In this lesson, you will learn how to architect a unified vector database schema to manage and query embeddings derived from different modalities like text, images, and audio.
The Challenge of Modality Fusion
When working with multi-modal data, you are often processing different data types through modality-specific encoders (e.g., CLIP for images/text or CLAP for audio/text). While the raw data formats differ, the resulting embeddings are simply high-dimensional vectors. To store them efficiently in a single vector database, you must ensure that all embeddings are projected into a common vector space or managed through a schema that allows for modality-aware retrieval.
Unified Schema Design
The most effective way to store these embeddings is to use a relational-vector hybrid approach. You maintain a central metadata table that defines the modality_type and a vector column (often stored as an array or a specialized BLOB) that holds the embedding.
Consider this schema pattern:
CREATE TABLE multi_modal_embeddings (
id UUID PRIMARY KEY,
modality_type VARCHAR(20), -- 'text', 'image', 'audio'
embedding VECTOR(768), -- Standardized dimension size
source_uri TEXT,
created_at TIMESTAMP
);
-- Indexing for high-performance similarity search
CREATE INDEX idx_vector_search
ON multi_modal_embeddings USING ivfflat (embedding)
WITH (lists = 100);
Navigating the Unified Space
Because you are storing different modalities in the same table, you must be careful with your similarity search queries. If your embedding model is a "joint-embedding" model (like CLIP), you can perform cross-modal retrieval, such as querying an image collection using a text prompt. If the models are independent, you should filter by modality_type within your query to ensure you aren't comparing an audio vector against an image vector, which would produce meaningless results.
-- Searching for images that match a text-based query
SELECT source_uri
FROM multi_modal_embeddings
WHERE modality_type = 'image'
ORDER BY embedding <=> '[0.12, -0.05, ...]'::vector
LIMIT 5;
By normalizing your embedding dimensions and tagging them with metadata, you create a scalable foundation that allows your application to navigate disparate media types as if they were part of a single, coherent intelligence layer.
Key Takeaway: Store multi-modal embeddings in a unified vector table by standardizing vector dimensions and using modality-specific metadata tags to maintain semantic integrity during similarity searches.