python/ebook_search/rerank.py

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+"""vLLM-backed optional reranking."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, replace
+from typing import TYPE_CHECKING
+
+from python.ebook_search.llm_interface import request_rerank
+
+if TYPE_CHECKING:
+    from python.ebook_search.config import RerankConfig
+    from python.ebook_search.search import SearchResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class RerankResult:
+    """A relevance score for one candidate chunk."""
+
+    chunk_id: int
+    score: float
+
+
+def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConfig) -> list[SearchResult]:
+    """Rerank candidates with a vLLM rerank endpoint."""
+    if not candidates:
+        return []
+
+    logger.info(
+        "ebook_rerank_request_start base_url=%s model=%s candidates=%s",
+        config.base_url,
+        config.model,
+        len(candidates),
+    )
+    scores = score_candidates(query, candidates, config)
+    results = sorted(
+        (
+            replace(
+                result,
+                score=final_rerank_score(result, scores[result.chunk_id].score, candidates),
+                rerank_score=scores[result.chunk_id].score,
+            )
+            for result in candidates
+        ),
+        key=lambda result: result.score,
+        reverse=True,
+    )
+    logger.info(
+        "ebook_rerank_request_complete base_url=%s model=%s candidates=%s",
+        config.base_url,
+        config.model,
+        len(results),
+    )
+    return results
+
+
+def score_candidates(
+    query: str,
+    candidates: list[SearchResult],
+    config: RerankConfig,
+) -> dict[int, RerankResult]:
+    """Score candidate chunks with the configured rerank API."""
+    body = request_rerank(query, [candidate.text for candidate in candidates], config)
+    if body is None:
+        return zero_rerank_scores(candidates)
+
+    scores = parse_vllm_scores(body, candidates)
+    for result in scores.values():
+        logger.debug("ebook_rerank_candidate_scored chunk_id=%s score=%s", result.chunk_id, result.score)
+    return scores
+
+
+def parse_vllm_scores(body: object, candidates: list[SearchResult]) -> dict[int, RerankResult]:
+    """Parse vLLM rerank scores into chunk-id keyed results."""
+    if not isinstance(body, dict):
+        logger.debug("ebook_rerank_response_not_object", extra={"response": body})
+        return zero_rerank_scores(candidates)
+
+    results = body.get("results") or body.get("data")
+    if not isinstance(results, list):
+        logger.debug("ebook_rerank_response_missing_results", extra={"response": body})
+        return zero_rerank_scores(candidates)
+
+    scores = zero_rerank_scores(candidates)
+    for item in results:
+        if not isinstance(item, dict):
+            continue
+        index = item.get("index")
+        score = item.get("relevance_score", item.get("score"))
+        if not isinstance(index, int) or index < 0 or index >= len(candidates):
+            continue
+        if not isinstance(score, int | float):
+            continue
+        chunk_id = candidates[index].chunk_id
+        scores[chunk_id] = RerankResult(chunk_id=chunk_id, score=clamp_score(float(score)))
+    return scores
+
+
+def zero_rerank_scores(candidates: list[SearchResult]) -> dict[int, RerankResult]:
+    """Return zero relevance scores for all candidate chunks."""
+    return {candidate.chunk_id: RerankResult(chunk_id=candidate.chunk_id, score=0.0) for candidate in candidates}
+
+
+def clamp_score(score: float) -> float:
+    """Clamp a rerank score into the supported 0.0 to 1.0 range."""
+    return min(max(score, 0.0), 1.0)
+
+
+def final_rerank_score(result: SearchResult, rerank_score: float, candidates: list[SearchResult]) -> float:
+    """Combine rerank relevance with normalized hybrid retrieval evidence."""
+    return rerank_score * normalized_hybrid_score(result, candidates)
+
+
+def normalized_hybrid_score(result: SearchResult, candidates: list[SearchResult]) -> float:
+    """Normalize a candidate hybrid score against the rerank candidate set."""
+    hybrid_scores = [
+        candidate.fused_score if candidate.fused_score is not None else candidate.score for candidate in candidates
+    ]
+    low = min(hybrid_scores)
+    high = max(hybrid_scores)
+    if high == low:
+        return 1.0
+
+    score = result.fused_score if result.fused_score is not None else result.score
+    return (score - low) / (high - low)