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| 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 | |