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| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def search_chunks_embedding(query, chunks, embeddings, model, top_k=5): | |
| query_embedding = model.encode([query], convert_to_numpy=True) | |
| scores = cosine_similarity(query_embedding, embeddings)[0] | |
| ranked = sorted( | |
| zip(chunks, scores), | |
| key=lambda x: x[1], | |
| reverse=True | |
| ) | |
| return ranked[:top_k] | |
| def retrieve_top_chunks_with_scores(query_embedding, chunks, embeddings, top_k=5): | |
| scores = cosine_similarity([query_embedding], embeddings)[0] | |
| ranked = sorted( | |
| zip(chunks, scores), | |
| key=lambda x: x[1], | |
| reverse=True | |
| ) | |
| return ranked[:top_k] | |