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Retrieval Augmented Generation (RAG)
/Optimization & Extension (Advanced Customization)
/Computational Efficiency

Computational Efficiency

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Computational Efficiency

Making RAG systems faster and more cost-effective is crucial for real-world applications. Here are key approaches to achieve this:

Search Parameter Tuning:

  • efConstruction: Controls how carefully the system builds its search index - higher values create better indexes but take longer
  • efSearch: Determines how thoroughly the system searches - higher values find more relevant results but take more time
  • M: Sets how many connections each data point has in the search network - more connections improve accuracy but use more memory

Vector Compression Techniques:

  • Scalar Quantization: Simplifies vector numbers (like rounding 3.14159 to 3.1) to save space while keeping most accuracy
  • Product Quantization: Breaks vectors into smaller pieces that can be stored more efficiently using lookup tables
  • Dimensionality Reduction: Keeps only the most important information in vectors, like compressing a photo while preserving the main details

Model Optimization:

  • Knowledge Distillation: Teaches smaller, faster models to mimic the behavior of larger, more powerful ones
  • Smaller Models: Uses compact models like DistilBERT that require less computing power while maintaining good performance
  • Quantization: Converts model calculations to use simpler number formats that process faster on computers

Smart Storage Strategies:

  • Query Result Caching: Remembers answers to common questions so they don't need to be calculated again
  • Embedding Caching: Stores already-calculated vector representations to avoid repeating work
  • Multi-Tier Retrieval: Uses quick, simple filters first before using more resource-intensive methods

These optimizations can reduce computing costs by 10-100 times while maintaining 95% or more of the original quality, making RAG systems practical for everyday applications.