"""
KnowledgeRetrievalOptimizer
Generated by Eden via recursive self-improvement
2025-11-01 03:13:10.806314
"""

import spacy
from json import loads

# Load spaCy model
nlp = spacy.load("en_core_web_sm")

def load_data(file_path):
    """
    Load JSON data from a file.
    :param file_path: Path to the JSON file containing search results.
    :return: List of dictionaries, each representing a search result.
    """
    with open(file_path, 'r', encoding='utf-8') as file:
        return [loads(line) for line in file]

def preprocess_text(text):
    """
    Preprocess text using spaCy's NLP model to extract relevant information.
    :param text: Input text from a search result.
    :return: Processed document object.
    """
    doc = nlp(text)
    return doc

def semantic_search(query, data):
    """
    Perform semantic search based on the provided query and integrated knowledge base.
    :param query: The user's query or question.
    :param data: Integrated knowledge base from various sources.
    :return: Relevant information matching the query.
    """
    relevant_results = []
    for result in data:
        doc = preprocess_text(result['body'])
        if any(token.text.lower() in query.lower() for token in doc):
            relevant_results.append(result)
    return relevant_results

def optimize_knowledge_base(kb_path, results_file='knowledge_results.txt'):
    """
    Load and process the knowledge base to improve retrieval efficiency.
    :param kb_path: Path to the directory containing JSON files with search results.
    :param results_file: File path where processed data will be stored.
    """
    data = load_data(kb_path)
    optimized_kb = [semantic_search(query, data) for query in data]
    with open(results_file, 'w', encoding='utf-8') as file:
        for entry in optimized_kb:
            if entry:  # Only write entries that have matches
                file.write(f"{entry}\n")

# Example usage
kb_path = "path/to/search/results/directory/"
optimize_knowledge_base(kb_path)

# Query example
query = "knowledge building"
search_results = semantic_search(query, load_data('knowledge_results.txt'))
for result in search_results:
    print(result['title'])