Multimodal Vector Search

Multimodal Vector Search

Unified vector search across text, images, audio, and video.

Overview

HeliosDB Multimodal Vector Search provides:

• Native support for multiple modalities (text, image, audio, video)
• CLIP-based cross-modal search
• Unified embedding space for semantic similarity
• High-performance ANN (Approximate Nearest Neighbor) indexes

Quick Start

-- Create table with vector column
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  image BYTEA,
  embedding vector(1536)
);

-- Create vector index
CREATE INDEX idx_docs_embedding ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

-- Search by similarity
SELECT id, content
FROM documents
ORDER BY embedding <-> to_vector('search query embedding')
LIMIT 10;

Key Features

Feature	Description
8 Modalities	Text, image, audio, video, code, tables, graphs, time-series
CLIP Integration	Cross-modal search (search images with text)
ANN Indexes	IVFFlat, HNSW for billion-scale search
Hybrid Search	Combine vector + keyword + graph search
GPU Acceleration	CUDA-accelerated similarity computation

Documentation

Document	Description
MULTIMODAL_SEARCH_ARCHITECTURE.md	System architecture
CLIP_INTEGRATION_APPROACH.md	CLIP model integration

Related

• GraphRAG: /docs/features/graphrag/
• Full-Text Search: /docs/guides/user/FULL_TEXT_SEARCH_TUNING_GUIDE.md
• Vector Examples: /docs/guides/user/VECTOR_INSERT_EXAMPLES.md

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Status: Production Ready Version: v7.0