Fix: Render architecture diagram correctly in README

end-to-end-use-cases/technical_blogger/readme.md

@@ -1,38 +1,42 @@
-# Technical Blog Generator with RAG and Llama
+# ✍️ Technical Blog Generator with Llama 🦙
 
-This project provides a practical recipe for building an AI-powered technical blog generator leveraging LLama 4. It demonstrates how to combine the power of a Llama4 with a local, in-memory vector database (Qdrant) to synthesize accurate, relevant, and well-structured technical blog posts from your existing documentation.
+This project provides a practical recipe for building an AI-powered technical blog generator leveraging **Llama 4**. It demonstrates how to combine the power of Llama 4 with a local, in-memory vector database (Qdrant) to synthesize accurate, relevant, and well-structured technical blog posts from your existing documentation.
 
-##  Features :
+---
+
+## ✨ Features
 
 Integrating a Llama LLM with a vector database via a RAG approach offers significant advantages over using an LLM alone:
 
-* **Grounded Content**: The LLM is "grounded" in your specific technical documentation. This drastically reduces the likelihood of hallucinations and ensures the generated content is factually accurate and directly relevant to your knowledge base.
-* **Up-to-Date Information**: By updating your local knowledge base (the data you ingest into Qdrant), the system can stay current with the latest information without requiring the expensive and time-consuming process of retraining the entire LLM.
-* **Domain-Specific Expertise**: The generated blogs are enriched with precise, domain-specific details, including code snippets, configuration examples, and architectural explanations, all directly drawn from the provided context.
-* **Structured Output**: The system is prompted to produce highly structured output, featuring clear sections, subsections, and even descriptions for diagrams, making the blog post nearly ready for publication.
+* **🔍 Grounded Content**: The LLM is "grounded" in your specific technical documentation. This drastically reduces the likelihood of hallucinations and ensures the generated content is factually accurate and directly relevant to your knowledge base.
+* **🔄 Up-to-Date Information**: By updating your local knowledge base (the data you ingest into Qdrant), the system can stay current with the latest information without requiring the expensive and time-consuming process of retraining the entire LLM.
+* **💡 Domain-Specific Expertise**: The generated blogs are enriched with precise, domain-specific details, including code snippets, configuration examples, and architectural explanations, all directly drawn from the provided context.
+* **📐 Structured Output**: The system is prompted to produce highly structured output, featuring clear sections, subsections, and even descriptions for diagrams, making the blog post nearly ready for publication.
 
-## Architecture Overview
+---
 
-The system follows a standard RAG pipeline, adapted for local development:
+## 🏗️ Architecture Overview
 
-1.  **Data Ingestion**: Your technical documentation is processed and split into smaller, semantically meaningful chunks of text.
-2.  **Indexing**: An embedding model (e.g., `all-MiniLM-L6-v2`) converts these text chunks into numerical vector embeddings. These vectors are then stored in an **in-memory Qdrant vector database**.
-3.  **Retrieval**: When a user specifies a blog topic, a query embedding is generated. This embedding is used to search the Qdrant database for the most relevant document chunks from your ingested knowledge base.
-4.  **Generation**: The retrieved relevant chunks, combined with the user's desired topic and a carefully crafted system prompt, are fed into the Llama model via its API. The Llama model then generates a comprehensive and detailed technical blog post based on this provided context.
+The system follows a standard RAG pipeline, adapted for local development:
 
+1.  **📚 Data Ingestion**: Your technical documentation is processed and split into smaller, semantically meaningful chunks of text.
+2.  **📊 Indexing**: An embedding model (e.g., `all-MiniLM-L6-v2`) converts these text chunks into numerical vector embeddings. These vectors are then stored in an **in-memory Qdrant vector database**.
+3.  **🔎 Retrieval**: When a user specifies a blog topic, a query embedding is generated. This embedding is used to search the Qdrant database for the most relevant document chunks from your ingested knowledge base.
+4.  **📝 Generation**: The retrieved relevant chunks, combined with the user's desired topic and a carefully crafted system prompt, are fed into the Llama model via its API. The Llama model then generates a comprehensive and detailed technical blog post based on this provided context.
 
+## 🛠️ Prerequisites
 
-## Prerequisites
+* 🐍 Python 3.8 or higher
+* 🔑 A Llama API key (obtained from [Llama's official site](https://www.llama.com/))
+* 📦 `pip` for installing Python packages
 
-* Python 3.8 or higher
-* A Llama API key (obtained from [Llama's official site](https://www.llama.com/))
-* `pip` for installing Python packages
+---
 
-## Getting Started
+## 🚀 Getting Started
 
 Follow these steps to set up and run the technical blog generator.
 
-### Step 1: Clone the Repository
+### Step 1: Clone the Repository ⬇️
 
 First, clone the `llama-cookbook` repository and navigate to the specific recipe directory:
 
@@ -40,14 +44,7 @@ First, clone the `llama-cookbook` repository and navigate to the specific recipe
 git clone [https://github.com/your-github-username/llama-cookbook.git](https://github.com/your-github-username/llama-cookbook.git) # Replace with actual repo URL if different
 cd llama-cookbook/end-to-end-use-cases/technical_blogger
 
-
-
-
-Step 2: Set Up Your Python Environment
-
-Step 3: Configure Your API Key
-
-Step 4: Prepare Your Knowledge Base (Data Ingestion)
-
-Step 5: Run the Notebook
-
+### Step 2: Set Up Your Python Environment 🧑‍💻
+### Step 3: Configure Your API Key 🔐
+### Step 4: Prepare Your Knowledge Base (Data Ingestion) 🧠
+### Step 5: Run the Notebook ▶️