teradata-hnsw-vector-search

name	Teradata HNSW Vector Search
description	Approximate nearest neighbor search using HNSW algorithm

Teradata HNSW Vector Search

Property	Value
Skill Name	Teradata HNSW Vector Search
Description	Approximate nearest neighbor search using HNSW algorithm
Category	Vector Search
Primary Function	HNSW
Framework	MLE

• Minimum Version: Teradata 20.00 |

Core Capabilities

• Automated table structure analysis via DBC.ColumnsV
• Dynamic SQL generation for HNSW
• Complete workflow from data preparation to results interpretation
• Data quality validation and preprocessing guidance
• Parameter optimization and tuning

Key Parameters

• IDColumn: Unique vector identifier
• TargetColumns: Column(s) containing vector data
• M: Connections per layer (default 16)
• EfConstruction: Build-time search width (default 200)
• DistanceMetric: 'COSINE', 'EUCLIDEAN', 'INNERPRODUCT'
• TopK: Number of nearest neighbors to return (Predict)
• EfSearch: Search-time accuracy parameter (Predict)

Use Cases

1. Semantic similarity search
2. Recommendation systems
3. Image/document retrieval
4. RAG (Retrieval-Augmented Generation)
5. Duplicate detection

Example Usage

-- HNSW execution
SELECT * FROM HNSW (
    ON {USER_DATABASE}.{VECTOR_TABLE} AS InputTable
    USING
    IDColumn ('{ID_COLUMN}')
    TargetColumns ('{VECTOR_COLUMN}')
    M (16)                             -- Neighbors per layer
    EfConstruction (200)               -- Construction search size
    DistanceMetric ('COSINE')          -- 'COSINE','EUCLIDEAN','INNERPRODUCT'
) AS dt;

Scripts Included

Core Analytics Scripts

• table_analysis.sql: Automatic table structure discovery
• preprocessing.sql: Data preparation and feature engineering
• model_training.sql: HNSW execution
• evaluation.sql: Results analysis and metrics
• complete_workflow_template.sql: End-to-end workflow

Utility Scripts

• data_quality_checks.sql: Comprehensive data validation
• parameter_tuning.sql: Parameter optimization
• diagnostic_queries.sql: Results diagnostics and interpretation

Best Practices

• Always run table_analysis.sql first to understand your data structure
• Validate data quality before executing the analytical function
• Use parameter_tuning.sql to find optimal configuration
• Review diagnostic_queries.sql output for model/results validation

Limitations

• Requires Teradata Vantage 20.00+ with ClearScape Analytics
• Input data must meet function-specific requirements
• Results depend on data quality and parameter configuration

Teradata HNSW Vector Search - ClearScape Analytics skill for Teradata Vantage