Update SVS compression documentation to focus on vector compression and quantization features

[Copilot]

This PR refactors the SVS compression documentation to focus on the core features of vector compression and quantization using LVQ and LeanVec terminology, as these are directly reflected in the API and implementation.

Key Changes

Title and Focus Shift

• Changed document title from "Intel scalable vector search (SVS) compression" to "Vector Compression and Quantization Techniques"
• Updated description to emphasize vector compression and quantization capabilities rather than Intel-specific branding
• Shifted focus from Intel SVS as a product to the underlying compression and quantization techniques

Content Improvements

• Enhanced LVQ section: Added clarification that LVQ learns parameters directly from data and uses representative vectors for compression parameter learning
• Improved LeanVec section: Better explained the method as combining dimensionality reduction with quantization after reducing vector dimensions
• Updated compression comparison table: Improved formatting and corrected observations, including more precise language about memory savings and performance trade-offs
• Simplified multi-level compression explanation: Streamlined the two-level compression section while maintaining technical accuracy
• Enhanced naming conventions: Updated to show both LVQ and LeanVec examples with clearer bit allocation explanations

Practical Considerations

• Restructured the learning parameters section with clearer "Practical Considerations" subsections
• Improved explanations of initial training requirements and data drift handling
• Fixed broken internal reference link to point to the correct SVS-VAMANA index documentation

Warning Updates

• Generalized the hardware dependency warning to focus on the broader concept of platform capabilities rather than specific Intel/Redis Open Source distinctions

Benefits

This update makes the documentation more focused on the actual compression and quantization features that users interact with through the API, using the LVQ and LeanVec terminology that appears in configuration options. The content now better explains the function and benefits of each variant, the role of multi-level compression, and practical considerations for parameter learning and data drift management.

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