Update vector search tutorial terminology to Couchbase 8.0 standards

README.md

@@ -3,8 +3,10 @@
 This repository demonstrates how to build a powerful semantic search engine using Couchbase as the backend database, combined with various AI-powered embedding and language model providers such as OpenAI, Azure OpenAI, Anthropic (Claude), Cohere, Hugging Face, Jina AI, Mistral AI, and Voyage AI.
 
 Each example provides two distinct approaches:
-- **FTS (Full Text Search)**: Uses Couchbase's vector search capabilities with pre-created search indices
-- **GSI (Global Secondary Index)**: Leverages Couchbase's native SQL++ queries with vector similarity functions
+- **Search Vector Index**: Uses Couchbase's Search-based vector search capabilities with pre-created search indices (in `search_based/` directories)
+- **Hyperscale & Composite Vector Indexes**: Leverages Couchbase's native SQL++ queries with vector similarity functions (in `query_based/` directories)
+
+For more information on vector indexes, see the [Couchbase documentation](https://docs.couchbase.com/cloud/vector-index/use-vector-indexes.html).
 
 Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it essential for applications that require intelligent information retrieval.
 
@@ -33,11 +35,11 @@ Semantic search goes beyond simple keyword matching by understanding the context
 
 ### 2. Choose Your Approach:
 
-#### For FTS (Full Text Search) Examples:
-Use the provided `{model}_index.json` index definition file in each model's `fts/` directory to create a new vector search index in your Couchbase cluster.
+#### For Search Vector Index Examples:
+Use the provided `{model}_index.json` index definition file in each model's `search_based/` directory to create a new vector search index in your Couchbase cluster.
 
-#### For GSI (Global Secondary Index) Examples:
-No additional setup required. GSI index will be created in each model's example.
+#### For Hyperscale & Composite Vector Index Examples:
+No additional setup required. Vector indexes will be created in each model's example in the `query_based/` directory.
 
 ### 3. Run the notebook file
 
@@ -75,9 +77,9 @@ Each notebook implements a semantic search function that performs similarity sea
 
 The system implements caching functionality using `CouchbaseCache` to improve performance for repeated queries.
 
-## Couchbase Vector Search Index (FTS Approach Only)
+## Couchbase Search Vector Index (Search-based Approach Only)
 
-For FTS examples, you'll need to create a vector search index using the provided JSON configuration files. For more information on creating a vector search index, please follow the [instructions](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html). The following is an example for Azure OpenAI Model.
+For Search Vector Index examples, you'll need to create a vector search index using the provided JSON configuration files. For more information on creating a vector search index, please follow the [instructions](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html). The following is an example for Azure OpenAI Model.
 
 ```json
 {

awsbedrock/query_based/frontmatter.md

@@ -0,0 +1,26 @@
+---
+# frontmatter
+path: "/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-or-composite-vector-index"
+alt_paths:
+  - "/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-vector-index"
+  - "/tutorial-aws-bedrock-couchbase-rag-with-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Amazon Bedrockand Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Amazon Bedrockand Couchbase Hyperscale and Composite Vector Index
+description:
+  - Learn how to build a semantic search engine using Amazon Bedrockand 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:
+  - Hyperscale Vector Index
+  - Composite Vector Index
+  - Artificial Intelligence
+  - LangChain
+  - Amazon Bedrock
+sdk_language:
+  - python
+length: 60 Mins
+---

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 Amazon Bedrockand Couchbase Search Vector Index
+short_title: RAG with Amazon Bedrockand Couchbase 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 Amazon Bedrockand Couchbase 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