from langswarm.agent import LangSwarmAgent
from retrievers import LegalRetriever  # Placeholder for your retriever implementation
from rerankers import LegalReranker  # Placeholder for your reranker implementation

class LegalDocumentWorkflow:
    """
    Workflow for retrieving and analyzing legal documents for case preparation.

    Specification:
    - Initialize a LangSwarm agent.
    - Set up a legal retriever and a legal domain-specific reranker.
    - Fetch, process, and load legal data into the retriever backend.
    - Handle user queries and provide ranked legal document results.
    - Implement fallback for cases where no documents are retrieved or reranked.
    """

    def __init__(self, retriever_config, reranker_config, legal_data):
        """
        Initialize the legal document assistant workflow.

        Args:
            retriever_config (dict): Configuration for setting up the legal retriever.
            reranker_config (dict): Configuration for the legal domain-specific reranker.
            legal_data (list): Data for populating the retriever backend.
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retriever and reranker
        self.retriever = LegalRetriever(**retriever_config)
        self.reranker = LegalReranker(**reranker_config)

        # Data Fetch and Processing
        self.processed_data = self.process_data(legal_data)

        # Data Load
        self.load_data_to_retriever()

    def process_data(self, raw_data):
        """
        Process the raw data into retriever-compatible formats.

        Args:
            raw_data (list): List of raw legal data.

        Returns:
            list: Processed data ready for loading.
        """
        return [{"text": entry.strip(), "metadata": {"type": "legal"}} for entry in raw_data]

    def load_data_to_retriever(self):
        """
        Load the processed data into the retriever backend.
        """
        self.retriever.add_documents(self.processed_data)

    def run(self, query):
        """
        Execute the legal document assistant workflow.

        Args:
            query (str): User's query.

        Returns:
            str: Response generated by the LangSwarm agent, or None if no results are found.
        """
        # Retrieve documents
        documents = self.retriever.query(query)

        if not documents:
            print("No legal documents retrieved.")
            return None

        # Rerank documents
        reranked_documents = self.reranker.rerank(query, documents)

        if not reranked_documents:
            print("No documents could be reranked.")
            return None

        # Concatenate input for the agent
        context = reranked_documents[0]["text"]
        agent_input = f"Query: {query}\nContext: {context}"

        # Generate and return response
        return self.agent.generate_response(agent_input)


# **Usage Example**

if __name__ == "__main__":
    # Example configuration and legal data
    retriever_config = {"backend": "faiss", "index_path": "legal_index.faiss"}
    reranker_config = {"model_name": "legal-bert"}  # Example domain-specific model
    legal_data = [
        "Case 1: LangSwarm v. AI Tech Corp.",
        "Case 2: Copyright law in the age of AI.",
        "Case 3: Privacy and data protection regulations."
    ]

    # Initialize workflow
    legal_assistant = LegalDocumentWorkflow(retriever_config, reranker_config, legal_data)

    # User query
    user_query = "What are the key points in LangSwarm v. AI Tech Corp.?"

    # Run the workflow
    response = legal_assistant.run(user_query)

    if response:
        print("Legal Assistant Response:", response)
    else:
        print("No relevant response could be generated.")
