converting to pydantic-settings

python/ebook_search/search.py

@@ -35,7 +35,6 @@ if TYPE_CHECKING:
     from python.ebook_search.config import EbookSearchConfig
 
 logger = logging.getLogger(__name__)
-BM25_CANDIDATE_LIMIT = 120
 
 
 @dataclass(frozen=True)
@@ -111,6 +110,7 @@ def search_ebooks(
         reciprocal_rank_fusion,
         retrieval.vector_results,
         retrieval.lexical_results,
+        rank_constant=config.rrf_rank_constant,
     )
     timings.append(timing)
     if config.rerank.enabled and rerank:
@@ -216,7 +216,7 @@ def vector_candidates(engine: Engine, query: str, config: EbookSearchConfig) ->
             raise ValueError(msg)
 
         embedding = embed_query(query, config)
-        limit = max(config.rerank.candidates, config.top_k) * 4
+        limit = max(config.rerank.candidates, config.top_k) * config.vector_candidate_multiplier
         embedding_table = get_embedding_table(model.dimension)
 
         embedding_param = literal(embedding, type_=Vector(model.dimension))
@@ -263,7 +263,7 @@ def bm25_candidates(query: str, config: EbookSearchConfig) -> list[SearchResult]
         logger.info("ebook_bm25_search_complete corpus=0 candidates=0")
         return []
 
-    scored_records = score_bm25_corpus(query, corpus, limit=BM25_CANDIDATE_LIMIT)
+    scored_records = score_bm25_corpus(query, corpus, limit=config.bm25_candidate_limit)
     results = [
         replace(search_result_from_row(record), score=score, vector_score=None, bm25_score=score)
         for record, score in scored_records