feat(retrieval): 实现 Embedding/Vector/RRF/Reranker 混合检索引擎

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/retrieval/reranker.py

@@ -0,0 +1,60 @@
+"""Reranker 统一接口与轻量实现。
+
+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
+确定性精排跑通链路；后续替换实现即可。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol, runtime_checkable
+
+from app.textutils import tokens
+
+
+@dataclass
+class RankedCandidate:
+    block_id: str
+    score: float
+    text: str = ""  # 块正文，供轻量精排计算词面重叠
+
+
+@runtime_checkable
+class RerankerProvider(Protocol):
+    """统一 Reranker 接口：输入候选块，输出按相关性重排后的候选块。"""
+
+    model_id: str
+
+    async def rerank(self, query: str, candidates: list[RankedCandidate]) -> list[RankedCandidate]: ...
+
+
+class LexicalReranker:
+    """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。"""
+
+    model_id = "lexical-v1"
+
+    def __init__(self, lexical_weight: float = 0.5) -> None:
+        self.lexical_weight = lexical_weight
+
+    async def rerank(self, query: str, candidates: list[RankedCandidate]) -> list[RankedCandidate]:
+        if not candidates:
+            return []
+
+        # 把原始分数（RRF 等）归一化到 [0,1]，便于与重叠度同量纲加权
+        scores = [c.score for c in candidates]
+        lo, hi = min(scores), max(scores)
+        span = (hi - lo) or 1.0
+
+        query_tokens = set(tokens(query))
+        ranked: list[RankedCandidate] = []
+        for c in candidates:
+            norm = (c.score - lo) / span
+            if query_tokens:
+                overlap = len(query_tokens & set(tokens(c.text))) / len(query_tokens)
+            else:
+                overlap = 0.0
+            final = self.lexical_weight * overlap + (1 - self.lexical_weight) * norm
+            ranked.append(RankedCandidate(block_id=c.block_id, score=final, text=c.text))
+
+        ranked.sort(key=lambda c: c.score, reverse=True)
+        return ranked