File size: 5,843 Bytes
aeb6dbc e6cd231 aeb6dbc b691127 aeb6dbc b691127 aeb6dbc caf7eaa aeb6dbc 121b0b5 aeb6dbc 3090a99 dbcbb17 a92172b dbcbb17 3090a99 aeb6dbc e6cd231 da0bc38 aeb6dbc 777b2da aeb6dbc 2459d65 b691127 2459d65 b691127 2459d65 b691127 aeb6dbc ebde808 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
#
# 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 datetime import date
from enum import IntEnum, Enum
import rag.utils.es_conn
import rag.utils.infinity_conn
import rag.utils
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"
LIGHTEN = int(os.environ.get('LIGHTEN', "0"))
REQUEST_WAIT_SEC = 2
REQUEST_MAX_WAIT_SEC = 300
LLM = get_base_config("user_default_llm", {})
LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
LLM_BASE_URL = LLM.get("base_url")
CHAT_MDL = EMBEDDING_MDL = RERANK_MDL = ASR_MDL = IMAGE2TEXT_MDL = ""
if not LIGHTEN:
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": "gpt-35-turbo",
"embedding_model": "text-embedding-ada-002",
"image2text_model": "gpt-4-vision-preview",
"asr_model": "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",
}
}
if LLM_FACTORY:
CHAT_MDL = default_llm[LLM_FACTORY]["chat_model"] + f"@{LLM_FACTORY}"
ASR_MDL = default_llm[LLM_FACTORY]["asr_model"] + f"@{LLM_FACTORY}"
IMAGE2TEXT_MDL = default_llm[LLM_FACTORY]["image2text_model"] + f"@{LLM_FACTORY}"
EMBEDDING_MDL = default_llm["BAAI"]["embedding_model"] + "@BAAI"
RERANK_MDL = default_llm["BAAI"]["rerank_model"] + "@BAAI"
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")
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", str(date.today()))
DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
DATABASE = decrypt_database_config(name=DATABASE_TYPE)
# 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")
DOC_ENGINE = os.environ.get('DOC_ENGINE', "elasticsearch")
if DOC_ENGINE == "elasticsearch":
docStoreConn = rag.utils.es_conn.ESConnection()
elif DOC_ENGINE == "infinity":
docStoreConn = rag.utils.infinity_conn.InfinityConnection()
else:
raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
retrievaler = search.Dealer(docStoreConn)
kg_retrievaler = kg_search.KGSearch(docStoreConn)
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
FORBIDDEN = 403
NOT_FOUND = 404
|