ragflow/api/settings.py

#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import os
from enum import IntEnum, Enum
from api.utils.file_utils import get_project_base_directory
from api.utils.log_utils import LoggerFactory, getLogger

# Logger
LoggerFactory.set_directory(
    os.path.join(
        get_project_base_directory(),
        "logs",
        "api"))
# {CRITICAL: 50, FATAL:50, ERROR:40, WARNING:30, WARN:30, INFO:20, DEBUG:10, NOTSET:0}
LoggerFactory.LEVEL = 30

stat_logger = getLogger("stat")
access_logger = getLogger("access")
database_logger = getLogger("database")
chat_logger = getLogger("chat")

from rag.utils.es_conn import ELASTICSEARCH
from rag.nlp import search
from graphrag import search as kg_search
from api.utils import get_base_config, decrypt_database_config

API_VERSION = "v1"
RAG_FLOW_SERVICE_NAME = "ragflow"
SERVER_MODULE = "rag_flow_server.py"
TEMP_DIRECTORY = os.path.join(get_project_base_directory(), "temp")
RAG_FLOW_CONF_PATH = os.path.join(get_project_base_directory(), "conf")

SUBPROCESS_STD_LOG_NAME = "std.log"

ERROR_REPORT = True
ERROR_REPORT_WITH_PATH = False

MAX_TIMESTAMP_INTERVAL = 60
SESSION_VALID_PERIOD = 7 * 24 * 60 * 60

REQUEST_TRY_TIMES = 3
REQUEST_WAIT_SEC = 2
REQUEST_MAX_WAIT_SEC = 300

USE_REGISTRY = get_base_config("use_registry")

default_llm = {
    "Tongyi-Qianwen": {
        "chat_model": "qwen-plus",
        "embedding_model": "text-embedding-v2",
        "image2text_model": "qwen-vl-max",
        "asr_model": "paraformer-realtime-8k-v1",
    },
    "OpenAI": {
        "chat_model": "gpt-3.5-turbo",
        "embedding_model": "text-embedding-ada-002",
        "image2text_model": "gpt-4-vision-preview",
        "asr_model": "whisper-1",
    },
    "Azure-OpenAI": {
        "chat_model": "azure-gpt-35-turbo",
        "embedding_model": "azure-text-embedding-ada-002",
        "image2text_model": "azure-gpt-4-vision-preview",
        "asr_model": "azure-whisper-1",
    },
    "ZHIPU-AI": {
        "chat_model": "glm-3-turbo",
        "embedding_model": "embedding-2",
        "image2text_model": "glm-4v",
        "asr_model": "",
    },
    "Ollama": {
        "chat_model": "qwen-14B-chat",
        "embedding_model": "flag-embedding",
        "image2text_model": "",
        "asr_model": "",
    },
    "Moonshot": {
        "chat_model": "moonshot-v1-8k",
        "embedding_model": "",
        "image2text_model": "",
        "asr_model": "",
    },
    "DeepSeek": {
        "chat_model": "deepseek-chat",
        "embedding_model": "",
        "image2text_model": "",
        "asr_model": "",
    },
    "VolcEngine": {
        "chat_model": "",
        "embedding_model": "",
        "image2text_model": "",
        "asr_model": "",
    },
    "BAAI": {
        "chat_model": "",
        "embedding_model": "BAAI/bge-large-zh-v1.5",
        "image2text_model": "",
        "asr_model": "",
        "rerank_model": "BAAI/bge-reranker-v2-m3",
    }
}
LLM = get_base_config("user_default_llm", {})
LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
LLM_BASE_URL = LLM.get("base_url")

if LLM_FACTORY not in default_llm:
    print(
        "\33[91m【ERROR】\33[0m:",
        f"LLM factory {LLM_FACTORY} has not supported yet, switch to 'Tongyi-Qianwen/QWen' automatically, and please check the API_KEY in service_conf.yaml.")
    LLM_FACTORY = "Tongyi-Qianwen"
CHAT_MDL = default_llm[LLM_FACTORY]["chat_model"]
EMBEDDING_MDL = default_llm["BAAI"]["embedding_model"]
RERANK_MDL = default_llm["BAAI"]["rerank_model"]
ASR_MDL = default_llm[LLM_FACTORY]["asr_model"]
IMAGE2TEXT_MDL = default_llm[LLM_FACTORY]["image2text_model"]

API_KEY = LLM.get("api_key", "")
PARSERS = LLM.get(
    "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,knowledge_graph:Knowledge Graph,email:Email")

# distribution
DEPENDENT_DISTRIBUTION = get_base_config("dependent_distribution", False)
RAG_FLOW_UPDATE_CHECK = False

HOST = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1")
HTTP_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port")

SECRET_KEY = get_base_config(
    RAG_FLOW_SERVICE_NAME,
    {}).get(
        "secret_key",
    "infiniflow")
TOKEN_EXPIRE_IN = get_base_config(
    RAG_FLOW_SERVICE_NAME, {}).get(
        "token_expires_in", 3600)

NGINX_HOST = get_base_config(
    RAG_FLOW_SERVICE_NAME, {}).get(
        "nginx", {}).get("host") or HOST
NGINX_HTTP_PORT = get_base_config(
    RAG_FLOW_SERVICE_NAME, {}).get(
        "nginx", {}).get("http_port") or HTTP_PORT

RANDOM_INSTANCE_ID = get_base_config(
    RAG_FLOW_SERVICE_NAME, {}).get(
        "random_instance_id", False)

PROXY = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("proxy")
PROXY_PROTOCOL = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("protocol")

DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
DATABASE = decrypt_database_config(name=DATABASE_TYPE)

# Switch
# upload
UPLOAD_DATA_FROM_CLIENT = True

# authentication
AUTHENTICATION_CONF = get_base_config("authentication", {})

# client
CLIENT_AUTHENTICATION = AUTHENTICATION_CONF.get(
    "client", {}).get(
        "switch", False)
HTTP_APP_KEY = AUTHENTICATION_CONF.get("client", {}).get("http_app_key")
GITHUB_OAUTH = get_base_config("oauth", {}).get("github")
FEISHU_OAUTH = get_base_config("oauth", {}).get("feishu")
WECHAT_OAUTH = get_base_config("oauth", {}).get("wechat")

# site
SITE_AUTHENTICATION = AUTHENTICATION_CONF.get("site", {}).get("switch", False)

# permission
PERMISSION_CONF = get_base_config("permission", {})
PERMISSION_SWITCH = PERMISSION_CONF.get("switch")
COMPONENT_PERMISSION = PERMISSION_CONF.get("component")
DATASET_PERMISSION = PERMISSION_CONF.get("dataset")

HOOK_MODULE = get_base_config("hook_module")
HOOK_SERVER_NAME = get_base_config("hook_server_name")

ENABLE_MODEL_STORE = get_base_config('enable_model_store', False)
# authentication
USE_AUTHENTICATION = False
USE_DATA_AUTHENTICATION = False
AUTOMATIC_AUTHORIZATION_OUTPUT_DATA = True
USE_DEFAULT_TIMEOUT = False
AUTHENTICATION_DEFAULT_TIMEOUT = 7 * 24 * 60 * 60  # s
PRIVILEGE_COMMAND_WHITELIST = []
CHECK_NODES_IDENTITY = False

retrievaler = search.Dealer(ELASTICSEARCH)
kg_retrievaler = kg_search.KGSearch(ELASTICSEARCH)


class CustomEnum(Enum):
    @classmethod
    def valid(cls, value):
        try:
            cls(value)
            return True
        except BaseException:
            return False

    @classmethod
    def values(cls):
        return [member.value for member in cls.__members__.values()]

    @classmethod
    def names(cls):
        return [member.name for member in cls.__members__.values()]


class PythonDependenceName(CustomEnum):
    Rag_Source_Code = "python"
    Python_Env = "miniconda"


class ModelStorage(CustomEnum):
    REDIS = "redis"
    MYSQL = "mysql"


class RetCode(IntEnum, CustomEnum):
    SUCCESS = 0
    NOT_EFFECTIVE = 10
    EXCEPTION_ERROR = 100
    ARGUMENT_ERROR = 101
    DATA_ERROR = 102
    OPERATING_ERROR = 103
    CONNECTION_ERROR = 105
    RUNNING = 106
    PERMISSION_ERROR = 108
    AUTHENTICATION_ERROR = 109
    UNAUTHORIZED = 401
    SERVER_ERROR = 500