How To
![Serverless semantic search with ELSER in Python: Exploring Summer Olympic games history](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Felastic-de_151512_720x420_01_V1.jpg&w=3840&q=100)
Serverless semantic search with ELSER in Python: Exploring Summer Olympic games history
This blog shows how to fetch information from an Elasticsearch index, in a natural language expression, using semantic search. We will load previous olympic games data set and then use the ELSER model to perform semantic searches.
![Protecting Sensitive and PII information in RAG with Elasticsearch and LlamaIndex](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Frag-with-llamaindex-and-elasticsearch%2Frag-highlevel.png&w=3840&q=100)
Protecting Sensitive and PII information in RAG with Elasticsearch and LlamaIndex
How to protect sensitive and PII data in a RAG application with Elasticsearch and LlamaIndex.
![Building advanced visualizations with Kibana and Vega](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2F03-strobes.jpg&w=3840&q=100)
Building advanced visualizations with Kibana and Vega
Have you struggled to build the Kibana visualizations you need using Lens and TSDB? Learn how to create complex visualizations using Kibana and Vega.
![Build a Conversational Search for your Customer Success Application with Elasticsearch and OpenAI](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Fconversational-search-for-customer-success%2Farchitecture.png&w=3840&q=100)
Build a Conversational Search for your Customer Success Application with Elasticsearch and OpenAI
Explore how to enhance your customer success application by implementing a conversational search feature using advanced technologies such as Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)
![Introducing Learning To Rank in Elasticsearch](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Felastictelescope.jpg&w=3840&q=100)
Introducing Learning To Rank in Elasticsearch
Discover how to Learning To Rank can help you to improve your search ranking and how to implement it in Elasticsearch
![semantic_text with Amazon Bedrock](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Fsemantic-text-with-amazon-bedrock%2Fcover.png&w=3840&q=100)
semantic_text with Amazon Bedrock
Using semantic_text new feature, and AWS Bedrock as inference endpoint service
![Elasticsearch open inference API adds Amazon Bedrock support](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Fblog-aws-inference.png&w=3840&q=100)
Elasticsearch open inference API adds Amazon Bedrock support
Elasticsearch open inference API adds support for embeddings generated from models hosted on Amazon Bedrock."
![Playground: Experiment with RAG using Bedrock Anthropic Models and Elasticsearch in minutes](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Fbedrock-playground%2Fimage.png&w=3840&q=100)
Playground: Experiment with RAG using Bedrock Anthropic Models and 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.
![Search complex documents using Unstructured.io and Elasticsearch vector database](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2F18-roadway.jpeg&w=3840&q=100)
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
![Combining sparse, dense and geo fields](/search-labs/_next/image?url=%2Fsearch-labs%2Fassets%2Fimages%2Fred_and_green_apple.jpg&w=3840&q=100)
Combining sparse, dense and geo fields
Learn how to combine multiple sparse, dense and geo fields in custom ways and what is the best way to do it?