Vector Databases

Pinecone

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

const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY! });
const index = pc.index('my-index');

// Upsert
await index.namespace('docs').upsert([
  { id: 'doc-1', values: embedding, metadata: { source: 'manual', topic: 'auth' } },
]);

// Query with metadata filter
const results = await index.namespace('docs').query({
  vector: queryEmbedding,
  topK: 5,
  filter: { topic: { $eq: 'auth' } },
  includeMetadata: true,
});

ChromaDB (local/self-hosted)

import chromadb

client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_or_create_collection(
    name="documents",
    metadata={"hnsw:space": "cosine"},
)

# Add documents (auto-embeds with default model)
collection.add(
    ids=["doc1", "doc2"],
    documents=["Auth guide content", "API reference content"],
    metadatas=[{"source": "manual"}, {"source": "api"}],
)

# Query
results = collection.query(query_texts=["how does login work?"], n_results=5)

pgvector (PostgreSQL extension)

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT NOT NULL,
  embedding vector(1536),
  metadata JSONB DEFAULT '{}'
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- Similarity search
SELECT id, content, 1 - (embedding <=> $1::vector) AS similarity
FROM documents
WHERE metadata->>'source' = 'manual'
ORDER BY embedding <=> $1::vector
LIMIT 5;

Node.js with pgvector

import pgvector from 'pgvector';

await pgvector.registerTypes(pool);

await pool.query(
  'INSERT INTO documents (content, embedding) VALUES ($1, $2)',
  [text, pgvector.toSql(embedding)]
);

const { rows } = await pool.query(
  'SELECT *, 1 - (embedding <=> $1) AS similarity FROM documents ORDER BY embedding <=> $1 LIMIT $2',
  [pgvector.toSql(queryEmbedding), 5]
);

Qdrant

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

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

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

// Upsert
await client.upsert('documents', {
  points: [{ id: 1, vector: embedding, payload: { source: 'manual' } }],
});

// Search with filter
const results = await client.search('documents', {
  vector: queryEmbedding,
  limit: 5,
  filter: { must: [{ key: 'source', match: { value: 'manual' } }] },
});

Embedding Generation

import OpenAI from 'openai';

const openai = new OpenAI();

async function embed(texts: string[]): Promise<number[][]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small', // 1536 dims, cheapest
    input: texts,
  });
  return response.data.map((d) => d.embedding);
}

Anti-Patterns

Anti-Pattern	Fix
No metadata filtering	Always store filterable metadata with vectors
Wrong distance metric	Match metric to embedding model (cosine for OpenAI)
Embedding model mismatch	Same model for indexing and querying
No batching on upsert	Batch upserts (100-1000 vectors per call)
Storing raw text in vector DB	Store text in primary DB, only IDs + vectors in vector DB

Production Checklist

• Embedding model locked (changing requires full re-index)
• Batch upserts with error handling
• Metadata schema documented
• Index type configured (HNSW for most cases)
• Backup strategy for vector data
• Monitoring: query latency, index size, recall metrics