Efficient search over large text datasets can be achieved by using a combination of binary and int8 embeddings, significantly reducing memory and computation requirements. By embedding queries into dense fp32 embeddings and then quantizing them to binary, a binary index is used to quickly retrieve a subset of documents. These are then rescored using int8 embeddings, which are smaller and faster to load from disk, to achieve near-original search performance. This method allows for substantial savings in storage and memory while maintaining high retrieval accuracy, making it a cost-effective solution for large-scale text search applications. This matters because it enables faster and more efficient data retrieval, which is crucial for handling large datasets in various applications.
