Generated markdown tutorials from Jupyter Notebooks

Generated from: couchbase-examples/vector-search-cookbook

Author: actions-user

…gsi-RAG_with_Couchbase_and_SmolAgents.md …nts-gsi-RAG_with_Couchbase_SmolAgents.mdtutorial/markdown/generated/vector-search-cookbook/smolagents-gsi-RAG_with_Couchbase_and_SmolAgents.md renamed to tutorial/markdown/generated/vector-search-cookbook/smolagents-gsi-RAG_with_Couchbase_SmolAgents.md tutorial/markdown/generated/vector-search-cookbook/smolagents-gsi-RAG_with_Couchbase_and_SmolAgents.md renamed to tutorial/markdown/generated/vector-search-cookbook/smolagents-gsi-RAG_with_Couchbase_SmolAgents.md

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 <!--- *** WARNING ***: Autogenerated markdown file from jupyter notebook. ***DO NOT EDIT THIS FILE***. Changes should be made to the original notebook file. See commit message for source repo. -->
 
 
-[View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/smolagents/gsi/RAG_with_Couchbase_and_SmolAgents.ipynb)
+[View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/smolagents/gsi/RAG_with_Couchbase_SmolAgents.ipynb)
 
 # Introduction
 In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database, [OpenAI](https://openai.com) as the embedding and LLM provider, and [Hugging Face smolagents](https://huggingface.co/docs/smolagents/en/index) as an agent framework. 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 using GSI (Global Secondary Index) 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-smolagents-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](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/smolagents/gsi/RAG_with_Couchbase_and_SmolAgents.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/smolagents/gsi/RAG_with_Couchbase_SmolAgents.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.