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Dria is a hub of public RAG models for developers to both contribute and utilize a shared embedding lake. This notebook demonstrates how to use the Dria API for data retrieval tasks.


Ensure you have the dria package installed. You can install it using pip:

%pip install --upgrade --quiet dria

Configure API Key

Set up your Dria API key for access.

import os

os.environ["DRIA_API_KEY"] = "DRIA_API_KEY"

Initialize Dria Retriever

Create an instance of DriaRetriever.

from langchain.retrievers import DriaRetriever

api_key = os.getenv("DRIA_API_KEY")
retriever = DriaRetriever(api_key=api_key)
API Reference:DriaRetriever

Create Knowledge Base

Create a knowledge on Dria's Knowledge Hub

contract_id = retriever.create_knowledge_base(
name="France's AI Development",
category="Artificial Intelligence",
description="Explore the growth and contributions of France in the field of Artificial Intelligence.",

Add Data

Load data into your Dria knowledge base.

texts = [
"The first text to add to Dria.",
"Another piece of information to store.",
"More data to include in the Dria knowledge base.",

ids = retriever.add_texts(texts)
print("Data added with IDs:", ids)

Retrieve Data

Use the retriever to find relevant documents given a query.

query = "Find information about Dria."
result = retriever.invoke(query)
for doc in result:

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