from langswarm.agent import LangSwarmAgent
from retrievers import EmbeddingRetriever  # Placeholder for your retriever implementation
from rerankers import CombinedReranker  # Placeholder for your reranker implementation

class RecommendationSystemWorkflow:
    """
    Workflow for providing personalized recommendations based on user preferences.

    Specification:
    - Initialize a LangSwarm agent.
    - Set up an embedding-based retriever and a combined reranker.
    - Fetch, process, and load recommendation data into the retriever backend.
    - Handle user queries and generate personalized recommendations.
    - Implement fallback for cases where no documents are retrieved or reranked.
    """

    def __init__(self, retriever_config, reranker_config, item_data):
        """
        Initialize the recommendation system workflow.

        Args:
            retriever_config (dict): Configuration for setting up the embedding-based retriever.
            reranker_config (dict): Configuration for the combined reranker.
            item_data (list): Data for populating the retriever backend.
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retriever and reranker
        self.retriever = EmbeddingRetriever(**retriever_config)
        self.reranker = CombinedReranker(**reranker_config)

        # Data Fetch and Processing
        self.processed_data = self.process_data(item_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 item data.

        Returns:
            list: Processed data ready for loading.
        """
        return [{"text": entry.strip(), "metadata": {"type": "item"}} 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, user_profile):
        """
        Execute the recommendation workflow.

        Args:
            user_profile (str): User's preferences or query.

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

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

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

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

        # Concatenate input for the agent
        context = "\n".join([doc["text"] for doc in reranked_documents[:3]])  # Top 3 recommendations
        agent_input = f"User Profile: {user_profile}\nRecommendations:\n{context}"

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


# **Usage Example**

if __name__ == "__main__":
    # Example configuration and item data
    retriever_config = {"backend": "pinecone", "api_key": "your-api-key"}
    reranker_config = {"weights": [0.8, 0.2]}  # Example weights for relevance and popularity
    item_data = [
        "Item 1: LangSwarm integration toolkit.",
        "Item 2: Advanced AI solutions guide.",
        "Item 3: Personalized recommendation strategies.",
        "Item 4: Multi-agent system development resources."
    ]

    # Initialize workflow
    recommendation_system = RecommendationSystemWorkflow(retriever_config, reranker_config, item_data)

    # User preferences
    user_profile = "Looking for tools to integrate AI and multi-agent systems."

    # Run the workflow
    response = recommendation_system.run(user_profile)

    if response:
        print("Personalized Recommendations:", response)
    else:
        print("No relevant recommendations could be generated.")
