final_book_retriever.py

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 = similarities[0][index]
        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)