"""
ImproveKnowledgeRetrieval
Generated by Eden via recursive self-improvement
2025-11-01 06:50:10.445631
"""

import numpy as np

class ImprovedRetriever:
    """
    A class to improve knowledge retrieval capabilities.
    
    Methods:
        preprocess_query(query): Preprocesses the input query for better matching.
        retrieve_information(query, dataset): Retrieves relevant information based on the processed query.
    """

    def __init__(self):
        self.dataset = None

    def preprocess_query(self, query: str) -> np.ndarray:
        """
        Preprocesses the input query by converting it to a numerical vector using TF-IDF.

        Args:
            query (str): The input query string.

        Returns:
            np.ndarray: A vector representation of the query.
        """
        from sklearn.feature_extraction.text import TfidfVectorizer
        vectorizer = TfidfVectorizer()
        return vectorizer.fit_transform([query]).toarray()[0]

    def retrieve_information(self, query: str, dataset: list) -> dict:
        """
        Retrieves relevant information based on the processed query.

        Args:
            query (str): The input query string.
            dataset (list): A list of documents or records to search through.

        Returns:
            dict: A dictionary containing relevant information and scores.
        """
        from sklearn.metrics.pairwise import cosine_similarity
        preprocessed_query = self.preprocess_query(query)
        document_vectors = np.array([self.preprocess_query(doc) for doc in dataset])
        
        similarities = cosine_similarity(preprocessed_query.reshape(1, -1), document_vectors).flatten()
        
        # Get the top 3 most relevant documents
        indices = np.argsort(similarities)[::-1][:3]
        relevant_documents = [dataset[i] for i in indices]
        
        return {doc: score for doc, score in zip(relevant_documents, similarities[indices])}

# Example Usage
if __name__ == "__main__":
    dataset = [
        "Python is a programming language.",
        "Machine learning models require large amounts of data.",
        "Eden is an AI system with unique capabilities."
    ]
    
    retriever = ImprovedRetriever()
    query = "What is Eden?"
    results = retriever.retrieve_information(query, dataset)
    
    for doc, score in results.items():
        print(f"Document: {doc} - Score: {score}")