vector-databases

name	vector-databases
description	Use when integrating vector databases for semantic search, embeddings storage, or RAG systems. Covers Pinecone, Qdrant, Chroma, pgvector, and Weaviate with TypeScript examples.

Vector Databases

Options Overview

DB	Best for	Hosting
Pinecone	Production, fully managed, simple API	Cloud only
Qdrant	Self-hosted, rich filtering, open source	Self-hosted / Cloud
Chroma	Local dev, prototyping, embedded	Local / Self-hosted
pgvector	Already using PostgreSQL	Self-hosted (Postgres ext)
Weaviate	GraphQL API, multi-modal	Self-hosted / Cloud

---

Pinecone

bun add @pinecone-database/pinecone

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY! })

// Create index (one-time setup)
await pc.createIndex({
  name: 'my-index',
  dimension: 1536, // match your embedding model
  metric: 'cosine',
  spec: { serverless: { cloud: 'aws', region: 'us-east-1' } },
})

const index = pc.index('my-index')

// Upsert vectors
await index.upsert([
  { id: 'doc-1', values: embeddingArray, metadata: { text: 'content', source: 'file.pdf' } },
  { id: 'doc-2', values: embeddingArray2, metadata: { text: 'more content' } },
])

// Query
const results = await index.query({
  vector: queryEmbedding,
  topK: 5,
  includeMetadata: true,
  filter: { source: { $eq: 'file.pdf' } }, // metadata filtering
})

// Delete
await index.deleteOne('doc-1')
await index.deleteMany({ filter: { source: { $eq: 'old-file.pdf' } } })

---

Qdrant

# Run locally
docker run -p 6333:6333 qdrant/qdrant

bun add @qdrant/js-client-rest

import { QdrantClient } from '@qdrant/js-client-rest'

const client = new QdrantClient({ url: 'http://localhost:6333' })

// Create collection
await client.createCollection('my-collection', {
  vectors: { size: 1536, distance: 'Cosine' },
})

// Upsert points
await client.upsert('my-collection', {
  points: [
    { id: 1, vector: embeddingArray, payload: { text: 'content', category: 'tech' } },
  ],
})

// Search
const results = await client.search('my-collection', {
  vector: queryEmbedding,
  limit: 5,
  filter: {
    must: [{ key: 'category', match: { value: 'tech' } }],
  },
  with_payload: true,
})

results.forEach(r => console.log(r.score, r.payload?.text))

---

Chroma (Local Dev)

pip install chromadb  # server
bun add chromadb      # client

import { ChromaClient } from 'chromadb'

const client = new ChromaClient()

// Get or create collection
const collection = await client.getOrCreateCollection({ name: 'docs' })

// Add documents (Chroma handles embedding if you configure an embedding function)
await collection.add({
  ids: ['id1', 'id2'],
  documents: ['First document text', 'Second document text'],
  metadatas: [{ source: 'file1.txt' }, { source: 'file2.txt' }],
})

// Or add pre-computed embeddings
await collection.add({
  ids: ['id3'],
  embeddings: [embeddingArray],
  documents: ['Third document'],
})

// Query
const results = await collection.query({
  queryEmbeddings: [queryEmbedding],
  nResults: 5,
})

---

pgvector (PostgreSQL)

-- Enable extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create table
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT NOT NULL,
  embedding vector(1536),
  metadata JSONB
);

-- Create index for fast ANN search
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
-- Or HNSW (better recall):
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- Insert
INSERT INTO documents (content, embedding) VALUES ('text', '[0.1, 0.2, ...]'::vector);

-- Cosine similarity search
SELECT id, content, 1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 5;

// With Drizzle ORM
import { vector } from 'drizzle-orm/pg-core'

export const documents = pgTable('documents', {
  id: serial('id').primaryKey(),
  content: text('content').notNull(),
  embedding: vector('embedding', { dimensions: 1536 }),
})

---

Embedding Models

Model	Dimensions	Best for
text-embedding-3-small	1536	Cost-efficient, OpenAI
text-embedding-3-large	3072	Best quality, OpenAI
nomic-embed-text	768	Free, local via Ollama
mxbai-embed-large	1024	Strong open source, local

Distance Metrics

• Cosine → most common for text (measures angle, ignores magnitude)
• Dot product → fast, use when vectors are normalized
• Euclidean → for spatial/image embeddings