Choosing the Right Vector Database for RAG and AI Applications

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Choosing the Right Vector Database for RAG and AI Applications

Vipin Vashisth Last Updated : 08 Jun, 2026

22 min read

Modern AI applications rely on understanding meaning rather than matching keywords. As large language models, semantic search, and RAG systems have become mainstream, vector databases have emerged as critical infrastructure for storing and retrieving high-dimensional embeddings at scale.

Choosing the right vector database can have a major impact on performance, scalability, cost, and developer experience. In this article, we’ll compare six leading vector databases like Pinecone, Weaviate, Qdrant, Milvus, pgvector, and ChromaDB, to help you identify the best fit for your use case.

Table of contents

Understanding Vector Databases

How Vector Search Works

Why Traditional Databases Struggle with Semantic Search

Quick Comparison

Creating a Sample Dataset

Performance Comparison

Conclusion

Understanding Vector Databases

Before looking at particular databases, you have to get what vector databases are, and also why they even matter. Traditional databases keep structured rows and columns. Vector databases, on the other hand, store something that feels way more abstract mathematical patterns of meaning, often called embeddings.

What Is a Vector Database?

A vector database is basically a specialized storage system built to store and also query high-dimensional vector data. Imagine a vector as one long arrangement of numbers. For example, [0.12, -0.45, 0.78, …] , it is encoding the semantic substance of a certain piece of content. One sentence can turn into a vector with 384 values, or sometimes 1536, depending on the model.

Then, when you end up with many, many thousands of those vectors, you can’t just brute force search them every time. You need an efficient method to quickly retrieve the vectors that are most similar to some question or input. And that’s basically what a vector database is for. It arranges vectors so that nearest-neighbor lookups run extremely fast.

What Are Embeddings?

Embeddings are kind of numerical representations of data that are generated by machine learning models, sort of outputs. An embedding model takes in your text image, or audio and then spits out a fixed-size array of floats. Those numbers, they end up reflecting semantic relationships. Like, “king” and “queen” would end up with embeddings that are nearer to each other than “king” and “bicycle”.

Some of the most widely used embedding models are:

OpenAI text-embedding-3-small: 1536 dimensions, excellent quality

sentence-transformers/all-MiniLM-L6-v2: 384 dimensions, free and fast

Cohere embed-v3: 1024 dimensions, great multilingual support

Google text-embedding-004: 768 dimensions, strong general-purpose model

The embedding model you end up choosing really impacts the quality of your vector search, no joke. For best results always stick with the same model for indexing and also for querying, otherwise things can drift.

How Vector Search Works

Vector search is basically about finding items that are semantically similar to a query. You take your query, convert it into a vector, and then ask the database to look for stored vectors that are “close” to it. The database typically relies on approximate nearest-neighbor algorithms, or ANN, so it can locate good matches without having to scan every single vector one by one

Three main similarity measures power vector search are:

Cosine Similarity: Cosine similarity looks at the angle between two vectors. A score of 1 means they’re pointing in the same direction, which is close to identical. 0 signals they’re unrelated. -1 they’re opposite, kind of negating each other. cosine_similarity(A, B) = (A · B) / (||A|| × ||B||)

Euclidean Distance: Euclidean distance measures the straight line distance between two points in a high dimensional space. euclidean_distance(A, B) = √(Σ(Aᵢ – Bᵢ)²)

Dot Product Similarity: Dot product multiplies corresponding elements, then adds them together. In practice a higher dot product usually suggests stronger resemblance or greater similarity. dot_product(A, B) = Σ(Aᵢ × Bᵢ)

Why Traditional Databases Struggle with Semantic Search

SQL databases like PostgreSQL and MySQL are pretty great at exact queries: WHERE name = 'John' or WHERE price L2 (Euclidean) Raw distance matters

Cosine Direction matters more than magnitude

Inner product (negative) Vectors are normalized

Vector Indexes: On top of that, pgvector supports two index types for speeding up similarity queries:

IVFFlat: it cuts the vector space into cells and searches only in nearby ones. it’s quicker to build, but you usually get slightly lower recall.

HNSW: it’s a graph based index with better recall and typically faster queries. it takes longer to build, and it tends to use more memory.

Getting Started with pgvector

Installing the Extension

pip install psycopg2-binary pgvector sentence-transformers

import psycopg2 from pgvector.psycopg2 import register_vector

Connect to PostgreSQL

conn = psycopg2.connect( host="localhost", port=5432, database="vector_demo", user="postgres", password="postgres", )

cur = conn.cursor()

Register the vector type

register_vector(conn)

Enable the pgvector extension

cur.execute("CREATE EXTENSION IF NOT EXISTS vector;") conn.commit()

print("Connected to PostgreSQL") print("pgvector extension enabled")

Check PostgreSQL and pgvector versions

cur.execute("SELECT version();") pg_version = cur.fetchone()[0]

cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector';") pgv_version = cur.fetchone()[0]

print(f"PostgreSQL: {pg_version.split(',')[0]}") print(f"pgvector : v{pgv_version}")

Output:

Connected to PostgreSQL

pgvector extension enabled

PostgreSQL: PostgreSQL 16.2 on x86_64-pc-linux-gnu

pgvector: v0.7.2

Creating Vector Columns

Create the articles table with a vector column

cur.execute("DROP TABLE IF EXISTS ai_articles;")

cur.execute( """ CREATE TABLE ai_articles ( id SERIAL PRIMARY KEY, doc_id TEXT NOT NULL, title TEXT NOT NULL, body TEXT NOT NULL, category TEXT, author TEXT, year INTEGER, rating FLOAT, embedding vector(384) ); """ )

Create an HNSW index on the embedding column

cur.execute( """ CREATE INDEX ON ai_articles USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64); """ )

conn.commit()

print("Table 'ai_articles' created with vector(384) column") print("HNSW index created on embedding column with cosine distance")

Output:

Table 'ai_articles' created with vector(384) column HNSW index created on embedding column (cosine distance)

Inserting Embeddings

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

print("Inserting documents into PostgreSQL...\n")

for doc in documents: embedding = model.encode(doc["text"]) # Returns a NumPy array

cur.execute( """ INSERT INTO ai_articles ( doc_id, title, body, category, author, year, rating, embedding ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s) """, ( doc["id"], doc["title"], doc["text"], doc["category"], doc["author"], doc["year"], doc["rating"], embedding.tolist(), ), )

print(f"Inserted: {doc['id']} — {doc['title']}")

conn.commit()

Verify count

cur.execute("SELECT COUNT(*) FROM ai_articles;") count = cur.fetchone()[0]

print(f"\nTotal rows inserted: {co