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# Source code for langchain_community.utilities.dria_index

    import logging
    from typing import Any, Dict, List, Optional, Union

    logger = logging.getLogger(__name__)

    class DriaAPIWrapper:
        """Wrapper around Dria API.

        This wrapper facilitates interactions with Dria's vector search
        and retrieval services, including creating knowledge bases, inserting data,
        and fetching search results.

        Attributes:
            api_key: Your API key for accessing Dria.
            contract_id: The contract ID of the knowledge base to interact with.
            top_n: Number of top results to fetch for a search.
        """

        def __init__(
            self, api_key: str, contract_id: Optional[str] = None, top_n: int = 10
        ):
            try:
                from dria import Dria, Models
            except ImportError:
                logger.error(
                    """Dria is not installed. Please install Dria to use this wrapper.

                    You can install Dria using the following command:
                    pip install dria
                    """
                )
                return

            self.api_key = api_key
            self.models = Models
            self.contract_id = contract_id
            self.top_n = top_n
            self.dria_client = Dria(api_key=self.api_key)
            if self.contract_id:
                self.dria_client.set_contract(self.contract_id)

        def create_knowledge_base(
            self,
            name: str,
            description: str,
            category: str,
            embedding: str,
        ) -> str:
            """Create a new knowledge base."""
            contract_id = self.dria_client.create(
                name=name, embedding=embedding, category=category, description=description
            )
            logger.info(f"Knowledge base created with ID: {contract_id}")
            self.contract_id = contract_id
            return contract_id

        def insert_data(self, data: List[Dict[str, Any]]) -> str:
            """Insert data into the knowledge base."""
            response = self.dria_client.insert_text(data)
            logger.info(f"Data inserted: {response}")
            return response

        def search(self, query: str) -> List[Dict[str, Any]]:
            """Perform a text-based search."""
            results = self.dria_client.search(query, top_n=self.top_n)
            logger.info(f"Search results: {results}")
            return results

        def query_with_vector(self, vector: List[float]) -> List[Dict[str, Any]]:
            """Perform a vector-based query."""
            vector_query_results = self.dria_client.query(vector, top_n=self.top_n)
            logger.info(f"Vector query results: {vector_query_results}")
            return vector_query_results

        def run(self, query: Union[str, List[float]]) -> Optional[List[Dict[str, Any]]]:
            """Method to handle both text-based searches and vector-based queries.

            Args:
                query: A string for text-based search or a list of floats for
                vector-based query.

            Returns:
                The search or query results from Dria.
            """
            if isinstance(query, str):
                return self.search(query)
            elif isinstance(query, list) and all(isinstance(item, float) for item in query):
                return self.query_with_vector(query)
            else:
                logger.error(
                    """Invalid query type. Please provide a string for text search or a
                    list of floats for vector query."""
                )
                return None