Learn how to implement a plugin and enable ChatGPT users to extend ChatGPT with any content indexed in Elasticsearch, using the Elastic documentation.

What is a ChatGPT plugin?

Architecture

Plugin code

Plugin file structure

Python code

Plugin manifest

OpenAPI definition

Deploying the Elastic plugin in Google Cloud Platform (GCP)

Setup

Build and deploy

Installing the plugin in ChatGPT

Let’s test our plugin!

ChatGPT and Elasticsearch: A plugin to use ChatGPT with your Elastic data

Explore how integrating Elasticsearch as a caching layer optimizes Generative AI performance by reducing token costs and response times, demonstrated through real-world testing and practical examples.

RAG overview

Challenges of generative AI at scale

Elastic as caching layer

Evaluating semantic similarity: tolerance vs. resistance

Semantic tolerance

Semantic resistance

Illustrating semantic similarity with an HR example

Impact of the similarity parameter on semantic tolerance and resistance

Use cases

Testing it out

Wrap-up

 RAG is a highly efficient mechanism in natural language processing. It operates by taking a given prompt or question and retrieving relevant information from a large dataset.

What is Retriever-Augmented Generation (RAG)?

Semantic Tolerance, mirroring Recall, is a concept where the similarity function is evaluated with a broader lens, allowing for a wider range of semantic similarities between questions. This leniency can result in more matches, potentially reducing the computational load on the LLM. However, it may also lead to less precise matches.

What is Semantic Tolerance in the context of Elasticsearch?

Optimizing GenAI: integrate Elasticsearch as a GenAI caching layer

Elasticsearch as a GenAI caching layer

Author

Baha Azarmi

Articles

ChatGPT and Elasticsearch: A plugin to use ChatGPT with your Elastic data

Elasticsearch as a GenAI caching layer