MaxKB/apps/application/serializers/common.py
2025-06-12 15:21:59 +08:00

144 lines
7.2 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

# coding=utf-8
"""
@project: MaxKB
@Author虎虎
@file common.py
@date2025/6/9 13:42
@desc:
"""
from datetime import datetime
from typing import List
from django.core.cache import cache
from django.db.models import QuerySet
from django.utils.translation import gettext_lazy as _
from application.chat_pipeline.step.chat_step.i_chat_step import PostResponseHandler
from application.models import Application, WorkFlowVersion, ChatRecord, Chat
from common.constants.cache_version import Cache_Version
from models_provider.models import Model
from models_provider.tools import get_model_credential
class ChatInfo:
def __init__(self,
chat_id: str,
chat_user_id: str,
chat_user_type: str,
knowledge_id_list: List[str],
exclude_document_id_list: list[str],
application_id: str,
application: Application,
work_flow_version: WorkFlowVersion = None,
debug=False):
"""
:param chat_id: 对话id
:param chat_user_id 对话用户id
:param chat_user_type 对话用户类型
:param knowledge_id_list: 知识库列表
:param exclude_document_id_list: 排除的文档
:param application_id 应用id
:param application: 应用信息
:param debug 是否是调试
"""
self.chat_id = chat_id
self.chat_user_id = chat_user_id
self.chat_user_type = chat_user_type
self.application = application
self.knowledge_id_list = knowledge_id_list
self.exclude_document_id_list = exclude_document_id_list
self.application_id = application_id
self.chat_record_list: List[ChatRecord] = []
self.work_flow_version = work_flow_version
self.debug = debug
@staticmethod
def get_no_references_setting(knowledge_setting, model_setting):
no_references_setting = knowledge_setting.get(
'no_references_setting', {
'status': 'ai_questioning',
'value': '{question}'})
if no_references_setting.get('status') == 'ai_questioning':
no_references_prompt = model_setting.get('no_references_prompt', '{question}')
no_references_setting['value'] = no_references_prompt if len(no_references_prompt) > 0 else "{question}"
return no_references_setting
def to_base_pipeline_manage_params(self):
knowledge_setting = self.application.knowledge_setting
model_setting = self.application.model_setting
model_id = self.application.model.id if self.application.model is not None else None
model_params_setting = None
if model_id is not None:
model = QuerySet(Model).filter(id=model_id).first()
credential = get_model_credential(model.provider, model.model_type, model.model_name)
model_params_setting = credential.get_model_params_setting_form(model.model_name).get_default_form_data()
return {
'knowledge_id_list': self.knowledge_id_list,
'exclude_document_id_list': self.exclude_document_id_list,
'exclude_paragraph_id_list': [],
'top_n': knowledge_setting.get('top_n') or 3,
'similarity': knowledge_setting.get('similarity') or 0.6,
'max_paragraph_char_number': knowledge_setting.get('max_paragraph_char_number') or 5000,
'history_chat_record': self.chat_record_list,
'chat_id': self.chat_id,
'dialogue_number': self.application.dialogue_number,
'problem_optimization_prompt': self.application.problem_optimization_prompt if self.application.problem_optimization_prompt is not None and len(
self.application.problem_optimization_prompt) > 0 else _(
"() contains the user's question. Answer the guessed user's question based on the context ({question}) Requirement: Output a complete question and put it in the <data></data> tag"),
'prompt': model_setting.get(
'prompt') if 'prompt' in model_setting and len(model_setting.get(
'prompt')) > 0 else Application.get_default_model_prompt(),
'system': model_setting.get(
'system', None),
'model_id': model_id,
'problem_optimization': self.application.problem_optimization,
'stream': True,
'model_setting': model_setting,
'model_params_setting': model_params_setting if self.application.model_params_setting is None or len(
self.application.model_params_setting.keys()) == 0 else self.application.model_params_setting,
'search_mode': self.application.knowledge_setting.get('search_mode') or 'embedding',
'no_references_setting': self.get_no_references_setting(self.application.knowledge_setting, model_setting),
'user_id': self.application.user_id,
'application_id': self.application.id
}
def to_pipeline_manage_params(self, problem_text: str, post_response_handler: PostResponseHandler,
exclude_paragraph_id_list, chat_user_id: str, chat_user_type, stream=True,
form_data=None):
if form_data is None:
form_data = {}
params = self.to_base_pipeline_manage_params()
return {**params, 'problem_text': problem_text, 'post_response_handler': post_response_handler,
'exclude_paragraph_id_list': exclude_paragraph_id_list, 'stream': stream, 'chat_user_id': chat_user_id,
'chat_user_type': chat_user_type, 'form_data': form_data}
def append_chat_record(self, chat_record: ChatRecord):
chat_record.problem_text = chat_record.problem_text[0:10240] if chat_record.problem_text is not None else ""
chat_record.answer_text = chat_record.answer_text[0:40960] if chat_record.problem_text is not None else ""
is_save = True
# 存入缓存中
for index in range(len(self.chat_record_list)):
record = self.chat_record_list[index]
if record.id == chat_record.id:
self.chat_record_list[index] = chat_record
is_save = False
break
if is_save:
self.chat_record_list.append(chat_record)
if not self.debug:
if not QuerySet(Chat).filter(id=self.chat_id).exists():
Chat(id=self.chat_id, application_id=self.application.id, abstract=chat_record.problem_text[0:1024],
chat_user_id=self.chat_user_id, chat_user_type=self.chat_user_type).save()
else:
QuerySet(Chat).filter(id=self.chat_id).update(update_time=datetime.now())
# 插入会话记录
chat_record.save()
def set_cache(self):
cache.set(Cache_Version.CHAT.get_key(key=self.chat_id), self, version=Cache_Version.CHAT.get_version(),
timeout=60 * 30)
@staticmethod
def get_cache(chat_id):
return cache.get(Cache_Version.CHAT.get_key(key=chat_id), version=Cache_Version.CHAT.get_version())