feat:恢复ichat 功能和 defaut 下的文件
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parent
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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 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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c.relname AS table_name,
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a.attname AS column_name,
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pg_catalog.format_type(a.atttypid, a.atttypmod) AS data_type,
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d.description AS column_comment
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FROM
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pg_class c
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JOIN pg_namespace n ON n.oid = c.relnamespace
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JOIN pg_attribute a ON a.attrelid = c.oid
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LEFT JOIN pg_description d ON d.objoid = a.attrelid AND d.objsubid = a.attnum
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WHERE
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n.nspname = 'public'
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AND c.relname = ANY(%s)
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AND a.attnum > 0
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AND NOT a.attisdropped
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ORDER BY
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c.relname, a.attnum;
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"""
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cur.execute(query, (table_names,))
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schema = {}
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for table_name, column_name, data_type, comment in cur.fetchall():
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if comment and "备注" in comment:
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comment = comment.split("备注")[0].strip()
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schema.setdefault(table_name, []).append(
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f"{column_name}-{comment}"
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)
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cur.close()
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return [
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{"table": table, "text": f"表 {table} 包含列：\n" + "\n".join(columns)}
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for table, columns in schema.items()
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]
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# def get_schema_text(conn, table_names: list):
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# cur = conn.cursor()
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# # 获取字段、类型、注释
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# column_query = """
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# SELECT
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# c.relname AS table_name,
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# a.attname AS column_name,
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# pg_catalog.format_type(a.atttypid, a.atttypmod) AS data_type,
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# d.description AS column_comment
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# FROM
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# pg_class c
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# JOIN pg_namespace n ON n.oid = c.relnamespace
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# JOIN pg_attribute a ON a.attrelid = c.oid
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# LEFT JOIN pg_description d ON d.objoid = a.attrelid AND d.objsubid = a.attnum
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# WHERE
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# n.nspname = 'public'
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# AND c.relname = ANY(%s)
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# AND a.attnum > 0
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# AND NOT a.attisdropped
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# ORDER BY
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# c.relname, a.attnum;
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# """
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# # 获取外键信息
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# fk_query = """
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# SELECT
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# conrelid::regclass::text AS table_name,
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# a.attname AS column_name,
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# confrelid::regclass::text AS foreign_table,
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# af.attname AS foreign_column
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# FROM
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# pg_constraint
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# JOIN pg_class ON conrelid = pg_class.oid
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# JOIN pg_namespace n ON pg_class.relnamespace = n.oid
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# JOIN pg_attribute a ON a.attrelid = conrelid AND a.attnum = ANY(conkey)
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# JOIN pg_attribute af ON af.attrelid = confrelid AND af.attnum = ANY(confkey)
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# WHERE
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# contype = 'f'
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# AND n.nspname = 'public'
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# AND conrelid::regclass::text = ANY(%s);
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# """
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# cur.execute(column_query, (table_names,))
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# columns = cur.fetchall()
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# cur.execute(fk_query, (table_names,))
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# fks = cur.fetchall()
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# # 构建外键字典
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# fk_map = {} # {(table, column): "foreign_table(foreign_column)"}
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# for table, column, f_table, f_column in fks:
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# fk_map[(table, column)] = f"{f_table}({f_column})"
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# # 组织输出结构
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# schema = {}
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# for table, column, dtype, comment in columns:
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# fk_note = f" -> {fk_map[(table, column)]}" if (table, column) in fk_map else ""
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# comment_note = f" -- {comment}" if comment else ""
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# schema.setdefault(table, []).append(f"{column} ({dtype}{fk_note}{comment_note})")
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# cur.close()
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# # 生成文本
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# schema_text = ""
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# for table, cols in schema.items():
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# schema_text += f"表 {table} 包含列：\n - " + "\n - ".join(cols) + "\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]
|
||||
except psycopg2.ProgrammingError:
|
||||
result = cur.statusmessage
|
||||
cur.close()
|
||||
return result
|
||||
|
||||
def strip_sql_markdown(content: str) -> str:
|
||||
# 去掉包裹在 ```sql 或 ``` 中的内容
|
||||
match = re.search(r"```sql\s*(.*?)```", content, re.DOTALL | re.IGNORECASE)
|
||||
if match:
|
||||
return match.group(1).strip()
|
||||
else:
|
||||
return None
|
||||
|
||||
# ORM 写入包装函数
|
||||
def save_message_thread_safe(**kwargs):
|
||||
def _save():
|
||||
with transaction.atomic():
|
||||
Message.objects.create(**kwargs)
|
||||
threading.Thread(target=_save).start()
|
||||
|
|
@ -0,0 +1,155 @@
|
|||
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}],
|
||||
"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":user_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':'*'}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,286 @@
|
|||
import requests
|
||||
import json
|
||||
import faiss
|
||||
import numpy as np
|
||||
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, is_safe_sql, save_message_thread_safe, get_table_structures
|
||||
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"
|
||||
|
||||
# 文本向量化模型
|
||||
EM_MODEL = "m3e-base"
|
||||
API_BASE_EM = "http://106.0.4.200:9997/v1"
|
||||
|
||||
# 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
|
||||
|
||||
HEADERS = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
|
||||
def get_table_names(conn):
|
||||
sql = """
|
||||
SELECT tablename
|
||||
FROM pg_tables
|
||||
WHERE schemaname = 'public';
|
||||
"""
|
||||
cur = conn.cursor()
|
||||
cur.execute(sql)
|
||||
data = cur.fetchall()
|
||||
cur.close()
|
||||
return [row[0] for row in data]
|
||||
|
||||
# def get_relation_table(query):
|
||||
# conn = connect_db()
|
||||
# # table_names = TABLES
|
||||
# table_names = get_table_names(conn)
|
||||
# schemas = get_table_structures(conn, table_names)
|
||||
|
||||
# texts = [
|
||||
# f"这是一个数据库表结构，表名为 {s['table']}，其结构如下：{s['text']}"
|
||||
# for s in schemas
|
||||
# ]
|
||||
# table_names = [s["table"] for s in schemas]
|
||||
# embeddings = embed_text(texts)
|
||||
# index, index_table_map = create_index(embeddings, texts, table_names)
|
||||
|
||||
# results = search_similar_tables(query, index, index_table_map, top_k=3)
|
||||
|
||||
# if not results:
|
||||
# return "没有找到相关表结构"
|
||||
# return results
|
||||
|
||||
def get_relation_table(query: str):
|
||||
conn = connect_db()
|
||||
table_names = get_table_names(conn) # 只获取用户表
|
||||
schemas = get_table_structures(conn, table_names)
|
||||
texts = [s["text"] for s in schemas]
|
||||
table_names = [s["table"] for s in schemas]
|
||||
embeddings = embed_text(texts)
|
||||
|
||||
# 存储向量
|
||||
store_embeddings_pg(conn, embeddings, texts, table_names)
|
||||
|
||||
# 查询相似表
|
||||
results = search_similar_tables_pg(conn, query, top_k=5)
|
||||
|
||||
if len(results) == 0:
|
||||
return "没有找到相关表结构"
|
||||
# 只取相关表的结构
|
||||
schemas = get_table_structures(conn, results)
|
||||
|
||||
llm_results = format_schema_for_llm(schemas)
|
||||
return llm_results
|
||||
|
||||
def store_embeddings_pg(conn, embeddings: list[list[float]], texts: list[str], table_names: list[str]):
|
||||
cur = conn.cursor()
|
||||
for embedding, text, table_name in zip(embeddings, texts, table_names):
|
||||
cur.execute("""
|
||||
INSERT INTO table_embeddings (table_name, schema_text, embedding)
|
||||
VALUES (%s, %s, %s)
|
||||
ON CONFLICT (table_name) DO UPDATE
|
||||
SET schema_text = EXCLUDED.schema_text,
|
||||
embedding = EXCLUDED.embedding
|
||||
""", (table_name, text, embedding))
|
||||
conn.commit()
|
||||
cur.close()
|
||||
|
||||
def search_similar_tables_pg(conn, query: str, top_k: int = 5):
|
||||
# 第一步：将 query 转为 embedding
|
||||
query_embedding = embed_text([query])[0]
|
||||
# 第二步：embedding 转成 '[x, y, z]' 格式字符串
|
||||
embedding_str = ",".join(map(str, query_embedding))
|
||||
cur = conn.cursor()
|
||||
query = f"""
|
||||
SELECT table_name
|
||||
FROM table_embeddings
|
||||
ORDER BY embedding <-> '[{embedding_str}]'::vector
|
||||
LIMIT {top_k};
|
||||
"""
|
||||
cur.execute(query)
|
||||
results = [row[0] for row in cur.fetchall()]
|
||||
cur.close()
|
||||
return results
|
||||
|
||||
|
||||
def format_schema_for_llm(schemas: list[dict]) -> str:
|
||||
lines = []
|
||||
for schema in schemas:
|
||||
lines.append(f"【表名】：{schema['table']}")
|
||||
lines.append("【字段】：")
|
||||
for col in schema["text"].split("结构如下：")[1].split("\n"):
|
||||
if col.strip():
|
||||
lines.append(f" - {col.strip()}")
|
||||
lines.append("") # 空行分隔表
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def embed_text(texts: list[str]) -> list[list[float]]:
|
||||
paylaod = {
|
||||
"input":texts,
|
||||
"model":EM_MODEL
|
||||
}
|
||||
url = f"{API_BASE_EM}/embeddings"
|
||||
response = requests.post(url, headers=HEADERS, json=paylaod)
|
||||
json_data = response.json()
|
||||
return [e['embedding'] for e in json_data['data']]
|
||||
|
||||
# def search_similar_tables(query: str, index, index_table_map, top_k:int=3):
|
||||
# query_embedding = embed_text([query])[0]
|
||||
# distances, indices = index.search(np.array([query_embedding]).astype("float32"), int(top_k))
|
||||
# results = []
|
||||
# for i in indices[0]:
|
||||
# if i != -1 and i in index_table_map:
|
||||
# results.append(index_table_map[i])
|
||||
# return results
|
||||
|
||||
# def create_index(embeddings: list[list[float]], texts: list[str], table_names: list[str]):
|
||||
# print(len(embeddings), '-----------')
|
||||
# dim = len(embeddings[0])
|
||||
# index = faiss.IndexFlatL2(dim)
|
||||
# embeddings_np = np.array(embeddings).astype('float32')
|
||||
# index.add(embeddings_np)
|
||||
|
||||
# # 构建索引到表名的映射字典
|
||||
# index_table_map = {i: table_names[i] for i in range(len(table_names))}
|
||||
# return index, index_table_map
|
||||
|
||||
|
||||
|
||||
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"
|
||||
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}],
|
||||
"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":
|
||||
schema_text = get_relation_table(prompt)
|
||||
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":user_prompt}]
|
||||
print("user_prompt", user_prompt)
|
||||
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':'*'}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
import requests
|
||||
import json
|
||||
import faiss
|
||||
import numpy as np
|
||||
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"
|
||||
|
||||
# 文本向量化模型
|
||||
EM_MODEL = "m3e-base"
|
||||
|
||||
# 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
|
||||
|
||||
HEADERS = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
|
||||
# 表结构向量化
|
||||
def embed_text(texts: list[str]) -> list[list[float]]:
|
||||
url = f"{API_BASE}/embeddings"
|
||||
_payload = {
|
||||
"model": EM_MODEL,
|
||||
"input": texts
|
||||
}
|
||||
try:
|
||||
response = requests.post(url, headers=HEADERS, json=_payload)
|
||||
except requests.exceptions.RequestException as e:
|
||||
return JsonResponse({"error":f"Embedding API调用失败: {e}"}, status=500)
|
||||
print("embeddings", response["data"])
|
||||
return [e['embedding'] for e in response['data']]
|
||||
|
||||
|
||||
# 创建Faiss索引
|
||||
def create_index(embeddings: list[list[float]], texts: list[str], table_names: list[str]):
|
||||
index = faiss.IndexFlatL2(len(embeddings[0]))
|
||||
index.add(np.array(embeddings)).astype("float32")
|
||||
index_table_map = {i: {"table": table_names[i], "text": texts[i]} for i in range(len(table_names))}
|
||||
return index, index_table_map
|
||||
|
||||
# 查询
|
||||
def search_similar_tables(query:str, index, index_table_map, k:int=5):
|
||||
query_embedding = embed_text([query])[0]
|
||||
distances, indices = index.search(np.array([query_embedding]).astype("float32"), k)
|
||||
return [index_table_map[i] for i in indices[0]]
|
||||
|
||||
def get_tables(conn) -> list[str]:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("""
|
||||
SELECT tablename
|
||||
FROM pg_tables
|
||||
WHERE schemaname = 'public'
|
||||
AND tableowner = 'postgres';
|
||||
""")
|
||||
return [row[0] for row in cur.fetchall()]
|
||||
|
||||
|
||||
# 主函数：提取表结构、嵌入向量并存储到 FAISS
|
||||
def get_relation_table(query):
|
||||
conn = connect_db()
|
||||
table_names = get_tables(conn)
|
||||
schemas = get_schema_text(conn, table_names)
|
||||
texts = [s["text"] for s in schemas]
|
||||
# table_names = [s["table"] for s in schemas]
|
||||
embeddings = embed_text(texts)
|
||||
index, index_table_map = create_index(embeddings, texts, table_names)
|
||||
results = search_similar_tables(query, index, index_table_map)
|
||||
|
||||
for result in results:
|
||||
print(f"表名: {result['table']}\n结构: {result['text']}")
|
||||
if len(results) == 0:
|
||||
return "没有找到相关表结构"
|
||||
return results
|
||||
|
||||
|
||||
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"
|
||||
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}],
|
||||
"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":
|
||||
schema_text = get_relation_table(prompt)
|
||||
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":user_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':'*'}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,129 @@
|
|||
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'))
|
||||
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|
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|
|
@ -63,7 +63,7 @@ INSTALLED_APPS = [
|
|||
'apps.wf',
|
||||
'apps.ecm',
|
||||
'apps.hrm',
|
||||
'apps.ichat',
|
||||
#'apps.ichat',
|
||||
'apps.am',
|
||||
'apps.vm',
|
||||
'apps.rpm',
|
||||
|
|
|
|||
Loading…
Reference in New Issue