add: quick loading snippet for Python users

load_model.py

@@ -0,0 +1,59 @@
+"""
+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!")