| layout |
title |
description |
feature_area_category_name |
feature_area_solution_name |
how_to_get_started |
button_stack |
redesign-use-case |
Vector Database Use Cases |
OpenSearch as a vector database supports a range of applications. Following are a few examples of solutions you can build. |
Search |
Vector Database |
You can begin exploring OpenSearch's vector database functionality by downloading your preferred distribution. To learn more or start a discussion, join the Slack channel or check out our user forum and follow our blog for the latest on OpenSearch tools. |
|
|
{::comment}
Implementation note: HTML tables are used instead of markdown, because markdown
does not support the use of colspan which is needed to make all first columns
across tables the same width without worrying about the length of the header text.
{:/comment}
| Search |
| Visual search |
Create applications that allow users to take a photograph and search for similar images without having to manually tag images. |
| Semantic search |
Enhance search relevancy by powering vector search with text embedding models that capture semantic meaning and use hybrid scoring to blend term frequency models (BM25) for improved results. |
| Multimodal search |
Use state-of-the-art models that can fuse and encode text, image, and audio inputs to generate more accurate digital fingerprints of rich media and enable more relevant search and insights. |
| Generative AI agents |
Build intelligent agents with the power of generative AI while minimizing hallucinations by using OpenSearch to power retrieval augmented generation (RAG) workflows with large language models (LLMs). (Whether you refer to them as chatbots, automated conversation entities, question answering bots, or something else, OpenSearch’s vector database functionality can help them deliver better results). |
| Personalization |
| Recommendation engine |
Generate product and user embeddings using collaborative filtering techniques and use OpenSearch to power your recommendation engine. |
| User-level content targeting |
Personalize web pages by using OpenSearch to retrieve content ranked by user propensities using embeddings trained on user interactions. |
| Data Quality |
| Automate pattern matching and de-duplication |
Use similarity search for automating pattern matching and duplicates in data to facilitate data quality processes. |
| Vector database engine |
| Data and machine learning platforms |
Build your platform with an integrated, Apache 2.0-licensed vector database that provides a reliable and scalable solution to operationalize embeddings and power vector search. |