updated tutorial based upon 8.0 requirements

jinaai/query_based/frontmatter.md

@@ -0,0 +1,24 @@
+---
+# frontmatter
+path: "/tutorial-jina-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: Retrieval-Augmented Generation (RAG) with Jina AI using Couchbase Hyperscale and Composite Vector Index
+short_title: RAG with Couchbase and Jina AI
+description:
+  - Learn how to build a semantic search engine using Couchbase and Jina.
+  - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Jina embeddings and language models.
+  - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain, Couchbase Hyperscale and Composite Vector Index.
+content_type: tutorial
+filter: sdk
+technology:
+  - vector search
+tags:
+  - Hyperscale Vector Index
+  - Composite Vector Index
+  - Artificial Intelligence
+  - LangChain
+  - Jina AI
+sdk_language:
+  - python
+length: 60 Mins
+alt_paths: ["/tutorial-jina-couchbase-rag-with-hyperscale-vector-index", "/tutorial-jina-couchbase-rag-with-composite-vector-index"]
+---

jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb → jinaai/search_based/RAG_with_Couchbase_and_Jina_AI.ipynb

@@ -7,7 +7,7 @@
       },
       "source": [
         "# Introduction\n",
-        "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database and [Jina](https://jina.ai/) as the AI-powered embedding and language model provider, utilizing Full-Text Search (FTS). Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it an essential tool for applications that require intelligent information retrieval. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-global-secondary-index)"
+        "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database and [Jina](https://jina.ai/) as the AI-powered embedding and language model provider, utilizing Full-Text Search using search vector index. Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it an essential tool for applications that require intelligent information retrieval. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using Hyperscale or Composite indexes, please take a look at [this.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-hyperscale-or-composite-vector-index)"
       ]
     },
     {
@@ -522,7 +522,7 @@
       },
       "source": [
         "#  Setting Up the Couchbase Vector Store\n",
-        "A vector store is where we'll keep our embeddings. Unlike the FTS index, which is used for text-based search, the vector store is specifically designed to handle embeddings and perform similarity searches. When a user inputs a query, the search engine converts the query into an embedding and compares it against the embeddings stored in the vector store. This allows the engine to find documents that are semantically similar to the query, even if they don't contain the exact same words. By setting up the vector store in Couchbase, we create a powerful tool that enables our search engine to understand and retrieve information based on the meaning and context of the query, rather than just the specific words used."
+        "A vector store is where we'll keep our embeddings. Unlike the search vector index, which is used for text-based search, the vector store is specifically designed to handle embeddings and perform similarity searches. When a user inputs a query, the search engine converts the query into an embedding and compares it against the embeddings stored in the vector store. This allows the engine to find documents that are semantically similar to the query, even if they don't contain the exact same words. By setting up the vector store in Couchbase, we create a powerful tool that enables our search engine to understand and retrieve information based on the meaning and context of the query, rather than just the specific words used."
       ]
     },
     {

jinaai/fts/frontmatter.md → jinaai/search_based/frontmatter.md

@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-jina-couchbase-rag-with-fts"
+path: "/tutorial-jina-couchbase-rag-with-search-vector-index"
 title: Retrieval-Augmented Generation (RAG) with Couchbase and Jina AI using FTS
 short_title: RAG with Couchbase and Jina
 description:
@@ -12,7 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - Jina AI