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feat: Implement LLM streaming support and enhance event handling in review process

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This commit is contained in:
Primakov Alexandr Alexandrovich 2025-10-13 17:48:03 +03:00
parent 2f29ccff74
commit 1d953f554b
6 changed files with 107 additions and 45 deletions
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14
backend/app/agents/reviewer.py
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@ -315,6 +315,17 @@ class ReviewerAgent:
print(f" ⚠️ ПРОПУСК: patch пустой или слишком маленький")
continue
# Callback для LLM streaming
async def on_llm_chunk(chunk: str, file: str):
"""Handle LLM streaming chunks"""
if self._stream_callback:
await self._stream_callback({
"type": "llm_chunk",
"chunk": chunk,
"file_path": file,
"message": chunk
})
# Analyze diff with PR context
pr_info = state.get("pr_info", {})
comments = await self.analyzer.analyze_diff(
@ -322,7 +333,8 @@ class ReviewerAgent:
diff=patch,
language=language,
pr_title=pr_info.get("title", ""),
pr_description=pr_info.get("description", "")
pr_description=pr_info.get("description", ""),
on_llm_chunk=on_llm_chunk
)
print(f" 💬 Получено комментариев: {len(comments)}")

32
backend/app/agents/tools.py
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@ -99,7 +99,8 @@ class CodeAnalyzer:
diff: str,
language: Optional[str] = None,
pr_title: str = "",
pr_description: str = ""
pr_description: str = "",
on_llm_chunk: Optional[callable] = None
) -> List[Dict[str, Any]]:
"""Analyze code diff and return comments"""
@ -154,13 +155,32 @@ class CodeAnalyzer:
try:
print(f"\n⏳ Отправка запроса к Ollama ({self.llm.model})...")
# Создаем chain с LLM и JSON парсером
chain = self.llm | self.json_parser
# Собираем полный ответ из streaming chunks
full_response = ""
chunk_count = 0
# Получаем результат
result = await chain.ainvoke(prompt)
print(f"\n🤖 STREAMING AI ответ:")
print("-" * 80)
print(f"\n🤖 ОТВЕТ AI (распарсен через JsonOutputParser):")
# Используем streaming
async for chunk in self.llm.astream(prompt):
chunk_count += 1
full_response += chunk
# Отправляем chunk через callback
if on_llm_chunk:
await on_llm_chunk(chunk, file_path)
# Показываем в консоли
print(chunk, end='', flush=True)
print("\n" + "-" * 80)
print(f"✅ Получено {chunk_count} chunks, всего {len(full_response)} символов")
# Парсим финальный результат
result = self.json_parser.parse(full_response)
print(f"\n🤖 РАСПАРСЕННЫЙ результат:")
print("-" * 80)
print(json.dumps(result, ensure_ascii=False, indent=2)[:500] + "...")
print("-" * 80)

25
backend/app/api/reviews.py
View File

@ -147,19 +147,20 @@ async def run_review_task(review_id: int, pr_number: int, repository_id: int, db
"data": event
}
# Save to DB
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review_id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
# Save to DB (НЕ сохраняем llm_chunk - их слишком много)
if event.get("type") != "llm_chunk":
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review_id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
# Broadcast
# Broadcast (отправляем все события, включая llm_chunk)
await manager.broadcast(event_data)
except Exception as e:
print(f"Error in review event handler: {e}")

25
backend/app/api/webhooks.py
View File

@ -31,19 +31,20 @@ async def start_review_task(review_id: int, pr_number: int, repository_id: int):
"data": event
}
# Save to DB
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review_id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
# Save to DB (НЕ сохраняем llm_chunk - их слишком много)
if event.get("type") != "llm_chunk":
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review_id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
# Broadcast
# Broadcast (отправляем все события, включая llm_chunk)
await manager.broadcast(event_data)
except Exception as e:
print(f"Error in webhook review event handler: {e}")

32
backend/app/workers/task_worker.py
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@ -227,21 +227,22 @@ class ReviewTaskWorker:
print(f" Prepared event_data: {event_data}")
logger.info(f" 🔔 Broadcasting event: type={event.get('type')}, connections={len(manager.active_connections)}")
# Save event to database
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review.id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
print(f" ✓ Event saved to DB: {db_event.id}")
logger.debug(f" 💾 Event saved to DB: {db_event.id}")
# Save event to database (НЕ сохраняем llm_chunk - их слишком много)
if event.get("type") != "llm_chunk":
from app.models.review_event import ReviewEvent
db_event = ReviewEvent(
review_id=review.id,
event_type=event.get("type", "agent_update"),
step=event.get("step"),
message=event.get("message"),
data=event
)
db.add(db_event)
await db.commit()
print(f" ✓ Event saved to DB: {db_event.id}")
logger.debug(f" 💾 Event saved to DB: {db_event.id}")
# Broadcast to all connected clients
# Broadcast to all connected clients (отправляем все, включая llm_chunk)
print(f" Broadcasting to {len(manager.active_connections)} connections...")
await manager.broadcast(event_data)
print(f" ✓ Broadcast completed")
@ -250,6 +251,9 @@ class ReviewTaskWorker:
if event.get("type") == "agent_step":
step = event.get("step", "unknown")
logger.info(f" 📍 Step: {step}")
elif event.get("type") == "llm_chunk":
# Не логируем каждый chunk, слишком много
pass
elif event.get("type") == "llm_message":
message = event.get("message", "")[:100]
logger.info(f" 💬 LLM: {message}...")

24
frontend/src/components/ReviewStream.tsx
View File

@ -32,6 +32,7 @@ export const ReviewStream: React.FC<ReviewStreamProps> = ({ reviewId }) => {
const [currentStep, setCurrentStep] = useState<string>('');
const [isConnected, setIsConnected] = useState(false);
const [llmMessages, setLlmMessages] = useState<string[]>([]);
const [llmStreamingText, setLlmStreamingText] = useState<string>('');
useEffect(() => {
console.log('🔌 Connecting to WebSocket:', WS_URL);
@ -126,6 +127,12 @@ export const ReviewStream: React.FC<ReviewStreamProps> = ({ reviewId }) => {
setCurrentStep(step || '');
}
// Handle LLM streaming chunks
if (data.type === 'llm_chunk' || data.data?.type === 'llm_chunk') {
const chunk = data.data?.chunk || data.chunk || '';
setLlmStreamingText((prev) => prev + chunk);
}
// Collect LLM messages
if (data.type === 'llm_message' || data.data?.type === 'llm_message') {
const message = data.data?.message || data.message;
@ -136,6 +143,8 @@ export const ReviewStream: React.FC<ReviewStreamProps> = ({ reviewId }) => {
// Handle special events
if (data.type === 'review_started') {
console.log('🎬 Review started:', data.data?.message);
// Сбрасываем streaming текст при начале нового review
setLlmStreamingText('');
}
if (data.type === 'review_completed') {
@ -319,6 +328,21 @@ export const ReviewStream: React.FC<ReviewStreamProps> = ({ reviewId }) => {
{renderStepProgress()}
{/* LLM Streaming текст */}
{llmStreamingText && (
<div className="mt-6">
<h3 className="text-lg font-semibold text-dark-text-primary mb-3 flex items-center gap-2">
<span className="animate-pulse">🤖</span> AI генерирует ответ...
</h3>
<div className="bg-dark-card border border-dark-border rounded-lg p-4">
<pre className="text-sm text-dark-text-secondary font-mono whitespace-pre-wrap break-words max-h-96 overflow-y-auto">
{llmStreamingText}
<span className="animate-pulse">▋</span>
</pre>
</div>
</div>
)}
<div className="mt-6">
<h3 className="text-lg font-semibold text-dark-text-primary mb-3">
📝 События