mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
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import logging
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from typing import Any, Dict, List, Optional, Union
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logger = logging.getLogger(__name__)
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class DriaAPIWrapper:
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"""Wrapper around Dria API.
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This wrapper facilitates interactions with Dria's vector search
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and retrieval services, including creating knowledge bases, inserting data,
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and fetching search results.
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Attributes:
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api_key: Your API key for accessing Dria.
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contract_id: The contract ID of the knowledge base to interact with.
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top_n: Number of top results to fetch for a search.
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"""
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def __init__(
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self, api_key: str, contract_id: Optional[str] = None, top_n: int = 10
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):
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try:
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from dria import Dria, Models
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except ImportError:
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logger.error(
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"""Dria is not installed. Please install Dria to use this wrapper.
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You can install Dria using the following command:
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pip install dria
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"""
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)
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return
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self.api_key = api_key
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self.models = Models
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self.contract_id = contract_id
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self.top_n = top_n
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self.dria_client = Dria(api_key=self.api_key)
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if self.contract_id:
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self.dria_client.set_contract(self.contract_id)
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def create_knowledge_base(
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self,
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name: str,
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description: str,
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category: str,
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embedding: str,
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) -> str:
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"""Create a new knowledge base."""
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contract_id = self.dria_client.create(
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name=name, embedding=embedding, category=category, description=description
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)
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logger.info(f"Knowledge base created with ID: {contract_id}")
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self.contract_id = contract_id
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return contract_id
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def insert_data(self, data: List[Dict[str, Any]]) -> str:
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"""Insert data into the knowledge base."""
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response = self.dria_client.insert_text(data)
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logger.info(f"Data inserted: {response}")
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return response
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def search(self, query: str) -> List[Dict[str, Any]]:
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"""Perform a text-based search."""
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results = self.dria_client.search(query, top_n=self.top_n)
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logger.info(f"Search results: {results}")
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return results
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def query_with_vector(self, vector: List[float]) -> List[Dict[str, Any]]:
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"""Perform a vector-based query."""
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vector_query_results = self.dria_client.query(vector, top_n=self.top_n)
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logger.info(f"Vector query results: {vector_query_results}")
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return vector_query_results
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def run(self, query: Union[str, List[float]]) -> Optional[List[Dict[str, Any]]]:
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"""Method to handle both text-based searches and vector-based queries.
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Args:
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query: A string for text-based search or a list of floats for
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vector-based query.
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Returns:
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The search or query results from Dria.
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"""
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if isinstance(query, str):
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return self.search(query)
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elif isinstance(query, list) and all(isinstance(item, float) for item in query):
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return self.query_with_vector(query)
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else:
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logger.error(
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"""Invalid query type. Please provide a string for text search or a
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list of floats for vector query."""
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)
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return None
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