fix: Fix /api/version endpoint path and save all review events to DB
This commit is contained in:
Primakov Alexandr Alexandrovich
parent
47bbb4ebc4
commit
8d231b49db
@ -118,7 +118,7 @@ async def health_check():
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return {"status": "healthy"}
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@app.get("/version")
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@app.get("/api/version")
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async def get_version():
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"""Get backend version"""
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try:
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@ -128,7 +128,8 @@ async def get_version():
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else:
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version = "unknown"
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return {"version": version}
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except Exception:
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except Exception as e:
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print(f"Error reading version: {e}")
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return {"version": "unknown"}
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@ -169,6 +169,22 @@ class ReviewTaskWorker:
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# Send initial "review started" message
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logger.info(f" 📢 Отправка начального сообщения о старте review...")
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try:
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# Save initial event to database
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from app.models.review_event import ReviewEvent
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initial_db_event = ReviewEvent(
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review_id=review.id,
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event_type="review_started",
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message=f"Начало review для PR #{pull_request.pr_number}",
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data={
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"repository_id": repository.id,
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"repository_name": f"{repository.repo_owner}/{repository.repo_name}"
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}
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)
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db.add(initial_db_event)
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await db.commit()
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logger.info(f" 💾 Начальное событие сохранено в БД: {initial_db_event.id}")
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# Broadcast initial message
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initial_message = {
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"type": "review_started",
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"review_id": review.id,
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@ -184,6 +200,8 @@ class ReviewTaskWorker:
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logger.info(f" ✅ Начальное сообщение отправлено: {len(manager.active_connections)} подключений")
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except Exception as e:
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logger.error(f" ❌ Ошибка отправки начального сообщения: {e}")
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import traceback
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traceback.print_exc()
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# Create event handler
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async def on_review_event(event: dict):
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@ -240,6 +258,19 @@ class ReviewTaskWorker:
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# Send completion message
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try:
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# Save completion event to database
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from app.models.review_event import ReviewEvent
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completion_db_event = ReviewEvent(
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review_id=review.id,
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event_type="review_completed",
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message=f"Review завершен для PR #{pull_request.pr_number}",
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data={}
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)
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db.add(completion_db_event)
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await db.commit()
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logger.info(f" 💾 Событие завершения сохранено в БД: {completion_db_event.id}")
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# Broadcast completion message
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completion_message = {
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"type": "review_completed",
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"review_id": review.id,
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@ -250,8 +281,11 @@ class ReviewTaskWorker:
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}
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}
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await manager.broadcast(completion_message)
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logger.info(f" 📢 Сообщение о завершении отправлено: {len(manager.active_connections)} подключений")
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except Exception as e:
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logger.error(f" ❌ Ошибка отправки сообщения о завершении: {e}")
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import traceback
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traceback.print_exc()
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# Global worker instance
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@ -0,0 +1,71 @@
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#!/bin/bash
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# Скрипт для автоповышения версии backend
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# Вызывается из pre-commit hook или вручную
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VERSION_FILE="backend/VERSION"
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# Проверка существования файла
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if [ ! -f "$VERSION_FILE" ]; then
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echo "0.1.0" > "$VERSION_FILE"
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echo "✅ Создан файл версии: 0.1.0"
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exit 0
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fi
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# Чтение текущей версии
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CURRENT_VERSION=$(cat "$VERSION_FILE")
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# Разбор версии (MAJOR.MINOR.PATCH)
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IFS='.' read -ra VERSION_PARTS <<< "$CURRENT_VERSION"
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MAJOR="${VERSION_PARTS[0]}"
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MINOR="${VERSION_PARTS[1]}"
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PATCH="${VERSION_PARTS[2]}"
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# Проверка типа изменения по коммиту
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if [ $# -eq 1 ]; then
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VERSION_TYPE="$1"
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else
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# Автоопределение по последнему коммиту
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LAST_COMMIT=$(git log -1 --pretty=%B 2>/dev/null || echo "")
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if echo "$LAST_COMMIT" | grep -qiE "^(feat|feature):"; then
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VERSION_TYPE="minor"
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elif echo "$LAST_COMMIT" | grep -qiE "^(fix|bugfix):"; then
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VERSION_TYPE="patch"
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elif echo "$LAST_COMMIT" | grep -qiE "^(BREAKING|major):"; then
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VERSION_TYPE="major"
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else
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VERSION_TYPE="patch"
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fi
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fi
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# Повышение версии
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case "$VERSION_TYPE" in
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major)
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MAJOR=$((MAJOR + 1))
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MINOR=0
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PATCH=0
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;;
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minor)
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MINOR=$((MINOR + 1))
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PATCH=0
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;;
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patch|*)
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PATCH=$((PATCH + 1))
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;;
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esac
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NEW_VERSION="$MAJOR.$MINOR.$PATCH"
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# Запись новой версии
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echo "$NEW_VERSION" > "$VERSION_FILE"
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echo "📦 Версия обновлена: $CURRENT_VERSION → $NEW_VERSION"
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# Добавление файла в git если мы в hook
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if [ -n "$GIT_INDEX_FILE" ]; then
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git add "$VERSION_FILE"
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fi
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exit 0
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@ -1,137 +0,0 @@
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"""
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Тест стриминга LLM messages от LangGraph
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"""
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import asyncio
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from langgraph.graph import StateGraph, END
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from typing import TypedDict, Annotated
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import operator
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from langchain_ollama import OllamaLLM
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class TestState(TypedDict):
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messages: Annotated[list, operator.add]
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result: str
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async def llm_node(state: TestState) -> TestState:
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"""Нода с LLM вызовом"""
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print(" [LLM NODE] Вызов LLM...")
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llm = OllamaLLM(
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model="qwen2.5-coder:3b",
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base_url="http://localhost:11434",
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temperature=0.7
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)
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# Простой промпт для быстрого ответа
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prompt = "Напиши короткую проверку кода на Python (не более 100 символов)"
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response = await llm.ainvoke(prompt)
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print(f" [LLM NODE] Ответ получен: {response[:50]}...")
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return {
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"messages": [{"role": "ai", "content": response}],
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"result": response
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}
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def create_test_graph():
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"""Создает тестовый граф с LLM"""
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workflow = StateGraph(TestState)
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workflow.add_node("llm_call", llm_node)
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workflow.set_entry_point("llm_call")
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workflow.add_edge("llm_call", END)
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return workflow.compile()
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async def test_with_llm():
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"""Тест стриминга с LLM"""
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print("\n" + "="*80)
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print("ТЕСТ СТРИМИНГА LLM MESSAGES")
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print("="*80)
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graph = create_test_graph()
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initial_state: TestState = {
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"messages": [],
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"result": ""
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}
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# Тест: updates + messages
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print(f"\n🔍 Тест: stream_mode=['updates', 'messages']")
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print("-" * 80)
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event_count = 0
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messages_count = 0
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async for event in graph.astream(initial_state, stream_mode=["updates", "messages"]):
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event_count += 1
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if isinstance(event, tuple) and len(event) >= 2:
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event_type, event_data = event[0], event[1]
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print(f"\n📨 Event #{event_count}")
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print(f" Type: {event_type}")
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print(f" Data type: {type(event_data)}")
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if event_type == 'updates':
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print(f" ✅ Node update")
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if isinstance(event_data, dict):
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for node_name in event_data.keys():
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print(f" Node: {node_name}")
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elif event_type == 'messages':
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messages_count += 1
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print(f" 💬 LLM Messages (#{messages_count})")
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if isinstance(event_data, (list, tuple)):
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for i, msg in enumerate(event_data):
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print(f" Message {i+1}:")
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# Извлекаем контент
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if hasattr(msg, 'content'):
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content = msg.content
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print(f" Content: {content[:100]}...")
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elif isinstance(msg, dict):
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print(f" Dict: {msg}")
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else:
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print(f" Type: {type(msg)}")
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print(f" Str: {str(msg)[:100]}...")
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print(f"\n" + "="*80)
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print(f"✅ Всего событий: {event_count}")
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print(f"✅ Messages событий: {messages_count}")
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print("="*80)
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async def main():
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print("\n" + "="*80)
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print("ТЕСТИРОВАНИЕ LLM STREAMING В LANGGRAPH")
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print("="*80)
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print("\nПроверка Ollama...")
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try:
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# Проверяем что Ollama доступен
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from langchain_ollama import OllamaLLM
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test_llm = OllamaLLM(model="qwen2.5-coder:3b", base_url="http://localhost:11434")
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result = await test_llm.ainvoke("test")
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print("✅ Ollama работает!")
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except Exception as e:
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print(f"❌ Ошибка подключения к Ollama: {e}")
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print("\n⚠️ Убедитесь что Ollama запущен: ollama serve")
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print("⚠️ И модель загружена: ollama pull qwen2.5-coder:3b\n")
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return
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await test_with_llm()
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print("\n✅ Тестирование завершено\n")
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if __name__ == "__main__":
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asyncio.run(main())
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