Milvus is a cloud-native vector database designed for managing large-scale vector data, powering various AI applications from similarity search to recommendation systems.

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Setup

  1. Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  1. Install Milvus by following the official installation guide.

  2. Create a database and collection in Milvus for your vector data.

  3. Install Langtrace and Milvus Python SDK:

pip install langtrace-python-sdk pymilvus python-dotenv
You’ll need an API key from Langtrace. Sign up for Langtrace if you haven’t done so already.

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Usage

Here’s an example of how to use Langtrace with Milvus for vector operations:

This example demonstrates how to:

  1. Create a collection in Milvus
  2. Generate and insert vector embeddings
  3. Perform vector similarity search
  4. Execute metadata queries
  5. Use Langtrace to monitor and trace all vector operations

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Observing the Full Trace

When you run this example, Langtrace captures the entire pipeline of vector operations. You can observe:

  • Collection creation and management
  • Embedding generation process
  • Data insertion operations
  • Vector similarity search performance
  • Metadata query execution

All these operations are automatically traced and can be monitored through the Langtrace dashboard, providing deep insights into your vector database operations.

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Conclusion

Integrating Langtrace with Milvus provides comprehensive observability for your vector database operations. This integration enables you to:

  • Monitor and debug vector operations in real-time
  • Track performance metrics across your vector search pipeline
  • Optimize your vector database queries
  • Ensure reliable and efficient vector similarity search

Ready to enhance your Milvus workflows? Start exploring Langtrace x Milvus today, and take your vector database observability to the next level. 🚀

MongoDB Vector SearchFastAPI