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Qdrant is an open-source vector similarity search engine built for production-ready vector search applications. It provides a convenient API to store, search, and manage vectors with additional payload data. Obiguard provides a proxy to Qdrant, allowing you to use virtual keys and observability features.

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Obiguard SDK Integration with Qdrant

Obiguard provides a consistent API to interact with models from various providers. To integrate Qdrant with Obiguard:

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1. Install the Obiguard SDK

Add the Obiguard SDK to your application to interact with Qdrant through Obiguard’s gateway.
  • Python SDK
pip install obiguard

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2. Initialize Obiguard with a Virtual Key

To use Qdrant with Obiguard, get your API key from Qdrant App, then add it to Obiguard to create your Qdrant virtual key.
  • Python SDK
  • OpenAI Python SDK
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
)

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3. Use the Obiguard SDK to interact with Qdrant

  • Python SDK
  • OpenAI Python
  • cURL
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
  custom_host="QDRANT_HOST" # Replace with your Qdrant host
)

response = client.post(
  url="https://xxxx-xxx-xxx-xx-xxxxxx.us-west-2-0.aws.cloud.qdrant.io", # Qdrant search endpoint, you can use any Qdrant endpoint
)

print(response)