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| """Retrieval orchestrator: query string -> top-K RetrievalResult. | |
| Embedder is injected. Term extraction uses catalog.term_regex + catalog.registry. | |
| """ | |
| from __future__ import annotations | |
| from scripts.lib.catalog_loader import Catalog | |
| from scripts.lib.embedder import Embedder | |
| from scripts.lib.ranker import rank, RetrievalResult | |
| from scripts.lib.term_extraction import find_terms | |
| def retrieve( | |
| *, | |
| catalog: Catalog, | |
| query: str, | |
| embedder: Embedder, | |
| top_k: int = 5, | |
| semantic_weight: float = 1.0, | |
| structured_weight: float = 1.5, | |
| graph_weight: float = 0.7, | |
| authored_reference_forward_weight: float = 1.2, | |
| authored_reference_reverse_weight: float = 0.6, | |
| reverse_escort_top_n: int = 1, | |
| kind_quotas: dict[str, int] | None = None, | |
| kind_caps: dict[str, int] | None = None, | |
| score_penalties: dict[str, float] | None = None, | |
| ) -> list[RetrievalResult]: | |
| query_vec = embedder.embed_one(query) | |
| if len(query_vec) != catalog.embedding_dim: | |
| raise ValueError( | |
| f"embedder dimension {len(query_vec)} does not match " | |
| f"catalog.embedding_dim {catalog.embedding_dim}; " | |
| f"catalog was built with model={catalog.embedding_model}" | |
| ) | |
| matched_terms = find_terms(query, catalog.term_regex, catalog.registry) | |
| return rank( | |
| catalog, query_vec, matched_terms, | |
| top_k=top_k, | |
| semantic_weight=semantic_weight, | |
| structured_weight=structured_weight, | |
| graph_weight=graph_weight, | |
| authored_reference_forward_weight=authored_reference_forward_weight, | |
| authored_reference_reverse_weight=authored_reference_reverse_weight, | |
| reverse_escort_top_n=reverse_escort_top_n, | |
| kind_quotas=kind_quotas, | |
| kind_caps=kind_caps, | |
| score_penalties=score_penalties, | |
| ) | |