From b885b2fa3c2519c399ed4b9a3dd4c2f6f62235d1 Mon Sep 17 00:00:00 2001
From: Farzad Sunavala <40604067+farzad528@users.noreply.github.com>
Date: Mon, 27 Jan 2025 02:40:35 -0600
Subject: [PATCH] docs: added markdown headings to enable TOC in github pages
 (#808)

* docs: added markdown headings to enable TOC in github pages

Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com>

* minor renames

Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com>

* part 3 heading

Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com>

---------

Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com>
---
 docs/examples/rag_azuresearch.ipynb | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb
index 4350ec2b..4f405dea 100644
--- a/docs/examples/rag_azuresearch.ipynb
+++ b/docs/examples/rag_azuresearch.ipynb
@@ -33,6 +33,7 @@
    "metadata": {},
    "source": [
     "\n",
+    "## A recipe 🧑‍🍳 🐥 💚\n",
     "\n",
     "This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:\n",
     "- [Docling](https://ds4sd.github.io/docling/) for document parsing and chunking\n",
@@ -61,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Part 0: Prerequisites\n",
+    "### Part 0: Prerequisites\n",
     " - **Azure AI Search** resource\n",
     " - **Azure OpenAI** resource with a deployed embedding and chat completion model (e.g. `text-embedding-3-small` and `gpt-4o`) \n",
     " - **Docling 2.12+** (installs `docling_core` automatically)  Docling installed (Python 3.8+ environment)\n",
@@ -114,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Part 1: Parse the PDF with Docling\n",
+    "### Part 1: Parse the PDF with Docling\n",
     "\n",
     "We’ll parse the **Microsoft GraphRAG Research Paper** (~15 pages). Parsing should be relatively quick, even on CPU, but it will be faster on a GPU or MPS device if available.\n",
     "\n",
@@ -235,7 +236,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Part 2: Hierarchical Chunking\n",
+    "### Part 2: Hierarchical Chunking\n",
     "We convert the `Document` into smaller chunks for embedding and indexing. The built-in `HierarchicalChunker` preserves structure. "
    ]
   },
@@ -276,7 +277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Part 3: Create Azure Search Index and Push Chunk Embeddings\n",
+    "### Part 3: Create Azure Search Index and Push Chunk Embeddings\n",
     "We’ll define a vector index in Azure AI Search, then embed each chunk using Azure OpenAI and upload in batches."
    ]
   },
@@ -382,7 +383,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Embed and Upsert to Azure AI Search\n"
+    "#### Generate Embeddings and Upload to Azure AI Search\n"
    ]
   },
   {
@@ -497,8 +498,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Part 4: RAG Query with Azure OpenAI\n",
-    "Combine retrieval from Azure Search with Chat Completions (aka. grounding your LLM)"
+    "### Part 4: Perform RAG I\n",
+    "Combine retrieval from Azure AI Search with Azure OpenAI Chat Completions (aka. grounding your LLM)"
    ]
   },
   {