Using semantic_text new feature, and AWS Bedrock as inference endpoint service

Steps

Creating Endpoints

Embeddings Task

Completion Task

Creating Mappings

Indexing data

Asking questions

Strategy 1: API Calls

Retrieving chunks

Answering the question

Strategy 2: Playground

Conclusion

semantic_text with Amazon Bedrock

Elasticsearch open inference API adds support for embeddings generated from models hosted on Amazon Bedrock."

Using a Base Model in Amazon Bedrock

Creating an Inference API Endpoint in Elasticsearch

Let’s Perform Some Inference

That Was Easy. What Else Can We Do?

Limitless Possibilities

Elasticsearch open inference API adds Amazon Bedrock support

Ingest and search complex proprietary documents with Unstructured and Elasticsearch vector database for RAG applications

High level flow

Search complex documents using Unstructured.io and Elasticsearch vector database

Build a RAG system with Llama3 open source and Elastic.

Building RAG with Llama 3 open-source and Elastic

Dataset

Configure Ollama and Llama3

Elasticsearch Setup

Application Setup

Method 1: Elastic, Llamaindex, Llama 3 (8B) version running locally using Ollama.

_Step 1 : Install Required Dependencies._

_Step 2: Import required dependencies_

_Step 3: Document Processing_

_Step 4: LLM Configuration_

_Step 5: Semantic Search_

Method 2: Elastic, Langchain, ELSER v2, Llama 3 (8B) version running locally using Ollama.

Elasticsearch RAG: How to build RAG with Llama 3 open-source and Elastic

Learn how LangChain and Elasticsearch can accelerate your speed of innovation in the LLM and GenAI space.

Rapid RAG prototyping

LangChain integration package

A simple RAG application

Full retrieval flexibility through the Retriever

LangChain and Elastic collaborate to add vector database and semantic reranking for RAG

With LocalAI, you can compute text embeddings in air-gapped systems. GPU support is available. Here's how to set up LocalAI to compute embeddings for your data.

Introduction

How to set up LocalAI to compute embeddings for your data

Step 1: Set up LocalAI with docker-compose

Step 2: Configure LocalAI to use Sentence Transformers models

Step 3: Start the LocalAI server

Step 4: Create Elasticsearch `_inference` service

LocalAI is a free and open-source inference server compatible with the OpenAI API. It supports model inference using multiple backends, including Sentence Transformers for embedding and llama.cpp for text generation. LocalAI also supports GPU acceleration, so you can compute embeddings faster.

What is LocalAI?

LocalAI for GPU-powered text embeddings in air-gapped systems

How to set up LocalAI for GPU-powered text embeddings in air-gapped environments

Explore how to use the Elasticsearch JavaScript client and TypeScript support to craft ES|QL queries and handle their results as native JavaScript objects.

Implementation: ES|QL queries to TypeScript types with the Elasticsearch JavaScript client

ES|QL queries to TypeScript types

ES|QL queries to TypeScript types with the Elasticsearch JavaScript client

Explore how NVIDIA NIM enhances applications with NLP capabilities and learn how to integrate NVIDIA NIM with Elasticsearch.

NVIDIA NIM

NVIDIA API key

Integrating NVIDIA NIM with Elastic using LangChain: RAG example

Creating a meetup calculator with NVIDIA NIM, Elasticsearch & GenAI

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

Using NVIDIA NIM with Elasticsearch vector store

Explore how to quickly set up and ingest data into Elasticsearch for use as a vector database with Azure OpenAI On Your Data, enabling you to chat with your private data.

Background

Pre-requisites:

Spinning up the Elasticsearch Cluster

Ingesting the Data

Connecting Azure OpenAI On Your Data to Elasticsearch

Where to go from here?

Azure OpenAI On Your Data: How to use Elasticsearch as the Vector Database

Using Elasticsearch as a vector database for Azure OpenAI On Your Data

How to protect sensitive and PII data in a RAG application with Elasticsearch and LlamaIndex.

Terminology

RAG and Data Protection

Quick recap

RAG with a public LLM

Prerequisites

Simple RAG Application

Indexing Data

Querying

Masking PII in RAG

Using NLP Libraries

Using the LlamaIndex out-of-the-box `NERPIINodePostprocessor`

Using Local LLMs via `PIINodePostprocessor`

Run Mistral locally

RAG: How to protect sensitive and PII info with Elasticsearch & LlamaIndex

Protecting Sensitive and PII information in RAG with Elasticsearch and LlamaIndex

Integrations