Querying Vectors

Perform similarity search and retrieve vectors using JavaScript SDK or Postgres.

This feature is in alpha

Expect rapid changes, limited features, and possible breaking updates. Share feedback as we refine the experience and expand access.

Vector similarity search finds vectors most similar to a query vector using distance metrics. You can query vectors using the JavaScript SDK or directly from Postgres using SQL.

Comparison to pgvector

Vector buckets and any Foreign Data Wrappers (FDW) they use only support one similarity search algorithm, the <===> distance operator.

Basic similarity search#

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import { createClient } from '@supabase/supabase-js'
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const supabase = createClient('https://your-project-id.supabase.co', 'your-service-key')
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const index = supabase.storage.vectors.from('embeddings').index('documents-openai')
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// Query with a vector embedding
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const { data, error } = await index.queryVectors({
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  queryVector: {
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    float32: [0.1, 0.2, 0.3 /* ... embedding of 1536 dimensions ... */],
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  },
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  topK: 5,
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  returnDistance: true,
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  returnMetadata: true,
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})
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if (error) {
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  console.error('Query failed:', error)
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} else {
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  // Results are ranked by similarity (lowest distance = most similar)
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  data.vectors.forEach((result, rank) => {
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    console.log(`${rank + 1}. ${result.metadata?.title}`)
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    console.log(`   Similarity score: ${result.distance.toFixed(4)}`)
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  })
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}

Semantic search#

Find documents similar to a query by embedding the query text:

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import { createClient } from '@supabase/supabase-js'
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import OpenAI from 'openai'
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const supabase = createClient(...)
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const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })
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async function semanticSearch(query, topK = 5) {
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  // Embed the query
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  const queryEmbedding = await openai.embeddings.create({
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    model: 'text-embedding-3-small',
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    input: query
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  })
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  const queryVector = queryEmbedding.data[0].embedding
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  // Search for similar vectors
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  const { data, error } = await supabase.storage.vectors
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    .from('embeddings')
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    .index('documents-openai')
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    .queryVectors({
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      queryVector: { float32: queryVector },
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      topK,
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      returnDistance: true,
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      returnMetadata: true
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    })
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  if (error) {
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    throw error
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  }
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  return data.vectors.map((result) => ({
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    id: result.key,
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    title: result.metadata?.title,
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    similarity: 1 - result.distance, // Convert distance to similarity (0-1)
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    metadata: result.metadata
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  }))
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}
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// Usage
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const results = await semanticSearch('How do I use vector search?')
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results.forEach((result) => {
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  console.log(`${result.title} (${(result.similarity * 100).toFixed(1)}% similar)`)
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})

Filtered similarity search#

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const index = supabase.storage.vectors
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  .from('embeddings')
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  .index('documents-openai')
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// Search with metadata filter
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const { data } = await index.queryVectors({
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  queryVector: { float32: [...embedding...] },
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  topK: 10,
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  filter: {
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    // Filter by metadata fields
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    category: 'electronics',
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    in_stock: true,
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    price: { $lte: 500 } // Less than or equal to 500
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  },
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  returnDistance: true,
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  returnMetadata: true
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})

Retrieving specific vectors#

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const index = supabase.storage.vectors.from('embeddings').index('documents-openai')
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const { data, error } = await index.getVectors({
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  keys: ['doc-1', 'doc-2', 'doc-3'],
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  returnData: true,
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  returnMetadata: true,
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})
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if (!error) {
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  data.vectors.forEach((vector) => {
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    console.log(`${vector.key}: ${vector.metadata?.title}`)
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  })
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}

Listing vectors#

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const index = supabase.storage.vectors.from('embeddings').index('documents-openai')
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let nextToken = undefined
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let pageCount = 0
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do {
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  const { data, error } = await index.listVectors({
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    maxResults: 100,
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    nextToken,
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    returnData: false, // Don't return embeddings for faster response
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    returnMetadata: true,
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  })
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  if (error) break
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  pageCount++
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  console.log(`Page ${pageCount}: ${data.vectors.length} vectors`)
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  data.vectors.forEach((vector) => {
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    console.log(`  - ${vector.key}: ${vector.metadata?.title}`)
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  })
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  nextToken = data.nextToken
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} while (nextToken)

Hybrid search: Vectors + relational data#

Combine similarity search with SQL filtering and joins:

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async function hybridSearch(queryVector, filters) {
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  const index = supabase.storage.vectors.from('embeddings').index('documents-openai')
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  // Get similar vectors with filters
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  const { data: vectorResults } = await index.queryVectors({
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    queryVector: { float32: queryVector },
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    topK: 100,
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    filter: filters,
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    returnDistance: true,
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    returnMetadata: true,
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  })
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  // Get additional details from relational database
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  const { data: details } = await supabase
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    .from('documents')
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    .select('*')
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    .in(
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      'id',
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      vectorResults.vectors.map((v) => v.metadata?.doc_id)
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    )
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  // Merge results
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  return vectorResults.vectors.map((vector) => {
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    const detail = details?.find((d) => d.id === vector.metadata?.doc_id)
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    return {
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      ...vector,
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      ...detail,
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    }
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  })
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}

Real-world examples#

RAG (retrieval-augmented generation)#

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import OpenAI from 'openai'
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import { createClient } from '@supabase/supabase-js'
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async function retrieveContextForLLM(userQuery) {
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  const supabase = createClient(...)
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  const openai = new OpenAI()
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  // 1. Embed the user query
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  const queryEmbedding = await openai.embeddings.create({
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    model: 'text-embedding-3-small',
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    input: userQuery
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  })
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  // 2. Retrieve relevant documents
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  const { data: vectorResults } = await supabase.storage.vectors
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    .from('embeddings')
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    .index('documents-openai')
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    .queryVectors({
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      queryVector: { float32: queryEmbedding.data[0].embedding },
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      topK: 5,
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      returnMetadata: true
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    })
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  // 3. Use vectors to augment LLM prompt
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  const context = vectorResults.vectors
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    .map(v => v.metadata?.content || '')
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    .join('\n\n')
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  const response = await openai.chat.completions.create({
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    model: 'gpt-4',
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    messages: [
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      {
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        role: 'system',
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        content: `Use the following context to answer the user's question:\n\n${context}`
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      },
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      {
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        role: 'user',
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        content: userQuery
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      }
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    ]
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  })
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  return response.choices[0].message.content
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}

Product recommendations#

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async function recommendProducts(userEmbedding, topK = 5) {
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  const supabase = createClient(...)
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  // Find similar products
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  const { data } = await supabase.storage.vectors
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    .from('embeddings')
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    .index('products-openai')
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    .queryVectors({
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      queryVector: { float32: userEmbedding },
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      topK,
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      filter: {
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        in_stock: true
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      },
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      returnMetadata: true
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    })
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  return data.vectors.map((result) => ({
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    id: result.metadata?.product_id,
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    name: result.metadata?.name,
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    price: result.metadata?.price,
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    similarity: 1 - result.distance
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  }))
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}

Filtering before similarity search#

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// Use metadata filters to reduce search scope
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const { data } = await index.queryVectors({
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  queryVector,
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  topK: 100,
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  filter: {
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    category: 'electronics', // Pre-filter by category
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  },
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})