Generated markdown tutorials from Jupyter Notebooks

actions-user · actions-user · commit eee042208416 · 2025-10-31T09:39:33.000Z
Generated from: couchbase-examples/vector-search-cookbook
diff --git a/tutorial/markdown/generated/vector-search-cookbook/awsbedrock-fts-RAG_with_Couchbase_and_Bedrock.md b/tutorial/markdown/generated/vector-search-cookbook/awsbedrock-fts-RAG_with_Couchbase_and_Bedrock.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/awsbedrock-gsi-RAG_with_Couchbase_and_Bedrock.md b/tutorial/markdown/generated/vector-search-cookbook/awsbedrock-gsi-RAG_with_Couchbase_and_Bedrock.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/azure-fts-RAG_with_Couchbase_and_AzureOpenAI.md b/tutorial/markdown/generated/vector-search-cookbook/azure-fts-RAG_with_Couchbase_and_AzureOpenAI.md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/azure-gsi-RAG_with_Couchbase_and_AzureOpenAI.md b/tutorial/markdown/generated/vector-search-cookbook/azure-gsi-RAG_with_Couchbase_and_AzureOpenAI.md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/claudeai-fts-RAG_with_Couchbase_and_Claude(by_Anthropic).md b/tutorial/markdown/generated/vector-search-cookbook/claudeai-fts-RAG_with_Couchbase_and_Claude(by_Anthropic).md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/claudeai-gsi-RAG_with_Couchbase_and_Claude(by_Anthropic).md b/tutorial/markdown/generated/vector-search-cookbook/claudeai-gsi-RAG_with_Couchbase_and_Claude(by_Anthropic).md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/cohere-fts-RAG_with_Couchbase_and_Cohere.md b/tutorial/markdown/generated/vector-search-cookbook/cohere-fts-RAG_with_Couchbase_and_Cohere.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/cohere-gsi-RAG_with_Couchbase_and_Cohere.md b/tutorial/markdown/generated/vector-search-cookbook/cohere-gsi-RAG_with_Couchbase_and_Cohere.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/crewai-fts-RAG_with_Couchbase_and_CrewAI.md b/tutorial/markdown/generated/vector-search-cookbook/crewai-fts-RAG_with_Couchbase_and_CrewAI.md
@@ -34,7 +34,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 How to run this tutorial
 ----------------------
 This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run 
-interactively. You can access the original notebook here.
+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).
 
 You can either:
 - Download the notebook file and run it on [Google Colab](https://colab.research.google.com)
diff --git a/tutorial/markdown/generated/vector-search-cookbook/crewai-gsi-RAG_with_Couchbase_and_CrewAI.md b/tutorial/markdown/generated/vector-search-cookbook/crewai-gsi-RAG_with_Couchbase_and_CrewAI.md
@@ -31,11 +31,11 @@ length: 60 Mins
 
 ## Overview
 
-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/)
 
 ## How to Run This Tutorial
 
-This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run interactively. You can access the original notebook here.
+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).
 
 You can either:
 - Download the notebook file and run it on [Google Colab](https://colab.research.google.com)
diff --git a/tutorial/markdown/generated/vector-search-cookbook/crewai-short-term-memory-fts-CouchbaseStorage_Demo.md b/tutorial/markdown/generated/vector-search-cookbook/crewai-short-term-memory-fts-CouchbaseStorage_Demo.md
@@ -36,7 +36,7 @@ Here's a breakdown of each section:
 How to run this tutorial
 ----------------------
 This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run 
-interactively. You can access the original notebook here.
+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).
 
 You can either:
 - Download the notebook file and run it on [Google Colab](https://colab.research.google.com)
diff --git a/tutorial/markdown/generated/vector-search-cookbook/crewai-short-term-memory-gsi-CouchbaseStorage_Demo.md b/tutorial/markdown/generated/vector-search-cookbook/crewai-short-term-memory-gsi-CouchbaseStorage_Demo.md
@@ -31,7 +31,7 @@ length: 45 Mins
 
 ## Overview
 
-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.
+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)
 
 **Key Features:**
 - Custom CrewAI memory storage with Couchbase GSI vector search
@@ -41,6 +41,8 @@ This tutorial shows how to implement a custom memory backend for CrewAI agents u
 
 **Requirements:** Couchbase Server 8.0+ or Capella with Query Service enabled.
 
+You can access this notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb).
+
 ## Prerequisites
 
 ### Couchbase Setup
diff --git a/tutorial/markdown/generated/vector-search-cookbook/huggingface-fts-hugging_face.md b/tutorial/markdown/generated/vector-search-cookbook/huggingface-fts-hugging_face.md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/jinaai-fts-RAG_with_Couchbase_and_Jina_AI.md b/tutorial/markdown/generated/vector-search-cookbook/jinaai-fts-RAG_with_Couchbase_and_Jina_AI.md
@@ -32,7 +32,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/jinaai-gsi-RAG_with_Couchbase_and_Jina_AI.md b/tutorial/markdown/generated/vector-search-cookbook/jinaai-gsi-RAG_with_Couchbase_and_Jina_AI.md
@@ -31,7 +31,7 @@ length: 60 Mins
 
 ## Overview
 
-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.
+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)
 
 **Key Features:**
 - High-performance GSI vector search with BHIVE indexing
@@ -43,7 +43,7 @@ This tutorial demonstrates building a high-performance semantic search engine us
 
 ## How to Run This Tutorial
 
-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).
 
 ## Prerequisites
 
diff --git a/tutorial/markdown/generated/vector-search-cookbook/openrouter-deepseek-fts-RAG_with_Couchbase_and_Openrouter_Deepseek.md b/tutorial/markdown/generated/vector-search-cookbook/openrouter-deepseek-fts-RAG_with_Couchbase_and_Openrouter_Deepseek.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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/tutorial/markdown/generated/vector-search-cookbook/openrouter-deepseek-gsi-RAG_with_Couchbase_and_Openrouter_Deepseek.md b/tutorial/markdown/generated/vector-search-cookbook/openrouter-deepseek-gsi-RAG_with_Couchbase_and_Openrouter_Deepseek.md
@@ -33,7 +33,7 @@ In this guide, we will walk you through building a powerful semantic search engi
 
 # How to run this tutorial
 
-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).
+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).
 
 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.
 
