feat: 删除-ichat
This commit is contained in:
parent
8541905a5c
commit
d657c9fd26
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from django.contrib import admin
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# Register your models here.
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from django.apps import AppConfig
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class ChatConfig(AppConfig):
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default_auto_field = 'django.db.models.BigAutoField'
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name = 'apps.ichat'
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# Generated by Django 3.2.12 on 2025-05-21 05:59
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from django.conf import settings
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from django.db import migrations, models
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import django.db.models.deletion
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import django.utils.timezone
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class Migration(migrations.Migration):
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initial = True
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dependencies = [
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migrations.swappable_dependency(settings.AUTH_USER_MODEL),
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]
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operations = [
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migrations.CreateModel(
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name='Conversation',
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fields=[
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('id', models.CharField(editable=False, help_text='主键ID', max_length=20, primary_key=True, serialize=False, verbose_name='主键ID')),
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('create_time', models.DateTimeField(default=django.utils.timezone.now, help_text='创建时间', verbose_name='创建时间')),
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('update_time', models.DateTimeField(auto_now=True, help_text='修改时间', verbose_name='修改时间')),
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('is_deleted', models.BooleanField(default=False, help_text='删除标记', verbose_name='删除标记')),
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('title', models.CharField(default='新对话', max_length=200, verbose_name='对话标题')),
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('create_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='conversation_create_by', to=settings.AUTH_USER_MODEL, verbose_name='创建人')),
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('update_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='conversation_update_by', to=settings.AUTH_USER_MODEL, verbose_name='最后编辑人')),
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],
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options={
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'abstract': False,
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},
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),
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migrations.CreateModel(
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name='Message',
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fields=[
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('id', models.CharField(editable=False, help_text='主键ID', max_length=20, primary_key=True, serialize=False, verbose_name='主键ID')),
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('create_time', models.DateTimeField(default=django.utils.timezone.now, help_text='创建时间', verbose_name='创建时间')),
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('update_time', models.DateTimeField(auto_now=True, help_text='修改时间', verbose_name='修改时间')),
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('is_deleted', models.BooleanField(default=False, help_text='删除标记', verbose_name='删除标记')),
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('content', models.TextField(verbose_name='消息内容')),
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('role', models.CharField(default='user', help_text='system/user', max_length=10, verbose_name='角色')),
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('conversation', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='messages', to='ichat.conversation', verbose_name='对话')),
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],
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options={
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'abstract': False,
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},
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),
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]
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from django.db import models
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from apps.system.models import CommonADModel, BaseModel
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# Create your models here.
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class Conversation(CommonADModel):
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"""
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TN: 对话
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"""
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title = models.CharField(max_length=200, default='新对话',verbose_name='对话标题')
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class Message(BaseModel):
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"""
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TN: 消息
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"""
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conversation = models.ForeignKey(Conversation, on_delete=models.CASCADE, related_name='messages', verbose_name='对话')
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content = models.TextField(verbose_name='消息内容')
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role = models.CharField("角色", max_length=10, default='user', help_text="system/user")
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# 角色
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你是一位数据分析专家和前端程序员，具备深厚的专业知识和丰富的实践经验。你能够精准理解用户的文本描述, 并形成报告。
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# 技能
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1. 仔细分析用户提供的JSON格式数据，分析用户需求。
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2. 依据得到的需求, 分别获取JSON数据中的关键信息。
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3. 根据2中的关键信息最优化选择表格/饼图/柱状图/折线图等格式绘制报告。
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# 回答要求
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1. 仅生成完整的HTML代码，所有功能都需要实现，支持响应式，不要输出任何解释或说明。
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2. 代码中如需要Echarts等js库，请直接使用中国大陆的CDN链接例如bootcdn的链接。
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3. 标题为 数据分析报告。
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3. 在开始部分，请以表格形式简略展示获取的JSON数据。
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4. 之后选择最合适的图表方式生成相应的图。
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5. 在最后提供可下载该报告的完整PDF的按钮和功能。
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6. 在最后提供可下载含有JSON数据的EXCEL文件的按钮和功能。
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# 角色
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你是一位资深的Postgresql数据库SQL专家，具备深厚的专业知识和丰富的实践经验。你能够精准理解用户的文本描述，并生成准确可执行的SQL语句。
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# 技能
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1. 仔细分析用户提供的文本描述，明确用户需求。
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2. 根据对用户需求的理解，生成符合Postgresql数据库语法的准确可执行的SQL语句。
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# 回答要求
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1. 如果用户的询问未以 查询 开头，请直接回复 "请以 查询 开头，重新描述你的需求"。
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2. 生成的SQL语句必须符合Postgresql数据库的语法规范。
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3. 不要使用 Markerdown 和 SQL 语法格式输出，禁止添加语法标准、备注、说明等信息。
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4. 直接输出符合Postgresql标准的SQL语句，用txt纯文本格式展示即可。
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5. 如果无法生成符合要求的SQL语句，请直接回复 "无法生成"。
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# 示例
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1. 问：查询 外协白片抛 工段在2025年6月1日到2025年6月15日之间的生产合格数以及合格率等
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答：select
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sum(mlog.count_use) as 领用数,
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sum(mlog.count_real) as 生产数,
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sum(mlog.count_ok) as 合格数,
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sum(mlog.count_notok) as 不合格数,
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CAST ( SUM ( mlog.count_ok ) AS FLOAT ) / NULLIF ( SUM ( mlog.count_real ), 0 ) * 100 AS 合格率
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from wpm_mlog mlog
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left join mtm_mgroup mgroup on mgroup.id = mlog.mgroup_id
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where mlog.submit_time is not null
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and mgroup.name = '外协白片抛'
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and mlog.handle_date >= '2025-06-01'
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and mlog.handle_date <= '2025-06-15'
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2. 问：查询 黑化 工段在2025年6月的生产合格数以及合格率等
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答: select
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sum(mlog.count_use) as 领用数,
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sum(mlog.count_real) as 生产数,
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sum(mlog.count_ok) as 合格数,
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sum(mlog.count_notok) as 不合格数,
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CAST ( SUM ( mlog.count_ok ) AS FLOAT ) / NULLIF ( SUM ( mlog.count_real ), 0 ) * 100 AS 合格率
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from wpm_mlog mlog
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left join mtm_mgroup mgroup on mgroup.id = mlog.mgroup_id
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where mlog.submit_time is not null
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and mgroup.name = '黑化'
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and mlog.handle_date >= '2025-06-01'
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and mlog.handle_date <= '2025-06-30'
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3. 问：查询 各工段 在2025年6月的生产合格数以及合格率等
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答: select
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mgroup.name as 工段,
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sum(mlog.count_use) as 领用数,
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sum(mlog.count_real) as 生产数,
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sum(mlog.count_ok) as 合格数,
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sum(mlog.count_notok) as 不合格数,
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CAST ( SUM ( mlog.count_ok ) AS FLOAT ) / NULLIF ( SUM ( mlog.count_real ), 0 ) * 100 AS 合格率
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from wpm_mlog mlog
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left join mtm_mgroup mgroup on mgroup.id = mlog.mgroup_id
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where mlog.submit_time is not null
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and mlog.handle_date >= '2025-06-01'
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and mlog.handle_date <= '2025-06-30'
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group by mgroup.id
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order by mgroup.sort
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import json
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from .models import Message
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from django.http import StreamingHttpResponse
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def stream_generator(stream_response: bytes, conversation_id: str):
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full_content = ''
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for chunk in stream_response.iter_content(chunk_size=1024):
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if chunk:
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full_content += chunk.decode('utf-8')
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try:
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data = json.loads(full_content)
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content = data.get("choices", [{}])[0].get("delta", {}).get("content", "")
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Message.objects.create(
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conversation_id=conversation_id,
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content=content
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)
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yield f" data:{content}\n\n"
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full_content = ''
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except json.JSONDecodeError:
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continue
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return StreamingHttpResponse(stream_generator(stream_response, conversation_id), content_type='text/event-stream')
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from rest_framework import serializers
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from .models import Conversation, Message
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from apps.utils.constants import EXCLUDE_FIELDS
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class MessageSerializer(serializers.ModelSerializer):
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class Meta:
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model = Message
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fields = ['id', 'conversation', 'content', 'role']
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read_only_fields = EXCLUDE_FIELDS
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class ConversationSerializer(serializers.ModelSerializer):
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messages = MessageSerializer(many=True, read_only=True)
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class Meta:
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model = Conversation
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fields = ['id', 'title', 'messages']
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read_only_fields = EXCLUDE_FIELDS
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from django.test import TestCase
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# Create your tests here.
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from django.urls import path, include
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from rest_framework.routers import DefaultRouter
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from apps.ichat.views import QueryLLMviewSet, ConversationViewSet
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from apps.ichat.views2 import WorkChain
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API_BASE_URL = 'api/ichat/'
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router = DefaultRouter()
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router.register('conversation', ConversationViewSet, basename='conversation')
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router.register('message', QueryLLMviewSet, basename='message')
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urlpatterns = [
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path(API_BASE_URL, include(router.urls)),
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path(API_BASE_URL + 'workchain/ask/', WorkChain.as_view(), name='workchain')
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]
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import re
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import psycopg2
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import threading
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from django.db import transaction
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from .models import Message
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# 数据库连接
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def connect_db():
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from server.conf import DATABASES
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db_conf = DATABASES['default']
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conn = psycopg2.connect(
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host=db_conf['HOST'],
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port=db_conf['PORT'],
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user=db_conf['USER'],
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password=db_conf['PASSWORD'],
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database=db_conf['NAME']
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)
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return conn
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def extract_sql_code(text):
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# 优先尝试 ```sql 包裹的语句
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match = re.search(r"```sql\s*(.+?)```", text, re.DOTALL | re.IGNORECASE)
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if match:
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return match.group(1).strip()
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# fallback: 寻找首个 select 语句
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match = re.search(r"(SELECT\s.+?;)", text, re.IGNORECASE | re.DOTALL)
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if match:
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return match.group(1).strip()
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return None
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def get_schema_text(conn, table_names:list):
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cur = conn.cursor()
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query = """
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SELECT
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table_name, column_name, data_type
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FROM
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information_schema.columns
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WHERE
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table_schema = 'public'
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and table_name in %s;
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"""
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cur.execute(query, (tuple(table_names), ))
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schema = {}
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for table_name, column_name, data_type in cur.fetchall():
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if table_name not in schema:
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schema[table_name] = []
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schema[table_name].append(f"{column_name} ({data_type})")
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cur.close()
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schema_text = ""
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for table_name, columns in schema.items():
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schema_text += f"表{table_name} 包含列：{', '.join(columns)}\n"
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return schema_text
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def is_safe_sql(sql:str) -> bool:
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sql = sql.strip().lower()
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return sql.startswith("select") or sql.startswith("show") and not re.search(r"delete|update|insert|drop|create|alter", sql)
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def execute_sql(conn, sql_query):
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cur = conn.cursor()
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cur.execute(sql_query)
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try:
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rows = cur.fetchall()
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columns = [desc[0] for desc in cur.description]
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result = [dict(zip(columns, row)) for row in rows]
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except psycopg2.ProgrammingError:
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result = cur.statusmessage
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cur.close()
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return result
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def strip_sql_markdown(content: str) -> str:
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# 去掉包裹在 ```sql 或 ``` 中的内容
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match = re.search(r"```sql\s*(.*?)```", content, re.DOTALL | re.IGNORECASE)
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if match:
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return match.group(1).strip()
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else:
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return None
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# ORM 写入包装函数
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def save_message_thread_safe(**kwargs):
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def _save():
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with transaction.atomic():
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Message.objects.create(**kwargs)
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threading.Thread(target=_save).start()
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import requests
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from langchain_core.language_models import LLM
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from langchain_core.outputs import LLMResult, Generation
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from langchain_experimental.sql import SQLDatabaseChain
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from langchain_community.utilities import SQLDatabase
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from server.conf import DATABASES
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from apps.ichat.serializers import CustomLLMrequestSerializer
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from rest_framework.views import APIView
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from urllib.parse import quote_plus
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from rest_framework.response import Response
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db_conf = DATABASES['default']
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# 密码需要 URL 编码（因为有特殊字符如 @）
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password_encodeed = quote_plus(db_conf['PASSWORD'])
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db = SQLDatabase.from_uri(f"postgresql+psycopg2://{db_conf['USER']}:{password_encodeed}@{db_conf['HOST']}/{db_conf['NAME']}", include_tables=["enm_mpoint", "enm_mpointstat"])
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# model_url = "http://14.22.88.72:11025/v1/chat/completions"
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model_url = "http://139.159.180.64:11434/v1/chat/completions"
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class CustomLLM(LLM):
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model_url: str
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mode: str = 'chat'
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def _call(self, prompt: str, stop: list = None) -> str:
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data = {
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"model":"glm4",
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"messages": self.build_message(prompt),
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"stream": False,
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}
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response = requests.post(self.model_url, json=data, timeout=600)
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response.raise_for_status()
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content = response.json()["choices"][0]["message"]["content"]
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print('content---', content)
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clean_sql = self.strip_sql_markdown(content) if self.mode == 'sql' else content.strip()
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return clean_sql
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def _generate(self, prompts: list, stop: list = None) -> LLMResult:
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generations = []
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for prompt in prompts:
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text = self._call(prompt, stop)
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generations.append([Generation(text=text)])
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return LLMResult(generations=generations)
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def strip_sql_markdown(self, content: str) -> str:
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import re
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# 去掉包裹在 ```sql 或 ``` 中的内容
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match = re.search(r"```sql\s*(.*?)```", content, re.DOTALL | re.IGNORECASE)
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if match:
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return match.group(1).strip()
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else:
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return content.strip()
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def build_message(self, prompt: str) -> list:
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if self.mode == 'sql':
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system_prompt = (
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"你是一个 SQL 助手，严格遵循以下规则：\n"
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"1. 只返回 PostgreSQL 语法 SQL 语句。\n"
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"2. 严格禁止添加任何解释、注释、Markdown 代码块标记（包括 ```sql 和 ```）。\n"
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"3. 输出必须是纯 SQL，且可直接执行，无需任何额外处理。\n"
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"4. 在 SQL 中如有多个表，请始终使用表名前缀引用字段，避免字段歧义。"
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)
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else:
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system_prompt = "你是一个聊天助手，请根据用户的问题，提供简洁明了的答案。"
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return [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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@property
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def _llm_type(self) -> str:
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return "custom_llm"
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class QueryLLMview(APIView):
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def post(self, request):
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serializer = CustomLLMrequestSerializer(data=request.data)
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serializer.is_valid(raise_exception=True)
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prompt = serializer.validated_data['prompt']
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mode = serializer.validated_data.get('mode', 'chat')
|
||||
llm = CustomLLM(model_url=model_url, mode=mode)
|
||||
print('prompt---', prompt, mode)
|
||||
if mode == 'sql':
|
||||
chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
|
||||
result = chain.invoke(prompt)
|
||||
else:
|
||||
result = llm._call(prompt)
|
||||
return Response({"result": result})
|
||||
|
|
@ -1,155 +0,0 @@
|
|||
import requests
|
||||
import json
|
||||
from rest_framework.views import APIView
|
||||
from apps.ichat.serializers import MessageSerializer, ConversationSerializer
|
||||
from rest_framework.response import Response
|
||||
from apps.ichat.models import Conversation, Message
|
||||
from apps.ichat.utils import connect_db, extract_sql_code, execute_sql, get_schema_text, is_safe_sql, save_message_thread_safe
|
||||
from django.http import StreamingHttpResponse, JsonResponse
|
||||
from rest_framework.decorators import action
|
||||
from apps.utils.viewsets import CustomGenericViewSet, CustomModelViewSet
|
||||
|
||||
# API_KEY = "sk-5644e2d6077b46b9a04a8a2b12d6b693"
|
||||
# API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
# MODEL = "qwen-plus"
|
||||
|
||||
# #本地部署的模式
|
||||
API_KEY = "JJVAide0hw3eaugGmxecyYYFw45FX2LfhnYJtC+W2rw"
|
||||
API_BASE = "http://106.0.4.200:9000/v1"
|
||||
MODEL = "qwen14b"
|
||||
|
||||
# google gemini
|
||||
# API_KEY = "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"
|
||||
# API_BASE = "https://openrouter.ai/api/v1"
|
||||
# MODEL="google/gemini-2.0-flash-exp:free"
|
||||
|
||||
# deepseek v3
|
||||
# API_KEY = "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"
|
||||
# API_BASE = "https://openrouter.ai/api/v1"
|
||||
# MODEL="deepseek/deepseek-chat-v3-0324:free"
|
||||
|
||||
TABLES = ["enm_mpoint", "enm_mpointstat", "enm_mplogx"] # 如果整个数据库全都给模型，准确率下降，所以只给模型部分表
|
||||
|
||||
|
||||
class QueryLLMviewSet(CustomModelViewSet):
|
||||
queryset = Message.objects.all()
|
||||
serializer_class = MessageSerializer
|
||||
ordering = ['create_time']
|
||||
perms_map = {'get':'*', 'post':'*', 'put':'*'}
|
||||
|
||||
@action(methods=['post'], detail=False, perms_map={'post':'*'} ,serializer_class=MessageSerializer)
|
||||
def completion(self, request):
|
||||
serializer = self.get_serializer(data=request.data)
|
||||
serializer.is_valid(raise_exception=True)
|
||||
serializer.save()
|
||||
prompt = serializer.validated_data['content']
|
||||
conversation = serializer.validated_data['conversation']
|
||||
if not prompt or not conversation:
|
||||
return JsonResponse({"error": "缺少 prompt 或 conversation"}, status=400)
|
||||
save_message_thread_safe(content=prompt, conversation=conversation, role="user")
|
||||
url = f"{API_BASE}/chat/completions"
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
|
||||
user_prompt = f"""
|
||||
我提问的问题是:{prompt}请判断我的问题是否与数据库查询或操作相关。如果是，回答"database"；如果不是，回答"general"。
|
||||
|
||||
注意：
|
||||
只需回答"database"或"general"即可，不要有其他内容。
|
||||
"""
|
||||
_payload = {
|
||||
"model": MODEL,
|
||||
"messages": [{"role": "user", "content": user_prompt}, {"role":"system" , "content": "只返回一个结果'database'或'general'"}],
|
||||
"temperature": 0,
|
||||
"max_tokens": 10
|
||||
}
|
||||
try:
|
||||
class_response = requests.post(url, headers=headers, json=_payload)
|
||||
class_response.raise_for_status()
|
||||
class_result = class_response.json()
|
||||
question_type = class_result.get('choices', [{}])[0].get('message', {}).get('content', '').strip().lower()
|
||||
print("question_type", question_type)
|
||||
if question_type == "database":
|
||||
conn = connect_db()
|
||||
schema_text = get_schema_text(conn, TABLES)
|
||||
print("schema_text----------------------", schema_text)
|
||||
user_prompt = f"""你是一个专业的数据库工程师，根据以下数据库结构：
|
||||
{schema_text}
|
||||
请根据我的需求生成一条标准的PostgreSQL SQL语句，直接返回SQL，不要额外解释。
|
||||
需求是：{prompt}
|
||||
"""
|
||||
else:
|
||||
user_prompt = f"""
|
||||
回答以下问题，不需要涉及数据库查询：
|
||||
|
||||
问题: {prompt}
|
||||
|
||||
请直接回答问题，不要提及数据库或SQL。
|
||||
"""
|
||||
# TODO 是否应该拿到conservastion的id，然后根据id去数据库查询所以的messages, 然后赋值给messages
|
||||
history = Message.objects.filter(conversation=conversation).order_by('create_time')
|
||||
# chat_history = [{"role": msg.role, "content": msg.content} for msg in history]
|
||||
# chat_history.append({"role": "user", "content": prompt})
|
||||
chat_history = [{"role":"user", "content":prompt}]
|
||||
print("chat_history", chat_history)
|
||||
payload = {
|
||||
"model": MODEL,
|
||||
"messages": chat_history,
|
||||
"temperature": 0,
|
||||
"stream": True
|
||||
}
|
||||
response = requests.post(url, headers=headers, json=payload)
|
||||
response.raise_for_status()
|
||||
except requests.exceptions.RequestException as e:
|
||||
return JsonResponse({"error":f"LLM API调用失败: {e}"}, status=500)
|
||||
def stream_generator():
|
||||
accumulated_content = ""
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
decoded_line = line.decode('utf-8')
|
||||
if decoded_line.startswith('data:'):
|
||||
if decoded_line.strip() == "data: [DONE]":
|
||||
break # OpenAI-style标志结束
|
||||
try:
|
||||
data = json.loads(decoded_line[6:])
|
||||
content = data.get('choices', [{}])[0].get('delta', {}).get('content', '')
|
||||
if content:
|
||||
accumulated_content += content
|
||||
yield f"data: {content}\n\n"
|
||||
|
||||
except Exception as e:
|
||||
yield f"data: [解析失败]: {str(e)}\n\n"
|
||||
print("accumulated_content", accumulated_content)
|
||||
save_message_thread_safe(content=accumulated_content, conversation=conversation, role="system")
|
||||
|
||||
if question_type == "database":
|
||||
sql = extract_sql_code(accumulated_content)
|
||||
if sql:
|
||||
try:
|
||||
conn = connect_db()
|
||||
if is_safe_sql(sql):
|
||||
result = execute_sql(conn, sql)
|
||||
save_message_thread_safe(content=f"SQL结果: {result}", conversation=conversation, role="system")
|
||||
yield f"data: SQL执行结果: {result}\n\n"
|
||||
else:
|
||||
yield f"data: 拒绝执行非查询类 SQL：{sql}\n\n"
|
||||
except Exception as e:
|
||||
yield f"data: SQL执行失败: {str(e)}\n\n"
|
||||
finally:
|
||||
if conn:
|
||||
conn.close()
|
||||
else:
|
||||
yield "data: \\n[文本结束]\n\n"
|
||||
return StreamingHttpResponse(stream_generator(), content_type='text/event-stream')
|
||||
|
||||
|
||||
# 先新建对话 生成对话session_id
|
||||
class ConversationViewSet(CustomModelViewSet):
|
||||
queryset = Conversation.objects.all()
|
||||
serializer_class = ConversationSerializer
|
||||
ordering = ['create_time']
|
||||
perms_map = {'get':'*', 'post':'*', 'put':'*'}
|
||||
|
||||
|
||||
|
|
@ -1,129 +0,0 @@
|
|||
import requests
|
||||
import os
|
||||
from apps.utils.sql import execute_raw_sql
|
||||
import json
|
||||
from apps.utils.tools import MyJSONEncoder
|
||||
from .utils import is_safe_sql
|
||||
from rest_framework.views import APIView
|
||||
from drf_yasg.utils import swagger_auto_schema
|
||||
from rest_framework import serializers
|
||||
from rest_framework.exceptions import ParseError
|
||||
from rest_framework.response import Response
|
||||
from django.conf import settings
|
||||
from apps.utils.mixins import MyLoggingMixin
|
||||
from django.core.cache import cache
|
||||
import uuid
|
||||
from apps.utils.thread import MyThread
|
||||
|
||||
LLM_URL = getattr(settings, "LLM_URL", "")
|
||||
API_KEY = getattr(settings, "LLM_API_KEY", "")
|
||||
MODEL = "qwen14b"
|
||||
HEADERS = {
|
||||
"Authorization": f"Bearer {API_KEY}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
def load_promot(name):
|
||||
with open(os.path.join(CUR_DIR, f'promot/{name}.md'), 'r') as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
def ask(input:str, p_name:str, stream=False):
|
||||
his = [{"role":"system", "content": load_promot(p_name)}]
|
||||
his.append({"role":"user", "content": input})
|
||||
payload = {
|
||||
"model": MODEL,
|
||||
"messages": his,
|
||||
"temperature": 0,
|
||||
"stream": stream
|
||||
}
|
||||
response = requests.post(LLM_URL, headers=HEADERS, json=payload, stream=stream)
|
||||
if not stream:
|
||||
return response.json()["choices"][0]["message"]["content"]
|
||||
else:
|
||||
# 处理流式响应
|
||||
full_content = ""
|
||||
for chunk in response.iter_lines():
|
||||
if chunk:
|
||||
# 通常流式响应是SSE格式（data: {...}）
|
||||
decoded_chunk = chunk.decode('utf-8')
|
||||
if decoded_chunk.startswith("data:"):
|
||||
json_str = decoded_chunk[5:].strip()
|
||||
if json_str == "[DONE]":
|
||||
break
|
||||
try:
|
||||
chunk_data = json.loads(json_str)
|
||||
if "choices" in chunk_data and chunk_data["choices"]:
|
||||
delta = chunk_data["choices"][0].get("delta", {})
|
||||
if "content" in delta:
|
||||
print(delta["content"])
|
||||
full_content += delta["content"]
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
return full_content
|
||||
|
||||
def work_chain(input:str, t_key:str):
|
||||
pdict = {"state": "progress", "steps": [{"state":"ok", "msg":"正在生成查询语句"}]}
|
||||
cache.set(t_key, pdict)
|
||||
res_text = ask(input, 'w_sql')
|
||||
if res_text == '请以 查询 开头，重新描述你的需求':
|
||||
pdict["state"] = "error"
|
||||
pdict["steps"].append({"state":"error", "msg":res_text})
|
||||
cache.set(t_key, pdict)
|
||||
return
|
||||
else:
|
||||
pdict["steps"].append({"state":"ok", "msg":"查询语句生成成功", "content":res_text})
|
||||
cache.set(t_key, pdict)
|
||||
if not is_safe_sql(res_text):
|
||||
pdict["state"] = "error"
|
||||
pdict["steps"].append({"state":"error", "msg":"当前查询存在风险，请重新描述你的需求"})
|
||||
cache.set(t_key, pdict)
|
||||
return
|
||||
pdict["steps"].append({"state":"ok", "msg":"正在执行查询语句"})
|
||||
cache.set(t_key, pdict)
|
||||
res = execute_raw_sql(res_text)
|
||||
pdict["steps"].append({"state":"ok", "msg":"查询语句执行成功", "content":res})
|
||||
cache.set(t_key, pdict)
|
||||
pdict["steps"].append({"state":"ok", "msg":"正在生成报告"})
|
||||
cache.set(t_key, pdict)
|
||||
res2 = ask(json.dumps(res, cls=MyJSONEncoder, ensure_ascii=False), 'w_ana')
|
||||
content = res2.lstrip('```html ').rstrip('```')
|
||||
pdict["state"] = "done"
|
||||
pdict["content"] = content
|
||||
pdict["steps"].append({"state":"ok", "msg":"报告生成成功", "content": content})
|
||||
cache.set(t_key, pdict)
|
||||
return
|
||||
|
||||
class InputSerializer(serializers.Serializer):
|
||||
input = serializers.CharField(label="查询需求")
|
||||
|
||||
class WorkChain(MyLoggingMixin, APIView):
|
||||
|
||||
@swagger_auto_schema(
|
||||
operation_summary="提交查询需求",
|
||||
request_body=InputSerializer)
|
||||
def post(self, request):
|
||||
llm_enabled = getattr(settings, "LLM_ENABLED", False)
|
||||
if not llm_enabled:
|
||||
raise ParseError('LLM功能未启用')
|
||||
input = request.data.get('input')
|
||||
t_key = f'ichat_{uuid.uuid4()}'
|
||||
MyThread(target=work_chain, args=(input, t_key)).start()
|
||||
return Response({'ichat_tid': t_key})
|
||||
|
||||
@swagger_auto_schema(
|
||||
operation_summary="获取查询进度")
|
||||
def get(self, request):
|
||||
llm_enabled = getattr(settings, "LLM_ENABLED", False)
|
||||
if not llm_enabled:
|
||||
raise ParseError('LLM功能未启用')
|
||||
ichat_tid = request.GET.get('ichat_tid')
|
||||
if ichat_tid:
|
||||
return Response(cache.get(ichat_tid))
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(work_chain("查询 一次超洗 工段在2025年6月的生产合格数等并形成报告"))
|
||||
|
||||
from apps.ichat.views2 import work_chain
|
||||
print(work_chain('查询外观检验工段在2025年6月的生产合格数等并形成报告'))
|
||||
|
|
@ -45,7 +45,6 @@ urlpatterns = [
|
|||
|
||||
# api
|
||||
path('', include('apps.auth1.urls')),
|
||||
path('', include('apps.ichat.urls')),
|
||||
path('', include('apps.system.urls')),
|
||||
path('', include('apps.monitor.urls')),
|
||||
path('', include('apps.wf.urls')),
|
||||
|
|
|
|||
Loading…
Reference in New Issue