---
title: "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

```sql
-- 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 |
|----------|-------------|

## 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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