Merge main into semantic-kernel-tutorial

Author: azaddhirajkumar

test/test-markdown-frontmatter.js

@@ -6,7 +6,7 @@ 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', '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', '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', 'Semantic Kernel']
+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', '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', 'hyperscale vector index', 'composite vector index']
 

tutorial/markdown/generated/vector-search-cookbook/haystack-fts-RAG_with_Couchbase_Capella_and_OpenAI.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-openai-haystack-rag-with-fts"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack"
-short_title: "RAG with Openai and Haystack"
+title: "Retrieval-Augmented Generation (RAG) with OpenAI, Haystack and Couchbase Search Vector Index"
+short_title: "RAG with OpenAI, Haystack and Couchbase Search Vector Index"
 description:
-  - Learn how to build a semantic search engine using Couchbase's Search vector index.
+  - Learn how to build a semantic search engine using Couchbase's Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with the embeddings generated by OpenAI Services.
   - You will understand how to perform Retrieval-Augmented Generation (RAG) using Haystack, Couchbase and OpenAI services.
 content_type: tutorial

tutorial/markdown/generated/vector-search-cookbook/haystack-gsi-RAG_with_Couchbase_Capella_and_OpenAI.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-openai-haystack-rag-with-gsi"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack"
-short_title: "RAG with Openai and Haystack"
+title: "RAG with OpenAI, Haystack and Couchbase Hyperscale and Composite Vector Indexes"
+short_title: "RAG with OpenAI, Haystack and Couchbase CVI and HVI"
 description:
-  - Learn how to build a semantic search engine using Couchbase's GSI vector index.
+  - Learn how to build a semantic search engine using Couchbase's Hyperscale and Composite Vector Indexes.
   - This tutorial demonstrates how to integrate Couchbase's GSI vector search capabilities with OpenAI embeddings.
   - You will understand how to perform Retrieval-Augmented Generation (RAG) using Haystack, Couchbase and OpenAI services.
 content_type: tutorial

tutorial/markdown/generated/vector-search-cookbook/lamaindex-fts-RAG_with_Couchbase_Capella_and_OpenAI.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-openai-llamaindex-rag-with-fts"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and LlamaIndex"
-short_title: "RAG with Openai and LlamaIndex"
+title: "Retrieval-Augmented Generation (RAG) with OpenAI, LlamaIndex and Couchbase Search Vector Index"
+short_title: "RAG with OpenAI, LlamaIndex and Couchbase Search Vector Index"
 description:
-  - Learn how to build a semantic search engine using Couchbase's Search vector index.
+  - Learn how to build a semantic search engine using Couchbase's Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's search vector search capabilities with the embeddings generated by OpenAI Services.
   - You will understand how to perform Retrieval-Augmented Generation (RAG) using Llamaindex, Couchbase and OpenAI services.
 content_type: tutorial

tutorial/markdown/generated/vector-search-cookbook/lamaindex-gsi-RAG_with_Couchbase_Capella_and_OpenAI.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-openai-llamaindex-rag-with-gsi"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and LlamaIndex"
-short_title: "RAG with Openai and LlamaIndex"
+title: "RAG with OpenAI, LlamaIndex and Couchbase Hyperscale and Composite Vector Indexes"
+short_title: "RAG with OpenAI, LlamaIndex and Couchbase CVI and HVI"
 description:
-  - Learn how to build a semantic search engine using Couchbase's GSI vector search.
+  - Learn how to build a semantic search engine using Couchbase's Hyperscale and Composite Vector Indexes.
   - This tutorial demonstrates how to integrate Couchbase's GSI vector search capabilities with OpenAI embeddings.
   - You will understand how to perform Retrieval-Augmented Generation (RAG) using LlamaIndex and GSI vector indexes.
 content_type: tutorial