Get started with search

Serverless Stack

This guide helps you get started with search use cases using Elasticsearch, regardless of your deployment type. Whether you're using a self-managed cluster, Elastic Cloud Hosted, or an Elasticsearch serverless project, these core search primitives are available to you.

To follow along with this guide, you'll need:

• An Elasticsearch cluster, or any type of serverless project. Create a deployment or project if you don't have one yet.
• Connection details for your cluster. Refer to Connection details for more information.

Choose a quickstart based on your use case:

• Index basics: Learn how to create indices, add documents, and perform basic searches using the Elasticsearch APIs.
• Keyword search with Python: Build a keyword search application using the Python client.
• Semantic search: Implement semantic search using vector embeddings.

Once you're comfortable with the basics, explore different search techniques:

• Full-text search: Traditional text-based search with analysis and scoring.
• Vector search: Similarity search using dense or sparse vectors.
• Semantic search: Context-aware search using machine learning models.
• Hybrid search: Combine multiple search techniques for better results.

• Learn about ingestion and data preparation
• Explore querying options including Query DSL, ES|QL, and retrievers
• Understand ranking and relevance to improve search quality
• Build RAG (Retrieval Augmented Generation) applications

• Elasticsearch API documentation
• Elasticsearch reference documentation
• Query languages