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226 lines
12 KiB
Python
226 lines
12 KiB
Python
# coding=utf-8
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"""
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@project: maxkb
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@Author：虎
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@file： chat_message_serializers.py
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@date：2023/11/14 13:51
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@desc:
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"""
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import json
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from typing import List
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from uuid import UUID
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from django.core.cache import caches
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from django.db.models import QuerySet
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from langchain.chat_models.base import BaseChatModel
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from rest_framework import serializers
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from application.chat_pipeline.pipeline_manage import PiplineManage
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from application.chat_pipeline.step.chat_step.i_chat_step import PostResponseHandler
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from application.chat_pipeline.step.chat_step.impl.base_chat_step import BaseChatStep
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from application.chat_pipeline.step.generate_human_message_step.impl.base_generate_human_message_step import \
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BaseGenerateHumanMessageStep
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from application.chat_pipeline.step.reset_problem_step.impl.base_reset_problem_step import BaseResetProblemStep
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from application.chat_pipeline.step.search_dataset_step.impl.base_search_dataset_step import BaseSearchDatasetStep
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from application.models import ChatRecord, Chat, Application, ApplicationDatasetMapping
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from application.models.api_key_model import ApplicationPublicAccessClient, ApplicationAccessToken
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from common.constants.authentication_type import AuthenticationType
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from common.exception.app_exception import AppApiException, AppChatNumOutOfBoundsFailed
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from common.util.field_message import ErrMessage
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from common.util.rsa_util import decrypt
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from common.util.split_model import flat_map
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from dataset.models import Paragraph, Document
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from setting.models import Model, Status
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from setting.models_provider.constants.model_provider_constants import ModelProvideConstants
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chat_cache = caches['model_cache']
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class ChatInfo:
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def __init__(self,
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chat_id: str,
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chat_model: BaseChatModel,
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dataset_id_list: List[str],
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exclude_document_id_list: list[str],
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application: Application):
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"""
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:param chat_id: 对话id
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:param chat_model: 对话模型
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:param dataset_id_list: 数据集列表
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:param exclude_document_id_list: 排除的文档
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:param application: 应用信息
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"""
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self.chat_id = chat_id
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self.application = application
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self.chat_model = chat_model
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self.dataset_id_list = dataset_id_list
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self.exclude_document_id_list = exclude_document_id_list
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self.chat_record_list: List[ChatRecord] = []
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def to_base_pipeline_manage_params(self):
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dataset_setting = self.application.dataset_setting
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model_setting = self.application.model_setting
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return {
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'dataset_id_list': self.dataset_id_list,
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'exclude_document_id_list': self.exclude_document_id_list,
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'exclude_paragraph_id_list': [],
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'top_n': dataset_setting.get('top_n') if 'top_n' in dataset_setting else 3,
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'similarity': dataset_setting.get('similarity') if 'similarity' in dataset_setting else 0.6,
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'max_paragraph_char_number': dataset_setting.get(
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'max_paragraph_char_number') if 'max_paragraph_char_number' in dataset_setting else 5000,
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'history_chat_record': self.chat_record_list,
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'chat_id': self.chat_id,
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'dialogue_number': self.application.dialogue_number,
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'prompt': model_setting.get(
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'prompt') if 'prompt' in model_setting else Application.get_default_model_prompt(),
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'chat_model': self.chat_model,
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'model_id': self.application.model.id if self.application.model is not None else None,
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'problem_optimization': self.application.problem_optimization,
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'stream': True,
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}
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def to_pipeline_manage_params(self, problem_text: str, post_response_handler: PostResponseHandler,
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exclude_paragraph_id_list, client_id: str, client_type, stream=True):
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params = self.to_base_pipeline_manage_params()
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return {**params, 'problem_text': problem_text, 'post_response_handler': post_response_handler,
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'exclude_paragraph_id_list': exclude_paragraph_id_list, 'stream': stream, 'client_id': client_id,
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'client_type': client_type}
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def append_chat_record(self, chat_record: ChatRecord):
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# 存入缓存中
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self.chat_record_list.append(chat_record)
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if self.application.id is not None:
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# 插入数据库
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if not QuerySet(Chat).filter(id=self.chat_id).exists():
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Chat(id=self.chat_id, application_id=self.application.id, abstract=chat_record.problem_text).save()
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# 插入会话记录
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chat_record.save()
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def get_post_handler(chat_info: ChatInfo):
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class PostHandler(PostResponseHandler):
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def handler(self,
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chat_id: UUID,
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chat_record_id,
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paragraph_list: List[Paragraph],
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problem_text: str,
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answer_text,
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manage: PiplineManage,
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step: BaseChatStep,
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padding_problem_text: str = None,
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**kwargs):
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chat_record = ChatRecord(id=chat_record_id,
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chat_id=chat_id,
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problem_text=problem_text,
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answer_text=answer_text,
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details=manage.get_details(),
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message_tokens=manage.context['message_tokens'],
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answer_tokens=manage.context['answer_tokens'],
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run_time=manage.context['run_time'],
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index=len(chat_info.chat_record_list) + 1)
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chat_info.append_chat_record(chat_record)
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# 重新设置缓存
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chat_cache.set(chat_id,
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chat_info, timeout=60 * 30)
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return PostHandler()
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class ChatMessageSerializer(serializers.Serializer):
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chat_id = serializers.UUIDField(required=True, error_messages=ErrMessage.char("对话id"))
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message = serializers.CharField(required=True, error_messages=ErrMessage.char("用户问题"))
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stream = serializers.BooleanField(required=True, error_messages=ErrMessage.char("是否流式回答"))
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re_chat = serializers.BooleanField(required=True, error_messages=ErrMessage.char("是否重新回答"))
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application_id = serializers.UUIDField(required=False, allow_null=True, error_messages=ErrMessage.uuid("应用id"))
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client_id = serializers.CharField(required=True, error_messages=ErrMessage.char("客户端id"))
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client_type = serializers.CharField(required=True, error_messages=ErrMessage.char("客户端类型"))
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def is_valid(self, *, raise_exception=False):
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super().is_valid(raise_exception=True)
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if self.data.get('client_type') == AuthenticationType.APPLICATION_ACCESS_TOKEN.value:
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access_client = QuerySet(ApplicationPublicAccessClient).filter(id=self.data.get('client_id')).first()
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if access_client is None:
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access_client = ApplicationPublicAccessClient(id=self.data.get('client_id'),
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application_id=self.data.get('application_id'),
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access_num=0,
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intraday_access_num=0)
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access_client.save()
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application_access_token = QuerySet(ApplicationAccessToken).filter(
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application_id=self.data.get('application_id')).first()
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if application_access_token.access_num <= access_client.intraday_access_num:
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raise AppChatNumOutOfBoundsFailed(1002, "访问次数超过今日访问量")
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application = QuerySet(Application).filter(id=self.data.get("application_id")).first()
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if application.model.status == Status.ERROR:
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raise AppApiException(500, "当前模型不可用")
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if application.model.status == Status.DOWNLOAD:
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raise AppApiException(500, "模型正在下载中,请稍后再发起对话")
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def chat(self):
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self.is_valid(raise_exception=True)
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message = self.data.get('message')
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re_chat = self.data.get('re_chat')
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stream = self.data.get('stream')
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client_id = self.data.get('client_id')
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client_type = self.data.get('client_type')
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self.is_valid(raise_exception=True)
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chat_id = self.data.get('chat_id')
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chat_info: ChatInfo = chat_cache.get(chat_id)
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if chat_info is None:
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chat_info = self.re_open_chat(chat_id)
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chat_cache.set(chat_id,
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chat_info, timeout=60 * 30)
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pipline_manage_builder = PiplineManage.builder()
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# 如果开启了问题优化,则添加上问题优化步骤
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if chat_info.application.problem_optimization:
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pipline_manage_builder.append_step(BaseResetProblemStep)
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# 构建流水线管理器
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pipline_message = (pipline_manage_builder.append_step(BaseSearchDatasetStep)
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.append_step(BaseGenerateHumanMessageStep)
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.append_step(BaseChatStep)
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.build())
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exclude_paragraph_id_list = []
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# 相同问题是否需要排除已经查询到的段落
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if re_chat:
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paragraph_id_list = flat_map([row.paragraph_id_list for row in
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filter(lambda chat_record: chat_record == message,
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chat_info.chat_record_list)])
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exclude_paragraph_id_list = list(set(paragraph_id_list))
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# 构建运行参数
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params = chat_info.to_pipeline_manage_params(message, get_post_handler(chat_info), exclude_paragraph_id_list,
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client_id, client_type, stream)
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# 运行流水线作业
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pipline_message.run(params)
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return pipline_message.context['chat_result']
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@staticmethod
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def re_open_chat(chat_id: str):
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chat = QuerySet(Chat).filter(id=chat_id).first()
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if chat is None:
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raise AppApiException(500, "会话不存在")
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application = QuerySet(Application).filter(id=chat.application_id).first()
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if application is None:
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raise AppApiException(500, "应用不存在")
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model = QuerySet(Model).filter(id=application.model_id).first()
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chat_model = None
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if model is not None:
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# 对话模型
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chat_model = ModelProvideConstants[model.provider].value.get_model(model.model_type, model.model_name,
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json.loads(
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decrypt(model.credential)),
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streaming=True)
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# 数据集id列表
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dataset_id_list = [str(row.dataset_id) for row in
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QuerySet(ApplicationDatasetMapping).filter(
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application_id=application.id)]
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# 需要排除的文档
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exclude_document_id_list = [str(document.id) for document in
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QuerySet(Document).filter(
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dataset_id__in=dataset_id_list,
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is_active=False)]
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return ChatInfo(chat_id, chat_model, dataset_id_list, exclude_document_id_list, application)
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