ragflow/api/db/services/llm_service.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.
#
from api.db.services.user_service import TenantService
from api.settings import database_logger
from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel
from api.db import LLMType
from api.db.db_models import DB, UserTenant
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):
        objs = cls.query(tenant_id=tenant_id, llm_name=model_name)
        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
        else:
            assert False, "LLM type error"

        model_config = cls.get_api_key(tenant_id, mdlnm)
        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=llm_name if llm_name else mdlnm)
                if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
                    model_config = {"llm_factory": llm[0].fid, "api_key":"", "llm_name": llm_name if llm_name else mdlnm, "api_base": ""}
            if not model_config:
                if llm_name == "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"]
            )

    @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.llm_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 = 512
        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 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