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RAG in production: Operationalize your GenAI project
Generative AI

RAG in production: Operationalize your GenAI project

Retrieval Augmented Generation enables GenAI the ability to answer questions using information that was not part of the model's training dataset, unlocking significant increases in productivity and user experience. In this blog we discuss the considerations necessary to run RAG pipelines in production.

Tim Brophy

Intelligent RAG, Fetch Surrounding Chunks
Generative AIVector Search

Intelligent RAG, Fetch Surrounding Chunks

Explore Fetch Surrounding Chunking, an emerging pattern in RAG that uses intelligent chunking and Elasticsearch vector database to optimize LLM responses. This approach balances data input to enhance the accuracy and relevance of LLM-generated answers through semantic hybrid search.

Sunile Manjee

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

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

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

Max Jakob

How to Set Up LocalAI for GPU-Powered Text Embeddings in Air-Gapped Environments
Generative AIHow ToIntegrations

How to Set Up LocalAI for GPU-Powered Text Embeddings in Air-Gapped Environments

With LocalAI you can compute text embeddings in air-gapped environments. GPU support is available.

Valeriy Khakhutskyy

OpenAI function calling with Elasticsearch
Generative AI

OpenAI function calling with Elasticsearch

Explore OpenAI's function calling capabilities, allowing AI models to interact with external APIs and perform tasks beyond text generation. Learn to implement dynamic function calls, including fetching data from Elasticsearch, enhancing the model's real-time data access and complex operation handling. Discover practical use cases and step-by-step integration in this insightful blog.

Ashish Tiwari

Using NVIDIA NIM with Elasticsearch vector store
Generative AIIntegrationsHow To

Using NVIDIA NIM with Elasticsearch vector store

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.

Alex Salgado

Elasticsearch open inference API adds Azure AI Studio support
IntegrationsHow ToGenerative AIVector Search

Elasticsearch open inference API adds Azure AI Studio support

Elasticsearch open inference API adds support for embeddings generated from models hosted on Azure AI Studio and completion tasks from large language models such as Meta-Llama-3-8B-Instruct."

Mark Hoy

Elasticsearch open inference API adds support for Azure OpenAI chat completions
IntegrationsHow ToGenerative AI

Elasticsearch open inference API adds support for Azure OpenAI chat completions

Elasticsearch open inference API adds support for Azure Open AI chat completions, providing full developer access to the Azure AI ecosystem

Tim Grein

Elasticsearch delivers performance increase for users running the Elastic Search AI Platform on Arm-based architectures
Vector SearchGenerative AIIntegrations

Elasticsearch delivers performance increase for users running the Elastic Search AI Platform on Arm-based architectures

Benchmarking in preview provides up to 37% better performance on Microsoft Cobalt 100 Arm-based VMs

Yuvraj Gupta

Hemant Malik