from langswarm.agent import LangSwarmAgent
from retrievers import TemporalRetriever  # Placeholder for your retriever implementation

class TemporalRetrievalWorkflow:
    """
    Workflow for retrieving documents based on temporal constraints.

    Specification:
    - Initialize a LangSwarm agent.
    - Set up a temporal retriever.
    - Fetch, process, and load temporally annotated data into the retriever backend.
    - Handle user queries and apply temporal filters.
    - Implement fallback for cases where no documents are retrieved or match temporal constraints.
    """

    def __init__(self, retriever_config, temporal_data):
        """
        Initialize the temporal retrieval workflow.

        Args:
            retriever_config (dict): Configuration for setting up the temporal retriever.
            temporal_data (list): Data with temporal metadata for populating the retriever backend.
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retriever
        self.retriever = TemporalRetriever(**retriever_config)

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

        # Data Load
        self.load_data_to_retriever()

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

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

        Returns:
            list: Processed data ready for loading.
        """
        return [
            {"text": entry["text"].strip(), "metadata": {"timestamp": entry["timestamp"]}}
            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, start_date, end_date):
        """
        Execute the temporal retrieval workflow.

        Args:
            query (str): User's query.
            start_date (str): Start date for the temporal filter (ISO format).
            end_date (str): End date for the temporal filter (ISO format).

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

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

        # Apply temporal filter
        filtered_documents = [
            doc
            for doc in documents
            if start_date <= doc["metadata"]["timestamp"] <= end_date
        ]

        if not filtered_documents:
            print("No documents match the temporal filter.")
            return None

        # Concatenate input for the agent
        context = filtered_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 temporal data
    retriever_config = {"backend": "faiss", "index_path": "temporal_index.faiss"}
    temporal_data = [
        {"text": "Event 1: LangSwarm release.", "timestamp": "2025-01-01"},
        {"text": "Event 2: AI conference keynote.", "timestamp": "2025-02-15"},
        {"text": "Event 3: Research breakthrough.", "timestamp": "2025-03-10"}
    ]

    # Initialize workflow
    temporal_retrieval = TemporalRetrievalWorkflow(retriever_config, temporal_data)

    # User query
    user_query = "What events happened in early 2025?"
    start_date = "2025-01-01"
    end_date = "2025-02-28"

    # Run the workflow
    response = temporal_retrieval.run(user_query, start_date, end_date)

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