Jeff Vestal
Principal Customer Enterprise Architect
Jeff Vestal is Sr. Systems Engineer at E*Trade working in the Real-Time Data Pipeline group. He led the initiative to setup elastic’s Machine Learning and created an elastic slackbot integration.
Over ten years ago he started working with individual prop traders and market makers then moving to commercial online brokerages where the focus is bringing the best trading experience to hundreds of thousands of customers. Throughout, he has worked to ensure all types of data, financial transaction, performance metrics, and logs, get to their destination on-time while creating robust monitoring and alerting to allow for quick notification, response, and resolution when incidents occur.
Your search index is already an agent memory system: Persistent agent memory for Claude Code with Elasticsearch
Give your AI agent persistent cross-session memory using Elasticsearch: Hybrid recall, a knowledge graph, and cross-device handoffs. Three commands to install.
Fast vs. accurate: Measuring the recall of quantized vector search
Explaining how to measure recall for vector search in Elasticsearch with minimal setup.
Your first Elastic Agent: From a single query to an AI-powered chat
Learn how to use Elastic’s AI Agent builder to create specialized AI agents. In this blog, we'll be building a financial AI Agent.
ChatGPT and Elasticsearch revisited: Part 2 - The UI abides
This blog expands on Part 1 by introducing a fully functional web UI for our RAG-based search system. By the end, you'll have a working interface that ties the retrieval, search, and generation process together—while keeping things easy to tweak and explore.
Semantic search using the Open Crawler and Semantic Text
Learn how to use the Open Crawler in combination with Semantic Text to easily crawl web sites and make them semantically searchable.
Reranking with an Elasticsearch-hosted cross-encoder from Hugging Face
Learn how to use a model from Hugging Face to host and perform semantic-reranking in Elasticsearch.
Quickly create RAG apps with Vertex AI Gemini models and Elasticsearch playground
Quickly create a RAG app with Vertex AI Gemini models and Elasticsearch playground
Elasticsearch open inference API for Google AI Studio
Elasticsearch open inference API adds support for Google AI Studio
ChatGPT and Elasticsearch revisited: Building a chatbot using RAG
Learn how to create a chatbot using ChatGPT and Elasticsearch, utilizing all of the newest RAG features.
Introducing Retrievers - Search All the Things!
Learn about Elasticsearch retrievers, including Standard, kNN, text_expansion, and RRF. Discover how to use retrievers with examples.
Elastic Cloud adds Elasticsearch Vector Database optimized profile to Microsoft Azure
Elasticsearch added a new vector search optimized profile to Elastic Cloud on Microsoft Azure. Get started and learn how to use it here.
RAG & RBAC integration: Protect data and boost AI capabilities
Discover how Retrieval Augmented Generation (RAG) & Role-Based Access Control (RBAC) integrate to protect data and boost AI capabilities.
Elastic Cloud adds Elasticsearch Vector Database optimized instance to Google Cloud
Elasticsearch's vector search optimized profile for GCP is available. Learn more about it and how to use it in this blog.
Vector search & kNN implementation guide - API edition
Learn how to implement vector search and kNN using the Elasticsearch APIs via HTTP or Python.
Elasticsearch as a GenAI caching layer
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.
The generative AI societal shift: Elastic's Gen AI & LLMs journey
Learn how Elastic is at the forefront of the generative AI & Large Language Models (LLMs) revolution, helping users take LLMs to new...
ChatGPT and Elasticsearch: OpenAI meets private data
Integrate Elasticsearch's search relevance with ChatGPT's question-answering capability to enhance your domain-specific knowledge base.
Elasticsearch and LangChain: unlocking the potential of large language models (LLMs)
Explore the synergy between LangChain and Elasticsearch and how they are pushing the boundaries of what's possible with large language models (LLMs).