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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
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