vector-db-setup

name	vector-db-setup
description	Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

Vector Database Setup

Configure vector databases for semantic search and AI applications.

Core Workflow

1. Choose database: Select based on requirements
2. Setup connection: Configure client
3. Generate embeddings: Create vector representations
4. Index documents: Store with metadata
5. Query vectors: Semantic similarity search
6. Optimize: Tune for performance

Database Comparison

Database	Type	Best For	Scaling
Pinecone	Managed	Production, no ops	Automatic
Chroma	Embedded/Server	Development, local	Manual
pgvector	PostgreSQL ext	Existing Postgres	With Postgres
Qdrant	Self-hosted	Full control	Manual
Weaviate	Managed/Self	GraphQL-like API	Both

Embeddings Generation

OpenAI Embeddings

// embeddings/openai.ts
import OpenAI from 'openai';

const openai = new OpenAI();

export async function generateEmbedding(text: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small', // or text-embedding-3-large
    input: text,
  });

  return response.data[0].embedding;
}

export async function generateEmbeddings(texts: string[]): Promise<number[][]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: texts,
  });

  return response.data.map((d) => d.embedding);
}

Batch Processing

// embeddings/batch.ts
const BATCH_SIZE = 100;

export async function batchGenerateEmbeddings(
  texts: string[]
): Promise<number[][]> {
  const embeddings: number[][] = [];

  for (let i = 0; i < texts.length; i += BATCH_SIZE) {
    const batch = texts.slice(i, i + BATCH_SIZE);
    const batchEmbeddings = await generateEmbeddings(batch);
    embeddings.push(...batchEmbeddings);

    // Rate limiting
    if (i + BATCH_SIZE < texts.length) {
      await new Promise((resolve) => setTimeout(resolve, 100));
    }
  }

  return embeddings;
}

Pinecone Setup

Installation & Config

npm install @pinecone-database/pinecone

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

const pinecone = new Pinecone({
  apiKey: process.env.PINECONE_API_KEY!,
});

// Get or create index
export async function getIndex(indexName: string) {
  const indexes = await pinecone.listIndexes();

  if (!indexes.indexes?.find((i) => i.name === indexName)) {
    await pinecone.createIndex({
      name: indexName,
      dimension: 1536, // OpenAI embedding dimension
      metric: 'cosine',
      spec: {
        serverless: {
          cloud: 'aws',
          region: 'us-east-1',
        },
      },
    });

    // Wait for index to be ready
    await new Promise((resolve) => setTimeout(resolve, 60000));
  }

  return pinecone.(indexName);
}

Upsert & Query

// db/pinecone-ops.ts
import { getIndex } from './pinecone';
import { generateEmbedding, generateEmbeddings } from '../embeddings/openai';

const index = await getIndex('my-index');

interface Document {
  id: string;
  content: string;
  metadata: Record<string, any>;
}

// Upsert documents
export async function upsertDocuments(
  documents: Document[],
  namespace = 'default'
) {
  const embeddings = await generateEmbeddings(documents.map((d) => d.content));

  const vectors = documents.map((doc, i) => ({
    id: doc.id,
    values: embeddings[i],
    metadata: {
      content: doc.content,
      ...doc.metadata,
    },
  }));

  // Upsert in batches
  const BATCH_SIZE = ;
   ( i = ; i < vectors.; i += ) {
     batch = vectors.(i, i + );
     index.(namespace).(batch);
  }
}


   () {
   { topK = , namespace = , filter } = options;

   queryEmbedding =  (query);

   results =  index.(namespace).({
    : queryEmbedding,
    topK,
    : ,
    filter,
  });

   results.?.( ({
    : match.,
    : match.,
    : match.?.,
    : match.,
  }));
}


   () {
   index.(namespace).(ids);
}


   () {
   index.(namespace).({ filter });
}

Chroma Setup

Installation & Config

npm install chromadb

// db/chroma.ts
import { ChromaClient, OpenAIEmbeddingFunction } from 'chromadb';

const client = new ChromaClient({
  path: process.env.CHROMA_URL || 'http://localhost:8000',
});

const embedder = new OpenAIEmbeddingFunction({
  openai_api_key: process.env.OPENAI_API_KEY!,
  openai_model: 'text-embedding-3-small',
});

export async function getCollection(name: string) {
  return client.getOrCreateCollection({
    name,
    embeddingFunction: embedder,
    metadata: { 'hnsw:space': 'cosine' },
  });
}

Chroma Operations

// db/chroma-ops.ts
import { getCollection } from './chroma';

const collection = await getCollection('documents');

// Add documents (Chroma generates embeddings)
export async function addDocuments(documents: Document[]) {
  await collection.add({
    ids: documents.map((d) => d.id),
    documents: documents.map((d) => d.content),
    metadatas: documents.map((d) => d.metadata),
  });
}

// Query
export async function query(queryText: string, nResults = 5) {
  const results = await collection.query({
    queryTexts: [queryText],
    nResults,
  });

  return results.ids[0].map((id, i) => ({
    id,
    content: results.?.[][i],
    : results.?.[][i],
    : results.?.[][i],
  }));
}


   () {
   results =  collection.({
    : [queryText],
    nResults,
    : filter,
  });

   results;
}


   () {
   collection.({
    : [id],
    : [content],
    : metadata ? [metadata] : ,
  });
}


   () {
   collection.({ ids });
}

pgvector Setup

Installation

npm install pg pgvector

-- Enable extension
CREATE EXTENSION vector;

-- Create table
CREATE TABLE documents (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content TEXT NOT NULL,
  metadata JSONB,
  embedding vector(1536),
  created_at TIMESTAMP DEFAULT NOW()
);

-- Create index for similarity search
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

-- Or use HNSW (better for production)
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

pgvector Operations

// db/pgvector.ts
import { Pool } from 'pg';
import pgvector from 'pgvector/pg';

const pool = new Pool({
  connectionString: process.env.DATABASE_URL,
});

// Register pgvector type
await pgvector.registerType(pool);

// Insert document
export async function insertDocument(
  content: string,
  embedding: number[],
  metadata?: Record<string, any>
) {
  const result = await pool.query(
    `INSERT INTO documents (content, embedding, metadata)
     VALUES ($1, $2, $3)
     RETURNING id`,
    [content, pgvector.toSql(embedding), metadata]
  );

  return result.rows[0].id;
}

// Similarity search
export async function searchSimilar(
  queryEmbedding: number[],
  limit = 5,
  threshold = 0.7
) {
  const result =  pool.(
    ,
    [pgvector.(queryEmbedding), threshold, limit]
  );

   result.;
}


   () {
   result =  pool.(
    ,
    [pgvector.(queryEmbedding), filter, limit]
  );

   result.;
}


   () {
   result =  pool.(
    ,
    [pgvector.(queryEmbedding), textQuery, limit]
  );

   result.;
}

Qdrant Setup

npm install @qdrant/js-client-rest

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

const client = new QdrantClient({
  url: process.env.QDRANT_URL,
  apiKey: process.env.QDRANT_API_KEY,
});

// Create collection
export async function createCollection(name: string) {
  await client.createCollection(name, {
    vectors: {
      size: 1536,
      distance: 'Cosine',
    },
  });
}

// Upsert points
export async function upsertPoints(
  collection: string,
  points: Array<{
    id: string;
    vector: number[];
    payload: Record<string, any>;
  }>
) {
  await client.upsert(collection, {
    points: points.map((p) => ({
      id: p.id,
      vector: p.,
      : p.,
    })),
  });
}


   () {
   results =  client.(collection, {
    vector,
    limit,
    : filter
      ? {
          : .(filter).( ({
            key,
            : { value },
          })),
        }
      : ,
    : ,
  });

   results;
}

Document Processing Pipeline

// pipeline/ingest.ts
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { generateEmbeddings } from '../embeddings/openai';
import { upsertDocuments } from '../db/pinecone-ops';

interface RawDocument {
  id: string;
  content: string;
  source: string;
  metadata?: Record<string, any>;
}

export async function ingestDocuments(documents: RawDocument[]) {
  const splitter = new RecursiveCharacterTextSplitter({
    chunkSize: 1000,
    chunkOverlap: 200,
  });

  const chunks: Array<{
    id: string;
    content: string;
    metadata: Record<string, any>;
  }> = [];

  for (const doc of documents) {
    const splits =  splitter.(doc.);

    splits.( {
      chunks.({
        : ,
        : text,
        : {
          : doc.,
          : doc.,
          : index,
          ...doc.,
        },
      });
    });
  }

  
   (chunks);

   { : chunks. };
}

Best Practices

1. Choose the right dimension: Match embedding model
2. Use namespaces: Organize data logically
3. Add metadata: Enable filtering
4. Batch operations: Reduce API calls
5. Index appropriately: HNSW for speed, IVF for memory
6. Monitor performance: Track latency and recall
7. Cache embeddings: Avoid regenerating
8. Use hybrid search: Combine vector and keyword

Output Checklist

Every vector database setup should include:

• Database client configured
• Embedding generation function
• Collection/index creation
• Document upsert with metadata
• Similarity search function
• Filtered search capability
• Batch processing for large datasets
• Delete/update operations
• Error handling
• Performance monitoring