e}")
+        print("\n⚠️  Убедитесь что Ollama запущен: ollama serve")
+        print("⚠️  И модель загружена: ollama pull qwen2.5-coder:3b\n")
+        return
+    
+    await test_with_llm()
+    
+    print("\n✅ Тестирование завершено\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+