Build a Semantic Search Engine in 20 Minutes: Chroma & Qdrant Tutorial

Build a Semantic Search Engine in 20 Minutes: Chroma & Qdrant Tutorial

💡 What You Will Learn

Build a Semantic Search Engine in 20 Minutes: Chroma & Qdrant Tutorial

📜 Table of Contents

• Vector Database Guide
• Comparison
• Selection guide

Vector Database Guide

Vector databases store embeddings for semantic search. They are essential for RAG applications.

Comparison

import chromadb
client = chromadb.Client()
col = client.create_collection("docs")
col.add(documents=["RAG is retrieval augmented generation"], ids=["doc1"])
results = col.query(query_texts=["What is RAG"], n_results=1)

Selection guide

• Learning/prototyping: Chroma
• Small production: Qdrant
• Large scale: Milvus
• Fully managed: Pinecone