Explore the following tutorials to learn about implementing vector search applications using the OpenSearch vector database. For more information about using OpenSearch as a vector database, see Vector search.
Vector search 101
Getting started with vector search
Learn how to run a raw vector search
Getting started with semantic and hybrid search
Build your first AI search application
AI search types
Semantic search
Understands the meaning and intent behind a query to deliver more relevant results
Hybrid search
Improves relevance by combining keyword-based and semantic search techniques
Multimodal search
Enables searching across different types of data, such as text and images
Neural sparse search
Uses sparse vector representations and deep learning models for efficient retrieval
Conversational search with RAG
Combines natural dialogue with retrieval-augmented generation to provide contextual answers
Vector search applications
Vector operations
Learn how to generate embeddings and optimize vector storage
Semantic search
Implement semantic search using various machine learning models
Using semantic highlighting
Learn how to highlight the most semantically relevant sentences in the results