Learn how to implement image search with an example. This blog covers how to use vector search through image processing in Elasticsearch.

Case study: Finding your puppy with image search

The architecture

Download the code zip file and install the necessary packages

Util class

Dog class

Attributes

Methods

DogRepository Class

DogService Class

The main code

Phase 01: Registering the Puppy

Start registering the puppies

Registering Luigi

Registering all the others puppies

Visualizing the new dogs

Phase 02: Finding the lost dog

Get the results

Happy end

Conclusion

Resources

Implementing image search with Elasticsearch

Implementing image search: vector search via image processing in Elasticsaerch

Explore how NVIDIA NIM enhances applications with natural language processing capabilities. NVIDIA NIM offers features such as in-flight batching, which not only speeds up request processing but also integrates seamlessly with Elasticsearch to boost data indexing and search functionalities.

Introduction

NVIDIA NIM - A general explanation before you start

NVIDIA API KEY

Integrating NVIDIA NIM with Elastic using LangChain: A Example with RAG

Creating a Meetup Calculator

Initial Setup

Environment Variables

Meetup Scenario:

Store in Elasticsearch vector database

Using a model from NVIDIA NIM microservices

Creating a RAG with Elastic using LangChain

Response Chain Execution

References

NVIDIA & Elasticsearch: How to use NVIDIA NIM with Elasticsearch vector store

Using NVIDIA NIM with Elasticsearch vector store

Author

Alex Salgado

Articles

Implementing image search: vector search via image processing in Elasticsaerch

Using NVIDIA NIM with Elasticsearch vector store