Merge pull request #2869 from opensearch-project/main

New blog, blog edit, new community member profile, & new maintainer!

.github/CODEOWNERS

@@ -1 +1 @@
-*   @elfisher @AMoo-Miki @nknize @krisfreedain @peterzhuamazon @CEHENKLE @dtaivpp @kolchfa-aws @nateynateynate
+*   @elfisher @AMoo-Miki @nknize @krisfreedain @peterzhuamazon @CEHENKLE @dtaivpp @kolchfa-aws @nateynateynate @natebower

MAINTAINERS.md

@@ -15,6 +15,7 @@ This document contains a list of maintainers in this repo. See [opensearch-proje
 | David Tippett    | [dtaivpp](https://github.com/dtaivpp)               | Independent Consultant |
 | Fanit Kolchina   | [kolchfa-aws](https://github.com/kolchfa-aws)       | Amazon      |
 | Nate Boot        | [nateynateynate](https://github.com/nateynateynate) | Amazon      |
+| Nathan Bower        | [natebower](https://github.com/natebower)        | Amazon      |
 
 ### Emeritus
 

_community_members/dinujoh.md

@@ -0,0 +1,24 @@
+---
+short_name: dinujoh
+name: Dinu John
+photo: '/assets/media/community/members/dinujoh.jpg'
+title: 'OpenSearch Community Member: Dinu John'
+primary_title: Dinu John
+breadcrumbs:
+  icon: community
+  items:
+    - title: Community
+      url: /community/index.html
+    - title: Members
+      url: /community/members/index.html
+    - title: 'Dinu John's Profile'
+      url: '/community/members/dinu-john.html'
+github: dinujoh
+job_title_and_company: 'Senior Software Engineer at AWS - OpenSearch'
+personas:
+  - author
+permalink: '/community/members/dinu-john.html'
+redirect_from: '/authors/dinujoh/'
+---
+
+Dinu is a senior software engineer working on observability in OpenSearch at Amazon Web Services. He is a maintainer of the Data Prepper project.

_community_members/mkbn.md

@@ -0,0 +1,8 @@
+---
+short_name: mkbn
+name: Madhan Kumar Baskaran
+photo: '/assets/media/community/members/mkbn.jpg'
+github: madhankb
+linkedin: 'Madhan Kumar Baskaran'
+---
+**Madhan Kumar Baskaran** works as a Search Engineer at AWS. His primary focus involves assisting customers in constructing scalable search applications and analytics solutions. Based in Bengaluru, India, Madhan has a keen interest in data engineering and DevOps.

_posts/2024-05-14-explore-opensearch-2-14.md

@@ -42,9 +42,9 @@ Another addition to OpenSearch’s multiple data sources capabilities comes with
 OpenSearch 2.14 includes several new additions to OpenSearch’s **search and ML** toolkit to make ML-powered applications and integrations easier to build.
 
 ### Integrate any ML model and build solutions faster with API-native ingest
-An update to the [ML framework](http://%20https//opensearch.org/docs/latest/ml-commons-plugin/integrating-ml-models/) allows you to integrate any ML model and use models to enrich data streams through the [Ingest API](https://opensearch.org/docs/latest/api-reference/ingest-apis/index/). The model APIs now support user-defined model interfaces—strong typing for model inputs and outputs—for your custom models and AI services. These APIs are powered by AI connectors that can be integrated with any ML model hosting or AI API provider.
+An update to the [ML framework](http://opensearch.org/docs/latest/ml-commons-plugin/integrating-ml-models/) allows you to integrate any ML model and use models to enrich data streams through the [Ingest API](https://opensearch.org/docs/latest/api-reference/ingest-apis/index/). The model APIs now support user-defined model interfaces—strong typing for model inputs and outputs—for your custom models and AI services. These APIs are powered by AI connectors that can be integrated with any ML model hosting or AI API provider.
 
-Previously, the ML framework was limited to integrations with text embedding models, with an embedding ingest processor to generate vector embeddings through our Ingest API. This release introduces an [ML inference processor](https://opensearch.org/docs/latest/ingest-pipelines/processors/ml-inference/)that allows you to perform inferences on any integrated ML model to enrich your pipeline. Types of models can include name-entity-recognition (NER), optical character recognition (OCR), image metadata extractors (objection classifiers and detectors), multi-modal embeddings, language and personally identifiable information (PII) detectors, and more. These models let you support a plethora of use cases involving automated metadata generation and intelligent document processing—traditionally labor-intensive processes that can be slow, inefficient, and costly to scale. Now you can apply native integrations and abstractions through standardized APIs to easily transition between model providers and build faster.
+Previously, the ML framework was limited to integrations with text embedding models, with an embedding ingest processor to generate vector embeddings through our Ingest API. This release introduces an [ML inference processor](https://opensearch.org/docs/latest/ingest-pipelines/processors/ml-inference/) that allows you to perform inferences on any integrated ML model to enrich your pipeline. Types of models can include name-entity-recognition (NER), optical character recognition (OCR), image metadata extractors (objection classifiers and detectors), multi-modal embeddings, language and personally identifiable information (PII) detectors, and more. These models let you support a plethora of use cases involving automated metadata generation and intelligent document processing—traditionally labor-intensive processes that can be slow, inefficient, and costly to scale. Now you can apply native integrations and abstractions through standardized APIs to easily transition between model providers and build faster.
 
 ### Use OpenSearch as a semantic cache for LangChain applications
 OpenSearch now offers a semantic cache for LangChain applications. The semantic cache uses OpenSearch’s k-NN indexes to cache large language model (LLM) requests and responses, helping users reduce costs by reducing expensive LLM calls. Previously, you could build apps like chatbots on LangChain with the OpenSearch [vector store](https://python.langchain.com/docs/integrations/vectorstores/opensearch/) and the [retrieval augmented generation](https://python.langchain.com/docs/templates/rag-opensearch/) (RAG) template. Now you can also use OpenSearch as a [semantic cache](https://python.langchain.com/docs/integrations/llms/llm_caching/#opensearch-semantic-cache) to cache responses for LLM requests like “how do I use OpenSearch for vector search” so that responses to similar requests, like “instruct me on using OpenSearch as a vector database” can be handled without additional costly LLM calls.
@@ -68,4 +68,4 @@ OpenSearch’s current PGP public key is scheduled to expire on May 12, 2024. To
 You can find the latest version of OpenSearch on our [downloads page](https://opensearch.org/downloads.html). There’s more to learn about the new OpenSearch tools in the [release notes](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.14.0.md), [documentation release notes](https://github.com/opensearch-project/documentation-website/blob/main/release-notes/opensearch-documentation-release-notes-2.14.0.md/), and [documentation](https://opensearch.org/docs/latest/), and [OpenSearch Playground](https://playground.opensearch.org/app/home#/) is a great way to dig into the new visualization options. As always, we would appreciate your feedback on this release on our [community forum](https://forum.opensearch.org/).
 
 
-*OpenSearch is on the road! Our user conference has grown, and the first-ever OpenSearchCon India is coming up soon. We hope you’ll consider joining the OpenSearch community on June 26 in Bengaluru—visit* [*this page*](https://opensearch.org/events/opensearchcon/2024/india/index.html) *to learn more. And the third annual* [*OpenSearchCon North America*](https://opensearch.org/events/opensearchcon/2024/north-america/index.html) *is officially scheduled for September 24–26 in San Francisco! The* [*Call for Presentations*](https://opensearch.org/events/opensearchcon/2024/north-america/cfp.html) *is open now, so get your submissions in soon!*
+*OpenSearch is on the road! Our user conference has grown, and the first-ever OpenSearchCon India is coming up soon. We hope you’ll consider joining the OpenSearch community on June 26 in Bengaluru—visit* [*this page*](https://opensearch.org/events/opensearchcon/2024/india/index.html) *to learn more. And the third annual* [*OpenSearchCon North America*](https://opensearch.org/events/opensearchcon/2024/north-america/index.html) *is officially scheduled for September 24–26 in San Francisco! The* [*Call for Presentations*](https://opensearch.org/events/opensearchcon/2024/north-america/cfp.html) *is open now, so get your submissions in soon!*