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Fix: task limiter issue. (#7873)

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### What problem does this PR solve?

#7869

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
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Kevin Hu 2025-05-27 11:16:29 +08:00 committed by GitHub
parent aaefc3f44c
commit 959793e83c
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1 changed files with 3 additions and 4 deletions
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5
rag/svr/task_executor.py
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@ -103,6 +103,7 @@ MAX_CONCURRENT_MINIO = int(os.environ.get('MAX_CONCURRENT_MINIO', '10'))
task_limiter = trio.CapacityLimiter(MAX_CONCURRENT_TASKS) task_limiter = trio.CapacityLimiter(MAX_CONCURRENT_TASKS)
chunk_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS) chunk_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS)
minio_limiter = trio.CapacityLimiter(MAX_CONCURRENT_MINIO) minio_limiter = trio.CapacityLimiter(MAX_CONCURRENT_MINIO)
kg_limiter = trio.CapacityLimiter(2)
WORKER_HEARTBEAT_TIMEOUT = int(os.environ.get('WORKER_HEARTBEAT_TIMEOUT', '120')) WORKER_HEARTBEAT_TIMEOUT = int(os.environ.get('WORKER_HEARTBEAT_TIMEOUT', '120'))
stop_event = threading.Event() stop_event = threading.Event()
@ -539,8 +540,6 @@ async def do_handle_task(task):
chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback) chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
# Either using graphrag or Standard chunking methods # Either using graphrag or Standard chunking methods
elif task.get("task_type", "") == "graphrag": elif task.get("task_type", "") == "graphrag":
global task_limiter
task_limiter = trio.CapacityLimiter(2)
if not task_parser_config.get("graphrag", {}).get("use_graphrag", False): if not task_parser_config.get("graphrag", {}).get("use_graphrag", False):
return return
graphrag_conf = task["kb_parser_config"].get("graphrag", {}) graphrag_conf = task["kb_parser_config"].get("graphrag", {})
@ -548,9 +547,9 @@ async def do_handle_task(task):
chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language) chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
with_resolution = graphrag_conf.get("resolution", False) with_resolution = graphrag_conf.get("resolution", False)
with_community = graphrag_conf.get("community", False) with_community = graphrag_conf.get("community", False)
async with kg_limiter:
await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback) await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback)
progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts)) progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts))
task_limiter = trio.CapacityLimiter(MAX_CONCURRENT_TASKS)
return return
else: else:
# Standard chunking methods # Standard chunking methods