added kaggle button

Author: steven-ja

content/posts/machine learning/deep learning/NLP/Gemma2+RAG/index.md

@@ -15,6 +15,8 @@ categories: ["NLP"]
 
 
 
+*Open in:*[<img src="https://www.kaggle.com/static/images/logos/kaggle-logo-gray-300.png" width="45"/>](https://www.kaggle.com/code/stefanogiannini/gemma-2-rag-llamaindex-faiss/)
+
 
 
 ## 1. Introduction

public/index.json

@@ -12,7 +12,8 @@
       <pubDate>Sun, 14 Jul 2024 00:00:00 +0100</pubDate>
 
       <guid>http://localhost:1313/posts/machine-learning/deep-learning/nlp/gemma2&#43;rag/</guid>
-      <description>1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
+      <description>Open in:
+1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
 2. Setup and Import %pip install transformers accelerate bitsandbytes flash-attn faiss-cpu llama-index -Uq %pip install llama-index-embeddings-huggingface -q %pip install llama-index-llms-huggingface -q %pip install llama-index-embeddings-instructor llama-index-vector-stores-faiss -q import contextlib import os import torch device = torch.</description>
     </item>
 

public/index.xml

@@ -486,7 +486,8 @@
     <div class="card-body">
       <a href="/posts/machine-learning/deep-learning/nlp/gemma2&#43;rag/" class="post-card-link">
         <h5 class="card-title">Gemma-2 &#43; RAG &#43; LlamaIndex &#43; VectorDB</h5>
-        <p class="card-text post-summary">1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
+        <p class="card-text post-summary">Open in:
+1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
 2. Setup and Import %pip install transformers accelerate bitsandbytes flash-attn faiss-cpu llama-index -Uq %pip install llama-index-embeddings-huggingface -q %pip install llama-index-llms-huggingface -q %pip install llama-index-embeddings-instructor llama-index-vector-stores-faiss -q import contextlib import os import torch device = torch.</p>
       </a>
 

public/posts/index.html

@@ -12,7 +12,8 @@
       <pubDate>Sun, 14 Jul 2024 00:00:00 +0100</pubDate>
 
       <guid>http://localhost:1313/posts/machine-learning/deep-learning/nlp/gemma2&#43;rag/</guid>
-      <description>1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
+      <description>Open in:
+1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
 2. Setup and Import %pip install transformers accelerate bitsandbytes flash-attn faiss-cpu llama-index -Uq %pip install llama-index-embeddings-huggingface -q %pip install llama-index-llms-huggingface -q %pip install llama-index-embeddings-instructor llama-index-vector-stores-faiss -q import contextlib import os import torch device = torch.</description>
     </item>
 

public/posts/index.xml

@@ -528,7 +528,8 @@ <h1>Gemma-2 &#43; RAG &#43; LlamaIndex &#43; VectorDB</h1>
 
 
         <div class="post-content" id="post-content">
-          <h2 id="1-introduction">1. Introduction</h2>
+          <p><em>Open in:</em><a href="https://www.kaggle.com/code/stefanogiannini/gemma-2-rag-llamaindex-faiss/" target="_blank" rel="noopener"><img src="https://www.kaggle.com/static/images/logos/kaggle-logo-gray-300.png" width="45"/></a></p>
+<h2 id="1-introduction">1. Introduction</h2>
 <p>Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.</p>
 <p><a href="https://mermaid.live/edit#pako:eNp1kt9ugjAUxl-l6ZXLdA_AxRIF_0VQp9tuiheVHpEEWlLaZcb47ju2uOGWcXHC6fdr-fpxzjRTAmhAc83rI3mNUknwGbK3BjR5saBPOzIYPJMRcw1Za5VB0xQy33l05OSQRYWGzJA4Tv5jIrYZTv-KvjZ27y2k9J5KqQfGLOKGk7nMoTGFkt7WhI2rPQiBIAk1cKf4DRMHTNk72lKabLHAbyZyzIyFShr4NGQDRhfwwctWnzl9ztBOVRuCWGO0zTongBTda_gaum0LhmG02NyvdKGFW4rZBpoazwUyBQm66y52RMJW1tTWkEeyVVZjLHfBjcngCakl60UqsxVI46_60FJLr8--Q7l2K9b7yS3BCShv-OoOn_puzXrdFG_sumVpn1agK14IHKTzVUupOUIFKQ3wVcCB29Jcf-QFUW6N2p5kRgOMEvpUK5sfaXDgZYOdrQU3EBUcp6G6ISAK_G7iJzVT8lDk9PIFuI7TUA" target="_blank" rel="noopener"><img src="https://mermaid.ink/img/pako:eNp1kt9ugjAUxl-l6ZXLdA_AxRIF_0VQp9tuiheVHpEEWlLaZcb47ju2uOGWcXHC6fdr-fpxzjRTAmhAc83rI3mNUknwGbK3BjR5saBPOzIYPJMRcw1Za5VB0xQy33l05OSQRYWGzJA4Tv5jIrYZTv-KvjZ27y2k9J5KqQfGLOKGk7nMoTGFkt7WhI2rPQiBIAk1cKf4DRMHTNk72lKabLHAbyZyzIyFShr4NGQDRhfwwctWnzl9ztBOVRuCWGO0zTongBTda_gaum0LhmG02NyvdKGFW4rZBpoazwUyBQm66y52RMJW1tTWkEeyVVZjLHfBjcngCakl60UqsxVI46_60FJLr8--Q7l2K9b7yS3BCShv-OoOn_puzXrdFG_sumVpn1agK14IHKTzVUupOUIFKQ3wVcCB29Jcf-QFUW6N2p5kRgOMEvpUK5sfaXDgZYOdrQU3EBUcp6G6ISAK_G7iJzVT8lDk9PIFuI7TUA?type=png"></a></p>
 <h2 id="2-setup-and-import">2. Setup and Import</h2>