#
# 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
import time
import uuid
from copy import deepcopy
from api.db import LLMType, UserTenantRole
from api.db.db_models import init_database_tables as init_web_db, LLMFactories, LLM, TenantLLM
from api.db.services import UserService
from api.db.services.document_service import DocumentService
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMFactoriesService, LLMService, TenantLLMService, LLMBundle
from api.db.services.user_service import TenantService, UserTenantService
from api.settings import CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, LLM_FACTORY, API_KEY, LLM_BASE_URL
def init_superuser():
user_info = {
"id": uuid.uuid1().hex,
"password": "admin",
"nickname": "admin",
"is_superuser": True,
"email": "admin@ragflow.io",
"creator": "system",
"status": "1",
}
tenant = {
"id": user_info["id"],
"name": user_info["nickname"] + "‘s Kingdom",
"llm_id": CHAT_MDL,
"embd_id": EMBEDDING_MDL,
"asr_id": ASR_MDL,
"parser_ids": PARSERS,
"img2txt_id": IMAGE2TEXT_MDL
}
usr_tenant = {
"tenant_id": user_info["id"],
"user_id": user_info["id"],
"invited_by": user_info["id"],
"role": UserTenantRole.OWNER
}
tenant_llm = []
for llm in LLMService.query(fid=LLM_FACTORY):
tenant_llm.append(
{"tenant_id": user_info["id"], "llm_factory": LLM_FACTORY, "llm_name": llm.llm_name, "model_type": llm.model_type,
"api_key": API_KEY, "api_base": LLM_BASE_URL})
if not UserService.save(**user_info):
print("\033[93m【ERROR】\033[0mcan't init admin.")
return
TenantService.insert(**tenant)
UserTenantService.insert(**usr_tenant)
TenantLLMService.insert_many(tenant_llm)
print(
"【INFO】Super user initialized. \033[93memail: admin@ragflow.io, password: admin\033[0m. Changing the password after logining is strongly recomanded.")
chat_mdl = LLMBundle(tenant["id"], LLMType.CHAT, tenant["llm_id"])
msg = chat_mdl.chat(system="", history=[
{"role": "user", "content": "Hello!"}], gen_conf={})
if msg.find("ERROR: ") == 0:
print(
"\33[91m【ERROR】\33[0m: ",
"'{}' dosen't work. {}".format(
tenant["llm_id"],
msg))
embd_mdl = LLMBundle(tenant["id"], LLMType.EMBEDDING, tenant["embd_id"])
v, c = embd_mdl.encode(["Hello!"])
if c == 0:
print(
"\33[91m【ERROR】\33[0m:",
" '{}' dosen't work!".format(
tenant["embd_id"]))
factory_infos = [{
"name": "OpenAI",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
}, {
"name": "Tongyi-Qianwen",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
}, {
"name": "ZHIPU-AI",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
},
{
"name": "Ollama",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
}, {
"name": "Moonshot",
"logo": "",
"tags": "LLM,TEXT EMBEDDING",
"status": "1",
}, {
"name": "FastEmbed",
"logo": "",
"tags": "TEXT EMBEDDING",
"status": "1",
}, {
"name": "Xinference",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
},{
"name": "Youdao",
"logo": "",
"tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
"status": "1",
},{
"name": "DeepSeek",
"logo": "",
"tags": "LLM",
"status": "1",
},{
"name": "VolcEngine",
"logo": "",
"tags": "LLM, TEXT EMBEDDING",
"status": "1",
},{
"name": "BaiChuan",
"logo": "",
"tags": "LLM,TEXT EMBEDDING",
"status": "1",
},
# {
# "name": "文心一言",
# "logo": "",
# "tags": "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION",
# "status": "1",
# },
]
def init_llm_factory():
llm_infos = [
# ---------------------- OpenAI ------------------------
{
"fid": factory_infos[0]["name"],
"llm_name": "gpt-4o",
"tags": "LLM,CHAT,128K",
"max_tokens": 128000,
"model_type": LLMType.CHAT.value + "," + LLMType.IMAGE2TEXT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "gpt-3.5-turbo",
"tags": "LLM,CHAT,4K",
"max_tokens": 4096,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "gpt-3.5-turbo-16k-0613",
"tags": "LLM,CHAT,16k",
"max_tokens": 16385,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "text-embedding-ada-002",
"tags": "TEXT EMBEDDING,8K",
"max_tokens": 8191,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "text-embedding-3-small",
"tags": "TEXT EMBEDDING,8K",
"max_tokens": 8191,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "text-embedding-3-large",
"tags": "TEXT EMBEDDING,8K",
"max_tokens": 8191,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "whisper-1",
"tags": "SPEECH2TEXT",
"max_tokens": 25 * 1024 * 1024,
"model_type": LLMType.SPEECH2TEXT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "gpt-4",
"tags": "LLM,CHAT,8K",
"max_tokens": 8191,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "gpt-4-turbo",
"tags": "LLM,CHAT,8K",
"max_tokens": 8191,
"model_type": LLMType.CHAT.value
},{
"fid": factory_infos[0]["name"],
"llm_name": "gpt-4-32k",
"tags": "LLM,CHAT,32K",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[0]["name"],
"llm_name": "gpt-4-vision-preview",
"tags": "LLM,CHAT,IMAGE2TEXT",
"max_tokens": 765,
"model_type": LLMType.IMAGE2TEXT.value
},
# ----------------------- Qwen -----------------------
{
"fid": factory_infos[1]["name"],
"llm_name": "qwen-turbo",
"tags": "LLM,CHAT,8K",
"max_tokens": 8191,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[1]["name"],
"llm_name": "qwen-plus",
"tags": "LLM,CHAT,32K",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[1]["name"],
"llm_name": "qwen-max-1201",
"tags": "LLM,CHAT,6K",
"max_tokens": 5899,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[1]["name"],
"llm_name": "text-embedding-v2",
"tags": "TEXT EMBEDDING,2K",
"max_tokens": 2048,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[1]["name"],
"llm_name": "paraformer-realtime-8k-v1",
"tags": "SPEECH2TEXT",
"max_tokens": 25 * 1024 * 1024,
"model_type": LLMType.SPEECH2TEXT.value
}, {
"fid": factory_infos[1]["name"],
"llm_name": "qwen-vl-max",
"tags": "LLM,CHAT,IMAGE2TEXT",
"max_tokens": 765,
"model_type": LLMType.IMAGE2TEXT.value
},
# ---------------------- ZhipuAI ----------------------
{
"fid": factory_infos[2]["name"],
"llm_name": "glm-3-turbo",
"tags": "LLM,CHAT,",
"max_tokens": 128 * 1000,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[2]["name"],
"llm_name": "glm-4",
"tags": "LLM,CHAT,",
"max_tokens": 128 * 1000,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[2]["name"],
"llm_name": "glm-4v",
"tags": "LLM,CHAT,IMAGE2TEXT",
"max_tokens": 2000,
"model_type": LLMType.IMAGE2TEXT.value
},
{
"fid": factory_infos[2]["name"],
"llm_name": "embedding-2",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
},
# ------------------------ Moonshot -----------------------
{
"fid": factory_infos[4]["name"],
"llm_name": "moonshot-v1-8k",
"tags": "LLM,CHAT,",
"max_tokens": 7900,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[4]["name"],
"llm_name": "moonshot-v1-32k",
"tags": "LLM,CHAT,",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
}, {
"fid": factory_infos[4]["name"],
"llm_name": "moonshot-v1-128k",
"tags": "LLM,CHAT",
"max_tokens": 128 * 1000,
"model_type": LLMType.CHAT.value
},
# ------------------------ FastEmbed -----------------------
{
"fid": factory_infos[5]["name"],
"llm_name": "BAAI/bge-small-en-v1.5",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[5]["name"],
"llm_name": "BAAI/bge-small-zh-v1.5",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
}, {
}, {
"fid": factory_infos[5]["name"],
"llm_name": "BAAI/bge-base-en-v1.5",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
}, {
}, {
"fid": factory_infos[5]["name"],
"llm_name": "BAAI/bge-large-en-v1.5",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[5]["name"],
"llm_name": "sentence-transformers/all-MiniLM-L6-v2",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[5]["name"],
"llm_name": "nomic-ai/nomic-embed-text-v1.5",
"tags": "TEXT EMBEDDING,",
"max_tokens": 8192,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[5]["name"],
"llm_name": "jinaai/jina-embeddings-v2-small-en",
"tags": "TEXT EMBEDDING,",
"max_tokens": 2147483648,
"model_type": LLMType.EMBEDDING.value
}, {
"fid": factory_infos[5]["name"],
"llm_name": "jinaai/jina-embeddings-v2-base-en",
"tags": "TEXT EMBEDDING,",
"max_tokens": 2147483648,
"model_type": LLMType.EMBEDDING.value
},
# ------------------------ Youdao -----------------------
{
"fid": factory_infos[7]["name"],
"llm_name": "maidalun1020/bce-embedding-base_v1",
"tags": "TEXT EMBEDDING,",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
},
# ------------------------ DeepSeek -----------------------
{
"fid": factory_infos[8]["name"],
"llm_name": "deepseek-chat",
"tags": "LLM,CHAT,",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[8]["name"],
"llm_name": "deepseek-coder",
"tags": "LLM,CHAT,",
"max_tokens": 16385,
"model_type": LLMType.CHAT.value
},
# ------------------------ VolcEngine -----------------------
{
"fid": factory_infos[9]["name"],
"llm_name": "Skylark2-pro-32k",
"tags": "LLM,CHAT,32k",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[9]["name"],
"llm_name": "Skylark2-pro-4k",
"tags": "LLM,CHAT,4k",
"max_tokens": 4096,
"model_type": LLMType.CHAT.value
},
# ------------------------ BaiChuan -----------------------
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan2-Turbo",
"tags": "LLM,CHAT,32K",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan2-Turbo-192k",
"tags": "LLM,CHAT,192K",
"max_tokens": 196608,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan3-Turbo",
"tags": "LLM,CHAT,32K",
"max_tokens": 32768,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan3-Turbo-128k",
"tags": "LLM,CHAT,128K",
"max_tokens": 131072,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan4",
"tags": "LLM,CHAT,128K",
"max_tokens": 131072,
"model_type": LLMType.CHAT.value
},
{
"fid": factory_infos[10]["name"],
"llm_name": "Baichuan-Text-Embedding",
"tags": "TEXT EMBEDDING",
"max_tokens": 512,
"model_type": LLMType.EMBEDDING.value
},
]
for info in factory_infos:
try:
LLMFactoriesService.save(**info)
except Exception as e:
pass
for info in llm_infos:
try:
LLMService.save(**info)
except Exception as e:
pass
LLMFactoriesService.filter_delete([LLMFactories.name == "Local"])
LLMService.filter_delete([LLM.fid == "Local"])
LLMService.filter_delete([LLM.fid == "Moonshot", LLM.llm_name == "flag-embedding"])
TenantLLMService.filter_delete([TenantLLM.llm_factory == "Moonshot", TenantLLM.llm_name == "flag-embedding"])
LLMFactoriesService.filter_delete([LLMFactoriesService.model.name == "QAnything"])
LLMService.filter_delete([LLMService.model.fid == "QAnything"])
TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "QAnything"], {"llm_factory": "Youdao"})
## insert openai two embedding models to the current openai user.
print("Start to insert 2 OpenAI embedding models...")
tenant_ids = set([row["tenant_id"] for row in TenantLLMService.get_openai_models()])
for tid in tenant_ids:
for row in TenantLLMService.query(llm_factory="OpenAI", tenant_id=tid):
row = row.to_dict()
row["model_type"] = LLMType.EMBEDDING.value
row["llm_name"] = "text-embedding-3-small"
row["used_tokens"] = 0
try:
TenantLLMService.save(**row)
row = deepcopy(row)
row["llm_name"] = "text-embedding-3-large"
TenantLLMService.save(**row)
except Exception as e:
pass
break
for kb_id in KnowledgebaseService.get_all_ids():
KnowledgebaseService.update_by_id(kb_id, {"doc_num": DocumentService.get_kb_doc_count(kb_id)})
"""
drop table llm;
drop table llm_factories;
update tenant set parser_ids='naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One';
alter table knowledgebase modify avatar longtext;
alter table user modify avatar longtext;
alter table dialog modify icon longtext;
"""
def init_web_data():
start_time = time.time()
init_llm_factory()
if not UserService.get_all().count():
init_superuser()
print("init web data success:{}".format(time.time() - start_time))
if __name__ == '__main__':
init_web_db()
init_web_data()