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Quick-start: Load and use the ResearchIT Phase 6 reranker.
Usage:
python load_model.py
Or import in your code:
from load_model import load_reranker, predict_scores
"""
import numpy as np
def load_reranker(model_path: str = "production_model/reranker_v1.txt"):
"""Load the LightGBM reranker model."""
import lightgbm as lgb
model = lgb.Booster(model_file=model_path)
assert model.num_feature() == 37, f"Expected 37 features, got {model.num_feature()}"
return model
def predict_scores(model, features: np.ndarray) -> np.ndarray:
"""
Predict reranking scores for candidates.
Args:
model: LightGBM Booster
features: (N, 37) float32 array — see feature_schema.json for column order
Returns:
(N,) float64 array — higher score = more relevant
"""
assert features.shape[1] == 37, f"Expected 37 features, got {features.shape[1]}"
return model.predict(features)
# Feature schema (must match this exact order)
FEATURE_SCHEMA = [
"qdrant_cosine_score", "candidate_position", "candidate_citation_count",
"candidate_log_citations", "candidate_influential_citations",
"candidate_age_days", "candidate_recency_score", "query_citation_count",
"query_age_days", "year_diff", "same_primary_category", "co_citation_count",
"shared_author_count", "candidate_is_newer", "query_log_citations",
"citation_count_ratio", "age_ratio", "candidate_citations_per_year",
"query_num_references", "candidate_num_cited_by",
"ewma_longterm_similarity", "ewma_shortterm_similarity",
"ewma_negative_similarity", "cluster_importance",
"cluster_distance_to_medoid", "is_suppressed_category",
"onboarding_category_match", "user_total_saves", "user_total_dismissals",
"user_days_since_last_save", "user_session_save_count",
"cosine_x_recency", "cosine_x_citations", "category_x_recency",
"cosine_x_cocitation", "position_inverse", "citations_x_recency",
]
if __name__ == "__main__":
model = load_reranker()
print(f"Model loaded: {model.num_trees()} trees, {model.num_feature()} features")
# Test with dummy input
dummy = np.zeros((10, 37), dtype=np.float32)
scores = predict_scores(model, dummy)
print(f"Dummy scores: {scores[:5]}")
print("✅ Model works!")
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