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| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from langchain_community.embeddings import HuggingFaceBgeEmbeddings | |
| # Initialize BERT embeddings model | |
| model_name = "BAAI/bge-small-en-v1.5" | |
| encode_kwargs = {'normalize_embeddings': True} # Set True to compute cosine similarity | |
| embeddings_model = HuggingFaceBgeEmbeddings( | |
| model_name=model_name, | |
| model_kwargs={'device': 'cpu'}, | |
| encode_kwargs=encode_kwargs | |
| ) | |
| # Read CSV file | |
| data = pd.read_csv(r'books.csv', encoding='latin1') | |
| def retrieve_documents(query): | |
| documents = data['Book Title'].tolist() | |
| return documents | |
| def answer(query, min_similarity=0.7): | |
| # Retrieve documents | |
| retrieved_documents = retrieve_documents(query) | |
| # Embed query | |
| embedded_query = embeddings_model.embed_query(query) | |
| # Embed documents | |
| embedded_documents = embeddings_model.embed_documents(retrieved_documents) | |
| # Calculate cosine similarity between query and documents | |
| similarities = cosine_similarity([embedded_query], embedded_documents) | |
| # Rank documents based on similarity scores | |
| ranked_indices = np.argsort(similarities[0])[::-1] | |
| # Retrieve document details for documents with similarity score greater than min_similarity | |
| ranked_documents = [] | |
| for index in ranked_indices: | |
| similarity_score = round(similarities[0][index], 2) # Round similarity score to two decimal places | |
| if similarity_score > min_similarity: | |
| document_details = { | |
| "Book": data['Book Title'].iloc[index], | |
| "Author": data['Author'].iloc[index], | |
| "Edition": data['Edition'].iloc[index], | |
| "File Name": data['File_name'].iloc[index], | |
| "Similarity Score": similarity_score | |
| } | |
| ranked_documents.append(document_details) | |
| else: | |
| # Since documents are ranked in descending order of similarity, break the loop when similarity score falls below min_similarity | |
| break | |
| if not ranked_documents: | |
| print("No similar books found") | |
| return ranked_documents | |
| # Example usage | |
| #query = "machine learning" | |
| #result = answer(query) | |
| #print(result) | |