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48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
"""Step 3 scoring contract; provider selection remains with LLMClient by default."""
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from __future__ import annotations
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from typing import Any, Dict, List, Protocol, Sequence, TypedDict
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class ScoreResult(TypedDict, total=False):
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id: str
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outline: str
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final_score: float
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recommend_reason: str
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class ScoringBackend(Protocol):
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"""Evaluate clips identified by id/outline, returning final_score and reason.
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Input includes transcript, content and absolute start/end times. Return a
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JSON-compatible list; scores use 0–1. Missing/invalid results are handled by
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the pipeline's alignment and fallback guards. Implementations must retain
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clip identifiers; they do not select, reorder or cut the source video.
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"""
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def score(self, clips: Sequence[Dict[str, Any]]) -> List[ScoreResult]: ...
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SCORING_CONTRACT = '''
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## 本次评分输出契约（覆盖上文输入/输出示例）
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你的任务是评估每个片段的内容价值，依据 transcript 原文、content 和 outline。
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返回 JSON 数组，每个输入片段对应一个对象，保留原 id 和 outline：
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[{"id":"原 id","outline":"原 outline","final_score":0.8,"recommend_reason":"基于原文的简短理由"}]
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final_score 必须是 0 到 1 的有限数值；没有足够内容依据时不要编造高分。
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不得只返回 id 到推荐理由的字典，不得省略分数，不要改变 id 或 outline。
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'''
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class LLMScoringBackend:
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"""Current LLM scoring with a consistent schema for every category prompt."""
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def __init__(self, client: Any, prompt: str):
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self.client = client
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self.prompt = prompt + SCORING_CONTRACT
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def score(self, clips: Sequence[Dict[str, Any]]) -> List[ScoreResult]:
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response = self.client.call_with_retry(self.prompt, list(clips))
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results = self.client.parse_json_response(response)
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return results if isinstance(results, list) else []
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