| import os |
| import json |
| import numpy as np |
|
|
| def load_artifacts(precomputed_dir: str) -> tuple[np.ndarray, np.ndarray, list]: |
| """Loads the precomputed artifacts: jd_vec.npy, cand_vecs.npy, and cand_ids.json.""" |
| jd_vec_path = os.path.join(precomputed_dir, "jd_vec.npy") |
| cand_vecs_path = os.path.join(precomputed_dir, "cand_vecs.npy") |
| cand_ids_path = os.path.join(precomputed_dir, "cand_ids.json") |
| |
| jd_vec = np.load(jd_vec_path) |
| cand_vecs = np.load(cand_vecs_path) |
| |
| with open(cand_ids_path, "r", encoding="utf-8") as f: |
| cand_ids = json.load(f) |
| |
| return jd_vec, cand_vecs, cand_ids |
|
|
| def compute_C_all(jd_vec: np.ndarray, cand_vecs: np.ndarray) -> np.ndarray: |
| """Computes semantic similarity for all candidates in a single vectorized matrix multiplication.""" |
| |
| if len(jd_vec.shape) == 1: |
| jd_vec = jd_vec.reshape(1, -1) |
| if len(cand_vecs.shape) == 1: |
| cand_vecs = cand_vecs.reshape(1, -1) |
| |
| scores = (cand_vecs @ jd_vec.T).squeeze() |
| scores = np.clip(scores, 0.0, 1.0) |
| return np.atleast_1d(scores) |
|
|
| def get_C_map(jd_vec: np.ndarray, cand_vecs: np.ndarray, cand_ids: list) -> dict[str, float]: |
| """Computes similarity and returns a dictionary mapping candidate IDs to embedding scores.""" |
| scores = compute_C_all(jd_vec, cand_vecs) |
| return {str(cid): float(score) for cid, score in zip(cand_ids, scores)} |
|
|
