Renamed folder to blog_generator, updated references, and removed try-except block

end-to-end-use-cases/blog_generator/.env

@@ -0,0 +1,2 @@
+LLAMA_API_KEY= replace with llama api key
+QDRANT_API_KEY = replace the qdrant key

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

@@ -0,0 +1,63 @@
+# ✍️ 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 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
+
+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.
+* **tructured 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:
+
+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
+
+* Python 3.8 or higher
+* A Llama API key (obtained from [Llama's official site](https://www.llama.com/) or refer to the [Llama Developer Documentation](https://llama.developer.meta.com/docs/overview/))
+* A Qdrant account and API key (refer to the [Qdrant Cloud Account Setup documentation](https://qdrant.tech/documentation/cloud-account-setup/))
+* `pip` for installing Python packages
+
+---
+
+## Getting Started
+
+Follow these steps to set up and run the technical blog generator.
+
+### Step 1: Clone the Repository and setup your Python Environment
+
+First, clone the `llama-cookbook` repository and navigate to the specific recipe directory as per the below:
+
+```bash
+git clone https://github.com/meta-llama/llama-cookbook
+
+cd llama-cookbook/end-to-end-use-cases/technical_blogger
+
+pip install -r requirements.txt
+```
+
+
+### Step 2: Configure Your API Key 
+See the Prerequisites section for details on obtaining and configuring your Llama and Qdrant API keys.
+
+
+### Step 3: Prepare Your Knowledge Base (Data Ingestion) 
+Before generating a blog post, you'll need to prepare your knowledge base by populating a Qdrant collection with relevant data. You can use the provided [`setup_qdrant_collection.py`](setup_qdrant_collection.py) script to create and populate a Qdrant collection.
+
+For more information on setting up a Qdrant collection, refer to the [`setup_qdrant_collection.py`](setup_qdrant_collection.py) script.
+
+### Step 4: Run the Notebook 
+Once you've completed the previous steps, you can run the notebook to generate a technical blog post. Simply execute the cells in the [`Technical_Blog_Generator.ipynb`](Technical_Blog_Generator.ipynb) notebook, and it will guide you through the process of generating a high-quality blog post based on your technical documentation.

end-to-end-use-cases/blog_generator/requirements.txt

@@ -0,0 +1,6 @@
+requests
+qdrant-client
+sentence-transformers
+IPython
+jupyter
+python-dotenv

end-to-end-use-cases/blog_generator/setup_qdrant_collection.py

@@ -0,0 +1,104 @@
+
+
+
+"""
+Script to set up a Qdrant collection with provided markdown files.
+To use this script, replace the file paths in the NEW_COLLECTIONS list with your own markdown files.
+Then, run the script using Python: `python setup_qdrant_collection.py`
+"""
+
+
+
+from pathlib import Path
+from qdrant_client import QdrantClient, models
+from sentence_transformers import SentenceTransformer
+import uuid
+import re
+
+# Configuration - in case you want to create an online collection
+QDRANT_URL = "replace with your Qdrant URL"
+QDRANT_API_KEY = "replace with your qdrant API key"
+EMBEDDING_MODEL = 'all-MiniLM-L6-v2'
+
+# New files to process
+# IMPORTANT: Added the configuration for readme_blogs_latest here
+NEW_COLLECTIONS = [
+    {
+        "file_path": "path/to/your/markdown/file1.txt",
+        "collection_name": "example_collection_1"
+    },
+    {
+        "file_path": "path/to/your/markdown/file2.txt",
+        "collection_name": "example_collection_2"
+    }
+
+]
+
+def markdown_splitter(text, max_chunk=800):
+    sections = re.split(r'(?=^#+ .*)', text, flags=re.MULTILINE)
+    chunks = []
+    current_chunk = []
+    
+    for section in sections:
+        if len(''.join(current_chunk)) + len(section) > max_chunk:
+            chunks.append(''.join(current_chunk))
+            current_chunk = [section]
+        else:
+            current_chunk.append(section)
+    
+    if current_chunk:
+        chunks.append(''.join(current_chunk))
+    
+    return [{"text": chunk, "header": f"section_{i}"} for i, chunk in enumerate(chunks)]
+
+def get_qdrant_client():
+    return QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
+
+def get_embedding_model():
+    return SentenceTransformer(EMBEDDING_MODEL)
+
+def process_file(config):
+    client = get_qdrant_client()
+    embedding_model = get_embedding_model()
+    
+    # Create collection if not exists
+    if not client.collection_exists(config["collection_name"]):
+        client.create_collection(
+            collection_name=config["collection_name"],
+            vectors_config=models.VectorParams(
+                size=384,
+                distance=models.Distance.COSINE
+            )
+        )
+    
+    # Process and store documents
+    try:
+        text = Path(config["file_path"]).read_text(encoding='utf-8')
+        chunks = markdown_splitter(text)
+        
+        batch_size = 100
+        for i in range(0, len(chunks), batch_size):
+            batch = chunks[i:i+batch_size]
+            points = []
+            for chunk in batch:
+                embedding = embedding_model.encode(chunk["text"]).tolist()
+                points.append(
+                    models.PointStruct(
+                        id=str(uuid.uuid4()),
+                        vector=embedding,
+                        payload=chunk
+                    )
+                )
+            client.upsert(collection_name=config["collection_name"], points=points)
+        
+        print(f"Processed {len(chunks)} chunks for {config['collection_name']}")
+    except FileNotFoundError:
+        print(f"Error: The file at {config['file_path']} was not found. Skipping collection setup.")
+
+def setup_all_collections():
+    for config in NEW_COLLECTIONS:
+        process_file(config)
+    print("All collections created and populated successfully!")
+
+if __name__ == "__main__":
+    setup_all_collections()

end-to-end-use-cases/blog_generator/walkthrough.ipynb

@@ -0,0 +1,672 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9035a13b",
+   "metadata": {},
+   "source": [
+    "# Ensure the required libraries are installed i.e.\n",
+    "!pip install sentence-transformers qdrant-client requests IPython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2847fa",
+   "metadata": {},
+   "source": [
+    "# Step 1: Import necessary modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0930f7de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import uuid\n",
+    "import re\n",
+    "from pathlib import Path\n",
+    "from sentence_transformers import SentenceTransformer, CrossEncoder\n",
+    "from qdrant_client import QdrantClient, models\n",
+    "from qdrant_client.models import SearchRequest\n",
+    "import requests\n",
+    "from IPython.display import Markdown, display\n",
+    "import json\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02b715a9",
+   "metadata": {},
+   "source": [
+    "## Step 2: Define Configuration and Global Variables\n",
+    "\n",
+    "To use this example, follow these steps to configure your environment:\n",
+    "\n",
+    "1.  **Set up an account with Llama**: You can use the LLAMA API key with a model like `Llama-4-Maverick-17B-128E-Instruct-FP8`. However, you're not limited to this; you can choose any other inference provider's endpoint and respective LLAMA models that suit your needs.\n",
+    "2.  **Choose a Llama model or alternative**: Select a suitable Llama model for inference, such as `Llama-4-Maverick-17B-128E-Instruct-FP8`, or explore other available LLAMA models from your chosen inference provider.\n",
+    "3.  **Create a Qdrant account**: Sign up for a Qdrant account and generate an access token.\n",
+    "4.  **Set up a Qdrant collection**: Use the provided script (`setup_qdrant_collection.py`) to create and populate a Qdrant collection.\n",
+    "\n",
+    "For more information on setting up a Qdrant collection, refer to the `setup_qdrant_collection.py` script. This script demonstrates how to process files, split them into chunks, and store them in a Qdrant collection.\n",
+    "\n",
+    "Once you've completed these steps, you can define your configuration variables as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "a9030dae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LLAMA_API_KEY = os.getenv(\"LLAMA_API_KEY\") \n",
+    "if not LLAMA_API_KEY:\n",
+    "    raise ValueError(\"LLAMA_API_KEY not found. Please set it as an environment variable.\")\n",
+    "API_URL = \"https://api.llama.com/v1/chat/completions\"  # Replace with your chosen inference provider's API URL\n",
+    "HEADERS = {\n",
+    "    \"Content-Type\": \"application/json\",\n",
+    "    \"Authorization\": f\"Bearer {LLAMA_API_KEY}\"\n",
+    "}\n",
+    "LLAMA_MODEL = \"Llama-4-Maverick-17B-128E-Instruct-FP8\"  # Choose a suitable Llama model or replace with your preferred model\n",
+    "# Qdrant Configuration\n",
+    "QDRANT_URL = \"add your existing qdrant URL\"  # Replace with your Qdrant instance URL\n",
+    "QDRANT_API_KEY = os.getenv(\"QDRANT_API_KEY\") # Load from environment variable\n",
+    "if not QDRANT_API_KEY:\n",
+    "    raise ValueError(\"QDRANT_API_KEY not found. Please set it as an environment variable.\")\n",
+    "# The Qdrant collection to be queried. This should already exist.\n",
+    "MAIN_COLLECTION_NAME = \"readme_blogs_latest\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f13075f",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define Helper Functions\n",
+    "\n",
+    "In this step, we'll define several helper functions that are used throughout the blog generation process. These functions include:\n",
+    "\n",
+    "1.  **`get_qdrant_client`**: Returns a Qdrant client instance configured with your Qdrant URL and API key.\n",
+    "2.  **`query_qdrant`**: Queries Qdrant with hybrid search and reranking on a specified collection.\n",
+    "\n",
+    "These helper functions simplify the code and make it easier to manage the Qdrant interaction. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "87383cf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_qdrant_client():\n",
+    "    \"\"\"\n",
+    "    Returns a Qdrant client instance.\n",
+    "    \n",
+    "    :return: QdrantClient instance\n",
+    "\n",
+    "    \"\"\"\n",
+    "    return QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)\n",
+    "\n",
+    "def get_embedding_model():\n",
+    "    \"\"\"Returns the SentenceTransformer embedding model.\"\"\"\n",
+    "    return SentenceTransformer('all-MiniLM-L6-v2')\n",
+    "\n",
+    "def query_qdrant(query: str, client: QdrantClient, collection_name: str, top_k: int = 5) -> list:\n",
+    "    \"\"\"\n",
+    "    Query Qdrant with hybrid search and reranking on a specified collection.\n",
+    "    \n",
+    "    :param query: Search query\n",
+    "    :param client: QdrantClient instance\n",
+    "    :param collection_name: Name of the Qdrant collection\n",
+    "    :param top_k: Number of results to return (default: 5)\n",
+    "    :return: List of relevant chunks\n",
+    "    \"\"\"\n",
+    "    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+    "    query_embedding = embedding_model.encode(query).tolist()\n",
+    "    \n",
+    "    try:\n",
+    "        results = client.search(\n",
+    "            collection_name=collection_name,\n",
+    "            query_vector=query_embedding,\n",
+    "            limit=top_k*2\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error during Qdrant search on collection '{collection_name}': {e}\")\n",
+    "        return []\n",
+    "    \n",
+    "    if not results:\n",
+    "        print(\"No results found in Qdrant for the given query.\")\n",
+    "        return []\n",
+    "    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')\n",
+    "    pairs = [(query, hit.payload[\"text\"]) for hit in results]\n",
+    "    scores = cross_encoder.predict(pairs)\n",
+    "    \n",
+    "    sorted_results = [x for _, x in sorted(zip(scores, results), key=lambda pair: pair[0], reverse=True)]\n",
+    "    return sorted_results[:top_k]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df183a73",
+   "metadata": {},
+   "source": [
+    "## Step 4: Define the Main Blog Generation Function\n",
+    "\n",
+    "The `generate_blog` function is the core of our blog generation process. It takes a topic as input and uses the following steps to generate a comprehensive blog post:\n",
+    "\n",
+    "1.  **Retrieve relevant content**: Uses the `query_qdrant` function to retrieve relevant chunks from the Qdrant collection based on the input topic.\n",
+    "2.  **Construct a prompt**: Creates a prompt for the Llama model by combining the retrieved content with a system prompt and user input.\n",
+    "3.  **Generate the blog post**: Sends the constructed prompt to the Llama model via the chosen inference provider's API and retrieves the generated blog post.\n",
+    "\n",
+    "This function orchestrates the entire blog generation process, making it easy to produce high-quality content based on your technical documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "437395c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_blog(topic: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Generates a technical blog post based on a topic using RAG.\n",
+    "    \n",
+    "    :param topic: Topic for the blog post\n",
+    "    :return: Generated blog content\n",
+    "    \"\"\"\n",
+    "    client = get_qdrant_client()\n",
+    "    relevant_chunks = query_qdrant(topic, client, MAIN_COLLECTION_NAME)\n",
+    "    \n",
+    "    if not relevant_chunks:\n",
+    "        error_message = \"No relevant content found in the knowledge base. Cannot generate blog post.\"\n",
+    "        print(error_message)\n",
+    "        return error_message\n",
+    "\n",
+    "    context = \"\\n\".join([chunk.payload[\"text\"] for chunk in relevant_chunks])\n",
+    "    system_prompt = f\"\"\"\n",
+    "    You are a technical writer specializing in creating comprehensive documentation-based blog posts. \n",
+    "    Use the following context from technical documentation to write an in-depth blog post about {topic}.\n",
+    "    \n",
+    "    Requirements:\n",
+    "    1. Structure the blog with clear sections and subsections\n",
+    "    2. Include code structure and configuration details where relevant\n",
+    "    3. Explain architectural components using diagrams (describe in markdown)\n",
+    "    4. Add setup instructions and best practices\n",
+    "    5. Use technical terminology appropriate for developers\n",
+    "    \n",
+    "    Context:\n",
+    "    {context}\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    payload = {\n",
+    "        \"model\": LLAMA_MODEL,\n",
+    "        \"messages\": [\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": f\"Write a detailed technical blog post about {topic}\"}\n",
+    "        ],\n",
+    "        \"temperature\": 0.5,\n",
+    "        \"max_tokens\": 4096\n",
+    "    }\n",
+    "    \n",
+    "    try:\n",
+    "        response = requests.post(API_URL, headers=HEADERS, json=payload)\n",
+    "        \n",
+    "        if response.status_code == 200:\n",
+    "            response_json = response.json()\n",
+    "            blog_content = response_json.get('completion_message', {}).get('content', {}).get('text', '')\n",
+    "            \n",
+    "            markdown_content = f\"# {topic}\\n\\n{blog_content}\"\n",
+    "            output_path = Path(f\"{topic.replace(' ', '_')}_blog.md\")\n",
+    "            with open(output_path, \"w\", encoding=\"utf-8\") as f:\n",
+    "                f.write(markdown_content)\n",
+    "            \n",
+    "            print(f\"Blog post generated and saved to {output_path}.\")\n",
+    "            display(Markdown(markdown_content))\n",
+    "            return markdown_content\n",
+    "            \n",
+    "        else:\n",
+    "            error_message = f\"Error: {response.status_code} - {response.text}\"\n",
+    "            print(error_message)\n",
+    "            return error_message\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        error_message = f\"An unexpected error occurred: {str(e)}\"\n",
+    "        print(error_message)\n",
+    "        return error_message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a5a1e4c",
+   "metadata": {},
+   "source": [
+    "## Step 5: Execute the Blog Generation Process\n",
+    "\n",
+    "Now that we've defined the necessary functions, let's put them to use! To generate a blog post, simply call the `generate_blog` function with a topic of your choice.\n",
+    "\n",
+    "For example:\n",
+    "```python\n",
+    "topic = \"Building a Messenger Chatbot with Llama 3\"\n",
+    "blog_content = generate_blog(topic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43d9b978",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/f5/lntr7_gx6fd1y_1rtgwf2g9h0000gn/T/ipykernel_89390/1696310953.py:28: DeprecationWarning: `search` method is deprecated and will be removed in the future. Use `query_points` instead.\n",
+      "  results = client.search(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Blog post generated and saved to Building_a_Messenger_Chatbot_with_Llama_3_blog.md.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "# Building a Messenger Chatbot with Llama 3\n",
+       "\n",
+       "Building a Messenger Chatbot with Llama 3: A Step-by-Step Guide\n",
+       "===========================================================\n",
+       "\n",
+       "### Introduction\n",
+       "\n",
+       "In this blog post, we'll explore the process of building a Llama 3 enabled Messenger chatbot using the Messenger Platform. We'll cover the architecture, setup instructions, and best practices for integrating Llama 3 with the Messenger Platform.\n",
+       "\n",
+       "### Overview of the Messenger Platform\n",
+       "\n",
+       "The Messenger Platform is a powerful tool that allows businesses to connect with their customers through a Facebook business page. With the Messenger Platform, businesses can build chatbots that can respond to customer inquiries, provide support, and even offer personalized recommendations.\n",
+       "\n",
+       "### Architecture of the Llama 3 Enabled Messenger Chatbot\n",
+       "\n",
+       "The diagram below illustrates the components and overall data flow of the Llama 3 enabled Messenger chatbot demo.\n",
+       "\n",
+       "```markdown\n",
+       "+---------------+\n",
+       "|  Facebook    |\n",
+       "|  Business Page  |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (User Message)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Messenger    |\n",
+       "|  Platform      |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (Webhook Event)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Web Server    |\n",
+       "|  (e.g., Amazon  |\n",
+       "|   EC2 instance)  |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (API Request)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Llama 3       |\n",
+       "|  Model          |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (Generated Response)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Web Server    |\n",
+       "|  (e.g., Amazon  |\n",
+       "|   EC2 instance)  |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (API Response)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Messenger    |\n",
+       "|  Platform      |\n",
+       "+---------------+\n",
+       "        |\n",
+       "        |  (Bot Response)\n",
+       "        v\n",
+       "+---------------+\n",
+       "|  Facebook    |\n",
+       "|  Business Page  |\n",
+       "+---------------+\n",
+       "```\n",
+       "\n",
+       "The architecture consists of the following components:\n",
+       "\n",
+       "*   Facebook Business Page: The page where customers interact with the chatbot.\n",
+       "*   Messenger Platform: The platform that handles user messages and sends webhook events to the web server.\n",
+       "*   Web Server: The server that receives webhook events from the Messenger Platform, sends API requests to the Llama 3 model, and returns API responses to the Messenger Platform.\n",
+       "*   Llama 3 Model: The AI model that generates responses to user messages.\n",
+       "\n",
+       "### Setting Up the Messenger Chatbot\n",
+       "\n",
+       "To set up the Messenger chatbot, follow these steps:\n",
+       "\n",
+       "1.  **Create a Facebook Business Page**: Create a Facebook business page for your business.\n",
+       "2.  **Create a Facebook Developer Account**: Create a Facebook developer account and register your application.\n",
+       "3.  **Set Up the Messenger Platform**: Set up the Messenger Platform for your application and configure the webhook settings.\n",
+       "4.  **Set Up the Web Server**: Set up a web server (e.g., Amazon EC2 instance) to receive webhook events from the Messenger Platform.\n",
+       "5.  **Integrate with Llama 3**: Integrate the Llama 3 model with your web server to generate responses to user messages.\n",
+       "\n",
+       "### Configuring the Webhook\n",
+       "\n",
+       "To configure the webhook, follow these steps:\n",
+       "\n",
+       "1.  Go to the Facebook Developer Dashboard and navigate to the Messenger Platform settings.\n",
+       "2.  Click on \"Webhooks\" and then click on \"Add Subscription\".\n",
+       "3.  Enter the URL of your web server and select the \"messages\" and \"messaging_postbacks\" events.\n",
+       "4.  Verify the webhook by clicking on \"Verify\" and entering the verification token.\n",
+       "\n",
+       "### Handling Webhook Events\n",
+       "\n",
+       "To handle webhook events, you'll need to write code that processes the events and sends API requests to the Llama 3 model. Here's an example code snippet in Python:\n",
+       "```python\n",
+       "import os\n",
+       "import json\n",
+       "from flask import Flask, request\n",
+       "import requests\n",
+       "\n",
+       "app = Flask(__name__)\n",
+       "\n",
+       "# Llama 3 API endpoint\n",
+       "LLAMA_API_ENDPOINT = os.environ['LLAMA_API_ENDPOINT']\n",
+       "\n",
+       "# Verify the webhook\n",
+       "@app.route('/webhook', methods=['GET'])\n",
+       "def verify_webhook():\n",
+       "    mode = request.args.get('mode')\n",
+       "    token = request.args.get('token')\n",
+       "    challenge = request.args.get('challenge')\n",
+       "\n",
+       "    if mode == 'subscribe' and token == 'YOUR_VERIFY_TOKEN':\n",
+       "        return challenge\n",
+       "    else:\n",
+       "        return 'Invalid request', 403\n",
+       "\n",
+       "# Handle webhook events\n",
+       "@app.route('/webhook', methods=['POST'])\n",
+       "def handle_webhook():\n",
+       "    data = request.get_json()\n",
+       "    if data['object'] == 'page':\n",
+       "        for entry in data['entry']:\n",
+       "            for messaging_event in entry['messaging']:\n",
+       "                if messaging_event.get('message'):\n",
+       "                    # Get the user message\n",
+       "                    user_message = messaging_event['message']['text']\n",
+       "\n",
+       "                    # Send API request to Llama 3 model\n",
+       "                    response = requests.post(LLAMA_API_ENDPOINT, json={'prompt': user_message})\n",
+       "\n",
+       "                    # Get the generated response\n",
+       "                    generated_response = response.json()['response']\n",
+       "\n",
+       "                    # Send API response back to Messenger Platform\n",
+       "                    send_response(messaging_event['sender']['id'], generated_response)\n",
+       "\n",
+       "    return 'OK', 200\n",
+       "\n",
+       "# Send response back to Messenger Platform\n",
+       "def send_response(recipient_id, response):\n",
+       "    # Set up the API endpoint and access token\n",
+       "    endpoint = f'https://graph.facebook.com/v13.0/me/messages?access_token={os.environ[\"PAGE_ACCESS_TOKEN\"]}'\n",
+       "\n",
+       "    # Set up the API request payload\n",
+       "    payload = {\n",
+       "        'recipient': {'id': recipient_id},\n",
+       "        'message': {'text': response}\n",
+       "    }\n",
+       "\n",
+       "    # Send the API request\n",
+       "    requests.post(endpoint, json=payload)\n",
+       "\n",
+       "if __name__ == '__main__':\n",
+       "    app.run(debug=True)\n",
+       "```\n",
+       "\n",
+       "### Best Practices\n",
+       "\n",
+       "Here are some best practices to keep in mind when building a Messenger chatbot with Llama 3:\n",
+       "\n",
+       "*   **Test thoroughly**: Test your chatbot thoroughly to ensure that it responds correctly to user messages.\n",
+       "*   **Use a robust web server**: Use a robust web server that can handle a high volume of webhook events.\n",
+       "*   **Implement error handling**: Implement error handling to handle cases where the Llama 3 model fails to generate a response.\n",
+       "*   **Monitor performance**: Monitor the performance of your chatbot to ensure that it's responding quickly to user messages.\n",
+       "\n",
+       "### Conclusion\n",
+       "\n",
+       "Building a Messenger chatbot with Llama 3 is a powerful way to provide customer support and improve customer experience. By following the steps outlined in this blog post, you can build a chatbot that responds to user messages and provides personalized recommendations. Remember to test thoroughly, use a robust web server, implement error handling, and monitor performance to ensure that your chatbot is successful."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Building a Messenger Chatbot with Llama 3\n",
+      "\n",
+      "Building a Messenger Chatbot with Llama 3: A Step-by-Step Guide\n",
+      "===========================================================\n",
+      "\n",
+      "### Introduction\n",
+      "\n",
+      "In this blog post, we'll explore the process of building a Llama 3 enabled Messenger chatbot using the Messenger Platform. We'll cover the architecture, setup instructions, and best practices for integrating Llama 3 with the Messenger Platform.\n",
+      "\n",
+      "### Overview of the Messenger Platform\n",
+      "\n",
+      "The Messenger Platform is a powerful tool that allows businesses to connect with their customers through a Facebook business page. With the Messenger Platform, businesses can build chatbots that can respond to customer inquiries, provide support, and even offer personalized recommendations.\n",
+      "\n",
+      "### Architecture of the Llama 3 Enabled Messenger Chatbot\n",
+      "\n",
+      "The diagram below illustrates the components and overall data flow of the Llama 3 enabled Messenger chatbot demo.\n",
+      "\n",
+      "```markdown\n",
+      "+---------------+\n",
+      "|  Facebook    |\n",
+      "|  Business Page  |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (User Message)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Messenger    |\n",
+      "|  Platform      |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (Webhook Event)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Web Server    |\n",
+      "|  (e.g., Amazon  |\n",
+      "|   EC2 instance)  |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (API Request)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Llama 3       |\n",
+      "|  Model          |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (Generated Response)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Web Server    |\n",
+      "|  (e.g., Amazon  |\n",
+      "|   EC2 instance)  |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (API Response)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Messenger    |\n",
+      "|  Platform      |\n",
+      "+---------------+\n",
+      "        |\n",
+      "        |  (Bot Response)\n",
+      "        v\n",
+      "+---------------+\n",
+      "|  Facebook    |\n",
+      "|  Business Page  |\n",
+      "+---------------+\n",
+      "```\n",
+      "\n",
+      "The architecture consists of the following components:\n",
+      "\n",
+      "*   Facebook Business Page: The page where customers interact with the chatbot.\n",
+      "*   Messenger Platform: The platform that handles user messages and sends webhook events to the web server.\n",
+      "*   Web Server: The server that receives webhook events from the Messenger Platform, sends API requests to the Llama 3 model, and returns API responses to the Messenger Platform.\n",
+      "*   Llama 3 Model: The AI model that generates responses to user messages.\n",
+      "\n",
+      "### Setting Up the Messenger Chatbot\n",
+      "\n",
+      "To set up the Messenger chatbot, follow these steps:\n",
+      "\n",
+      "1.  **Create a Facebook Business Page**: Create a Facebook business page for your business.\n",
+      "2.  **Create a Facebook Developer Account**: Create a Facebook developer account and register your application.\n",
+      "3.  **Set Up the Messenger Platform**: Set up the Messenger Platform for your application and configure the webhook settings.\n",
+      "4.  **Set Up the Web Server**: Set up a web server (e.g., Amazon EC2 instance) to receive webhook events from the Messenger Platform.\n",
+      "5.  **Integrate with Llama 3**: Integrate the Llama 3 model with your web server to generate responses to user messages.\n",
+      "\n",
+      "### Configuring the Webhook\n",
+      "\n",
+      "To configure the webhook, follow these steps:\n",
+      "\n",
+      "1.  Go to the Facebook Developer Dashboard and navigate to the Messenger Platform settings.\n",
+      "2.  Click on \"Webhooks\" and then click on \"Add Subscription\".\n",
+      "3.  Enter the URL of your web server and select the \"messages\" and \"messaging_postbacks\" events.\n",
+      "4.  Verify the webhook by clicking on \"Verify\" and entering the verification token.\n",
+      "\n",
+      "### Handling Webhook Events\n",
+      "\n",
+      "To handle webhook events, you'll need to write code that processes the events and sends API requests to the Llama 3 model. Here's an example code snippet in Python:\n",
+      "```python\n",
+      "import os\n",
+      "import json\n",
+      "from flask import Flask, request\n",
+      "import requests\n",
+      "\n",
+      "app = Flask(__name__)\n",
+      "\n",
+      "# Llama 3 API endpoint\n",
+      "LLAMA_API_ENDPOINT = os.environ['LLAMA_API_ENDPOINT']\n",
+      "\n",
+      "# Verify the webhook\n",
+      "@app.route('/webhook', methods=['GET'])\n",
+      "def verify_webhook():\n",
+      "    mode = request.args.get('mode')\n",
+      "    token = request.args.get('token')\n",
+      "    challenge = request.args.get('challenge')\n",
+      "\n",
+      "    if mode == 'subscribe' and token == 'YOUR_VERIFY_TOKEN':\n",
+      "        return challenge\n",
+      "    else:\n",
+      "        return 'Invalid request', 403\n",
+      "\n",
+      "# Handle webhook events\n",
+      "@app.route('/webhook', methods=['POST'])\n",
+      "def handle_webhook():\n",
+      "    data = request.get_json()\n",
+      "    if data['object'] == 'page':\n",
+      "        for entry in data['entry']:\n",
+      "            for messaging_event in entry['messaging']:\n",
+      "                if messaging_event.get('message'):\n",
+      "                    # Get the user message\n",
+      "                    user_message = messaging_event['message']['text']\n",
+      "\n",
+      "                    # Send API request to Llama 3 model\n",
+      "                    response = requests.post(LLAMA_API_ENDPOINT, json={'prompt': user_message})\n",
+      "\n",
+      "                    # Get the generated response\n",
+      "                    generated_response = response.json()['response']\n",
+      "\n",
+      "                    # Send API response back to Messenger Platform\n",
+      "                    send_response(messaging_event['sender']['id'], generated_response)\n",
+      "\n",
+      "    return 'OK', 200\n",
+      "\n",
+      "# Send response back to Messenger Platform\n",
+      "def send_response(recipient_id, response):\n",
+      "    # Set up the API endpoint and access token\n",
+      "    endpoint = f'https://graph.facebook.com/v13.0/me/messages?access_token={os.environ[\"PAGE_ACCESS_TOKEN\"]}'\n",
+      "\n",
+      "    # Set up the API request payload\n",
+      "    payload = {\n",
+      "        'recipient': {'id': recipient_id},\n",
+      "        'message': {'text': response}\n",
+      "    }\n",
+      "\n",
+      "    # Send the API request\n",
+      "    requests.post(endpoint, json=payload)\n",
+      "\n",
+      "if __name__ == '__main__':\n",
+      "    app.run(debug=True)\n",
+      "```\n",
+      "\n",
+      "### Best Practices\n",
+      "\n",
+      "Here are some best practices to keep in mind when building a Messenger chatbot with Llama 3:\n",
+      "\n",
+      "*   **Test thoroughly**: Test your chatbot thoroughly to ensure that it responds correctly to user messages.\n",
+      "*   **Use a robust web server**: Use a robust web server that can handle a high volume of webhook events.\n",
+      "*   **Implement error handling**: Implement error handling to handle cases where the Llama 3 model fails to generate a response.\n",
+      "*   **Monitor performance**: Monitor the performance of your chatbot to ensure that it's responding quickly to user messages.\n",
+      "\n",
+      "### Conclusion\n",
+      "\n",
+      "Building a Messenger chatbot with Llama 3 is a powerful way to provide customer support and improve customer experience. By following the steps outlined in this blog post, you can build a chatbot that responds to user messages and provides personalized recommendations. Remember to test thoroughly, use a robust web server, implement error handling, and monitor performance to ensure that your chatbot is successful.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Specify the topic for the blog post\n",
+    "topic = \"Building a Messenger Chatbot with Llama 3\"\n",
+    "blog_content = generate_blog(topic)\n",
+    "print(blog_content)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "test_blogs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}