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

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

backend/app/retrieval/hybrid.py

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+"""RRF 排名融合与分数归一化。"""
+
+
+def rrf_fuse(ranked_lists: list[list[str]], k: int = 60) -> dict[str, float]:
+    """Reciprocal Rank Fusion：对多个「按相关性降序」的 block_id 列表做排名融合。
+
+    每个 block 的融合分 = Σ 1/(k + rank)，rank 从 1 开始。返回 block_id -> 融合分。
+    """
+    scores: dict[str, float] = {}
+    for ids in ranked_lists:
+        for rank, block_id in enumerate(ids, start=1):
+            scores[block_id] = scores.get(block_id, 0.0) + 1.0 / (k + rank)
+    return scores
+
+
+def normalize_scores(items: list[tuple[str, float]]) -> list[tuple[str, float]]:
+    """把 (block_id, score) 列表 min-max 归一化到 [0,1]，score 越大越相关。"""
+    if not items:
+        return []
+    values = [score for _, score in items]
+    lo, hi = min(values), max(values)
+    span = hi - lo
+    if span == 0:
+        return [(block_id, 1.0) for block_id, _ in items]
+    return [(block_id, round((score - lo) / span, 6)) for block_id, score in items]