Fix: Reduce excessive IO operations by loading LLM factory configurations (#6047)

…ions

### What problem does this PR solve?

This PR fixes an issue where the application was repeatedly reading the
llm_factories.json file from disk in multiple places, which could lead
to "Too many open files" errors under high load conditions. The fix
centralizes the file reading operation in the settings.py module and
stores the data in a global variable that can be accessed by other
modules.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [x] Performance Improvement
- [ ] Other (please describe):
This commit is contained in:
utopia2077 2025-03-14 09:54:38 +08:00 committed by GitHub
parent 47926f7d21
commit 2d4a60cae6
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4 changed files with 18 additions and 17 deletions

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@ -103,13 +103,8 @@ def init_llm_factory():
except Exception:
pass
factory_llm_infos = json.load(
open(
os.path.join(get_project_base_directory(), "conf", "llm_factories.json"),
"r",
)
)
for factory_llm_info in factory_llm_infos["factory_llm_infos"]:
factory_llm_infos = settings.FACTORY_LLM_INFOS
for factory_llm_info in factory_llm_infos:
llm_infos = factory_llm_info.pop("llm")
try:
LLMFactoriesService.save(**factory_llm_info)

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@ -13,13 +13,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import logging
import os
from api.db.services.user_service import TenantService
from api.utils.file_utils import get_project_base_directory
from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel, TTSModel
from api import settings
from api.db import LLMType
from api.db.db_models import DB
from api.db.db_models import LLMFactories, LLM, TenantLLM
@ -75,7 +73,7 @@ class TenantLLMService(CommonService):
# model name must be xxx@yyy
try:
model_factories = json.load(open(os.path.join(get_project_base_directory(), "conf/llm_factories.json"), "r"))["factory_llm_infos"]
model_factories = settings.FACTORY_LLM_INFOS
model_providers = set([f["name"] for f in model_factories])
if arr[-1] not in model_providers:
return model_name, None

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@ -16,6 +16,7 @@
import os
from datetime import date
from enum import IntEnum, Enum
import json
import rag.utils.es_conn
import rag.utils.infinity_conn
@ -24,6 +25,7 @@ from rag.nlp import search
from graphrag import search as kg_search
from api.utils import get_base_config, decrypt_database_config
from api.constants import RAG_FLOW_SERVICE_NAME
from api.utils.file_utils import get_project_base_directory
LIGHTEN = int(os.environ.get('LIGHTEN', "0"))
@ -40,6 +42,7 @@ PARSERS = None
HOST_IP = None
HOST_PORT = None
SECRET_KEY = None
FACTORY_LLM_INFOS = None
DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
DATABASE = decrypt_database_config(name=DATABASE_TYPE)
@ -61,7 +64,7 @@ kg_retrievaler = None
def init_settings():
global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE
global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE, FACTORY_LLM_INFOS
LIGHTEN = int(os.environ.get('LIGHTEN', "0"))
DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
DATABASE = decrypt_database_config(name=DATABASE_TYPE)
@ -69,6 +72,12 @@ def init_settings():
LLM_DEFAULT_MODELS = LLM.get("default_models", {})
LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
LLM_BASE_URL = LLM.get("base_url")
try:
with open(os.path.join(get_project_base_directory(), "conf", "llm_factories.json"), "r") as f:
FACTORY_LLM_INFOS = json.load(f)["factory_llm_infos"]
except Exception:
FACTORY_LLM_INFOS = []
global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
if not LIGHTEN:

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@ -16,14 +16,13 @@
import datetime
import json
import logging
import os
import re
from collections import defaultdict
import json_repair
from api import settings
from api.db import LLMType
from api.db.services.document_service import DocumentService
from api.db.services.llm_service import TenantLLMService, LLMBundle
from api.utils.file_utils import get_project_base_directory
from rag.settings import TAG_FLD
from rag.utils import num_tokens_from_string, encoder
@ -46,9 +45,9 @@ def chunks_format(reference):
def llm_id2llm_type(llm_id):
llm_id, _ = TenantLLMService.split_model_name_and_factory(llm_id)
fnm = os.path.join(get_project_base_directory(), "conf")
llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r"))
for llm_factory in llm_factories["factory_llm_infos"]:
llm_factories = settings.FACTORY_LLM_INFOS
for llm_factory in llm_factories:
for llm in llm_factory["llm"]:
if llm_id == llm["llm_name"]:
return llm["model_type"].strip(",")[-1]