I have been working on my RAG application and when it comes to using my data (although small), I was thinking about performance and size.

I decided to check Quantization.

Quantization can be a useful technique in my RAG (Retrieval-Augmented Generation) workflow, especially when dealing with high-dimensional embeddings. It essentially reduces the precision of embeddings—compressing them so that the memory footprint is lower and the similarity searches can be faster, all while preserving most of the semantic information. Let’s break down the concept and how I might integrate it into the app:

Why Use Quantization?

  1. Performance and Speed

    • Faster Searches: With quantized vectors, the distance computations become less expensive. This improvement is particularly significant when my vector db scales up, as the reduced precision can speed up nearest neighbor searches.
    • Reduced Storage Costs: Quantization compresses high-dimensional embeddings into lower precision representations (e.g., converting 32-bit floats to 8-bit integers). This reduction results in lower storage usage while keeping the overall retrieval quality intact.

  2. Trade-offs

    • Accuracy vs. Efficiency: Quantization introduces an approximation error. Depending on the algorithm (e.g., scalar quantization, product quantization), I might lose a bit of fine-grained precision. However, with properly tuned parameters, I can often achieve a very attractive balance between efficiency and accuracy.
    • Compatibility: I need to ensure that my vector db (in this case, Qdrant) supports the kind of quantized data representations I’m planning to use. Sometimes, additional adjustments in the query implementations or similarity metrics might be necessary.

Where to Fit Quantization in the Pipeline

In my RAG app, the ideal spot to introduce quantization is after I’ve chunked the markdown content, generated the embeddings with my C# Semantic Kernel, and right before I index them into Qdrant. This allows me to reduce the memory load and potentially make the searches more efficient without having to modify core logic for content processing and response generation.

Here’s a simplified timeline of my data flow with quantization:

  1. Chunking: Divide my markdown files into manageable parts.
  2. Embedding Generation: Use my C# Semantic Kernel to create embedding vectors.
  3. Quantization: Apply a quantization algorithm to these vectors, reducing their precision.
  4. Indexing: Write the quantized embeddings into Qdrant.
  5. Query Handling: Use the quantized representations for similarity search and retrieval-augmented generation.

Mermaid Diagram for the Augmented Pipeline

Below is a Mermaid diagram showing an updated flow in my RAG application, incorporating quantization:

flowchart TD A[Markdown Files] --> B[Chunk Content] B --> C[Generate Embeddings -C# Semantic Kernel-] C --> D[Quantization Process] D --> E[Write Quantized Vectors to Qdrant] E --> F[User Query via API] F --> G[Retrieve Related Quantized Docs] G --> H[Semantic Kernel for Answer Generation] H --> I[Return Answer to User]

Approaches to Quantization

  • Scalar Quantization: Each component of the vector is quantized independently, which is simpler and often faster.
  • Product Quantization (PQ): The vector is split into subspaces, and each subspace is quantized independently. This method can dramatically reduce memory usage while maintaining good retrieval performance.
  • Optimized PQ or other variants: Trade-offs can be further tuned by employing more advanced techniques that optimize the quantization process to better preserve similarity metrics.

Final Thoughts

Integrating quantization in my RAG application provides a robust way to balance performance, storage constraints, and retrieval accuracy. I plan to test different quantization methods to see which offers the best trade-off for my specific use case.

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