DocuSign and Elastic supercharge generative contract and search solutions
How does Elastic ensure flexibility and developer choice in AI models?
Elastic ensures flexibility and developer choice in AI models by allowing access to third-party PyTorch models, including those available on the HuggingFace model hub6. This enables developers to tap into a wide range of pre-trained models and use them within Elasticsearch6. Additionally, Elastic provides an open platform where developers can use a variety of tools and consistent standards, accelerating generative AI projects2.
What is the Elasticsearch Relevance Engine (ESRE)?
The Elasticsearch Relevance Engine (ESRE) is a collection of tools from Elastic that combines machine learning models, data transformation and storage (including vectors), and data search and retrieval1. ESRE includes features for data security and integration with other software, such as large language models (LLMs).
How does generative AI enhance enterprise search?
Generative AI enhances enterprise search by addressing the limitations of traditional keyword-based systems5. It leverages semantic search and natural language processing to better understand context and provide more accurate results5. Additionally, it offers retrieval augmented generation (RAG) capabilities, permissions, faceted search, hybrid search, and the ability to use multiple search techniques, improving overall search efficiency and effectiveness.