

Best practices for designing vector database schemas including vector dimensions, metadata structure, indexing strategies, and collection organization. Critical for performance, scalability, and maintainability.
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Vector database schema design determines how vectors and metadata are organized, indexed, and queried. Good design is critical for performance and scalability.
{
"embedding": [0.1, 0.2, ...], // Dense vector
"sparse_embedding": {1: 0.5, 42: 0.8} // Sparse vector (optional)
}
{
"id": "doc123",
"title": "...",
"category": "technology",
"timestamp": "2024-01-15",
"tags": ["AI", "ML"],
"author": "..."
}
# Index category for fast filtering
collection.create_index(
field_name="category",
index_params={"index_type": "HASH"}
)
# Partition by date
partitions = ["2024-01", "2024-02", "2024-03"]
# Search specific partition
results = collection.search(
data=query,
partition_names=["2024-03"]
)
Separate vectors for different modalities:
{
"text_embedding": [...],
"image_embedding": [...],
"combined_embedding": [...]
}
Not applicable (design practice).