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| import pandas as pd | |
| from sentence_transformers import SentenceTransformer, util | |
| model = SentenceTransformer("all-MiniLM-L6-v2") | |
| def get_relevant_passages(query, df, top_k=20): | |
| # Create a copy to avoid modifying the original dataframe | |
| df_copy = df.copy() | |
| # Ensure URL field is properly formatted | |
| if 'url' in df_copy.columns: | |
| # Clean up URLs if needed | |
| df_copy['url'] = df_copy['url'].astype(str) | |
| # Ensure URLs start with http or https | |
| mask = ~df_copy['url'].str.startswith(('http://', 'https://')) | |
| df_copy.loc[mask, 'url'] = 'https://www.shl.com/' + df_copy.loc[mask, 'url'].str.lstrip('/') | |
| # Format test_type for better representation | |
| def format_test_type(test_types): | |
| if isinstance(test_types, list): | |
| return ', '.join(test_types) | |
| return str(test_types) | |
| # Concatenate all fields into a single string per row | |
| corpus = df_copy.apply( | |
| lambda row: f"{row['description']} " | |
| f"Test types: {format_test_type(row['test_type'])}. " | |
| f"Adaptive support: {row['adaptive_support']}. " | |
| f"Remote support: {row['remote_support']}. " | |
| f"Duration: {row['duration'] if pd.notna(row['duration']) else 'N/A'} minutes.", | |
| axis=1 | |
| ).tolist() | |
| corpus_embeddings = model.encode(corpus, convert_to_tensor=True) | |
| query_embedding = model.encode(query, convert_to_tensor=True) | |
| hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)[0] | |
| return df_copy.iloc[[hit['corpus_id'] for hit in hits]] |