from langswarm.agent import LangSwarmAgent
from retrievers import MultilingualRetriever  # Placeholder for your retriever implementation
from translators import Translator  # Placeholder for your translation implementation

class MultilingualKnowledgeWorkflow:
    """
    Workflow for retrieving and translating knowledge across multiple languages.

    Specification:
    - Initialize a LangSwarm agent.
    - Set up a multilingual retriever and translator.
    - Fetch, process, and load multilingual data into the retriever backend.
    - Handle user queries and provide translated results.
    - Implement fallback for cases where no documents are retrieved or translation fails.
    """

    def __init__(self, retriever_config, translator_config, multilingual_data):
        """
        Initialize the multilingual knowledge retrieval workflow.

        Args:
            retriever_config (dict): Configuration for setting up the multilingual retriever.
            translator_config (dict): Configuration for setting up the translator.
            multilingual_data (list): Data for populating the retriever backend.
        """
        # Initialize LangSwarm agent
        self.agent = LangSwarmAgent()

        # Initialize retriever and translator
        self.retriever = MultilingualRetriever(**retriever_config)
        self.translator = Translator(**translator_config)

        # Data Fetch and Processing
        self.processed_data = self.process_data(multilingual_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 multilingual data.

        Returns:
            list: Processed data ready for loading.
        """
        return [{"text": entry["text"].strip(), "metadata": {"language": entry["language"]}} 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, target_language):
        """
        Execute the multilingual knowledge retrieval workflow.

        Args:
            query (str): User's query.
            target_language (str): Target language for translation.

        Returns:
            str: Translated knowledge 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 multilingual documents retrieved.")
            return None

        # Translate the top document
        top_document = documents[0]
        try:
            translated_text = self.translator.translate(top_document["text"], target_language)
        except Exception as e:
            print(f"Translation failed: {e}")
            return None

        # Concatenate input for the agent
        agent_input = f"Query: {query}\nTranslated Context: {translated_text}"

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


# **Usage Example**

if __name__ == "__main__":
    # Example configuration and multilingual data
    retriever_config = {"backend": "pinecone", "api_key": "your-api-key"}
    translator_config = {"service": "google", "api_key": "translator-api-key"}
    multilingual_data = [
        {"text": "¿Qué es LangSwarm?", "language": "es"},
        {"text": "LangSwarm nedir?", "language": "tr"},
        {"text": "Qu'est-ce que LangSwarm ?", "language": "fr"},
        {"text": "What is LangSwarm?", "language": "en"}
    ]

    # Initialize workflow
    multilingual_knowledge = MultilingualKnowledgeWorkflow(retriever_config, translator_config, multilingual_data)

    # User query
    user_query = "What is LangSwarm?"
    target_language = "fr"  # French

    # Run the workflow
    response = multilingual_knowledge.run(user_query, target_language)

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