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import numpy as np
import pickle
from sklearn.metrics.pairwise import cosine_similarity
def load_embeddings():
"""Load pre-computed embeddings and metadata."""
# Load embeddings
candidate_embeddings = np.load('data/processed/candidate_embeddings.npy')
company_embeddings = np.load('data/processed/company_embeddings.npy')
# Load metadata
with open('data/processed/candidates_processed.pkl', 'rb') as f:
candidates_df = pickle.load(f)
with open('data/processed/companies_processed.pkl', 'rb') as f:
companies_df = pickle.load(f)
return candidate_embeddings, company_embeddings, candidates_df, companies_df
def find_top_matches(candidate_idx, candidate_embeddings, company_embeddings, companies_df, top_k=10):
"""Find top K company matches for a candidate."""
# Get candidate embedding
candidate_vec = candidate_embeddings[candidate_idx].reshape(1, -1)
# Calculate similarities
similarities = cosine_similarity(candidate_vec, company_embeddings)[0]
# Get top K indices
top_indices = np.argsort(similarities)[::-1][:top_k]
# Build results
matches = []
for idx in top_indices:
matches.append({
'company_id': idx,
'company_name': companies_df.iloc[idx].get('name', f'Company {idx}'),
'job_title': companies_df.iloc[idx].get('title', 'N/A'),
'score': float(similarities[idx])
})
return matches |