Generative AI
Search complex documents using Unstructured.io and Elasticsearch vector database
Ingest and search complex proprietary documents with Unstructured and Elasticsearch vector database for RAG applications
How Generative AI will transform web accessibility
An experiment inspired by Be My Eyes and OpenAI to experiment with using Chat GPT 4o for web accessibility
Playground: Experiment with RAG applications with Elasticsearch in minutes
Playground is a low code interface for developers to explore grounding LLMs of their choice with their own private data, in minutes.
Elasticsearch vs. OpenSearch: Vector Search Performance Comparison
Elasticsearch is out-of-the-box 2x–12x faster than OpenSearch for vector search
Building RAG with Llama 3 open-source and Elastic
Build a RAG system with Llama3 open source and Elastic.
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.
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.
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.
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.
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.