feat(query): Implement Vector Index with HNSW Algorithm

[b41sh]

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Summary

This PR introduces a vector index to Databend, leveraging the Hierarchical Navigable Small World (HNSW) algorithm for efficient similarity search.

Key Features:

• Vector Index with HNSW: Implements a vector index based on the HNSW algorithm, enabling fast and accurate approximate nearest neighbor search on VECTOR data.
• Integration with VECTOR Data Type: Seamlessly integrates with the existing VECTOR data type, allowing users to create indexes on vector columns.

Implementation Details:

The implementation of the HNSW algorithm is primarily based on modifications to the excellent open-source HNSW implementation from github.com/qdrant/qdrant. We would like to express our sincere gratitude to the Qdrant team for their valuable work, which significantly accelerated the development of this feature.

part of: #17972

Tests

• Unit Test
• Logic Test
• Benchmark Test
• No Test - Explain why

Type of change

• Bug Fix (non-breaking change which fixes an issue)
• New Feature (non-breaking change which adds functionality)
• Breaking Change (fix or feature that could cause existing functionality not to work as expected)
• Documentation Update
• Refactoring
• Performance Improvement
• Other (please describe):

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