[P] Vector quantization methods

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txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

txtai has built-in support for quantizing vectors. The code above shows how 1-bit (binary) quantization can be applied. With 1-bit quantization, each dimension is transformed into a 1-bit value (0 or 1). Those bits are grouped into uint8's. This method can retain a surprising amount of accuracy, especially with high dimension models.

See the article below for more information along with benchmarks.

Article: …

binary code database embeddings language language model llm machinelearning orchestration quantization search semantic shows support value vector vectors workflows