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#
# 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.
#
from api.db.services.user_service import TenantService
from api.settings import database_logger
from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel, TTSModel
from api.db import LLMType
from api.db.db_models import DB
from api.db.db_models import LLMFactories, LLM, TenantLLM
from api.db.services.common_service import CommonService
class LLMFactoriesService(CommonService):
model = LLMFactories
class LLMService(CommonService):
model = LLM
class TenantLLMService(CommonService):
model = TenantLLM
@classmethod
@DB.connection_context()
def get_api_key(cls, tenant_id, model_name):
arr = model_name.split("@")
if len(arr) < 2:
objs = cls.query(tenant_id=tenant_id, llm_name=model_name)
else:
objs = cls.query(tenant_id=tenant_id, llm_name=arr[0], llm_factory=arr[1])
if not objs:
return
return objs[0]
@classmethod
@DB.connection_context()
def get_my_llms(cls, tenant_id):
fields = [
cls.model.llm_factory,
LLMFactories.logo,
LLMFactories.tags,
cls.model.model_type,
cls.model.llm_name,
cls.model.used_tokens
]
objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
return list(objs)
@classmethod
@DB.connection_context()
def model_instance(cls, tenant_id, llm_type,
llm_name=None, lang="Chinese"):
e, tenant = TenantService.get_by_id(tenant_id)
if not e:
raise LookupError("Tenant not found")
if llm_type == LLMType.EMBEDDING.value:
mdlnm = tenant.embd_id if not llm_name else llm_name
elif llm_type == LLMType.SPEECH2TEXT.value:
mdlnm = tenant.asr_id
elif llm_type == LLMType.IMAGE2TEXT.value:
mdlnm = tenant.img2txt_id if not llm_name else llm_name
elif llm_type == LLMType.CHAT.value:
mdlnm = tenant.llm_id if not llm_name else llm_name
elif llm_type == LLMType.RERANK:
mdlnm = tenant.rerank_id if not llm_name else llm_name
elif llm_type == LLMType.TTS:
mdlnm = tenant.tts_id if not llm_name else llm_name
else:
assert False, "LLM type error"
model_config = cls.get_api_key(tenant_id, mdlnm)
tmp = mdlnm.split("@")
fid = None if len(tmp) < 2 else tmp[1]
mdlnm = tmp[0]
if model_config: model_config = model_config.to_dict()
if not model_config:
if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
model_config = {"llm_factory": llm[0].fid, "api_key":"", "llm_name": mdlnm, "api_base": ""}
if not model_config:
if mdlnm == "flag-embedding":
model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
"llm_name": llm_name, "api_base": ""}
else:
if not mdlnm:
raise LookupError(f"Type of {llm_type} model is not set.")
raise LookupError("Model({}) not authorized".format(mdlnm))
if llm_type == LLMType.EMBEDDING.value:
if model_config["llm_factory"] not in EmbeddingModel:
return
return EmbeddingModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
if llm_type == LLMType.RERANK:
if model_config["llm_factory"] not in RerankModel:
return
return RerankModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
if llm_type == LLMType.IMAGE2TEXT.value:
if model_config["llm_factory"] not in CvModel:
return
return CvModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], lang,
base_url=model_config["api_base"]
)
if llm_type == LLMType.CHAT.value:
if model_config["llm_factory"] not in ChatModel:
return
return ChatModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
if llm_type == LLMType.SPEECH2TEXT:
if model_config["llm_factory"] not in Seq2txtModel:
return
return Seq2txtModel[model_config["llm_factory"]](
model_config["api_key"], model_config["llm_name"], lang,
base_url=model_config["api_base"]
)
if llm_type == LLMType.TTS:
if model_config["llm_factory"] not in TTSModel:
return
return TTSModel[model_config["llm_factory"]](
model_config["api_key"],
model_config["llm_name"],
base_url=model_config["api_base"],
)
@classmethod
@DB.connection_context()
def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None):
e, tenant = TenantService.get_by_id(tenant_id)
if not e:
raise LookupError("Tenant not found")
if llm_type == LLMType.EMBEDDING.value:
mdlnm = tenant.embd_id
elif llm_type == LLMType.SPEECH2TEXT.value:
mdlnm = tenant.asr_id
elif llm_type == LLMType.IMAGE2TEXT.value:
mdlnm = tenant.img2txt_id
elif llm_type == LLMType.CHAT.value:
mdlnm = tenant.llm_id if not llm_name else llm_name
elif llm_type == LLMType.RERANK:
mdlnm = tenant.rerank_id if not llm_name else llm_name
elif llm_type == LLMType.TTS:
mdlnm = tenant.tts_id if not llm_name else llm_name
else:
assert False, "LLM type error"
num = 0
try:
for u in cls.query(tenant_id = tenant_id, llm_name=mdlnm):
num += cls.model.update(used_tokens = u.used_tokens + used_tokens)\
.where(cls.model.tenant_id == tenant_id, cls.model.llm_name == mdlnm)\
.execute()
except Exception as e:
pass
return num
@classmethod
@DB.connection_context()
def get_openai_models(cls):
objs = cls.model.select().where(
(cls.model.llm_factory == "OpenAI"),
~(cls.model.llm_name == "text-embedding-3-small"),
~(cls.model.llm_name == "text-embedding-3-large")
).dicts()
return list(objs)
class LLMBundle(object):
def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese"):
self.tenant_id = tenant_id
self.llm_type = llm_type
self.llm_name = llm_name
self.mdl = TenantLLMService.model_instance(
tenant_id, llm_type, llm_name, lang=lang)
assert self.mdl, "Can't find mole for {}/{}/{}".format(
tenant_id, llm_type, llm_name)
self.max_length = 8192
for lm in LLMService.query(llm_name=llm_name):
self.max_length = lm.max_tokens
break
def encode(self, texts: list, batch_size=32):
emd, used_tokens = self.mdl.encode(texts, batch_size)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens):
database_logger.error(
"Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
return emd, used_tokens
def encode_queries(self, query: str):
emd, used_tokens = self.mdl.encode_queries(query)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens):
database_logger.error(
"Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
return emd, used_tokens
def similarity(self, query: str, texts: list):
sim, used_tokens = self.mdl.similarity(query, texts)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens):
database_logger.error(
"Can't update token usage for {}/RERANK".format(self.tenant_id))
return sim, used_tokens
def describe(self, image, max_tokens=300):
txt, used_tokens = self.mdl.describe(image, max_tokens)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens):
database_logger.error(
"Can't update token usage for {}/IMAGE2TEXT".format(self.tenant_id))
return txt
def transcription(self, audio):
txt, used_tokens = self.mdl.transcription(audio)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens):
database_logger.error(
"Can't update token usage for {}/SEQUENCE2TXT".format(self.tenant_id))
return txt
def tts(self, text):
for chunk in self.mdl.tts(text):
if isinstance(chunk,int):
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, chunk, self.llm_name):
database_logger.error(
"Can't update token usage for {}/TTS".format(self.tenant_id))
return
yield chunk
def chat(self, system, history, gen_conf):
txt, used_tokens = self.mdl.chat(system, history, gen_conf)
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, used_tokens, self.llm_name):
database_logger.error(
"Can't update token usage for {}/CHAT".format(self.tenant_id))
return txt
def chat_streamly(self, system, history, gen_conf):
for txt in self.mdl.chat_streamly(system, history, gen_conf):
if isinstance(txt, int):
if not TenantLLMService.increase_usage(
self.tenant_id, self.llm_type, txt, self.llm_name):
database_logger.error(
"Can't update token usage for {}/CHAT".format(self.tenant_id))
return
yield txt
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