Feature Description

Add support for byte vector storage in the OpenSearch vector store integration, leveraging OpenSearch 2.17's new byte vector capabilities with the Faiss engine. This would allow users to store vectors more efficiently by using 8-bit integers (-128 to 127) instead of floats, significantly reducing storage requirements while maintaining search quality.

Reason

Currently, the OpenSearch integration in LlamaIndex only supports float vectors. Adding byte vector support would require:

• Adding a new data_type parameter to OpensearchVectorClient
• Updating the index creation and query logic to handle byte vectors

Value of Feature

Storage Efficiency

• Byte vectors require 1/4 the storage space of 32-bit float vectors
• Reduced storage costs for large-scale deployments
• Faster network transfer for distributed systems

Performance Benefits

• Potentially faster similarity search due to reduced memory bandwidth requirements
• More efficient cache utilisation
• Better scalability for large vector databases