ObjectUIFields
Vector Field
Vector embeddings viewer for AI/ML data
The Vector Field component displays vector embeddings used in AI/ML applications. This is a read-only field that shows a preview of the embedding values and dimensionality.
Basic Usage
Basic Vector Display
{
"type": "vector",
"name": "embedding",
"label": "Text Embedding",
"dimensions": 768,
"readonly": true,
"value": [
0.1234,
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0.9012,
0.3456,
-0.789
]
}Different Dimensions
High-Dimensional Vector
{
"type": "vector",
"name": "image_embedding",
"label": "Image Embedding",
"dimensions": 1536,
"readonly": true,
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}Field Schema
interface VectorFieldSchema {
type: 'vector';
name: string; // Field name/ID
label?: string; // Field label
value?: number[]; // Vector array
readonly: true; // Always read-only
className?: string; // Additional CSS classes
// Vector Options
dimensions?: number; // Vector dimensionality
}Display Format
Vectors are displayed with a preview:
[0.1234, -0.5678, 0.9012...] (768D)Shows:
- First 3 values (formatted to 4 decimal places)
- Total dimensionality in parentheses
Common Embedding Dimensions
Different AI models use different embedding sizes:
- OpenAI text-embedding-3-small: 512, 1536, or 3072 dimensions (configurable)
- OpenAI text-embedding-3-large: 256, 1024, or 3072 dimensions (configurable)
- OpenAI text-embedding-ada-002: 1536 dimensions (fixed)
- Sentence Transformers (BERT): 768 dimensions
- OpenAI CLIP: 512 dimensions
- Word2Vec: 100-300 dimensions
- Custom Models: Variable dimensions
Data Format
Vectors are stored as arrays of floating-point numbers:
// Example embedding
const embedding: number[] = [
0.1234, -0.5678, 0.9012, 0.3456, -0.7890,
// ... 763 more values for 768D embedding
];Cell Renderer
In tables/grids, displays compact preview:
import { VectorCellRenderer } from '@object-ui/fields';
// Renders: [0.1234, -0.5678, 0.9012...] (768D)Vector Operations
Common operations with vector fields (performed on backend):
Similarity Search
// Find similar vectors using cosine similarity
const findSimilar = async (queryVector: number[], limit: number = 10) => {
return await db.raw(`
SELECT
id,
1 - (embedding <=> $1) as similarity
FROM documents
ORDER BY embedding <=> $1
LIMIT $2
`, [queryVector, limit]);
};Distance Metrics
- Cosine Similarity: Measures angle between vectors
- Euclidean Distance: Straight-line distance
- Dot Product: Inner product of vectors
Use Cases
- Semantic Search: Find similar documents, products, or content
- Recommendation Systems: Product recommendations, content suggestions
- Text Analysis: Document clustering, topic modeling
- Image Search: Similar image finding, visual search
- Anomaly Detection: Identify outliers in data
- AI Applications: Any machine learning feature vectors
Backend Implementation
Example vector storage and search:
// PostgreSQL with pgvector extension
import { Client } from 'pg';
// Create table with vector column
const createTable = async () => {
await db.raw(`
CREATE TABLE IF NOT EXISTS documents (
id SERIAL PRIMARY KEY,
content TEXT,
embedding vector(768)
);
CREATE INDEX ON documents
USING ivfflat (embedding vector_cosine_ops);
`);
};
// Generate embedding (using OpenAI)
const generateEmbedding = async (text: string): Promise<number[]> => {
const response = await openai.embeddings.create({
model: 'text-embedding-3-small',
input: text
});
return response.data[0].embedding;
};
// Store document with embedding
const storeDocument = async (content: string) => {
const embedding = await generateEmbedding(content);
return db.insert('documents', {
content,
embedding
});
};
// Search similar documents
const searchSimilar = async (query: string, limit = 10) => {
const queryEmbedding = await generateEmbedding(query);
return db.raw(`
SELECT
id,
content,
1 - (embedding <=> $1::vector) as similarity
FROM documents
ORDER BY embedding <=> $1::vector
LIMIT $2
`, [queryEmbedding, limit]);
};Database Support
Vector storage is supported by:
- PostgreSQL: pgvector extension
- Pinecone: Specialized vector database
- Weaviate: Vector search engine
- Milvus: Open-source vector database
- Qdrant: Vector similarity search engine
- Chroma: AI-native embedding database
Performance Considerations
- Indexing: Use approximate nearest neighbor (ANN) indices
- Quantization: Consider dimension reduction for storage
- Batch Operations: Generate embeddings in batches
- Caching: Cache frequently used embeddings
- Async Generation: Generate embeddings asynchronously
Example: Semantic Search
// 1. Generate embedding for user query
const query = "Find documentation about authentication";
const queryEmbedding = await generateEmbedding(query);
// 2. Search for similar documents
const results = await db.raw(`
SELECT
id,
title,
content,
1 - (embedding <=> $1::vector) as similarity
FROM documents
WHERE 1 - (embedding <=> $1::vector) > 0.7
ORDER BY embedding <=> $1::vector
LIMIT 10
`, [queryEmbedding]);
// 3. Display results with similarity scores
results.forEach(doc => {
console.log(`${doc.title} (${(doc.similarity * 100).toFixed(1)}% match)`);
});