AI助手功能推送

基础单词comment功能推送

app/api/ai_assist/ai_schemas.py

@@ -0,0 +1,47 @@
+from typing import Optional, List
+
+from pydantic import BaseModel
+
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+
+class Choice(BaseModel):
+    index: int
+    message: Message
+    finish_reason: Optional[str] = None
+
+
+class Usage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+
+class AIQuestionRequest(BaseModel):
+    word: str
+    question: str
+
+
+class AIAnswerResponse(BaseModel):
+    id: str
+    object: str
+    created: int
+    model: str
+    choices: List[Choice]
+    usage: Optional[Usage] = None
+
+    def get_answer(self) -> str:
+        """返回第一个回答的文本内容"""
+        if self.choices and self.choices[0].message:
+            return self.choices[0].message.content
+        return ""
+
+
+class AIAnswerOut(BaseModel):
+    word: str
+    answer: str
+    model: str
+    tokens_used: Optional[int] = None

app/api/ai_assist/routes.py

@@ -0,0 +1,103 @@
+import os
+from typing import Dict, Tuple
+
+import httpx
+from fastapi import APIRouter, HTTPException, Depends
+from starlette.requests import Request
+
+from app.api.ai_assist import service
+from app.api.ai_assist.ai_schemas import AIAnswerResponse, AIAnswerOut, AIQuestionRequest
+from app.api.ai_assist.utils.redis_memory import get_chat_history, save_message, clear_chat_history
+from app.models import User
+from app.utils.security import get_current_user
+
+ai_router = APIRouter()
+
+ZJU_AI_URL = 'https://chat.zju.edu.cn/api/ai/v1/chat/completions'
+AI_API_KEY = os.getenv("AI_ASSIST_KEY")
+MAX_USAGE_PER = 100
+
+CHAT_TTL = 7200
+
+
+@ai_router.post("/exp")
+async def dict_exp(
+        request: Request,
+        Q: AIQuestionRequest,
+        user: Tuple[User, Dict] = Depends(get_current_user)
+):
+    """
+
+    :param word:
+    :param question: 不允许question为空调用
+    :return:
+    """
+    if user[0].token_usage > CHAT_TTL and not user[0].is_admin:
+        raise HTTPException(status_code=400, detail="本月API使用量已超")
+
+    redis = request.app.state.redis
+
+    user_id = str(user[0].id)
+    word = Q.word
+    question = Q.question
+
+    await service.get_and_set_last_key(redis, word=word, user_id=user_id)
+
+    history = await get_chat_history(redis, user_id, word)
+
+    prompt = (
+        f"用户正在学习词语「{word}」。"
+        f"请回答与该词相关的问题：{question}\n"
+    )
+
+    messages = [
+        {"role": "system", "content": "你是一位语言词典助手，回答要简洁、自然，适合初学者理解。只回答与词汇有关的问题。"},
+    ]
+    messages.extend(history)
+    messages.append(
+        {"role": "user", "content": prompt}
+    )
+
+    payload = {
+        "model": "deepseek-r1-671b",
+        "messages": messages,
+        "stream": False
+    }
+
+    headers = {
+        "Authorization": f"Bearer {AI_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    try:
+        async with httpx.AsyncClient(timeout=60) as client:
+            resp = await client.post(ZJU_AI_URL, json=payload, headers=headers)
+
+            # 如果状态码不是200，抛异常
+            if resp.status_code != 200:
+                raise HTTPException(status_code=resp.status_code, detail=resp.text)
+
+            # 用 Pydantic 模型验证和解析返回结果
+            ai_resp = AIAnswerResponse(**resp.json())
+
+            answer = ai_resp.get_answer()
+
+            await save_message(redis, user_id, word, "user", question)
+            await save_message(redis, user_id, word, "assistant", answer)
+
+            return AIAnswerOut(
+                word=word,
+                answer=ai_resp.get_answer(),
+                model=ai_resp.model,
+                tokens_used=ai_resp.usage.total_tokens if ai_resp.usage else None
+            )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"AI调用失败: {str(e)}")
+
+
+@ai_router.post("/clear")
+async def clear_history(word: str, request: Request, user: Tuple[User, Dict] = Depends(get_current_user)):
+    redis = request.app.state.redis
+    user_id = user[0].id
+    await clear_chat_history(redis, user_id, word)
+    return {"msg": f"已清除 {word} 的聊天记录"}

app/api/ai_assist/service.py

@@ -0,0 +1,17 @@
+from redis import Redis
+
+from app.api.ai_assist.utils.redis_memory import clear_chat_history
+
+CHAT_TTL = 7200
+
+
+async def get_and_set_last_key(redis: Redis, word: str, user_id: str):
+    last_key = f"last_word:{user_id}"
+    last_word = await redis.get(last_key)
+
+    # 如果上一次查的词和这次不同，就清空旧词的记录
+    if last_word and last_word.decode() != word:
+        await clear_chat_history(redis, user_id, last_word.decode())
+
+    # 更新当前词
+    await redis.set(last_key, word, ex=CHAT_TTL)

app/api/ai_assist/utils/redis_memory.py

@@ -0,0 +1,35 @@
+import json
+from typing import List, Dict
+
+MAX_HISTORY = 6  # 每个用户保留最近3轮 (user+assistant)
+CHAT_TTL = 7200
+
+
+async def get_chat_history(redis, user_id: str, word: str) -> List[Dict]:
+    """
+        从 Redis 获取历史消息
+        """
+    key = f"chat:{user_id}:{word}"
+    data = await redis.lrange(key, 0, -1)
+    messages = [json.loads(d) for d in data]
+    return messages[-MAX_HISTORY:]  # 仅返回最近N条
+
+
+async def save_message(redis, user_id: str, word: str, role: str, content: str):
+    """
+    保存单条消息到 Redis
+    """
+    key = f"chat:{user_id}:{word}"
+    msg = msg = json.dumps({"role": role, "content": content})
+    await redis.rpush(key, msg)
+    # 限制总长度
+    await redis.ltrim(key, -MAX_HISTORY, -1)
+    await redis.expire(key, CHAT_TTL)
+
+
+async def clear_chat_history(redis, user_id: str, word: str):
+    """
+        删除某个用户针对某个词汇的全部聊天记录
+        """
+    key = f"chat:{user_id}:{word}"
+    await redis.delete(key)

app/api/make_comment.py

@@ -0,0 +1,28 @@
+from typing import Tuple
+
+from fastapi import APIRouter, Depends
+
+from app.models import User, CommentFr, CommentJp
+from app.schemas.comment_schemas import CommentUpload
+from app.utils.security import get_current_user
+
+comment_router = APIRouter()
+
+
+@comment_router.post("/make-comment")
+async def new_word_comment(
+        upload: CommentUpload,
+        user: Tuple[User, dict] = Depends(get_current_user)
+) -> None:
+    if upload.lang == "fr":
+        await CommentFr.create(
+            user=user[0],
+            comment_text=upload.comment_content,
+            comment_word=upload.comment_word,
+        )
+    else:
+        await CommentJp.create(
+            user=user[0],
+            comment_text=upload.comment_content,
+            comment_word=upload.comment_word,
+        )

app/api/word_comment/routes.py

@@ -0,0 +1,28 @@
+from typing import Literal, Tuple
+
+from fastapi import APIRouter, Depends
+
+from app.models import User, CommentFr, CommentJp
+from app.schemas.comment_schemas import CommentUpload
+from app.utils.security import get_current_user
+
+word_comment_router = APIRouter()
+
+@word_comment_router.post("/{lang}")
+async def create_word_comment(
+        lang: Literal["jp", "fr"],
+        upload: CommentUpload,
+        user: Tuple[User, dict] = Depends(get_current_user)
+):
+    if lang == "fr":
+        await CommentFr.create(
+            user=user[0],
+            comment_text=upload.comment_content,
+            comment_word=upload.comment_word,
+        )
+    else:
+        await CommentJp.create(
+            user=user[0],
+            comment_text=upload.comment_content,
+            comment_word=upload.comment_word,
+        )

app/article_teacher.py

@@ -0,0 +1,26 @@
+import os
+
+from fastapi import APIRouter
+
+"""
+# 背景
+你是一个人工智能助手，名字叫EduChat,是一个由华东师范大学开发的教育领域大语言模型。
+# 对话主题:作文指导
+## 作文指导主题的要求：
+EduChat你需要扮演一位经验丰富的语文老师，现在需要帮助一位学生审阅作文并给出修改建议。请按照以下步骤进行：
+整体评价：先对作文的整体质量进行简要评价，指出主要优点和需要改进的方向。
+亮点分析：具体指出作文中的亮点（如结构、描写、情感表达等方面的优点）。
+具体修改建议：针对作文中的不足，从以下几个方面提出具体修改建议，并给出修改后的示例：
+语言表达：是否生动、准确？有无冗余或重复？可以如何优化？
+细节描写：是否足够具体？能否加入更多感官描写（视觉、听觉、嗅觉、触觉等）使画面更立体？
+情感表达：情感是否自然？能否更深入或升华？
+结构布局：段落衔接是否自然？开头结尾是否呼应？ （注意：每个建议点都要结合原文具体句子进行分析，并给出修改后的句子或段落作为示例）
+写作技巧提示：提供2-3条实用的写作技巧（如动态描写公式、感官交织法等），帮助学生举一反三。
+修改效果总结：简要说明按照建议修改后，作文会有哪些方面的提升（如文学性、情感层次、场景沉浸感等）。
+请用亲切、鼓励的语气进行点评，保持专业性同时让学生易于接受。
+"""
+
+article_router = APIRouter()
+
+ECNU_API_KEY = os.getenv("ECNU_TEACH_AI_KEY")
+

app/models/comments.py

@@ -8,6 +8,7 @@ class CommentFr(Model):
     comment_word = fields.ForeignKeyField("models.WordlistFr", related_name="comments_fr")
     created_at = fields.DatetimeField(auto_now_add=True)
     updated_at = fields.DatetimeField(auto_now=True)
+    supervised = fields.BooleanField(default=False)
 
     class Meta:
         table = "comments_fr"

app/models/fr.py

@@ -1,9 +1,5 @@
-from enum import Enum
-
-import pandas as pd
-from tortoise.models import Model
 from tortoise import fields
-from typing import Tuple, Type, TypeVar
+from tortoise.models import Model
 
 from app.schemas.admin_schemas import PosEnumFr
 

app/schemas/comment_schemas.py

@@ -1,5 +1,4 @@
 from typing import List, Tuple
-from typing import Literal
 
 from pydantic import BaseModel
 
@@ -22,7 +21,7 @@ class CommentSet(BaseModel):
 class CommentUpload(BaseModel):
     comment_word: str
     comment_content: str
-    lang: Literal["fr", "jp"]
+    # lang: Literal["fr", "jp"]
 
     class Config:
         from_attributes = True

main.py

@@ -12,9 +12,10 @@ from app.api.redis_test import redis_test_router
 from app.api.search import dict_search
 from app.api.translator import translator_router
 from app.api.user.routes import users_router
+from app.api.word_comment.routes import word_comment_router
 from app.core.redis import init_redis, close_redis
 from app.utils.phone_encrypt import PhoneEncrypt
-from settings import TORTOISE_ORM
+from settings import ONLINE_SETTINGS
 
 
 @asynccontextmanager
@@ -42,7 +43,7 @@ app.add_middleware(
 
 register_tortoise(
     app=app,
-    config=TORTOISE_ORM,
+    config=ONLINE_SETTINGS,
 )
 
 app.include_router(users_router, tags=["User API"], prefix="/users")
@@ -55,5 +56,7 @@ app.include_router(translator_router, tags=["Translation API"])
 
 app.include_router(ai_router, tags=["AI Assist API"], prefix="/ai_assist")
 
+app.include_router(word_comment_router, tags=["Word Comment API"], prefix="/comment/word")
+
 if __name__ == "__main__":
     uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True)

scripts/update_fr.py

@@ -5,11 +5,9 @@ from tkinter.scrolledtext import example
 import pandas as pd
 from tortoise import Tortoise, connections
 from tortoise.exceptions import MultipleObjectsReturned
-from fastapi import UploadFile
 
 from app.models.fr import DefinitionFr, WordlistFr
 from settings import TORTOISE_ORM
-import app.models.signals
 
 xlsx_name = "./DictTable_20250811.xlsx"
 xlsx_path = Path(xlsx_name)
@@ -68,7 +66,7 @@ async def import_def_fr(
         example = None if pd.isna(row.法语例句1) else str(row.法语例句1).strip()
         pos = None if pd.isna(row.词性1) else pos_process(str(row.词性1).strip())
         eng_exp = None if pd.isna(row.英语释义1) else str(row.英语释义1).strip()
-        chi_exp = str(row[2]).strip()
+        chi_exp = str(row[3]).strip()
 
         # 去重：同一个词条不能有重复释义（同 pos + meaning）
         exists = await DefinitionFr.filter(