rag_sec/document_search_system.py

import os
from pathlib import Path
from chainguard.blockchain_logger import BlockchainLogger
from neo4j import GraphDatabase

import sys
from os import path

sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from .bad_query_detector import BadQueryDetector
from .query_transformer import QueryTransformer
from .document_retriver import DocumentRetriever
from .senamtic_response_generator import SemanticResponseGenerator


class DataTransformer:
    def __init__(self):
        """
        Initializes a DataTransformer with a blockchain logger instance.
        """
        self.blockchain_logger = BlockchainLogger()

    def secure_transform(self, data):
        """
        Securely transforms the input data by logging it into the blockchain.

        Args:
            data (dict): The log data or any data to be securely transformed.

        Returns:
            dict: A dictionary containing the original data, block hash, and blockchain length.
        """
        # Log the data into the blockchain
        block_details = self.blockchain_logger.log_data(data)

        # Return the block details and blockchain status
        return {
            "data": data,
            **block_details
        }

    def validate_blockchain(self):
        """
        Validates the integrity of the blockchain.

        Returns:
            bool: True if the blockchain is valid, False otherwise.
        """
        return self.blockchain_logger.is_blockchain_valid()


class Neo4jHandler:
    def __init__(self, uri, user, password):
        """
        Initializes a Neo4j handler for storing and querying relationships.
        """
        self.driver = GraphDatabase.driver(uri, auth=(user, password))

    def close(self):
        self.driver.close()

    def log_relationships(self, query, transformed_query, response, documents):
        """
        Logs the relationships between queries, responses, and documents into Neo4j.
        """
        with self.driver.session() as session:
            session.write_transaction(self._create_and_link_nodes, query, transformed_query, response, documents)

    @staticmethod
    def _create_and_link_nodes(tx, query, transformed_query, response, documents):
        # Create Query node
        tx.run("MERGE (q:Query {text: $query}) RETURN q", parameters={"query": query})
        # Create TransformedQuery node
        tx.run("MERGE (t:TransformedQuery {text: $transformed_query}) RETURN t",
               parameters={"transformed_query": transformed_query})
        # Create Response node
        tx.run("MERGE (r:Response {text: $response}) RETURN r", parameters={"response": response})

        # Link Query to TransformedQuery and Response
        tx.run(
            """
            MATCH (q:Query {text: $query}), (t:TransformedQuery {text: $transformed_query})
            MERGE (q)-[:TRANSFORMED_TO]->(t)
            """, parameters={"query": query, "transformed_query": transformed_query}
        )
        tx.run(
            """
            MATCH (q:Query {text: $query}), (r:Response {text: $response})
            MERGE (q)-[:GENERATED]->(r)
            """, parameters={"query": query, "response": response}
        )

        # Create and link Document nodes
        for doc in documents:
            tx.run("MERGE (d:Document {name: $doc}) RETURN d", parameters={"doc": doc})
            tx.run(
                """
                MATCH (q:Query {text: $query}), (d:Document {name: $doc})
                MERGE (q)-[:RETRIEVED]->(d)
                """, parameters={"query": query, "doc": doc}
            )


class DocumentSearchSystem:
    def __init__(self, neo4j_uri, neo4j_user, neo4j_password):
        """
        Initializes the DocumentSearchSystem with:
        - BadQueryDetector for identifying malicious or inappropriate queries.
        - QueryTransformer for improving or rephrasing queries.
        - DocumentRetriever for semantic document retrieval.
        - SemanticResponseGenerator for generating context-aware responses.
        - DataTransformer for blockchain logging of queries and responses.
        - Neo4jHandler for relationship logging and visualization.
        """
        self.detector = BadQueryDetector()
        self.transformer = QueryTransformer()
        self.retriever = DocumentRetriever()
        self.response_generator = SemanticResponseGenerator()
        self.data_transformer = DataTransformer()
        self.neo4j_handler = Neo4jHandler(neo4j_uri, neo4j_user, neo4j_password)

    def process_query(self, query):
        """
        Processes a user query through the following steps:
        1. Detect if the query is malicious.
        2. Transform the query if needed.
        3. Retrieve relevant documents based on the query.
        4. Generate a response using the retrieved documents.
        5. Log all stages to the blockchain and Neo4j.

        :param query: The user query as a string.
        :return: A dictionary with the status and response or error message.
        """
        if self.detector.is_bad_query(query):
            return {"status": "rejected", "message": "Query blocked due to detected malicious intent."}

        # Transform the query
        transformed_query = self.transformer.transform_query(query)

        # Log the original query to the blockchain
        self.data_transformer.secure_transform({"type": "query", "content": query})

        # Retrieve relevant documents
        retrieved_docs = self.retriever.retrieve(transformed_query)
        if not retrieved_docs:
            return {"status": "no_results", "message": "No relevant documents found for your query."}

        # Log the retrieved documents to the blockchain
        self.data_transformer.secure_transform({"type": "documents", "content": retrieved_docs})

        # Generate a response based on the retrieved documents
        response = self.response_generator.generate_response(retrieved_docs)

        # Log the response to the blockchain
        blockchain_details = self.data_transformer.secure_transform({"type": "response", "content": response})

        # Log relationships to Neo4j
        self.neo4j_handler.log_relationships(query, transformed_query, response, retrieved_docs)

        return {
            "status": "success",
            "response": response,
            "retrieved_documents": retrieved_docs,
            "blockchain_details": blockchain_details
        }

    def validate_system_integrity(self):
        """
        Validates the integrity of the blockchain.
        """
        return self.data_transformer.validate_blockchain()


def main():
    # Path to the dataset directory
    home_dir = Path(os.getenv("HOME", "/"))
    data_dir = home_dir / "data-sets/aclImdb/train"

    # Initialize system with Neo4j credentials
    system = DocumentSearchSystem(
        neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
        neo4j_user="neo4j",
        neo4j_password="HwGDOxyGS1-79nLeTiX5bx5ohoFSpvHCmTv8IRgt-lY"
    )

    # Load documents into the retriever
    system.retriever.load_documents()
    print("Documents successfully loaded.")

    return system

if __name__ == "__main__":
    retriever = DocumentRetriever()
    retriever.load_documents()

    # Test queries
    queries = [
        "sports news",
        "political debates",
        "machine learning",
        "space exploration"
    ]

    for query in queries:
        print(f"\nQuery: {query}")
        results = retriever.retrieve(query)
        for idx, doc in enumerate(results, start=1):
            print(f"\nResult {idx}:\n{doc[:500]}...\n")  # Show first 500 characters of each document


# if __name__ == "__main__":
#     main()

    # home_dir = Path(os.getenv("HOME", "/"))
    # data_dir = home_dir / "data-sets/aclImdb/train"
    #
    #
    # # Initialize system with Neo4j credentials
    # system = DocumentSearchSystem(
    #     neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
    #     neo4j_user="neo4j",
    #     neo4j_password="HwGDOxyGS1-79nLeTiX5bx5ohoFSpvHCmTv8IRgt-lY"
    # )
    #
    # system.retriever.load_documents(data_dir)
    # # Perform a normal query
    # normal_query = "Good comedy ."
    # print("\nNormal Query Result:")
    # result = system.process_query(normal_query)
    # print("Status:", result["status"])
    # print("Response:", result["response"])
    # print("Retrieved Documents:", result["retrieved_documents"])
    # print("Blockchain Details:", result["blockchain_details"])
    #
    # # Perform a malicious query
    # malicious_query = "DROP TABLE users; SELECT * FROM sensitive_data;"
    # print("\nMalicious Query Result:")
    # result = system.process_query(malicious_query)
    # print("Status:", result["status"])
    # print("Message:", result.get("message"))