from langswarm.agent import LangSwarmAgent
from retrievers import HybridRetriever, SQLRetriever  # Placeholder for your retriever implementations
from rerankers import CombinedReranker  # Placeholder for your reranker implementation

class EnterpriseKnowledgeBaseWorkflow:
    """
    Workflow for retrieving and ranking information from an enterprise knowledge base.

    Specification:
    - Initialize a LangSwarm agent.
    - Set up multiple retrievers and a combined reranker.
    - Fetch, process, and load data into respective retriever backends.
    - Handle user queries and generate contextual responses.
    - Implement fallback for cases where no documents are retrieved or reranked.
    """

    def __init__(self, retriever_configs, reranker_config, data_sources):
        """
        Initialize the enterprise knowledge base workflow.

        Args:
            retriever_configs (list): List of configurations for each retriever.
            reranker_config (dict): Configuration for the combined reranker.
            data_sources (list): List of data sources (files or database connections).
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retrievers
        self.retrievers = [HybridRetriever(**config) if config["type"] == "hybrid" else SQLRetriever(**config)
                           for config in retriever_configs]

        # Initialize reranker
        self.reranker = CombinedReranker(**reranker_config)

        # Data Fetch and Processing
        self.data = self.fetch_data(data_sources)
        self.processed_data = self.process_data(self.data)

        # Data Load
        self.load_data_to_retrievers(self.processed_data)

    def fetch_data(self, data_sources):
        """
        Fetch data from various sources.

        Args:
            data_sources (list): List of file paths or database queries.

        Returns:
            dict: Raw data fetched from each source, keyed by retriever index.
        """
        raw_data = {}
        for i, source in enumerate(data_sources):
            if isinstance(source, str):  # File source
                with open(source, 'r') as f:
                    raw_data[i] = f.readlines()
            elif callable(source):  # Database query function
                raw_data[i] = source()  # Assume the function returns a list of records
        return raw_data

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

        Args:
            raw_data (dict): Raw data keyed by retriever index.

        Returns:
            dict: Processed data ready for loading, keyed by retriever index.
        """
        processed_data = {}
        for idx, data in raw_data.items():
            processed_data[idx] = [{"text": entry.strip(), "metadata": {}} for entry in data]
        return processed_data

    def load_data_to_retrievers(self, processed_data):
        """
        Load the processed data into respective retriever backends.

        Args:
            processed_data (dict): Processed data keyed by retriever index.
        """
        for idx, retriever in enumerate(self.retrievers):
            retriever.add_documents(processed_data[idx])

    def run(self, query):
        """
        Execute the knowledge base workflow.

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

        Returns:
            str: Response generated by the LangSwarm agent, or None if no results are found.
        """
        # Retrieve documents from all sources
        all_documents = []
        for retriever in self.retrievers:
            documents = retriever.query(query)
            if not documents:
                print(f"No documents retrieved from retriever {retriever}.")
                continue
            all_documents.extend(documents)

        if not all_documents:
            print("No documents retrieved from any source.")
            return None

        # Rerank documents
        reranked_documents = self.reranker.rerank(query, all_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 configurations and data sources
    retriever_configs = [
        {"type": "hybrid", "backend": "pinecone", "api_key": "your-api-key"},
        {"type": "sql", "connection_string": "sqlite:///enterprise_knowledge.db"}
    ]
    reranker_config = {"weights": [0.7, 0.3]}  # Example configuration for weighted reranking
    data_sources = ["knowledge_base.txt", lambda: ["Record 1 from DB", "Record 2 from DB"]]

    # Initialize workflow
    knowledge_base = EnterpriseKnowledgeBaseWorkflow(retriever_configs, reranker_config, data_sources)

    # User query
    user_query = "What is our enterprise's vision?"

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

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