Table of Contents
Prerequisites
Before integrating Langtrace with Qdrant, ensure you have the following:- Qdrant client library installed
- Langtrace Python SDK installed
Installation
If you haven’t already installed the required libraries, you can do so using pip:Python
Basic Setup
To start using Langtrace with Qdrant, you need to initialize both clients:Python
Tracing Qdrant Operations
Langtrace allows you to trace various Qdrant operations. Here are some common operations and how to trace them:Adding Points to a Collection
Python
Searching in a Collection
Python
Creating a Collection
Python
Best Practices
- Meaningful Span Names: Choose descriptive names for your spans to easily identify operations in the Langtrace dashboard.
- Error Handling: Wrap your Qdrant operations in try-except blocks to capture and trace errors:
Python
- Performance Monitoring: Use Langtrace to monitor the performance of your Qdrant operations over time. This can help you identify slow queries or other bottlenecks in your application.
Troubleshooting
If you encounter issues with Langtrace and Qdrant integration, consider the following:- Check Connectivity: Ensure that both Qdrant and Langtrace servers are accessible from your application.
- Verify API Keys: Double-check that you’re using the correct Langtrace API key.
- Update Libraries: Ensure you’re using the latest versions of both the Qdrant client and Langtrace SDK.
By integrating Langtrace with Qdrant, you gain powerful observability capabilities for your vector search operations. This integration allows you to optimize performance, troubleshoot issues, and gain insights into your AI application’s behavior. Happy tracing!