from langswarm.agent import LangSwarmAgent
from retrievers import HybridRetriever, BM25Retriever  # Placeholder for retriever implementations
from rerankers import CombinedRerankingWorkflow  # Placeholder for reranking workflow implementation

class HybridRetrievalRerankingWorkflow:
    """
    Workflow for combining hybrid retrieval (dense + sparse) and reranking strategies.

    Specification:
    - Retrieve documents using both dense and sparse retrievers.
    - Combine results and apply multi-agent reranking for improved ranking.
    - Generate responses based on the top-ranked results.
    """

    def __init__(self, dense_config, sparse_config, reranker_configs, documents):
        """
        Initialize the workflow.

        Args:
            dense_config (dict): Configuration for the dense retriever.
            sparse_config (dict): Configuration for the sparse retriever.
            reranker_configs (list): List of reranker configurations.
            documents (list): List of documents to populate retrievers.
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retrievers
        self.dense_retriever = HybridRetriever(**dense_config)
        self.sparse_retriever = BM25Retriever(documents, **sparse_config)

        # Initialize reranking workflow
        rerankers = [config["reranker"](**config["params"]) for config in reranker_configs]
        self.reranker_workflow = CombinedRerankingWorkflow(rerankers)

        # Data Load
        self.load_data_to_dense_retriever(documents)

    def load_data_to_dense_retriever(self, documents):
        """
        Load documents into the dense retriever backend.

        Args:
            documents (list): List of documents.
        """
        self.dense_retriever.add_documents(documents)

    def run(self, query):
        """
        Execute the hybrid retrieval and reranking workflow.

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

        Returns:
            str: Response generated by the LangSwarm agent, or None if no results are found.
        """
        # Retrieve using dense and sparse retrievers
        dense_results = self.dense_retriever.query(query)
        sparse_results = self.sparse_retriever.query(query)

        # Combine results
        combined_results = dense_results + sparse_results

        # Rerank combined results
        reranked_results = self.reranker_workflow.run(query, combined_results)

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

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

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