Merge main into update-ruby-on-rails-tutorial

Resolved conflict in test/test-markdown-frontmatter.js by merging tags arrays from both branches. Kept 'Rails' and 'Couchbase ORM' tags from feature branch and added 'LlamaIndex', 'GraphQL', and 'Semantic Kernel' tags from main.

Author: teetangh

test/test-markdown-frontmatter.js

@@ -6,9 +6,9 @@ const chalk = require('chalk')
 // accepted data field values
 const sdk_languages = ['nodejs', 'scala', 'python', 'swift', 'csharp', 'objective-c', 'android-java', 'any', 'java', 'kotlin', 'dart', 'golang', 'ruby', 'c++']
 
-const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'Rails', 'Couchbase ORM', 'LangChain', 'OpenAI', 'Streamlit', 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'TAVs', 'Custom Queries', 'Data API', 'FTS', 'GSI', 'Hugging Face']
+const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'Rails', 'Couchbase ORM', 'LangChain', 'OpenAI', 'Streamlit', 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'TAVs', 'Custom Queries', 'Data API', 'FTS', 'GSI', 'Hugging Face', 'LlamaIndex', 'GraphQL', 'Semantic Kernel']
 
-const technologies = ['connectors', 'kv', 'query', 'capella', 'server', 'index', 'mobile', 'fts', 'sync gateway', 'eventing', 'analytics', 'udf', 'vector search', 'react', 'edge-server', 'app-services']
+const technologies = ['connectors', 'kv', 'query', 'capella', 'server', 'index', 'mobile', 'fts', 'sync gateway', 'eventing', 'analytics', 'udf', 'vector search', 'react', 'edge-server', 'app-services', 'hyperscale vector index', 'composite vector index']
 
 const content_types = ['quickstart', 'tutorial', 'learn']
 

tutorial/markdown/aspnet/semantic-kernel/semantic-kernel-tutorial.md

@@ -12,6 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
+  - FTS
   - Artificial Intelligence
   - Autogen
   - Ag2

…book/agentchat_RetrieveChat_couchbase.md …/ag2-agentchat_RetrieveChat_couchbase.mdtutorial/markdown/generated/vector-search-cookbook/agentchat_RetrieveChat_couchbase.md renamed to tutorial/markdown/generated/vector-search-cookbook/ag2-agentchat_RetrieveChat_couchbase.md tutorial/markdown/generated/vector-search-cookbook/agentchat_RetrieveChat_couchbase.md renamed to tutorial/markdown/generated/vector-search-cookbook/ag2-agentchat_RetrieveChat_couchbase.md

@@ -12,6 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
+  - GSI
   - Artificial Intelligence
   - LangChain
   - Amazon Bedrock
@@ -32,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-This tutorial is available as a Jupyter Notebook (`.ipynb` file) that you can run interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/awsbedrock/RAG_with_Couchbase_and_Bedrock.ipynb).
+This tutorial is available as a Jupyter Notebook (`.ipynb` file) that you can run interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb).
 
 You can either download the notebook file and run it on [Google Colab](https://colab.research.google.com/) or run it on your system by setting up the Python environment.
 

…ookbook/Bedrock_Agents_Custom_Control.md …pproach-Bedrock_Agents_Custom_Control.mdtutorial/markdown/generated/vector-search-cookbook/Bedrock_Agents_Custom_Control.md renamed to tutorial/markdown/generated/vector-search-cookbook/awsbedrock-agents-custom-control-approach-Bedrock_Agents_Custom_Control.md tutorial/markdown/generated/vector-search-cookbook/Bedrock_Agents_Custom_Control.md renamed to tutorial/markdown/generated/vector-search-cookbook/awsbedrock-agents-custom-control-approach-Bedrock_Agents_Custom_Control.md

@@ -12,6 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
+  - GSI
   - Artificial Intelligence
   - LangChain
   - OpenAI
@@ -31,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-This tutorial is available as a Jupyter Notebook (`.ipynb` file) that you can run interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/azure/RAG_with_Couchbase_and_AzureOpenAI.ipynb).
+This tutorial is available as a Jupyter Notebook (`.ipynb` file) that you can run interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb).
 
 You can either download the notebook file and run it on [Google Colab](https://colab.research.google.com/) or run it on your system by setting up the Python environment.