Faster, Cheaper Retrieval with Embedding Quantization
Embeddings are a fundamental component of most modern AI stack. When working with large document repositories, the computational costs of storing and retrieving embeddings can quickly become prohibitive. Fortunately, there's a solution: embedding quantization.
What is Embedding Quantization?
Embedding quantization is the process of compressing high-dimensional embedding vectors into a more compact representation such as binary. Instead of storing each number in a 32-bit float, each value is reduced to a single bit - 0 for negative numbers and 1 for positive numbers. This process reduces the storage and memory requirements by 32 times!
While quantization is a lossy compression technique, meaning some information is lost, the performance impact is surprisingly small. Experiments show quantized embeddings can achieve high 90%+ of the accuracy of the original embeddings. And with techniques like oversampling and re-ranking, you can get results very close to the uncompressed embeddings.
Benefits of Quantization
The primary benefits of embedding quantization are:
1. Reduced storage costs - By converting each element in the vector to a single bit (0 or 1), the storage requirement per element drops from 32 bits to 1 bit. For large datasets, this translates to major cost savings.
2. Faster retrieval speeds - The compact binary vectors allow for highly optimized similarity search algorithms like hamming distance. Retrieving nearest neighbors becomes much faster.
3. Lower memory footprint - With 32x compression, you can load more embeddings into memory for rapid processing.
How to Implement It
Implementing quantization is straightforward with modern vector databases and libraries.
Many vector databases like FAISS, Weaviate, and Milvus also natively support quantization out-of-the-box through simple configuration options.
When to Use Quantization
For applications dealing with large text repositories requiring embedding-based retrieval, quantization should be considered mandatory rather than optional. The memory, storage, and performance benefits are too significant to ignore, especially at production scale.
However, quantization may not be suitable for all use cases.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into binary embedding quantization, considerations for production, in the video below!👇
In summary, embedding quantization is a powerful technique that can dramatically accelerate and reduce the costs of embedding retrieval pipelines. By leveraging quantization, oversampling, and re-ranking, you can achieve close to original embedding accuracy at a fraction of the computational resources. For text processing at scale, it's a strategy worth serious consideration.
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Source of images/quotes:
🗞️ Blogs: https://huggingface.co/blog/embedding-quantization https://qdrant.tech/articles/binary-quantization/ https://qdrant.github.io/fastembed/qdrant/Binary_Quantization_with_Qdrant/#1-imports https://qdrant.tech/articles/binary-quantization-openai/# https://weaviate.io/blog/binary-quantization
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