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https://github.com/infiniflow/ragflow.git
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Move api.settings to common.settings (#11036)
### What problem does this PR solve? As title ### Type of change - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
332
common/settings.py
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332
common/settings.py
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import json
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import secrets
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from datetime import date
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import logging
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from common.constants import RAG_FLOW_SERVICE_NAME
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from common.file_utils import get_project_base_directory
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from common.config_utils import get_base_config, decrypt_database_config
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from common.misc_utils import pip_install_torch
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from common.constants import SVR_QUEUE_NAME, Storage
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import rag.utils
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import rag.utils.es_conn
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import rag.utils.infinity_conn
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import rag.utils.opensearch_conn
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from rag.utils.azure_sas_conn import RAGFlowAzureSasBlob
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from rag.utils.azure_spn_conn import RAGFlowAzureSpnBlob
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from rag.utils.minio_conn import RAGFlowMinio
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from rag.utils.opendal_conn import OpenDALStorage
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from rag.utils.s3_conn import RAGFlowS3
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from rag.utils.oss_conn import RAGFlowOSS
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from rag.nlp import search
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LLM = None
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LLM_FACTORY = None
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LLM_BASE_URL = None
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CHAT_MDL = ""
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EMBEDDING_MDL = ""
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RERANK_MDL = ""
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ASR_MDL = ""
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IMAGE2TEXT_MDL = ""
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CHAT_CFG = ""
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EMBEDDING_CFG = ""
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RERANK_CFG = ""
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ASR_CFG = ""
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IMAGE2TEXT_CFG = ""
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API_KEY = None
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PARSERS = None
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HOST_IP = None
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HOST_PORT = None
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SECRET_KEY = None
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FACTORY_LLM_INFOS = None
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ALLOWED_LLM_FACTORIES = None
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DATABASE_TYPE = os.getenv("DB_TYPE", "mysql")
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DATABASE = decrypt_database_config(name=DATABASE_TYPE)
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# authentication
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AUTHENTICATION_CONF = None
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# client
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CLIENT_AUTHENTICATION = None
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HTTP_APP_KEY = None
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GITHUB_OAUTH = None
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FEISHU_OAUTH = None
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OAUTH_CONFIG = None
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DOC_ENGINE = os.getenv('DOC_ENGINE', 'elasticsearch')
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docStoreConn = None
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retriever = None
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kg_retriever = None
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# user registration switch
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REGISTER_ENABLED = 1
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# sandbox-executor-manager
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SANDBOX_HOST = None
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STRONG_TEST_COUNT = int(os.environ.get("STRONG_TEST_COUNT", "8"))
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SMTP_CONF = None
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MAIL_SERVER = ""
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MAIL_PORT = 000
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MAIL_USE_SSL = True
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MAIL_USE_TLS = False
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MAIL_USERNAME = ""
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MAIL_PASSWORD = ""
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MAIL_DEFAULT_SENDER = ()
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MAIL_FRONTEND_URL = ""
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# move from rag.settings
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ES = {}
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INFINITY = {}
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AZURE = {}
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S3 = {}
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MINIO = {}
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OSS = {}
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OS = {}
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DOC_MAXIMUM_SIZE: int = 128 * 1024 * 1024
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DOC_BULK_SIZE: int = 4
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EMBEDDING_BATCH_SIZE: int = 16
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PARALLEL_DEVICES: int = 0
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STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO')
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STORAGE_IMPL = None
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def get_svr_queue_name(priority: int) -> str:
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if priority == 0:
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return SVR_QUEUE_NAME
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return f"{SVR_QUEUE_NAME}_{priority}"
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def get_svr_queue_names():
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return [get_svr_queue_name(priority) for priority in [1, 0]]
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def _get_or_create_secret_key():
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secret_key = os.environ.get("RAGFLOW_SECRET_KEY")
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if secret_key and len(secret_key) >= 32:
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return secret_key
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# Check if there's a configured secret key
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configured_key = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("secret_key")
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if configured_key and configured_key != str(date.today()) and len(configured_key) >= 32:
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return configured_key
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# Generate a new secure key and warn about it
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import logging
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new_key = secrets.token_hex(32)
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logging.warning(f"SECURITY WARNING: Using auto-generated SECRET_KEY. Generated key: {new_key}")
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return new_key
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class StorageFactory:
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storage_mapping = {
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Storage.MINIO: RAGFlowMinio,
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Storage.AZURE_SPN: RAGFlowAzureSpnBlob,
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Storage.AZURE_SAS: RAGFlowAzureSasBlob,
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Storage.AWS_S3: RAGFlowS3,
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Storage.OSS: RAGFlowOSS,
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Storage.OPENDAL: OpenDALStorage
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}
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@classmethod
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def create(cls, storage: Storage):
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return cls.storage_mapping[storage]()
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def init_settings():
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global DATABASE_TYPE, DATABASE
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DATABASE_TYPE = os.getenv("DB_TYPE", "mysql")
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DATABASE = decrypt_database_config(name=DATABASE_TYPE)
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global ALLOWED_LLM_FACTORIES, LLM_FACTORY, LLM_BASE_URL
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llm_settings = get_base_config("user_default_llm", {}) or {}
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llm_default_models = llm_settings.get("default_models", {}) or {}
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LLM_FACTORY = llm_settings.get("factory", "") or ""
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LLM_BASE_URL = llm_settings.get("base_url", "") or ""
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ALLOWED_LLM_FACTORIES = llm_settings.get("allowed_factories", None)
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global REGISTER_ENABLED
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try:
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REGISTER_ENABLED = int(os.environ.get("REGISTER_ENABLED", "1"))
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except Exception:
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pass
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global FACTORY_LLM_INFOS
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try:
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with open(os.path.join(get_project_base_directory(), "conf", "llm_factories.json"), "r") as f:
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FACTORY_LLM_INFOS = json.load(f)["factory_llm_infos"]
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except Exception:
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FACTORY_LLM_INFOS = []
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global API_KEY
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API_KEY = llm_settings.get("api_key")
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global PARSERS
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PARSERS = llm_settings.get(
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"parsers", "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,email:Email,tag:Tag"
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)
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global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
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chat_entry = _parse_model_entry(llm_default_models.get("chat_model", CHAT_MDL))
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embedding_entry = _parse_model_entry(llm_default_models.get("embedding_model", EMBEDDING_MDL))
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rerank_entry = _parse_model_entry(llm_default_models.get("rerank_model", RERANK_MDL))
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asr_entry = _parse_model_entry(llm_default_models.get("asr_model", ASR_MDL))
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image2text_entry = _parse_model_entry(llm_default_models.get("image2text_model", IMAGE2TEXT_MDL))
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global CHAT_CFG, EMBEDDING_CFG, RERANK_CFG, ASR_CFG, IMAGE2TEXT_CFG
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CHAT_CFG = _resolve_per_model_config(chat_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
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EMBEDDING_CFG = _resolve_per_model_config(embedding_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
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RERANK_CFG = _resolve_per_model_config(rerank_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
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ASR_CFG = _resolve_per_model_config(asr_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
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IMAGE2TEXT_CFG = _resolve_per_model_config(image2text_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
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CHAT_MDL = CHAT_CFG.get("model", "") or ""
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EMBEDDING_MDL = os.getenv("TEI_MODEL", "BAAI/bge-small-en-v1.5") if "tei-" in os.getenv("COMPOSE_PROFILES", "") else ""
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RERANK_MDL = RERANK_CFG.get("model", "") or ""
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ASR_MDL = ASR_CFG.get("model", "") or ""
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IMAGE2TEXT_MDL = IMAGE2TEXT_CFG.get("model", "") or ""
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global HOST_IP, HOST_PORT
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HOST_IP = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1")
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HOST_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port")
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global SECRET_KEY
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SECRET_KEY = _get_or_create_secret_key()
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# authentication
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authentication_conf = get_base_config("authentication", {})
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global CLIENT_AUTHENTICATION, HTTP_APP_KEY, GITHUB_OAUTH, FEISHU_OAUTH, OAUTH_CONFIG
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# client
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CLIENT_AUTHENTICATION = authentication_conf.get("client", {}).get("switch", False)
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HTTP_APP_KEY = authentication_conf.get("client", {}).get("http_app_key")
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GITHUB_OAUTH = get_base_config("oauth", {}).get("github")
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FEISHU_OAUTH = get_base_config("oauth", {}).get("feishu")
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OAUTH_CONFIG = get_base_config("oauth", {})
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global DOC_ENGINE, docStoreConn, ES, OS, INFINITY
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DOC_ENGINE = os.environ.get("DOC_ENGINE", "elasticsearch")
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# DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch")
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lower_case_doc_engine = DOC_ENGINE.lower()
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if lower_case_doc_engine == "elasticsearch":
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ES = get_base_config("es", {})
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docStoreConn = rag.utils.es_conn.ESConnection()
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elif lower_case_doc_engine == "infinity":
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INFINITY = get_base_config("infinity", {"uri": "infinity:23817"})
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docStoreConn = rag.utils.infinity_conn.InfinityConnection()
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elif lower_case_doc_engine == "opensearch":
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OS = get_base_config("os", {})
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docStoreConn = rag.utils.opensearch_conn.OSConnection()
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else:
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raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
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global AZURE, S3, MINIO, OSS
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if STORAGE_IMPL_TYPE in ['AZURE_SPN', 'AZURE_SAS']:
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AZURE = get_base_config("azure", {})
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elif STORAGE_IMPL_TYPE == 'AWS_S3':
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S3 = get_base_config("s3", {})
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elif STORAGE_IMPL_TYPE == 'MINIO':
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MINIO = decrypt_database_config(name="minio")
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elif STORAGE_IMPL_TYPE == 'OSS':
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OSS = get_base_config("oss", {})
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global STORAGE_IMPL
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STORAGE_IMPL = StorageFactory.create(Storage[STORAGE_IMPL_TYPE])
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global retriever, kg_retriever
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retriever = search.Dealer(docStoreConn)
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from graphrag import search as kg_search
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kg_retriever = kg_search.KGSearch(docStoreConn)
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global SANDBOX_HOST
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if int(os.environ.get("SANDBOX_ENABLED", "0")):
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SANDBOX_HOST = os.environ.get("SANDBOX_HOST", "sandbox-executor-manager")
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global SMTP_CONF
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SMTP_CONF = get_base_config("smtp", {})
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global MAIL_SERVER, MAIL_PORT, MAIL_USE_SSL, MAIL_USE_TLS, MAIL_USERNAME, MAIL_PASSWORD, MAIL_DEFAULT_SENDER, MAIL_FRONTEND_URL
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MAIL_SERVER = SMTP_CONF.get("mail_server", "")
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MAIL_PORT = SMTP_CONF.get("mail_port", 000)
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MAIL_USE_SSL = SMTP_CONF.get("mail_use_ssl", True)
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MAIL_USE_TLS = SMTP_CONF.get("mail_use_tls", False)
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MAIL_USERNAME = SMTP_CONF.get("mail_username", "")
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MAIL_PASSWORD = SMTP_CONF.get("mail_password", "")
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mail_default_sender = SMTP_CONF.get("mail_default_sender", [])
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if mail_default_sender and len(mail_default_sender) >= 2:
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MAIL_DEFAULT_SENDER = (mail_default_sender[0], mail_default_sender[1])
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MAIL_FRONTEND_URL = SMTP_CONF.get("mail_frontend_url", "")
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global DOC_MAXIMUM_SIZE, DOC_BULK_SIZE, EMBEDDING_BATCH_SIZE
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DOC_MAXIMUM_SIZE = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
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DOC_BULK_SIZE = int(os.environ.get("DOC_BULK_SIZE", 4))
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EMBEDDING_BATCH_SIZE = int(os.environ.get("EMBEDDING_BATCH_SIZE", 16))
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def check_and_install_torch():
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global PARALLEL_DEVICES
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try:
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pip_install_torch()
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import torch.cuda
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PARALLEL_DEVICES = torch.cuda.device_count()
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logging.info(f"found {PARALLEL_DEVICES} gpus")
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except Exception:
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logging.info("can't import package 'torch'")
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def _parse_model_entry(entry):
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if isinstance(entry, str):
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return {"name": entry, "factory": None, "api_key": None, "base_url": None}
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if isinstance(entry, dict):
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name = entry.get("name") or entry.get("model") or ""
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return {
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"name": name,
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"factory": entry.get("factory"),
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"api_key": entry.get("api_key"),
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"base_url": entry.get("base_url"),
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}
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return {"name": "", "factory": None, "api_key": None, "base_url": None}
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def _resolve_per_model_config(entry_dict, backup_factory, backup_api_key, backup_base_url):
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name = (entry_dict.get("name") or "").strip()
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m_factory = entry_dict.get("factory") or backup_factory or ""
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m_api_key = entry_dict.get("api_key") or backup_api_key or ""
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m_base_url = entry_dict.get("base_url") or backup_base_url or ""
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if name and "@" not in name and m_factory:
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name = f"{name}@{m_factory}"
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return {
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"model": name,
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"factory": m_factory,
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"api_key": m_api_key,
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"base_url": m_base_url,
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}
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def print_rag_settings():
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logging.info(f"MAX_CONTENT_LENGTH: {DOC_MAXIMUM_SIZE}")
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logging.info(f"MAX_FILE_COUNT_PER_USER: {int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))}")
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Reference in New Issue
Block a user