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):

api/db/init_data.py

     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)

api/db/services/llm_service.py

 #  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
 
         # 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

api/settings.py

 import os
 from datetime import date
 from enum import IntEnum, Enum
+import json
 import rag.utils.es_conn
 import rag.utils.infinity_conn
 
 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"))
 
 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)
 
 
 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)
     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:

rag/prompts.py

 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
 
 
 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]