Dria

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.

Installation

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",
    embedding=DriaRetriever.models.jina_embeddings_v2_base_en.value,
    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:
    print(doc)

Related

• Retriever conceptual guide
• Retriever how-to guides