yxxandClaude Code
c6cde2500b
- 检索调优参数(rrf_k/rerank/rerank_candidates/score_threshold)透传到引擎实际执行 - Recall 去重，避免同一 Note 多 Block 重复导致 Recall 超 1 - RAG 运行改为后台异步执行：创建即 queued + 202，支持取消与 SSE 实时事件 - 数据集元数据校验，坏文件隔离跳过；citation_required 语义修正 - modes 空/重复校验；配置快照记录模型版本与索引元信息 Co-Authored-By: Claude Code <noreply@anthropic.com>
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""Reranker 统一接口与轻量实现。
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真实默认是 BGE reranker 类 Cross-Encoder，第一阶段先用词面重叠 + 原始分数加权的
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确定性精排跑通链路；后续替换实现即可。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Protocol, runtime_checkable
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from app.textutils import tokens
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@dataclass
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class RankedCandidate:
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block_id: str
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score: float
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text: str = "" # 块正文，供轻量精排计算词面重叠
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@runtime_checkable
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class RerankerProvider(Protocol):
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"""统一 Reranker 接口：输入候选块，输出按相关性重排后的候选块。"""
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model_id: str
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version: str
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async def rerank(self, query: str, candidates: list[RankedCandidate]) -> list[RankedCandidate]: ...
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class LexicalReranker:
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"""轻量精排：query 与块正文的词面重叠度，与归一化后的原始分数加权求和。"""
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model_id = "lexical-v1"
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version = "1"
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def __init__(self, lexical_weight: float = 0.5) -> None:
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self.lexical_weight = lexical_weight
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async def rerank(self, query: str, candidates: list[RankedCandidate]) -> list[RankedCandidate]:
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if not candidates:
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return []
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# 把原始分数（RRF 等）归一化到 [0,1]，便于与重叠度同量纲加权
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scores = [c.score for c in candidates]
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lo, hi = min(scores), max(scores)
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span = (hi - lo) or 1.0
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query_tokens = set(tokens(query))
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ranked: list[RankedCandidate] = []
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for c in candidates:
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norm = (c.score - lo) / span
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if query_tokens:
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overlap = len(query_tokens & set(tokens(c.text))) / len(query_tokens)
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else:
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overlap = 0.0
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final = self.lexical_weight * overlap + (1 - self.lexical_weight) * norm
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ranked.append(RankedCandidate(block_id=c.block_id, score=final, text=c.text))
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ranked.sort(key=lambda c: c.score, reverse=True)
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return ranked
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