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380 lines
11 KiB
Python
380 lines
11 KiB
Python
"""
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陪练场景准备服务 - Python 原生实现
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功能：
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- 根据课程ID获取知识点
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- 调用 AI 生成陪练场景配置
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- 解析并返回结构化场景数据
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提供稳定可靠的陪练场景提取能力。
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"""
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.exceptions import ExternalServiceError
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from .ai_service import AIService, AIResponse
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from .llm_json_parser import parse_with_fallback, clean_llm_output
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from .prompts.practice_scene_prompts import (
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SYSTEM_PROMPT,
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USER_PROMPT,
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PRACTICE_SCENE_SCHEMA,
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DEFAULT_SCENE_TYPE,
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DEFAULT_DIFFICULTY,
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)
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logger = logging.getLogger(__name__)
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# ==================== 数据结构 ====================
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@dataclass
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class PracticeScene:
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"""陪练场景数据结构"""
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name: str
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description: str
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background: str
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ai_role: str
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objectives: List[str]
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keywords: List[str]
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type: str = DEFAULT_SCENE_TYPE
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difficulty: str = DEFAULT_DIFFICULTY
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@dataclass
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class PracticeSceneResult:
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"""陪练场景生成结果"""
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success: bool
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scene: Optional[PracticeScene] = None
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raw_response: Dict[str, Any] = field(default_factory=dict)
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ai_provider: str = ""
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ai_model: str = ""
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ai_tokens: int = 0
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ai_latency_ms: int = 0
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knowledge_points_count: int = 0
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error: str = ""
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# ==================== 服务类 ====================
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class PracticeSceneService:
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"""
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陪练场景准备服务
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使用 Python 原生实现。
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使用示例：
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```python
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service = PracticeSceneService()
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result = await service.prepare_practice_knowledge(
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db=db_session,
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course_id=1
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)
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if result.success:
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print(result.scene.name)
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print(result.scene.objectives)
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```
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"""
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def __init__(self):
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"""初始化服务"""
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self.ai_service = AIService(module_code="practice_scene")
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async def prepare_practice_knowledge(
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self,
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db: AsyncSession,
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course_id: int
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) -> PracticeSceneResult:
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"""
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准备陪练所需的知识内容并生成场景
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陪练知识准备的 Python 实现。
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Args:
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db: 数据库会话（支持多租户，由调用方传入对应租户的数据库连接）
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course_id: 课程ID
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Returns:
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PracticeSceneResult: 包含场景配置和元信息的结果对象
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"""
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try:
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logger.info(f"开始陪练知识准备 - course_id: {course_id}")
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# 1. 查询知识点
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knowledge_points = await self._fetch_knowledge_points(db, course_id)
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if not knowledge_points:
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logger.warning(f"课程没有知识点 - course_id: {course_id}")
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return PracticeSceneResult(
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success=False,
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error=f"课程 {course_id} 没有可用的知识点"
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)
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logger.info(f"获取到 {len(knowledge_points)} 个知识点 - course_id: {course_id}")
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# 2. 格式化知识点为文本
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knowledge_text = self._format_knowledge_points(knowledge_points)
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# 3. 调用 AI 生成场景
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ai_response = await self._call_ai_generation(knowledge_text)
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logger.info(
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f"AI 生成完成 - provider: {ai_response.provider}, "
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f"tokens: {ai_response.total_tokens}, latency: {ai_response.latency_ms}ms"
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)
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# 4. 解析 JSON 结果
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scene_data = self._parse_scene_response(ai_response.content)
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if not scene_data:
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logger.error(f"场景解析失败 - course_id: {course_id}")
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return PracticeSceneResult(
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success=False,
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raw_response={"ai_output": ai_response.content},
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ai_provider=ai_response.provider,
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ai_model=ai_response.model,
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ai_tokens=ai_response.total_tokens,
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ai_latency_ms=ai_response.latency_ms,
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knowledge_points_count=len(knowledge_points),
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error="AI 输出解析失败"
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)
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# 5. 构建场景对象
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scene = self._build_scene_object(scene_data)
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logger.info(
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f"陪练场景生成成功 - course_id: {course_id}, "
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f"scene_name: {scene.name}, type: {scene.type}"
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)
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return PracticeSceneResult(
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success=True,
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scene=scene,
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raw_response=scene_data,
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ai_provider=ai_response.provider,
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ai_model=ai_response.model,
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ai_tokens=ai_response.total_tokens,
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ai_latency_ms=ai_response.latency_ms,
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knowledge_points_count=len(knowledge_points)
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)
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except Exception as e:
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logger.error(
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f"陪练知识准备失败 - course_id: {course_id}, error: {e}",
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exc_info=True
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)
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return PracticeSceneResult(
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success=False,
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error=str(e)
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)
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async def _fetch_knowledge_points(
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self,
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db: AsyncSession,
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course_id: int
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) -> List[Dict[str, Any]]:
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"""
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从数据库获取课程知识点
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获取课程知识点
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"""
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# 知识点查询 SQL：
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# SELECT kp.name, kp.description
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# FROM knowledge_points kp
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# INNER JOIN course_materials cm ON kp.material_id = cm.id
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# WHERE kp.course_id = {course_id}
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# AND kp.is_deleted = 0
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# AND cm.is_deleted = 0
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# ORDER BY kp.id;
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sql = text("""
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SELECT kp.name, kp.description
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FROM knowledge_points kp
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INNER JOIN course_materials cm ON kp.material_id = cm.id
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WHERE kp.course_id = :course_id
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AND kp.is_deleted = 0
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AND cm.is_deleted = 0
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ORDER BY kp.id
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""")
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try:
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result = await db.execute(sql, {"course_id": course_id})
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rows = result.fetchall()
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knowledge_points = []
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for row in rows:
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knowledge_points.append({
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"name": row[0],
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"description": row[1] or ""
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})
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return knowledge_points
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except Exception as e:
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logger.error(f"查询知识点失败: {e}")
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raise ExternalServiceError(f"数据库查询失败: {e}")
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def _format_knowledge_points(self, knowledge_points: List[Dict[str, Any]]) -> str:
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"""
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将知识点列表格式化为文本
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Args:
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knowledge_points: 知识点列表
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Returns:
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格式化后的文本
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"""
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lines = []
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for i, kp in enumerate(knowledge_points, 1):
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name = kp.get("name", "")
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description = kp.get("description", "")
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if description:
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lines.append(f"{i}. {name}\n {description}")
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else:
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lines.append(f"{i}. {name}")
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return "\n\n".join(lines)
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async def _call_ai_generation(self, knowledge_text: str) -> AIResponse:
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"""
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调用 AI 生成陪练场景
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Args:
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knowledge_text: 格式化后的知识点文本
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Returns:
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AI 响应对象
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"""
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# 构建用户消息
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user_message = USER_PROMPT.format(knowledge_points=knowledge_text)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_message}
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]
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# 调用 AI（自动降级：4sapi.com → OpenRouter）
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response = await self.ai_service.chat(
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messages=messages,
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temperature=0.7, # 适中的创意性
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prompt_name="practice_scene_generation"
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)
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return response
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def _parse_scene_response(self, ai_output: str) -> Optional[Dict[str, Any]]:
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"""
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解析 AI 输出的场景 JSON
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使用 LLM JSON Parser 进行多层兜底解析
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Args:
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ai_output: AI 原始输出
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Returns:
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解析后的字典，失败返回 None
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"""
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# 先清洗输出
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cleaned_output, rules = clean_llm_output(ai_output)
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if rules:
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logger.debug(f"AI 输出已清洗: {rules}")
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# 使用带 Schema 校验的解析
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result = parse_with_fallback(
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cleaned_output,
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schema=PRACTICE_SCENE_SCHEMA,
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default=None,
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validate_schema=True,
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on_error="none"
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)
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return result
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def _build_scene_object(self, scene_data: Dict[str, Any]) -> PracticeScene:
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"""
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从解析的字典构建场景对象
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Args:
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scene_data: 解析后的场景数据
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Returns:
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PracticeScene 对象
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"""
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# 提取 scene 字段（JSON 格式为 {"scene": {...}}）
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scene = scene_data.get("scene", scene_data)
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return PracticeScene(
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name=scene.get("name", "陪练场景"),
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description=scene.get("description", ""),
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background=scene.get("background", ""),
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ai_role=scene.get("ai_role", "AI扮演客户"),
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objectives=scene.get("objectives", []),
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keywords=scene.get("keywords", []),
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type=scene.get("type", DEFAULT_SCENE_TYPE),
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difficulty=scene.get("difficulty", DEFAULT_DIFFICULTY)
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)
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def scene_to_dict(self, scene: PracticeScene) -> Dict[str, Any]:
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"""
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将场景对象转换为字典
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便于 API 响应序列化
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Args:
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scene: PracticeScene 对象
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Returns:
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字典格式的场景数据
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"""
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return {
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"scene": {
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"name": scene.name,
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"description": scene.description,
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"background": scene.background,
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"ai_role": scene.ai_role,
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"objectives": scene.objectives,
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"keywords": scene.keywords,
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"type": scene.type,
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"difficulty": scene.difficulty
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}
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}
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# ==================== 全局实例 ====================
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practice_scene_service = PracticeSceneService()
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# ==================== 便捷函数 ====================
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async def prepare_practice_knowledge(
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db: AsyncSession,
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course_id: int
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) -> PracticeSceneResult:
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"""
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准备陪练所需的知识内容（便捷函数）
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Args:
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db: 数据库会话
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course_id: 课程ID
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Returns:
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PracticeSceneResult 结果对象
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"""
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return await practice_scene_service.prepare_practice_knowledge(db, course_id)
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