真实ai对话

This commit is contained in:
wanjia 2025-06-09 18:00:00 +08:00
parent d534bd732d
commit 816d3fdb3a
4 changed files with 738 additions and 38 deletions

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from django.core.management.base import BaseCommand
from rlhf.models import Conversation, Message, Feedback, DetailedFeedback, FeedbackTag
from django.db.models import Count, Avg, Sum, Q, F
from django.utils import timezone
from django.contrib.auth import get_user_model
import json
from datetime import datetime, timedelta
User = get_user_model()
class Command(BaseCommand):
help = '分析RLHF反馈数据生成统计报告'
def add_arguments(self, parser):
parser.add_argument(
'--export',
action='store_true',
help='导出数据到JSON文件',
)
parser.add_argument(
'--days',
type=int,
default=30,
help='分析最近的天数',
)
def handle(self, *args, **options):
self.stdout.write(self.style.SUCCESS("=" * 60))
self.stdout.write(self.style.SUCCESS("🤖 在线人类反馈强化学习系统 - 数据分析报告"))
self.stdout.write(self.style.SUCCESS("=" * 60))
# 基本统计
feedback_stats = self.get_feedback_stats()
self.stdout.write(self.style.SUCCESS(f"\n📊 反馈统计:"))
self.stdout.write(f" 总反馈数量: {feedback_stats['total_feedback']}")
self.stdout.write(f" 正面反馈: {feedback_stats['positive_feedback']} ({feedback_stats['positive_rate']:.1f}%)")
self.stdout.write(f" 负面反馈: {feedback_stats['negative_feedback']}")
self.stdout.write(f" 平均反馈分数: {feedback_stats['avg_feedback']:.2f}")
# 对话统计
conv_stats = self.get_conversation_stats()
self.stdout.write(self.style.SUCCESS(f"\n💬 对话统计:"))
self.stdout.write(f" 总对话数量: {conv_stats['total_conversations']}")
self.stdout.write(f" 总消息数量: {conv_stats['total_messages']}")
self.stdout.write(f" 平均每对话消息数: {conv_stats['avg_messages_per_conversation']:.1f}")
# 标签统计
tag_stats = self.get_tag_stats()
self.stdout.write(self.style.SUCCESS(f"\n🏷️ 标签统计:"))
self.stdout.write(f" 最常用的正面标签:")
for tag in tag_stats['top_positive']:
self.stdout.write(f" - {tag['tag_name']}: {tag['count']}")
self.stdout.write(f" 最常用的负面标签:")
for tag in tag_stats['top_negative']:
self.stdout.write(f" - {tag['tag_name']}: {tag['count']}")
# 每日趋势
days = options['days']
daily_trend = self.get_daily_feedback_trend(days)
self.stdout.write(self.style.SUCCESS(f"\n📈 最近{days}天反馈趋势:"))
for day in daily_trend:
self.stdout.write(f" {day['date']}: {day['total']}条反馈 (正面率: {day['positive_rate']:.1f}%)")
# 用户统计
user_stats = self.get_user_stats()
self.stdout.write(self.style.SUCCESS(f"\n👥 用户统计:"))
self.stdout.write(f" 总用户数量: {user_stats['total_users']}")
self.stdout.write(f" 活跃标注用户: {user_stats['active_users']}")
self.stdout.write(f" 平均每用户标注量: {user_stats['avg_annotations_per_user']:.1f}")
# 导出数据
if options['export']:
filename = self.export_data_to_json()
self.stdout.write(self.style.SUCCESS(f"\n✅ 数据已导出到: {filename}"))
def get_feedback_stats(self):
"""获取反馈统计信息"""
# 基本反馈统计
basic_feedback = Feedback.objects.aggregate(
total=Count('id'),
positive=Sum(Case(When(feedback_value__gt=0, then=1), default=0)),
negative=Sum(Case(When(feedback_value__lt=0, then=1), default=0)),
avg=Avg('feedback_value')
)
# 详细反馈统计
detailed_feedback = DetailedFeedback.objects.aggregate(
total=Count('id'),
positive=Count('id', filter=Q(feedback_type='positive')),
negative=Count('id', filter=Q(feedback_type='negative'))
)
# 合并统计
total = (basic_feedback['total'] or 0) + (detailed_feedback['total'] or 0)
positive = (basic_feedback['positive'] or 0) + (detailed_feedback['positive'] or 0)
negative = (basic_feedback['negative'] or 0) + (detailed_feedback['negative'] or 0)
# 计算平均分和正面比例
avg_feedback = basic_feedback['avg'] or 0
positive_rate = (positive / total * 100) if total > 0 else 0
return {
'total_feedback': total,
'positive_feedback': positive,
'negative_feedback': negative,
'avg_feedback': avg_feedback,
'positive_rate': positive_rate
}
def get_conversation_stats(self):
"""获取对话统计信息"""
total_conversations = Conversation.objects.count()
total_messages = Message.objects.count()
# 计算每个对话的消息数量分布
conversation_messages = Message.objects.values('conversation').annotate(count=Count('id'))
avg_messages = conversation_messages.aggregate(Avg('count'))['count__avg'] or 0
return {
'total_conversations': total_conversations,
'total_messages': total_messages,
'avg_messages_per_conversation': avg_messages
}
def get_tag_stats(self):
"""获取标签使用统计"""
# 分析DetailedFeedback中的标签使用情况
# 注意由于标签可能存储为JSON字符串这里需要解析
# 首先获取所有的标签
all_tags = FeedbackTag.objects.all()
tag_id_to_name = {str(tag.id): tag.tag_name for tag in all_tags}
# 计算每个标签的使用次数
tag_counts = {}
for feedback in DetailedFeedback.objects.all():
if feedback.feedback_tags:
try:
# 尝试解析JSON标签列表
tag_ids = json.loads(feedback.feedback_tags)
if isinstance(tag_ids, list):
for tag_id in tag_ids:
tag_id = str(tag_id)
if tag_id in tag_counts:
tag_counts[tag_id] += 1
else:
tag_counts[tag_id] = 1
except (json.JSONDecodeError, TypeError):
# 如果不是有效的JSON可能是单个标签ID
tag_id = str(feedback.feedback_tags)
if tag_id in tag_counts:
tag_counts[tag_id] += 1
else:
tag_counts[tag_id] = 1
# 获取排名前5的正面和负面标签
positive_tags = FeedbackTag.objects.filter(tag_type='positive')
negative_tags = FeedbackTag.objects.filter(tag_type='negative')
top_positive = []
for tag in positive_tags:
tag_id = str(tag.id)
if tag_id in tag_counts:
top_positive.append({
'tag_name': tag.tag_name,
'count': tag_counts[tag_id]
})
top_negative = []
for tag in negative_tags:
tag_id = str(tag.id)
if tag_id in tag_counts:
top_negative.append({
'tag_name': tag.tag_name,
'count': tag_counts[tag_id]
})
# 按使用次数排序
top_positive.sort(key=lambda x: x['count'], reverse=True)
top_negative.sort(key=lambda x: x['count'], reverse=True)
# 取前5
return {
'top_positive': top_positive[:5],
'top_negative': top_negative[:5]
}
def get_daily_feedback_trend(self, days=30):
"""获取每日反馈趋势"""
# 计算开始日期
start_date = timezone.now().date() - timedelta(days=days)
# 基本反馈按日期分组
basic_daily = Feedback.objects.filter(timestamp__date__gte=start_date) \
.values('timestamp__date') \
.annotate(
date=F('timestamp__date'),
total=Count('id'),
positive=Sum(Case(When(feedback_value__gt=0, then=1), default=0)),
negative=Sum(Case(When(feedback_value__lt=0, then=1), default=0))
) \
.order_by('date')
# 详细反馈按日期分组
detailed_daily = DetailedFeedback.objects.filter(created_at__date__gte=start_date) \
.values('created_at__date') \
.annotate(
date=F('created_at__date'),
total=Count('id'),
positive=Count('id', filter=Q(feedback_type='positive')),
negative=Count('id', filter=Q(feedback_type='negative'))
) \
.order_by('date')
# 合并两种反馈数据
daily_data = {}
for item in basic_daily:
date_str = item['date'].strftime('%Y-%m-%d')
daily_data[date_str] = {
'date': date_str,
'total': item['total'],
'positive': item['positive'],
'negative': item['negative']
}
for item in detailed_daily:
date_str = item['date'].strftime('%Y-%m-%d')
if date_str in daily_data:
daily_data[date_str]['total'] += item['total']
daily_data[date_str]['positive'] += item['positive']
daily_data[date_str]['negative'] += item['negative']
else:
daily_data[date_str] = {
'date': date_str,
'total': item['total'],
'positive': item['positive'],
'negative': item['negative']
}
# 计算正面反馈比例
for date_str, data in daily_data.items():
data['positive_rate'] = (data['positive'] / data['total'] * 100) if data['total'] > 0 else 0
# 转换为列表并按日期排序
result = list(daily_data.values())
result.sort(key=lambda x: x['date'])
return result
def get_user_stats(self):
"""获取用户统计信息"""
# 总用户数
total_users = User.objects.count()
# 有反馈记录的用户数
users_with_feedback = User.objects.filter(
Q(feedback__isnull=False) | Q(detailed_feedback__isnull=False)
).distinct().count()
# 最近30天活跃的标注用户
thirty_days_ago = timezone.now() - timedelta(days=30)
active_users = User.objects.filter(
Q(feedback__timestamp__gte=thirty_days_ago) |
Q(detailed_feedback__created_at__gte=thirty_days_ago)
).distinct().count()
# 计算每个用户的标注量
user_annotations = {}
for feedback in Feedback.objects.all():
user_id = str(feedback.user_id)
if user_id in user_annotations:
user_annotations[user_id] += 1
else:
user_annotations[user_id] = 1
for feedback in DetailedFeedback.objects.all():
user_id = str(feedback.user_id)
if user_id in user_annotations:
user_annotations[user_id] += 1
else:
user_annotations[user_id] = 1
# 计算平均每用户标注量
if user_annotations:
avg_annotations = sum(user_annotations.values()) / len(user_annotations)
else:
avg_annotations = 0
return {
'total_users': total_users,
'users_with_feedback': users_with_feedback,
'active_users': active_users,
'avg_annotations_per_user': avg_annotations
}
def export_data_to_json(self):
"""导出数据到JSON文件"""
data = {
'conversations': [],
'feedback_summary': self.get_feedback_stats(),
'tag_stats': self.get_tag_stats(),
'daily_trend': self.get_daily_feedback_trend(30),
'export_time': timezone.now().isoformat()
}
# 导出对话和消息数据
for conv in Conversation.objects.all().prefetch_related('messages'):
conv_data = {
'id': str(conv.id),
'created_at': conv.created_at.isoformat(),
'user_id': str(conv.user_id),
'is_submitted': conv.is_submitted,
'messages': []
}
for msg in conv.messages.all().order_by('timestamp'):
msg_data = {
'id': str(msg.id),
'role': msg.role,
'content': msg.content,
'timestamp': msg.timestamp.isoformat(),
'feedback': []
}
# 获取消息的反馈
for fb in Feedback.objects.filter(message_id=msg.id):
msg_data['feedback'].append({
'id': str(fb.id),
'type': 'basic',
'value': fb.feedback_value,
'user_id': str(fb.user_id),
'timestamp': fb.timestamp.isoformat()
})
# 获取详细反馈
for dfb in DetailedFeedback.objects.filter(message_id=msg.id):
try:
tags = json.loads(dfb.feedback_tags) if dfb.feedback_tags else []
except (json.JSONDecodeError, TypeError):
tags = [dfb.feedback_tags] if dfb.feedback_tags else []
msg_data['feedback'].append({
'id': str(dfb.id),
'type': 'detailed',
'feedback_type': dfb.feedback_type,
'tags': tags,
'custom_tags': dfb.custom_tags,
'custom_content': dfb.custom_content,
'is_inline': dfb.is_inline,
'user_id': str(dfb.user_id),
'timestamp': dfb.created_at.isoformat()
})
conv_data['messages'].append(msg_data)
data['conversations'].append(conv_data)
# 保存到文件
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
filename = f'rlhf_data_export_{timestamp}.json'
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
return filename

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from django.core.management.base import BaseCommand, CommandError
from rlhf.models import (
Conversation, Message, Feedback, FeedbackTag, DetailedFeedback,
ConversationSubmission, ConversationEvaluation, SystemConfig
)
from django.utils import timezone
import json
import uuid
import os
from django.contrib.auth import get_user_model
User = get_user_model()
class Command(BaseCommand):
help = '导入/导出RLHF数据'
def add_arguments(self, parser):
parser.add_argument(
'--import-file',
help='导入JSON数据文件的路径',
)
parser.add_argument(
'--export-file',
help='导出JSON数据文件的路径',
)
parser.add_argument(
'--import-tags',
action='store_true',
help='从init_tags.py导入标签',
)
parser.add_argument(
'--clear',
action='store_true',
help='在导入前清除现有数据',
)
def handle(self, *args, **options):
if options['import_file']:
self.import_data(options['import_file'], options['clear'])
elif options['export_file']:
self.export_data(options['export_file'])
elif options['import_tags']:
self.import_tags_from_init_file()
else:
self.stdout.write(self.style.WARNING('请指定导入文件路径或导出文件路径'))
def import_data(self, file_path, clear=False):
"""从JSON文件导入数据"""
if not os.path.exists(file_path):
raise CommandError(f'文件不存在: {file_path}')
try:
with open(file_path, 'r', encoding='utf-8') as f:
data = json.load(f)
if clear:
self.stdout.write(self.style.WARNING('正在清除现有数据...'))
self._clear_data()
# 导入对话
for conv_data in data.get('conversations', []):
self._import_conversation(conv_data)
# 导入配置
for config in data.get('system_configs', []):
self._import_system_config(config)
self.stdout.write(self.style.SUCCESS(f'成功导入数据从: {file_path}'))
except Exception as e:
raise CommandError(f'导入数据失败: {str(e)}')
def export_data(self, file_path):
"""导出数据到JSON文件"""
try:
data = {
'conversations': [],
'system_configs': [],
'tags': [],
'export_time': timezone.now().isoformat()
}
# 导出标签
for tag in FeedbackTag.objects.all():
data['tags'].append({
'id': str(tag.id),
'tag_name': tag.tag_name,
'tag_type': tag.tag_type,
'description': tag.description,
'created_at': tag.created_at.isoformat()
})
# 导出系统配置
for config in SystemConfig.objects.all():
data['system_configs'].append({
'id': str(config.id),
'config_key': config.config_key,
'config_value': config.config_value,
'config_type': config.config_type,
'description': config.description,
'created_at': config.created_at.isoformat(),
'updated_at': config.updated_at.isoformat()
})
# 导出对话
for conv in Conversation.objects.all().prefetch_related('messages'):
conv_data = {
'id': str(conv.id),
'user_id': str(conv.user_id),
'is_submitted': conv.is_submitted,
'created_at': conv.created_at.isoformat(),
'messages': []
}
for msg in conv.messages.all().order_by('timestamp'):
msg_data = {
'id': str(msg.id),
'role': msg.role,
'content': msg.content,
'timestamp': msg.timestamp.isoformat(),
'feedback': []
}
# 获取基本反馈
for fb in Feedback.objects.filter(message_id=msg.id):
msg_data['feedback'].append({
'id': str(fb.id),
'type': 'basic',
'feedback_value': fb.feedback_value,
'user_id': str(fb.user_id),
'timestamp': fb.timestamp.isoformat()
})
# 获取详细反馈
for dfb in DetailedFeedback.objects.filter(message_id=msg.id):
msg_data['feedback'].append({
'id': str(dfb.id),
'type': 'detailed',
'feedback_type': dfb.feedback_type,
'feedback_tags': dfb.feedback_tags,
'custom_tags': dfb.custom_tags,
'custom_content': dfb.custom_content,
'is_inline': dfb.is_inline,
'user_id': str(dfb.user_id),
'created_at': dfb.created_at.isoformat(),
'updated_at': dfb.updated_at.isoformat()
})
conv_data['messages'].append(msg_data)
data['conversations'].append(conv_data)
with open(file_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
self.stdout.write(self.style.SUCCESS(f'成功导出数据到: {file_path}'))
except Exception as e:
raise CommandError(f'导出数据失败: {str(e)}')
def import_tags_from_init_file(self):
"""从init_tags.py导入标签"""
try:
# 定义标签数据
positive_tags = [
('有帮助', '回答对问题有实际帮助'),
('准确', '信息准确可靠'),
('清晰', '表达清楚易懂'),
('完整', '回答全面完整'),
('友好', '语调友善亲切'),
('创新', '提供了新颖的观点')
]
negative_tags = [
('不准确', '包含错误信息'),
('不相关', '回答偏离主题'),
('不完整', '回答过于简略'),
('不清晰', '表达模糊难懂'),
('不友好', '语调生硬冷淡'),
('重复', '内容重复冗余')
]
# 插入正面标签
for tag_name, description in positive_tags:
FeedbackTag.objects.get_or_create(
tag_name=tag_name,
defaults={
'id': str(uuid.uuid4()),
'tag_type': 'positive',
'description': description,
'created_at': timezone.now()
}
)
# 插入负面标签
for tag_name, description in negative_tags:
FeedbackTag.objects.get_or_create(
tag_name=tag_name,
defaults={
'id': str(uuid.uuid4()),
'tag_type': 'negative',
'description': description,
'created_at': timezone.now()
}
)
self.stdout.write(self.style.SUCCESS('成功导入标签'))
except Exception as e:
raise CommandError(f'导入标签失败: {str(e)}')
def _clear_data(self):
"""清除现有数据"""
DetailedFeedback.objects.all().delete()
Feedback.objects.all().delete()
Message.objects.all().delete()
Conversation.objects.all().delete()
ConversationSubmission.objects.all().delete()
ConversationEvaluation.objects.all().delete()
self.stdout.write(self.style.SUCCESS('已清除现有数据'))
def _import_conversation(self, conv_data):
"""导入单个对话"""
# 检查用户是否存在
user_id = conv_data.get('user_id')
if not User.objects.filter(id=user_id).exists():
self.stdout.write(self.style.WARNING(f'用户不存在: {user_id},将使用第一个管理员用户'))
user = User.objects.filter(is_superuser=True).first()
if not user:
raise CommandError('找不到管理员用户')
user_id = user.id
# 创建对话
conv = Conversation.objects.create(
id=conv_data.get('id', str(uuid.uuid4())),
user_id=user_id,
is_submitted=conv_data.get('is_submitted', False),
created_at=timezone.parse_datetime(conv_data.get('created_at', timezone.now().isoformat()))
)
# 创建消息
for msg_data in conv_data.get('messages', []):
msg = Message.objects.create(
id=msg_data.get('id', str(uuid.uuid4())),
conversation=conv,
role=msg_data.get('role', 'user'),
content=msg_data.get('content', ''),
timestamp=timezone.parse_datetime(msg_data.get('timestamp', timezone.now().isoformat()))
)
# 创建反馈
for fb_data in msg_data.get('feedback', []):
if fb_data.get('type') == 'basic':
Feedback.objects.create(
id=fb_data.get('id', str(uuid.uuid4())),
message=msg,
conversation=conv,
user_id=fb_data.get('user_id', user_id),
feedback_value=fb_data.get('feedback_value', 0),
timestamp=timezone.parse_datetime(fb_data.get('timestamp', timezone.now().isoformat()))
)
elif fb_data.get('type') == 'detailed':
DetailedFeedback.objects.create(
id=fb_data.get('id', str(uuid.uuid4())),
message=msg,
conversation=conv,
user_id=fb_data.get('user_id', user_id),
feedback_type=fb_data.get('feedback_type', 'neutral'),
feedback_tags=fb_data.get('feedback_tags', '[]'),
custom_tags=fb_data.get('custom_tags', ''),
custom_content=fb_data.get('custom_content', ''),
is_inline=fb_data.get('is_inline', True),
created_at=timezone.parse_datetime(fb_data.get('created_at', timezone.now().isoformat())),
updated_at=timezone.parse_datetime(fb_data.get('updated_at', timezone.now().isoformat()))
)
def _import_system_config(self, config_data):
"""导入系统配置"""
SystemConfig.objects.update_or_create(
config_key=config_data.get('config_key'),
defaults={
'id': config_data.get('id', str(uuid.uuid4())),
'config_value': config_data.get('config_value', ''),
'config_type': config_data.get('config_type', 'string'),
'description': config_data.get('description', ''),
'created_at': timezone.parse_datetime(config_data.get('created_at', timezone.now().isoformat())),
'updated_at': timezone.parse_datetime(config_data.get('updated_at', timezone.now().isoformat()))
}
)

View File

@ -2,21 +2,22 @@ import requests
import json
import time
import logging
from django.conf import settings
logger = logging.getLogger(__name__)
class SiliconFlowClient:
def __init__(self, api_key="sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf", model="Qwen/QwQ-32B"):
def __init__(self, api_key=None, model=None):
"""
初始化SiliconFlow客户端
"""
self.api_key = api_key
self.model = model
self.api_key = api_key or getattr(settings, 'SILICONFLOW_API_KEY', "sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf")
self.model = model or getattr(settings, 'DEFAULT_AI_MODEL', "Qwen/QwQ-32B")
self.base_url = "https://api.siliconflow.cn/v1"
self.messages = []
self.system_message = None
logger.info(f"初始化SiliconFlow客户端 - 模型: {model}")
logger.info(f"初始化SiliconFlow客户端 - 模型: {self.model}")
def set_model(self, model):
"""设置使用的模型"""
@ -64,6 +65,7 @@ class SiliconFlowClient:
"Content-Type": "application/json"
}
logger.debug(f"发送请求到SiliconFlow API模型{self.model}")
response = requests.post(
f"{self.base_url}/chat/completions",
json=payload,
@ -108,6 +110,7 @@ class SiliconFlowClient:
"Content-Type": "application/json"
}
logger.debug(f"发送流式请求到SiliconFlow API模型{self.model}")
response = requests.post(
f"{self.base_url}/chat/completions",
json=payload,
@ -159,12 +162,15 @@ class SiliconFlowClient:
yield error_msg
@classmethod
def get_available_models(cls, api_key="sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf"):
def get_available_models(cls, api_key=None):
"""
获取可用的模型列表
"""
import os
if not api_key:
api_key = getattr(settings, 'SILICONFLOW_API_KEY', "sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf")
# 尝试多种网络配置
proxy_configs = [
# 不使用代理
@ -243,9 +249,9 @@ class SiliconFlowClient:
logger.warning(f"网络配置 {i+1} 异常: {str(e)}")
continue
# 所有配置都失败了
logger.error("所有网络配置都失败,无法获取模型列表")
raise Exception("无法连接到SiliconFlow API服务器")
# 所有配置都失败了,返回预定义的模型列表
logger.error("所有网络配置都失败,使用预定义模型列表")
return cls._get_fallback_models()
@classmethod
def _get_fallback_models(cls):

View File

@ -22,6 +22,9 @@ from datetime import datetime, timedelta
from django.db import transaction
from django.db.models.functions import TruncDate
from apps.user.authentication import CustomTokenAuthentication
from .siliconflow_client import SiliconFlowClient
from django.conf import settings
import logging
# 创建统一响应格式的基类
@ -237,25 +240,45 @@ class ConversationViewSet(StandardResponseMixin, viewsets.ModelViewSet):
def _generate_ai_response(self, user_message, conversation):
"""
生成AI回复
这里只是一个示例实际应用中需要对接真实的AI服务
生成AI回复通过调用SiliconFlow API
"""
# 从系统配置获取当前使用的模型
model_config = SystemConfig.objects.filter(config_key='current_model').first()
model_name = model_config.config_value if model_config else "默认模型"
logger = logging.getLogger(__name__)
# 获取历史消息作为上下文
history_messages = Message.objects.filter(conversation=conversation).order_by('timestamp')
history = []
for msg in history_messages:
history.append({"role": msg.role, "content": msg.content})
# 在这里调用实际的AI API
# 例如如果使用SiliconFlow API
# response = sf_client.chat(user_message, history)
# 这里仅作为示例,返回一个固定的回复
return f"这是AI({model_name})的回复:我已收到您的消息「{user_message}」。根据您的问题,我的建议是..."
try:
# 从系统配置获取当前使用的模型
model_config = SystemConfig.objects.filter(config_key='current_model').first()
model_name = model_config.config_value if model_config else getattr(settings, 'DEFAULT_AI_MODEL', "Qwen/QwQ-32B")
# 初始化SiliconFlow客户端
sf_client = SiliconFlowClient(
api_key=getattr(settings, 'SILICONFLOW_API_KEY', None),
model=model_name
)
# 获取系统提示词(如果有)
system_prompt_config = SystemConfig.objects.filter(config_key='system_prompt').first()
if system_prompt_config and system_prompt_config.config_value:
sf_client.set_system_message(system_prompt_config.config_value)
# 获取历史消息作为上下文
history_messages = Message.objects.filter(conversation=conversation).order_by('timestamp')
# 添加历史消息到客户端
for msg in history_messages:
sf_client.add_message(msg.role, msg.content)
# 调用API获取回复
logger.info(f"正在调用AI API (模型: {model_name}) 处理消息: {user_message[:50]}...")
response = sf_client.chat(user_message)
# 记录AI回复到日志
logger.info(f"AI回复成功回复长度: {len(response)}")
return response
except Exception as e:
logger.exception(f"AI API调用失败: {str(e)}")
return f"很抱歉AI服务暂时不可用: {str(e)}"
def _update_annotation_stats(self, user_id):
"""更新用户的标注统计信息"""
@ -653,7 +676,6 @@ class ConversationEvaluationViewSet(StandardResponseMixin, viewsets.ModelViewSet
data=ConversationEvaluationSerializer(evaluation).data
)
class SystemConfigViewSet(StandardResponseMixin, viewsets.ModelViewSet):
queryset = SystemConfig.objects.all()
serializer_class = SystemConfigSerializer
@ -718,15 +740,30 @@ class SystemConfigViewSet(StandardResponseMixin, viewsets.ModelViewSet):
@action(detail=False, methods=['get'])
def models(self, request):
# 返回可用的模型列表
return self.get_standard_response(
data={
'models': [
{'id': 'model1', 'name': 'GPT-3.5'},
{'id': 'model2', 'name': 'GPT-4'},
{'id': 'model3', 'name': 'Claude'},
{'id': 'model4', 'name': 'LLaMA'},
{'id': 'model5', 'name': 'Qwen'}
]
}
)
"""返回可用的模型列表"""
from .siliconflow_client import SiliconFlowClient
from django.conf import settings
import logging
logger = logging.getLogger(__name__)
try:
# 从SiliconFlow获取可用模型列表
logger.info("正在获取可用模型列表...")
models = SiliconFlowClient.get_available_models(
api_key=getattr(settings, 'SILICONFLOW_API_KEY', None)
)
logger.info(f"成功获取 {len(models)} 个可用模型")
return self.get_standard_response(
data={'models': models}
)
except Exception as e:
logger.exception(f"获取模型列表失败: {str(e)}")
# 使用预定义的模型列表作为备选
fallback_models = SiliconFlowClient._get_fallback_models()
return self.get_standard_response(
code=200, # 仍然返回200以避免前端错误
message="无法从API获取模型列表使用预定义列表",
data={'models': fallback_models}
)