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Resources

Evolution of the Elasticsearch .NET Client

Learn how OpenAI chat completions and Elasticsearch can be used to summarize, translate or perform question & answering on any text.

The Essence of Continuous Innovation at Elastic

Using the new completion API

Exciting future ahead

Elasticsearch open inference API adds support for OpenAI chat completions

Learn to implement a RAG system using LlamaIndex, Elasticsearch and locally running Mistral.

Terminology

What are we going to build ?

High level flow

Steps in building the RAG application

Run Mistral locally

Install Elasticsearch

RAG Application

Indexing Data

Create IngestionPipeline

Querying

Some Caveats:

What’s Next ?

RAG (Retrieval Augmented Generation) with LlamaIndex, Elasticsearch and Mistral

Learn more about how to use Cohere embeddings with Elastic built search experiences!

Elastic's approach to shipping: frequent, production ready iterations

Elasticsearch vector database for Cohere embeddings

Elastic kNN search for Cohere vector embeddings

Elasticsearch open inference API adds support for Cohere Embeddings

“Elasticsearch integrates semantic reranking with Cohere’s Rerank models, with the inclusion of Rerank into our open Inference API.”

Elastic’s approach to hybrid search

Cohere’s approach to reranking

Building a RAG implementation with Elasticsearch and Cohere

Elasticsearch vector database

Cohere reranking

Elasticsearch’s accelerated roadmap to semantic reranking and retrievers

Cohere’s generative model capabilities

Elasticsearch open Inference API adds support for Cohere’s Rerank 3 model

How to execute and manage ES|QL queries in PHP

ES|QL

Use case

Map into an object or a custom class

From ES|QL to PHP objects

How to export ES|QL queries as Pandas dataframes in Python

From ES|QL to Pandas dataframes in Python

Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.

Limitations of LLMs

Approaches to enhance LLMs

RAG using Elasticsearch and Cohere Command model through Amazon Bedrock

Why Cohere?

Why Elasticsearch?

Why Amazon Bedrock?

Solution overview

Step by step explanation

Conclusion

Generative AI is a type of Artificial Intelligence (AI) that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by Machine Learning (ML) models—very large models that are pre-trained on vast corpora of data and commonly referred to as Foundation Models (FMs).

What is generative AI?

LLMs have certain limitations. One notable constraint is that they are trained on general domain corpora, making them less effective on domain-specific tasks. Also, LLMs are trained offline until a knowledge cutoff date. It will be agnostic of any developments that have happened after the knowledge cutoff date. This may lead to inaccurate interpretations.

What are the limitations of Large Language Models (LLMs)?

RAG using Elasticsearch & Cohere through Amazon Bedrock

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch

Learn to split workplace documents into passages, transform these passages into embeddings in Elasticsearch and integrate Amazon Bedrock LLM.

1. Install packages and import modules

2. Init Amazon Bedrock client

3. Connect to Elasticsearch

4. Download the dataset

5. Split documents into passages

6. Index data into elasticsearch

7. Init Amazon Bedrock LLM

8. Asking a question

Trying it out

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities you need to build generative AI applications, simplifying development while maintaining privacy and security. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

Integrations

ES|QL queries to Java objects