Vector Database
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.
Building multilingual RAG with Elastic and Mistral
Building a multilingual RAG application using Elastic and Mixtral 8x22B model
Mistral AI embedding models now available via Elasticsearch Open Inference API
Learn more about how to use Mistral embeddings with Elastic built search experiences!
Introducing the sparse vector query: Searching sparse vectors with inference or precomputed query vectors
Learn about the Elasticsearch sparse vector query, how it works, and how to effectively use it.
Bit vectors in Elasticsearch
Discover what are bit vectors, their practical implications and how to use them in Elasticsearch.