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CodeSignal

Optimizing and Scaling Qdrant for Vector Search

via CodeSignal

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Overview

This course focuses on **scaling Qdrant deployments**, improving retrieval efficiency, reducing search latency, and handling real-time updates for production workloads.

Syllabus

  • Unit 1: Query Latency in Vector Search
    • Precomputing Nearest Neighbors in Qdrant
    • Compute Cosine Similarity and Find Nearest Neighbors
    • Search Function Implementation Using Embeddings and Nearest Neighbors
  • Unit 2: Dynamic Search Space Reduction
    • Testing Query Filtering with Varying Thresholds
    • Measuring Function Performance with Varying Thresholds
    • Enhancing Filter Function for User-Friendly Search
  • Unit 3: Real Time Stream Processing
    • Enhancing Real-Time Data Streaming with Logging
    • Implement Real-Time Data Streaming with Performance Monitoring
    • Performance Monitoring Enhancement for Streaming Data Insertion

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