landing_page/qdrant-landing/content/documentation/embeddings/azure.md

title, weight

title	weight
Azure OpenAI	950

Using Azure OpenAI with Qdrant

Azure OpenAI is Microsoft's platform for AI embeddings, focusing on powerful text and data analytics. These embeddings are suitable for high-precision vector searches in Qdrant.

Installation

You can install the required packages using the following pip command:

pip install openai azure-identity python-dotenv qdrant-client

Code Example

import os
import openai
import dotenv
import qdrant_client
from qdrant_client.models import Batch
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

dotenv.load_dotenv()

# Set to True if using Azure Active Directory for authentication
use_azure_active_directory = False

# Qdrant client setup
qdrant_client = qdrant_client.QdrantClient(url="http://localhost:6333")

# Azure OpenAI Authentication
if not use_azure_active_directory:
    endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
    api_key = os.environ["AZURE_OPENAI_API_KEY"]

    client = openai.AzureOpenAI(
        azure_endpoint=endpoint,
        api_key=api_key,
        api_version="2023-09-01-preview"
    )
else:
    endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
    client = openai.AzureOpenAI(
        azure_endpoint=endpoint,
        azure_ad_token_provider=get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"),
        api_version="2023-09-01-preview"
    )

# Deployment name of the model in Azure OpenAI Studio
deployment = "your-deployment-name"  # Replace with your deployment name

# Generate embeddings using the Azure OpenAI client
text_input = "The food was delicious and the waiter..."
embeddings_response = client.embeddings.create(
    model=deployment,
    input=text_input
)

# Extract the embedding vector from the response
embedding_vector = embeddings_response.data[0].embedding

# Insert the embedding into Qdrant
qdrant_client.upsert(
    collection_name="MyCollection",
    points=Batch(
        ids=[1],  # This ID can be dynamically assigned or managed
        vectors=[embedding_vector],
    )
)

print("Embedding successfully upserted into Qdrant.")