couchbase-examples · giriraj-singh-couchbase · Nov 24, 2025 · Nov 24, 2025 · Nov 24, 2025
diff --git a/awsbedrock/gsi/.env.sample → awsbedrock/query_based/.env.sample b/awsbedrock/gsi/.env.sample → awsbedrock/query_based/.env.sample
diff --git a/.../gsi/RAG_with_Couchbase_and_Bedrock.ipynb → ...ased/RAG_with_Couchbase_and_Bedrock.ipynb b/.../gsi/RAG_with_Couchbase_and_Bedrock.ipynb → ...ased/RAG_with_Couchbase_and_Bedrock.ipynb
diff --git a/awsbedrock/gsi/frontmatter.md → awsbedrock/query_based/frontmatter.md b/awsbedrock/gsi/frontmatter.md → awsbedrock/query_based/frontmatter.md
@@ -1,22 +1,24 @@
 ---
 # frontmatter
-path: "/tutorial-aws-bedrock-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using GSI index
-short_title: RAG with Couchbase and Amazon Bedrock using GSI index
+path: "/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using GSI.
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Claude language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - Amazon Bedrock
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-vector-index", "/tutorial-aws-bedrock-couchbase-rag-with-composite-vector-index"]
 ---
diff --git a/awsbedrock/fts/.env.sample → awsbedrock/search_based/.env.sample b/awsbedrock/fts/.env.sample → awsbedrock/search_based/.env.sample
diff --git a/.../fts/RAG_with_Couchbase_and_Bedrock.ipynb → ...ased/RAG_with_Couchbase_and_Bedrock.ipynb b/.../fts/RAG_with_Couchbase_and_Bedrock.ipynb → ...ased/RAG_with_Couchbase_and_Bedrock.ipynb
diff --git a/awsbedrock/fts/frontmatter.md → awsbedrock/search_based/frontmatter.md b/awsbedrock/fts/frontmatter.md → awsbedrock/search_based/frontmatter.md
@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-aws-bedrock-couchbase-rag-with-fts"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using FTS service
-short_title: RAG with Couchbase and Amazon Bedrock using FTS service
+path: "/tutorial-aws-bedrock-couchbase-rag-with-search-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock with Search Vector Index
+short_title: RAG with Couchbase and Amazon Bedrock with Search Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using FTS service.
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Claude language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - Amazon Bedrock

diff --git a/azure/gsi/.env.sample → azure/query_based/.env.sample b/azure/gsi/.env.sample → azure/query_based/.env.sample
diff --git a/.../RAG_with_Couchbase_and_AzureOpenAI.ipynb → .../RAG_with_Couchbase_and_AzureOpenAI.ipynb b/.../RAG_with_Couchbase_and_AzureOpenAI.ipynb → .../RAG_with_Couchbase_and_AzureOpenAI.ipynb
diff --git a/azure/gsi/frontmatter.md → azure/query_based/frontmatter.md b/azure/gsi/frontmatter.md → azure/query_based/frontmatter.md
@@ -1,22 +1,24 @@
 ---
 # frontmatter
-path: "/tutorial-azure-openai-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI using GSI index
-short_title: RAG with Couchbase and Azure OpenAI using GSI index
+path: "/tutorial-azure-openai-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using GSI.
+  - Learn how to build a semantic search engine using Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Azure OpenAI embeddings.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - OpenAI
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-azure-openai-couchbase-rag-with-hyperscale-vector-index", "/tutorial-azure-openai-couchbase-rag-with-composite-vector-index"]
 ---
diff --git a/.../RAG_with_Couchbase_and_AzureOpenAI.ipynb → .../RAG_with_Couchbase_and_AzureOpenAI.ipynb b/.../RAG_with_Couchbase_and_AzureOpenAI.ipynb → .../RAG_with_Couchbase_and_AzureOpenAI.ipynb
diff --git a/azure/fts/azure_index.json → azure/search_based/azure_index.json b/azure/fts/azure_index.json → azure/search_based/azure_index.json
diff --git a/azure/fts/frontmatter.md → azure/search_based/frontmatter.md b/azure/fts/frontmatter.md → azure/search_based/frontmatter.md
@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-azure-openai-couchbase-rag-with-fts"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI using FTS service
-short_title: RAG with Couchbase and Azure OpenAI using FTS service
+path: "/tutorial-azure-openai-couchbase-rag-with-search-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI with Search Vector Index
+short_title: RAG with Couchbase and Azure OpenAI with Search Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using FTS service.
+  - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Azure OpenAI embeddings.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - OpenAI

diff --git a/claudeai/gsi/.env.sample → claudeai/query_based/.env.sample b/claudeai/gsi/.env.sample → claudeai/query_based/.env.sample
diff --git a/..._Couchbase_and_Claude(by_Anthropic).ipynb → ..._Couchbase_and_Claude(by_Anthropic).ipynb b/..._Couchbase_and_Claude(by_Anthropic).ipynb → ..._Couchbase_and_Claude(by_Anthropic).ipynb
diff --git a/claudeai/gsi/frontmatter.md → claudeai/query_based/frontmatter.md b/claudeai/gsi/frontmatter.md → claudeai/query_based/frontmatter.md
@@ -1,22 +1,24 @@
 ---
 # frontmatter
-path: "/tutorial-openai-claude-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude using GSI index
-short_title: RAG with Couchbase, OpenAI, and Claude using GSI index
+path: "/tutorial-openai-claude-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude with Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase, OpenAI, and Claude with Couchbase Hyperscale and Composite Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using GSI.
+  - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude with Couchbase Hyperscale and Composite Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenAI embeddings and use Claude as the language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - OpenAI
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-openai-claude-couchbase-rag-with-hyperscale-vector-index", "/tutorial-openai-claude-couchbase-rag-with-composite-vector-index"]
 ---
diff --git a/claudeai/fts/.env.sample → claudeai/search_based/.env.sample b/claudeai/fts/.env.sample → claudeai/search_based/.env.sample
diff --git a/..._Couchbase_and_Claude(by_Anthropic).ipynb → ..._Couchbase_and_Claude(by_Anthropic).ipynb b/..._Couchbase_and_Claude(by_Anthropic).ipynb → ..._Couchbase_and_Claude(by_Anthropic).ipynb
diff --git a/claudeai/fts/claude_index.json → claudeai/search_based/claude_index.json b/claudeai/fts/claude_index.json → claudeai/search_based/claude_index.json
diff --git a/claudeai/fts/frontmatter.md → claudeai/search_based/frontmatter.md b/claudeai/fts/frontmatter.md → claudeai/search_based/frontmatter.md
@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-openai-claude-couchbase-rag-with-fts"
-title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude using FTS service
-short_title: RAG with Couchbase, OpenAI, and Claude using FTS service
+path: "/tutorial-openai-claude-couchbase-rag-with-search-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude with Search Vector Index
+short_title: RAG with Couchbase, OpenAI, and Claude with Search Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using FTS service.
+  - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenAI embeddings and use Claude as the language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - OpenAI

diff --git a/cohere/gsi/.env.sample → cohere/query_based/.env.sample b/cohere/gsi/.env.sample → cohere/query_based/.env.sample
diff --git a/...e/gsi/RAG_with_Couchbase_and_Cohere.ipynb → ...based/RAG_with_Couchbase_and_Cohere.ipynb b/...e/gsi/RAG_with_Couchbase_and_Cohere.ipynb → ...based/RAG_with_Couchbase_and_Cohere.ipynb
diff --git a/cohere/fts/frontmatter.md → cohere/query_based/frontmatter.md b/cohere/fts/frontmatter.md → cohere/query_based/frontmatter.md
@@ -1,22 +1,24 @@
 ---
 # frontmatter
-path: "/tutorial-cohere-couchbase-rag-with-fts"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere using FTS service
-short_title: RAG with Couchbase and Cohere using FTS service
+path: "/tutorial-cohere-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Cohere using FTS service.
+  - Learn how to build a semantic search engine using Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Cohere embeddings and language models.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - Cohere
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-cohere-couchbase-rag-with-hyperscale-vector-index", "/tutorial-cohere-couchbase-rag-with-composite-vector-index"]
 ---
diff --git a/cohere/fts/.env.sample → cohere/search_based/.env.sample b/cohere/fts/.env.sample → cohere/search_based/.env.sample
diff --git a/...e/fts/RAG_with_Couchbase_and_Cohere.ipynb → ...based/RAG_with_Couchbase_and_Cohere.ipynb b/...e/fts/RAG_with_Couchbase_and_Cohere.ipynb → ...based/RAG_with_Couchbase_and_Cohere.ipynb
diff --git a/cohere/fts/cohere_index.json → cohere/search_based/cohere_index.json b/cohere/fts/cohere_index.json → cohere/search_based/cohere_index.json
diff --git a/cohere/gsi/frontmatter.md → cohere/search_based/frontmatter.md b/cohere/gsi/frontmatter.md → cohere/search_based/frontmatter.md
@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-cohere-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with GSI
-short_title: RAG with Couchbase and Cohere with GSI
+path: "/tutorial-cohere-couchbase-rag-with-search-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with Search Vector Index
+short_title: RAG with Couchbase and Cohere with Search Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Cohere using GSI.
+  - Learn how to build a semantic search engine using Couchbase and Cohere using Search Vector Index.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Cohere embeddings and language models.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - Cohere

diff --git a/crewai-short-term-memory/fts/.env.sample → ...short-term-memory/query_based/.env.sample b/crewai-short-term-memory/fts/.env.sample → ...short-term-memory/query_based/.env.sample
diff --git a/...rm-memory/gsi/CouchbaseStorage_Demo.ipynb → ...y/query_based/CouchbaseStorage_Demo.ipynb b/...rm-memory/gsi/CouchbaseStorage_Demo.ipynb → ...y/query_based/CouchbaseStorage_Demo.ipynb
diff --git a/crewai-short-term-memory/gsi/frontmatter.md → ...rt-term-memory/query_based/frontmatter.md b/crewai-short-term-memory/gsi/frontmatter.md → ...rt-term-memory/query_based/frontmatter.md
@@ -1,8 +1,8 @@
 ---
 # frontmatter
-path: "/tutorial-crewai-short-term-memory-couchbase-with-global-secondary-index"
-title: Implementing Short-Term Memory for CrewAI Agents with Couchbase with GSI
-short_title: CrewAI Short-Term Memory with Couchbase with GSI
+path: "/tutorial-crewai-short-term-memory-couchbase-with-hyperscale-or-composite-vector-index"
+title: Implementing Short-Term Memory for CrewAI Agents with Couchbase with Couchbase Hyperscale and Composite Vector Index
+short_title: CrewAI Short-Term Memory with Couchbase with Couchbase Hyperscale and Composite Vector Index
 description:
   - Learn how to implement short-term memory for CrewAI agents using Couchbase's vector search capabilities with GSI.
   - This tutorial demonstrates how to store and retrieve agent interactions using semantic search.
@@ -12,11 +12,13 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - CrewAI
 sdk_language:
   - python
 length: 45 Mins
+alt_paths: ["/tutorial-crewai-short-term-memory-couchbase-with-hyperscale-vector-index", "/tutorial-crewai-short-term-memory-couchbase-with-composite-vector-index"]
 ---
diff --git a/crewai-short-term-memory/gsi/.env.sample → ...hort-term-memory/search_based/.env.sample b/crewai-short-term-memory/gsi/.env.sample → ...hort-term-memory/search_based/.env.sample
diff --git a/...rm-memory/fts/CouchbaseStorage_Demo.ipynb → .../search_based/CouchbaseStorage_Demo.ipynb b/...rm-memory/fts/CouchbaseStorage_Demo.ipynb → .../search_based/CouchbaseStorage_Demo.ipynb
diff --git a/crewai-short-term-memory/fts/crew_index.json → ...-term-memory/search_based/crew_index.json b/crewai-short-term-memory/fts/crew_index.json → ...-term-memory/search_based/crew_index.json
diff --git a/crewai-short-term-memory/fts/frontmatter.md → ...t-term-memory/search_based/frontmatter.md b/crewai-short-term-memory/fts/frontmatter.md → ...t-term-memory/search_based/frontmatter.md
@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-crewai-short-term-memory-couchbase-with-fts"
+path: "/tutorial-crewai-short-term-memory-couchbase-with-search-vector-index"
 title: Implementing Short-Term Memory for CrewAI Agents with Couchbase using FTS Service
 short_title: CrewAI Short-Term Memory with Couchbase using FTS
 description:
@@ -12,7 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - CrewAI

diff --git a/crewai/gsi/.env.sample → crewai/query_based/.env.sample b/crewai/gsi/.env.sample → crewai/query_based/.env.sample
diff --git a/...i/gsi/RAG_with_Couchbase_and_CrewAI.ipynb → ...based/RAG_with_Couchbase_and_CrewAI.ipynb b/...i/gsi/RAG_with_Couchbase_and_CrewAI.ipynb → ...based/RAG_with_Couchbase_and_CrewAI.ipynb
diff --git a/crewai/gsi/frontmatter.md → crewai/query_based/frontmatter.md b/crewai/gsi/frontmatter.md → crewai/query_based/frontmatter.md
@@ -1,8 +1,8 @@
 ---
 # frontmatter
-path: "/tutorial-crewai-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI with GSI
-short_title: RAG with Couchbase and CrewAI with GSI
+path: "/tutorial-crewai-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI with Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase and CrewAI with Couchbase Hyperscale and Composite Vector Index
 description:
   - Learn how to build a semantic search engine using Couchbase and CrewAI.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with CrewAI's agent-based approach.
@@ -12,11 +12,13 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - LangChain
   - CrewAI
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-crewai-couchbase-rag-with-hyperscale-vector-index", "/tutorial-crewai-couchbase-rag-with-composite-vector-index"]
 ---
diff --git a/crewai/fts/.env.sample → crewai/search_based/.env.sample b/crewai/fts/.env.sample → crewai/search_based/.env.sample
diff --git a/...i/fts/RAG_with_Couchbase_and_CrewAI.ipynb → ...based/RAG_with_Couchbase_and_CrewAI.ipynb b/...i/fts/RAG_with_Couchbase_and_CrewAI.ipynb → ...based/RAG_with_Couchbase_and_CrewAI.ipynb
diff --git a/crewai/fts/crew_index.json → crewai/search_based/crew_index.json b/crewai/fts/crew_index.json → crewai/search_based/crew_index.json
diff --git a/crewai/fts/frontmatter.md → crewai/search_based/frontmatter.md b/crewai/fts/frontmatter.md → crewai/search_based/frontmatter.md
@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-crewai-couchbase-rag-with-fts"
+path: "/tutorial-crewai-couchbase-rag-with-search-vector-index"
 title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI using FTS Service
 short_title: RAG with Couchbase and CrewAI using FTS
 description:
@@ -12,7 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - CrewAI

diff --git a/...G_with_Couchbase_Capella_and_OpenAI.ipynb → ...G_with_Couchbase_Capella_and_OpenAI.ipynb b/...G_with_Couchbase_Capella_and_OpenAI.ipynb → ...G_with_Couchbase_Capella_and_OpenAI.ipynb
diff --git a/haystack/gsi/frontmatter.md → haystack/query_based/frontmatter.md b/haystack/gsi/frontmatter.md → haystack/query_based/frontmatter.md
@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-openai-haystack-rag-with-gsi"
+path: "/tutorial-openai-haystack-rag-with-hyperscale-or-composite-vector-index"
 title: "RAG with OpenAI, Haystack and Couchbase Hyperscale and Composite Vector Indexes"
 short_title: "RAG with OpenAI, Haystack and Couchbase CVI and HVI"
 description:
@@ -15,8 +15,10 @@ tags:
   - OpenAI
   - Artificial Intelligence
   - Haystack
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
 sdk_language:
   - python
 length: 60 Mins
+alt_paths: ["/tutorial-openai-haystack-rag-with-hyperscale-vector-index", "/tutorial-openai-haystack-rag-with-composite-vector-index"]
 ---
diff --git a/haystack/fts/requirements.txt → haystack/query_based/requirements.txt b/haystack/fts/requirements.txt → haystack/query_based/requirements.txt
diff --git a/...G_with_Couchbase_Capella_and_OpenAI.ipynb → ...G_with_Couchbase_Capella_and_OpenAI.ipynb b/...G_with_Couchbase_Capella_and_OpenAI.ipynb → ...G_with_Couchbase_Capella_and_OpenAI.ipynb
diff --git a/haystack/fts/frontmatter.md → haystack/search_based/frontmatter.md b/haystack/fts/frontmatter.md → haystack/search_based/frontmatter.md
@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-openai-haystack-rag-with-fts"
+path: "/tutorial-openai-haystack-rag-with-search-vector-index"
 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:
@@ -15,7 +15,7 @@ tags:
   - OpenAI
   - Artificial Intelligence
   - Haystack
-  - FTS
+  - Search Vector Index
 sdk_language:
   - python
 length: 60 Mins

diff --git a/haystack/fts/fts_index.json → haystack/search_based/fts_index.json b/haystack/fts/fts_index.json → haystack/search_based/fts_index.json
diff --git a/haystack/gsi/requirements.txt → haystack/search_based/requirements.txt b/haystack/gsi/requirements.txt → haystack/search_based/requirements.txt
diff --git a/huggingface/fts/.env.sample → huggingface/query_based/.env.sample b/huggingface/fts/.env.sample → huggingface/query_based/.env.sample
diff --git a/huggingface/gsi/frontmatter.md → huggingface/query_based/frontmatter.md b/huggingface/gsi/frontmatter.md → huggingface/query_based/frontmatter.md
@@ -1,8 +1,8 @@
 ---
 # frontmatter
-path: "/tutorial-huggingface-couchbase-vector-search-with-global-secondary-index"
-title: Using Hugging Face Embeddings with Couchbase Vector Search with GSI
-short_title: Hugging Face with Couchbase Vector Search with GSI
+path: "/tutorial-huggingface-couchbase-vector-search-with-hyperscale-or-composite-vector-index"
+title: Using Hugging Face Embeddings with Couchbase Vector Search with Couchbase Hyperscale and Composite Vector Index
+short_title: Hugging Face with Couchbase Vector Search with Couchbase Hyperscale and Composite Vector Index
 description:
   - Learn how to generate embeddings using Hugging Face and store them in Couchbase.
   - This tutorial demonstrates how to use Couchbase's vector search capabilities with Hugging Face embeddings.
@@ -12,10 +12,12 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Hyperscale Vector Index
+  - Composite Vector Index
   - Artificial Intelligence
   - Hugging Face
 sdk_language:
   - python
 length: 30 Mins
+alt_paths: ["/tutorial-huggingface-couchbase-vector-search-with-hyperscale-vector-index", "/tutorial-huggingface-couchbase-vector-search-with-composite-vector-index"]
 ---
diff --git a/huggingface/gsi/hugging_face.ipynb → huggingface/query_based/hugging_face.ipynb b/huggingface/gsi/hugging_face.ipynb → huggingface/query_based/hugging_face.ipynb
diff --git a/huggingface/gsi/.env.sample → huggingface/search_based/.env.sample b/huggingface/gsi/.env.sample → huggingface/search_based/.env.sample
diff --git a/huggingface/fts/frontmatter.md → huggingface/search_based/frontmatter.md b/huggingface/fts/frontmatter.md → huggingface/search_based/frontmatter.md
@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-huggingface-couchbase-vector-search-with-fts"
+path: "/tutorial-huggingface-couchbase-vector-search-with-search-vector-index"
 title: Using Hugging Face Embeddings with Couchbase Vector Search using FTS Service
 short_title: Hugging Face with Couchbase Vector Search using FTS Service
 description:
@@ -12,7 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - Hugging Face
 sdk_language:

diff --git a/huggingface/fts/hugging_face.ipynb → huggingface/search_based/hugging_face.ipynb b/huggingface/fts/hugging_face.ipynb → huggingface/search_based/hugging_face.ipynb
diff --git a/huggingface/fts/huggingface_index.json → ...gface/search_based/huggingface_index.json b/huggingface/fts/huggingface_index.json → ...gface/search_based/huggingface_index.json
diff --git a/jinaai/gsi/.env.sample → jinaai/query_based/.env.sample b/jinaai/gsi/.env.sample → jinaai/query_based/.env.sample
diff --git a/.../gsi/RAG_with_Couchbase_and_Jina_AI.ipynb → ...ased/RAG_with_Couchbase_and_Jina_AI.ipynb b/.../gsi/RAG_with_Couchbase_and_Jina_AI.ipynb → ...ased/RAG_with_Couchbase_and_Jina_AI.ipynb