diff --git a/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb b/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb
index 3bde9949..c48aad5b 100644
--- a/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb
+++ b/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# How to run this tutorial\n",
     "\n",
-    "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).\n",
+    "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/fts/RAG_with_Couchbase_and_Bedrock.ipynb).\n",
     "\n",
     "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."
    ]
diff --git a/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb b/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb
index 2b2aa27d..f67fe449 100644
--- a/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb
+++ b/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb
@@ -15,7 +15,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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).\n",
+        "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).\n",
         "\n",
         "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."
       ]
@@ -710,7 +710,8 @@
             "\n",
             "As he progressed at the Ally Pally, the Manchester United fan was sent a good luck message by the club's former midfielder and ex-England captain David Beckham. In 12 months, Littler's Instagram followers have risen from 4,000 to 1.3m. Commercial backers include a clothing range, cereal firm and train company and he will appear in a reboot of the TV darts show Bullseye. Google say he was the most searched-for athlete online in the UK during 2024. On the back of his success, Littler darts, boards, cabinets, shirts are being snapped up in big numbers. \"This Christmas the junior magnetic dartboard is selling out, we're talking over 100,000. They're 20 quid and a great introduction for young children,\" said Garry Plummer, the boss of sponsors Target Darts, who first signed a deal with Littler's family when he was aged 12. \"All the toy shops want it, they all want him - 17, clean, doesn't drink, wonderful.\"\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -859,7 +860,8 @@
             "\n",
             "As he progressed at the Ally Pally, the Manchester United fan was sent a good luck message by the club's former midfielder and ex-England captain David Beckham. In 12 months, Littler's Instagram followers have risen from 4,000 to 1.3m. Commercial backers include a clothing range, cereal firm and train company and he will appear in a reboot of the TV darts show Bullseye. Google say he was the most searched-for athlete online in the UK during 2024. On the back of his success, Littler darts, boards, cabinets, shirts are being snapped up in big numbers. \"This Christmas the junior magnetic dartboard is selling out, we're talking over 100,000. They're 20 quid and a great introduction for young children,\" said Garry Plummer, the boss of sponsors Target Darts, who first signed a deal with Littler's family when he was aged 12. \"All the toy shops want it, they all want him - 17, clean, doesn't drink, wonderful.\"\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1072,4 +1074,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb b/azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb
index 7d476a6d..6a9937fd 100644
--- a/azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb
+++ b/azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb
@@ -16,7 +16,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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).\n",
+        "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/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb).\n",
         "\n",
         "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."
       ]
@@ -944,4 +944,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb b/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb
index 99caf0a0..9d37da37 100644
--- a/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb
+++ b/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb
@@ -16,7 +16,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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).\n",
+        "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).\n",
         "\n",
         "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."
       ]
@@ -665,7 +665,8 @@
             "Littler was hugged by his parents after victory over Meikle\n",
             "\n",
             "Littler returned to Alexandra Palace to a boisterous reception from more than 3,000 spectators and delivered an astonishing display in the fourth set. He was on for a nine-darter after his opening two throws in both of the first two legs and completed the set in 32 darts - the minimum possible is 27. The teenager will next play after Christmas against European Championship winner Ritchie Edhouse, the 29th seed, or Ian White, and is seeded to meet Humphries in the semi-finals. Having entered last year's event ranked 164th, Littler is up to fourth in the world and will go to number two if he reaches the final again this time. He has won 10 titles in his debut professional year, including the Premier League and Grand Slam of Darts. After reaching the World Championship final as a debutant aged just 16, Littler's life has been transformed and interest in darts has rocketed. Google say he was the most searched-for athlete online in the UK during 2024. This Christmas, more than 100,000 children are expected to be opening Littler-branded magnetic dartboards as presents. His impact has helped double the number of junior academies and has prompted plans to expand the World Championship. Littler was named BBC Young Sports Personality of the Year on Tuesday and was runner-up to athlete Keely Hodgkinson for the main award.\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -810,7 +811,8 @@
             "A tearful Luke Littler hit a tournament record 140.91 set average as he started his bid for the PDC World Championship title with a dramatic 3-1 win over Ryan Meikle. The 17-year-old made headlines around the world when he reached the tournament final in January, where he lost to Luke Humphries. Starting this campaign on Saturday, Littler was millimetres away from a nine-darter when he missed double 12 as he blew Meikle away in the fourth and final set of the second-round match. Littler was overcome with emotion at the end, cutting short his on-stage interview. \"It was probably the toughest game I've ever played. I had to fight until the end,\" he said later in a news conference. \"As soon as the question came on stage and then boom, the tears came. It was just a bit too much to speak on stage. \"It is the worst game I have played. I have never felt anything like that tonight.\" Admitting to nerves during the match, he told Sky Sports: \"Yes, probably the biggest time it's hit me. Coming into it I was fine, but as soon as [referee] George Noble said 'game on', I couldn't throw them.\" Littler started slowly against Meikle, who had two darts for the opening set, but he took the lead by twice hitting double 20. Meikle did not look overawed against his fellow Englishman and levelled, but Littler won the third set and exploded into life in the fourth. The tournament favourite hit four maximum 180s as he clinched three straight legs in 11, 10 and 11 darts for a record set average, and 100.85 overall. Meanwhile, two seeds crashed out on Saturday night – five-time world champion Raymond van Barneveld lost to Welshman Nick Kenny, while England's Ryan Joyce beat Danny Noppert. Australian Damon Heta was another to narrowly miss out on a nine-darter, just failing on double 12 when throwing for the match in a 3-1 win over Connor Scutt. Ninth seed Heta hit four 100-plus checkouts to come from a set down against Scutt in a match in which both men averaged more than 97.\n",
             "\n",
             "Littler was hugged by his parents after victory over Meikle\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1032,7 +1034,8 @@
             "These achievements highlight Littler's exceptional talent and his continued rise in professional darts.\n",
             "Time taken: 1.09 seconds\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1097,4 +1100,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb b/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
index 45731367..08d3e782 100644
--- a/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
+++ b/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
@@ -16,7 +16,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/claudeai/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb).\n",
+        "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/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb).\n",
         "\n",
         "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."
       ]
@@ -1046,4 +1046,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb b/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
index 609604d6..3efc06d0 100644
--- a/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
+++ b/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb
@@ -16,7 +16,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/claudeai/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb).\n",
+        "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/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb).\n",
         "\n",
         "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."
       ]
@@ -701,7 +701,8 @@
             "Score: 0.5792, Text: After Fifa awards Saudi Arabia the hosting rights for the men's 2034 World Cup, BBC analysis editor Ros Atkins looks at how we got here and the controversies surrounding the decision.\n",
             "--------------------------------------------------------------------------------\n",
             "Score: 0.5877, Text: FA still to decide on endorsing Saudi World Cup bid\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -837,7 +838,8 @@
             "Score: 0.5792, Text: After Fifa awards Saudi Arabia the hosting rights for the men's 2034 World Cup, BBC analysis editor Ros Atkins looks at how we got here and the controversies surrounding the decision.\n",
             "--------------------------------------------------------------------------------\n",
             "Score: 0.5877, Text: FA still to decide on endorsing Saudi World Cup bid\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1084,4 +1086,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb b/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb
index e920b00f..7f37963a 100644
--- a/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb
+++ b/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb
@@ -17,7 +17,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/cohere/RAG_with_Couchbase_and_Cohere.ipynb).\n",
+        "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/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb).\n",
         "\n",
         "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."
       ]
@@ -804,7 +804,8 @@
             "--------------------------------------------------------------------------------\n",
             "Score: 0.6322, Text: 'Self-doubt, errors & big changes' - inside the crisis at Man City\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1015,4 +1016,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb b/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb
index d024ad09..9b724761 100644
--- a/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb
+++ b/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb
@@ -17,7 +17,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/cohere/RAG_with_Couchbase_and_Cohere.ipynb).\n",
+        "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/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb).\n",
         "\n",
         "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."
       ]
@@ -651,7 +651,8 @@
             "--------------------------------------------------------------------------------\n",
             "Distance: 0.3677, Text: 'Self-doubt, errors & big changes' - inside the crisis at Man City\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -798,7 +799,8 @@
             "--------------------------------------------------------------------------------\n",
             "Distance: 0.3677, Text: 'Self-doubt, errors & big changes' - inside the crisis at Man City\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1054,4 +1056,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb b/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb
index 3482469c..de6676af 100644
--- a/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb
+++ b/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb
@@ -13,7 +13,7 @@
         "How to run this tutorial\n",
         "----------------------\n",
         "This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run \n",
-        "interactively. You can access the original notebook here.\n",
+        "interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb).\n",
         "\n",
         "You can either:\n",
         "- Download the notebook file and run it on [Google Colab](https://colab.research.google.com)\n",
@@ -1209,4 +1209,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb b/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb
index 5bbece55..16079a09 100644
--- a/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb
+++ b/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb
@@ -21,7 +21,7 @@
       "id": "407ff72e",
       "metadata": {},
       "source": [
-        "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization.\n",
+        "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-fts)\n",
         "\n",
         "**Key Features:**\n",
         "- Custom CrewAI memory storage with Couchbase GSI vector search\n",
@@ -29,7 +29,9 @@
         "- Agent memory persistence across conversations\n",
         "- Performance benchmarks showing GSI benefits\n",
         "\n",
-        "**Requirements:** Couchbase Server 8.0+ or Capella with Query Service enabled."
+        "**Requirements:** Couchbase Server 8.0+ or Capella with Query Service enabled.\n",
+        "\n",
+        "You can access this notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb)."
       ]
     },
     {
@@ -1650,7 +1652,8 @@
             "Distance: 0.22963345721993045 (lower = more similar)\n",
             "--------------------------------------------------------------------------------\n",
             "Context: **Manchester City’s Impeccable Form: A Reflection of Guardiola’s Philosophy**\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1741,4 +1744,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb b/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb
index 90e7f78e..ad9c4e61 100644
--- a/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb
+++ b/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb
@@ -11,7 +11,7 @@
         "How to run this tutorial\n",
         "----------------------\n",
         "This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run \n",
-        "interactively. You can access the original notebook here.\n",
+        "interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb).\n",
         "\n",
         "You can either:\n",
         "- Download the notebook file and run it on [Google Colab](https://colab.research.google.com)\n",
diff --git a/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb b/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb
index dc0274f1..ddd66fe4 100644
--- a/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb
+++ b/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb
@@ -21,7 +21,7 @@
       "id": "7e91202c",
       "metadata": {},
       "source": [
-        "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. 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 FTS index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-using-fts)"
+        "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. 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 FTS index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-with-fts/)"
       ]
     },
     {
@@ -37,7 +37,7 @@
       "id": "4e84bba4",
       "metadata": {},
       "source": [
-        "This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run interactively. You can access the original notebook here.\n",
+        "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/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb).\n",
         "\n",
         "You can either:\n",
         "- Download the notebook file and run it on [Google Colab](https://colab.research.google.com)\n",
@@ -1575,4 +1575,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
index d31b6509..270e49de 100644
--- a/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
+++ b/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
@@ -12,7 +12,11 @@
                 "- Haystack framework for the RAG pipeline\n",
                 "- OpenAI for embeddings and text generation\n",
                 "\n",
-                "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles."
+                "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-global-secondary-index)\n",
+                "\n",
+                "\n",
+                "This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run \n",
+                "interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb)."
             ]
         },
         {
diff --git a/haystack/fts/frontmatter.md b/haystack/fts/frontmatter.md
index 53149c44..91b1a588 100644
--- a/haystack/fts/frontmatter.md
+++ b/haystack/fts/frontmatter.md
@@ -1,10 +1,10 @@
 ---
 # frontmatter
-path: "/tutorial-openai-haystack-rag"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack"
-short_title: "RAG with Openai and Haystack"
+path: "/tutorial-openai-haystack-rag-with-fts"
+title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack using FTS Service"
+short_title: "RAG with Openai and Haystack using FTS"
 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 FTS Service.
   - 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
diff --git a/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
index d07c49b0..e6982d15 100644
--- a/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
+++ b/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
@@ -14,9 +14,12 @@
     "- Haystack framework for the RAG pipeline\n",
     "- OpenAI for embeddings and text generation\n",
     "\n",
-    "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations.\n",
+    "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-fts)\n",
     "\n",
-    "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 will equip you with the knowledge to create a fully functional RAG system using OpenAI Services and Haystack with Couchbase's advanced GSI vector search."
+    "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 will equip you with the knowledge to create a fully functional RAG system using OpenAI Services and Haystack with Couchbase's advanced GSI vector search.\n",
+    "\n",
+    "This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run \n",
+    "interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb)."
    ]
   },
   {
diff --git a/haystack/gsi/frontmatter.md b/haystack/gsi/frontmatter.md
index 2d51dc11..2c9c5df8 100644
--- a/haystack/gsi/frontmatter.md
+++ b/haystack/gsi/frontmatter.md
@@ -1,8 +1,8 @@
 ---
 # frontmatter
-path: "/tutorial-openai-haystack-rag"
-title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack"
-short_title: "RAG with Openai and Haystack"
+path: "/tutorial-openai-haystack-rag-with-global-secondary-index"
+title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack with GSI"
+short_title: "RAG with Openai and Haystack with GSI"
 description:
   - Learn how to build a semantic search engine using Couchbase's GSI vector index.
   - This tutorial demonstrates how to integrate Couchbase's GSI vector search capabilities with OpenAI embeddings.
diff --git a/huggingface/fts/hugging_face.ipynb b/huggingface/fts/hugging_face.ipynb
index 4e3265a3..31b436a8 100644
--- a/huggingface/fts/hugging_face.ipynb
+++ b/huggingface/fts/hugging_face.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# How to run this tutorial\n",
     "\n",
-    "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/huggingface/hugging_face.ipynb).\n",
+    "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/huggingface/fts/hugging_face.ipynb).\n",
     "\n",
     "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."
    ]
diff --git a/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb b/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb
index 1193e4e6..434db0a8 100644
--- a/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb
+++ b/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb
@@ -16,7 +16,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/jinaai/RAG_with_Couchbase_and_Jina_AI.ipynb).\n",
+        "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/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb).\n",
         "\n",
         "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."
       ]
@@ -837,7 +837,8 @@
             "--------------------------------------------------------------------------------\n",
             "Score: 0.6207, Text: Manchester City boss Pep Guardiola has won 18 trophies since he arrived at the club in 2016\n",
             "\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1106,4 +1107,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb b/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb
index ffbdcb3c..6ee9b44f 100644
--- a/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb
+++ b/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb
@@ -21,7 +21,7 @@
    "id": "569c4838",
    "metadata": {},
    "source": [
-    "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system.\n",
+    "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-fts)\n",
     "\n",
     "**Key Features:**\n",
     "- High-performance GSI vector search with BHIVE indexing\n",
@@ -45,7 +45,7 @@
    "id": "c1f64ee4",
    "metadata": {},
    "source": [
-    "This tutorial is available as a Jupyter Notebook that you can run interactively on [Google Colab](https://colab.research.google.com/) or locally by setting up the Python environment."
+    "This tutorial is available as a Jupyter Notebook that you can run interactively on [Google Colab](https://colab.research.google.com/) or locally by setting up the Python environment. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb)."
    ]
   },
   {
diff --git a/lamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/llamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
similarity index 100%
rename from lamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
rename to llamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
diff --git a/lamaindex/fts/frontmatter.md b/llamaindex/fts/frontmatter.md
similarity index 100%
rename from lamaindex/fts/frontmatter.md
rename to llamaindex/fts/frontmatter.md
diff --git a/lamaindex/fts/fts_index.json b/llamaindex/fts/fts_index.json
similarity index 100%
rename from lamaindex/fts/fts_index.json
rename to llamaindex/fts/fts_index.json
diff --git a/lamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/llamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
similarity index 100%
rename from lamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
rename to llamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
diff --git a/lamaindex/gsi/___frontmatter._____md b/llamaindex/gsi/___frontmatter._____md
similarity index 100%
rename from lamaindex/gsi/___frontmatter._____md
rename to llamaindex/gsi/___frontmatter._____md
diff --git a/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb b/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
index 0b2dc603..387bb2bd 100644
--- a/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
+++ b/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
@@ -14,7 +14,7 @@
    "source": [
     "# How to run this tutorial\n",
     "\n",
-    "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/openrouter-deepseek/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb).\n",
+    "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/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb).\n",
     "\n",
     "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."
    ]
diff --git a/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb b/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
index 7191f308..cbde7f8c 100644
--- a/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
+++ b/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb
@@ -14,7 +14,7 @@
       "source": [
         "# How to run this tutorial\n",
         "\n",
-        "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/openrouter-deepseek/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb).\n",
+        "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/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb).\n",
         "\n",
         "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."
       ]
@@ -681,7 +681,8 @@
             "A tearful Luke Littler hit a tournament record 140.91 set average as he started his bid for the PDC World Championship title with a dramatic 3-1 win over Ryan Meikle. The 17-year-old made headlines around the world when he reached the tournament final in January, where he lost to Luke Humphries. Starting this campaign on Saturday, Littler was millimetres away from a nine-darter when he missed double 12 as he blew Meikle away in the fourth and final set of the second-round match. Littler was overcome with emotion at the end, cutting short his on-stage interview. \"It was probably the toughest game I've ever played. I had to fight until the end,\" he said later in a news conference. \"As soon as the question came on stage and then boom, the tears came. It was just a bit too much to speak on stage. \"It is the worst game I have played. I have never felt anything like that tonight.\" Admitting to nerves during the match, he told Sky Sports: \"Yes, probably the biggest time it's hit me. Coming into it I was fine, but as soon as [referee] George Noble said 'game on', I couldn't throw them.\" Littler started slowly against Meikle, who had two darts for the opening set, but he took the lead by twice hitting double 20. Meikle did not look overawed against his fellow Englishman and levelled, but Littler won the third set and exploded into life in the fourth. The tournament favourite hit four maximum 180s as he clinched three straight legs in 11, 10 and 11 darts for a record set average, and 100.85 overall. Meanwhile, two seeds crashed out on Saturday night – five-time world champion Raymond van Barneveld lost to Welshman Nick Kenny, while England's Ryan Joyce beat Danny Noppert. Australian Damon Heta was another to narrowly miss out on a nine-darter, just failing on double 12 when throwing for the match in a 3-1 win over Connor Scutt. Ninth seed Heta hit four 100-plus checkouts to come from a set down against Scutt in a match in which both men averaged more than 97.\n",
             "\n",
             "Littler was hugged by his parents after victory over Meikle\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -829,7 +830,8 @@
             "A tearful Luke Littler hit a tournament record 140.91 set average as he started his bid for the PDC World Championship title with a dramatic 3-1 win over Ryan Meikle. The 17-year-old made headlines around the world when he reached the tournament final in January, where he lost to Luke Humphries. Starting this campaign on Saturday, Littler was millimetres away from a nine-darter when he missed double 12 as he blew Meikle away in the fourth and final set of the second-round match. Littler was overcome with emotion at the end, cutting short his on-stage interview. \"It was probably the toughest game I've ever played. I had to fight until the end,\" he said later in a news conference. \"As soon as the question came on stage and then boom, the tears came. It was just a bit too much to speak on stage. \"It is the worst game I have played. I have never felt anything like that tonight.\" Admitting to nerves during the match, he told Sky Sports: \"Yes, probably the biggest time it's hit me. Coming into it I was fine, but as soon as [referee] George Noble said 'game on', I couldn't throw them.\" Littler started slowly against Meikle, who had two darts for the opening set, but he took the lead by twice hitting double 20. Meikle did not look overawed against his fellow Englishman and levelled, but Littler won the third set and exploded into life in the fourth. The tournament favourite hit four maximum 180s as he clinched three straight legs in 11, 10 and 11 darts for a record set average, and 100.85 overall. Meanwhile, two seeds crashed out on Saturday night – five-time world champion Raymond van Barneveld lost to Welshman Nick Kenny, while England's Ryan Joyce beat Danny Noppert. Australian Damon Heta was another to narrowly miss out on a nine-darter, just failing on double 12 when throwing for the match in a 3-1 win over Connor Scutt. Ninth seed Heta hit four 100-plus checkouts to come from a set down against Scutt in a match in which both men averaged more than 97.\n",
             "\n",
             "Littler was hugged by his parents after victory over Meikle\n",
-            "\n... (output truncated for brevity)\n"
+            "\n",
+            "... (output truncated for brevity)\n"
           ]
         }
       ],
@@ -1084,4 +1086,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
\ No newline at end of file
+}