958eaeda8a
- main.py: 加 _CNJSONResponse 修复 datetime 序列化时区(+00:00→+08:00)
- schemas/__init__.py: 加 _fmt_dt 函数和 sync_to_platform 字段
- ai_service.py: 评论 max_tokens 从 300 提升到 500 避免截断
- scheduler.py: datetime.utcnow() 全部改为 datetime.now()（北京时间）
- docker-compose.yml: MySQL 容器加 TZ=Asia/Shanghai
- Interactions.vue: 文章标题链接从系统配置读取域名，格式为 {域名}/huihui-h5/#/news/share?id={id}&login=no
259 lines
11 KiB
Python
Executable File
259 lines
11 KiB
Python
Executable File
"""AI服务 - 人格生成、内容创作"""
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import json
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import random
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import re
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from typing import Optional
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import httpx
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, update
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from app.models import AIModelConfig, TokenStat
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from app.utils.crypto import decrypt
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from app.core.logger import logger
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from datetime import date
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class AIService:
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"""AI大模型服务"""
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# 人格候选池
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CHARACTER_TYPES = ["开朗", "内敛", "毒舌", "温和", "理性", "感性", "幽默", "严谨"]
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LANGUAGE_STYLES = ["严肃", "幽默", "文艺", "吐槽", "口语化", "学术", "简洁", "丰富"]
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INTEREST_TAGS_POOL = [
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"科技", "财经", "娱乐", "体育", "政治", "文化", "教育", "医疗",
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"汽车", "房产", "旅游", "美食", "军事", "国际", "环保", "农业"
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]
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INTERACT_TENDENCIES = ["爱评论", "爱点赞", "爱收藏", "潜水", "爱转发", "爱回复"]
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async def _get_default_model(self, db: AsyncSession) -> Optional[AIModelConfig]:
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result = await db.execute(
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select(AIModelConfig).where(
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AIModelConfig.is_default == 1, AIModelConfig.is_enabled == 1
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)
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)
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return result.scalar_one_or_none()
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async def _call_api(
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self, db: AsyncSession, prompt: str, system_prompt: str = None,
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max_tokens: int = None
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) -> tuple[str, int]:
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"""调用AI接口，返回(内容, token数)"""
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model = await self._get_default_model(db)
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if not model:
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# 无模型配置时返回随机预设
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return "", 0
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api_key = decrypt(model.api_key_enc) if model.api_key_enc else ""
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base_url = model.api_base_url or "https://api.openai.com/v1"
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headers = {"Content-Type": "application/json"}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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payload = {
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"model": model.model_version or "gpt-3.5-turbo",
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"messages": messages,
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"temperature": model.temperature,
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"max_tokens": max_tokens or model.max_tokens,
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}
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import asyncio as _asyncio
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last_err = None
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for attempt in range(3): # 最多重试3次
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try:
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async with httpx.AsyncClient(timeout=model.timeout_seconds) as client:
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resp = await client.post(
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f"{base_url}/chat/completions",
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headers=headers,
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json=payload,
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)
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# 429 限流：等待后重试
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if resp.status_code == 429:
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wait = 30 * (attempt + 1) # 30s, 60s, 90s
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logger.warning(f"AI接口限流(429)，{wait}s后重试({attempt+1}/3)")
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await _asyncio.sleep(wait)
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continue
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resp.raise_for_status()
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data = resp.json()
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text = data["choices"][0]["message"]["content"].strip()
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tokens = data.get("usage", {}).get("total_tokens", 0)
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await self._record_token_usage(db, tokens, data.get("usage", {}), model.model_name)
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logger.bind(ai_call=True).info(
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f"AI调用成功 model={model.model_name} tokens={tokens}"
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)
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return text, tokens
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except Exception as e:
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last_err = e
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if attempt < 2:
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await _asyncio.sleep(5 * (attempt + 1))
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logger.error(f"AI调用失败(已重试3次): {last_err}")
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return "", 0
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async def generate_personality(self, nickname: str, account: str) -> dict:
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"""生成用户人格（含fallback随机生成）"""
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# 如无AI配置，使用随机生成
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from app.core.database import AsyncSessionLocal
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try:
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async with AsyncSessionLocal() as db:
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model = await self._get_default_model(db)
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if not model:
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return self._random_personality()
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prompt = f"""请为以下虚拟新闻读者生成一个独特的人格档案，要求真实自然、贴合中国用户特征。
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用户昵称：{nickname}
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请严格以JSON格式返回，不要有其他内容：
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{{
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"character_type": "从[开朗/内敛/毒舌/温和/理性/感性/幽默/严谨]中选一个",
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"language_style": "从[严肃/幽默/文艺/吐槽/口语化/学术/简洁/丰富]中选一个",
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"interest_tags": ["兴趣1", "兴趣2", "兴趣3"],
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"interact_tendency": "从[爱评论/爱点赞/爱收藏/潜水/爱转发/爱回复]中选一个",
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"word_count_min": 最少字数(10-50整数),
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"word_count_max": 最多字数(50-200整数),
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"personality_desc": "一句话描述此人的性格特点（30字以内）"
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}}"""
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content, _ = await self._call_api(db, prompt, max_tokens=300)
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if content:
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try:
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# 提取JSON
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json_match = re.search(r'\{.*\}', content, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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except Exception:
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pass
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return self._random_personality()
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except Exception as e:
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logger.error(f"人格生成失败: {e}")
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return self._random_personality()
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def _random_personality(self) -> dict:
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"""随机生成人格（无AI时的备用方案）"""
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interests = random.sample(self.INTEREST_TAGS_POOL, random.randint(2, 4))
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char = random.choice(self.CHARACTER_TYPES)
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style = random.choice(self.LANGUAGE_STYLES)
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tendency = random.choice(self.INTERACT_TENDENCIES)
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w_min = random.randint(15, 40)
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w_max = random.randint(60, 150)
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return {
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"character_type": char,
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"language_style": style,
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"interest_tags": interests,
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"interact_tendency": tendency,
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"word_count_min": w_min,
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"word_count_max": w_max,
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"personality_desc": f"一个{char}性格、{tendency}的新闻读者",
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}
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async def generate_comment(
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self, db: AsyncSession, article_title: str, article_content: str,
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personality_prompt: str, word_min: int = 20, word_max: int = 80
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) -> tuple[str, int]:
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"""生成文章评论"""
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system_prompt = f"""你是一名真实的社区用户，正在阅读新闻后发表评论。{personality_prompt}
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重要规则：
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- 评论必须积极正面、文明友善，绝对不包含任何政治敏感、色情、暴力、侮辱、歧视内容
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- 不要提及具体政治人物、党派、政策批评、社会矛盾等敏感话题
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- 内容围绕文章本身展开，表达个人感受、分享观点、提出建设性问题
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- 语言朴实自然，像普通网友留言，不夸张不煽情"""
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prompt = f"""请根据以下新闻文章写一条评论。
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文章标题：{article_title}
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文章摘要：{article_content[:200] if article_content else '（无摘要）'}
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要求：
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1. 评论字数 {word_min}~{word_max} 字
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2. 内容积极正面，贴近文章主题
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3. 语气自然真实，符合普通读者口吻
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4. 必须是完整的句子，不能被截断，以句号/感叹号/问号结尾
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5. 只输出评论正文，不要加任何前缀或解释
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评论："""
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return await self._call_api(db, prompt, system_prompt, max_tokens=500)
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async def generate_reply(
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self, db: AsyncSession, article_title: str, parent_comment: str,
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personality_prompt: str, word_min: int = 15, word_max: int = 60
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) -> tuple[str, int]:
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"""生成回复"""
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system_prompt = f"""你是一名真实的社区用户。{personality_prompt}
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重要规则：回复必须积极正面、文明友善，不含任何敏感违规内容。"""
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prompt = f"""文章：{article_title}
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原评论：{parent_comment}
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请对上面的评论写一条友善自然的回复，{word_min}~{word_max}字，直接输出回复内容。"""
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return await self._call_api(db, prompt, system_prompt, max_tokens=150)
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async def test_model(self, db: AsyncSession, model_id: int, test_prompt: str) -> dict:
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"""测试模型可用性"""
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result = await db.execute(select(AIModelConfig).where(AIModelConfig.id == model_id))
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model = result.scalar_one_or_none()
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if not model:
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return {"success": False, "error": "模型不存在"}
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api_key = decrypt(model.api_key_enc) if model.api_key_enc else ""
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base_url = model.api_base_url or "https://api.openai.com/v1"
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headers = {"Content-Type": "application/json"}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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payload = {
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"model": model.model_version or "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": test_prompt}],
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"max_tokens": 200,
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}
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try:
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import time
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start = time.time()
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async with httpx.AsyncClient(timeout=model.timeout_seconds) as client:
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resp = await client.post(f"{base_url}/chat/completions", headers=headers, json=payload)
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resp.raise_for_status()
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data = resp.json()
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elapsed = round(time.time() - start, 2)
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content = data["choices"][0]["message"]["content"]
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tokens = data.get("usage", {}).get("total_tokens", 0)
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return {
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"success": True, "content": content,
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"tokens": tokens, "elapsed_seconds": elapsed,
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}
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except Exception as e:
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return {"success": False, "error": str(e)}
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async def _record_token_usage(
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self, db: AsyncSession, total: int, usage: dict, model_name: str
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):
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"""记录Token消耗"""
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today = date.today()
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from sqlalchemy.dialects.mysql import insert as mysql_insert
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try:
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existing = await db.execute(
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select(TokenStat).where(TokenStat.stat_date == today)
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)
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stat = existing.scalar_one_or_none()
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if stat:
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stat.total_tokens += total
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stat.prompt_tokens += usage.get("prompt_tokens", 0)
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stat.completion_tokens += usage.get("completion_tokens", 0)
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stat.call_count += 1
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else:
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stat = TokenStat(
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stat_date=today,
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model_name=model_name,
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total_tokens=total,
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prompt_tokens=usage.get("prompt_tokens", 0),
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completion_tokens=usage.get("completion_tokens", 0),
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call_count=1,
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)
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db.add(stat)
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except Exception as e:
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logger.error(f"记录Token消耗失败: {e}")
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ai_service = AIService()
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