Vector Database
Elasticsearch open Inference API adds support for AlibabaCloud AI Search
Discover how to use Elasticsearch vector database with AlibabaCloud AI Search, which offers inference, reranking, and embedding capabilities.
Understanding BSI IT Grundschutz: A recipe for GenAI powered search on your (private) PDF treasure
An easy approach to create embeddings for and apply semantic GenAI powered search (RAG) to documents as part of the BSI IT Grundschutz using Elastic's new semantic_text field type and the Playground in Elastic.
Elasticsearch open inference API adds native chunking support for Hugging Face
Elasticsearch open inference API extends support for models from Hugging Face, and brings native chunking to Hugging Face users
Unlocking multilingual insights: translating datasets with Python, LangChain, and Vector Database
Learn how to translate a dataset from one language to another and use Elastic's vector database capabilities to gain more insights.
A tutorial on building local agent using LangGraph, LLaMA3 and Elasticsearch vector store from scratch
This article will provide a detailed tutorial on implementing a local, reliable agent using LangGraph, combining concepts from Adaptive RAG, Corrective RAG, and Self-RAG papers, and integrating Langchain, Elasticsearch Vector Store, Tavily AI for web search, and LLaMA3 via Ollama.
Looking back: A timeline of vector search innovations
Looking back at Elastic's vector search innovations in Elasticsearch and Lucene
Vector embeddings made simple with the Elasticsearch-DSL client for Python
Learn how to ingest and search dense vectors in Python using the Elasticsearch-DSL client.
Advanced RAG Techniques Part 2: Querying and Testing
Discussing and implementing techniques which may increase RAG performance. Part 2 of 2, focusing on querying and testing an advanced RAG pipeline.
Advanced RAG Techniques Part 1: Data Processing
Discussing and implementing techniques which may increase RAG performance. Part 1 of 2, focusing on the data processing and ingestion component of an advanced RAG pipeline.
Smart ordering system with Phi-3 small models and Elastic
Deploying Phi-3 models on Azure AI Studio and using them with Elastic Open Inference Service to create a RAG application.