diff --git a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/README.md b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/README.md
index a5934a47..49e56660 100644
--- a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/README.md
+++ b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/README.md
@@ -11,41 +11,39 @@ The pipeline demonstrates how to:
 
 ## Prerequisites
 
-- A Docker runtime with 8GB+ free ram
-  - GPU is optional, but recommended
+- Python 3.x
 - Elasticsearch cluster (cloud or local)
 - OpenAI API key - Setup an OpenAI account and create a [secret key](https://platform.openai.com/docs/quickstart)
+- 8GB+ RAM
+- GPU (optional but recommended)
 
-## Quick Start
+## Execution Options
 
-This example runs four stages as docker compose services:
+This project can be run in two different ways:
 
-```mermaid
-graph TD
-    verify-file-structure --> generate-embeddings
-    generate-embeddings --> index-content
-    index-content --> search-and-analyze
-```
+### 1. Jupyter Notebook
 
-First, copy [env.example](env.example) to `.env` and fill in values noted inside.
+We provide a Google Colab notebook that allows you to explore the entire pipeline interactively:
+- [Open the Multimodal RAG Pipeline Notebook](notebook/01-mmrag-blog-quick-start.ipynb)
+- This notebook includes step-by-step instructions and explanations for each stage of the pipeline
 
-Now, enter below to run the pipeline:
-```bash
-docker compose run --build --rm search-and-analyze
-```
+### 2. Docker 
 
-The first time takes a while to build the image and download ImageBind weights.
+For containerized execution that ensures consistent environment:
+- Follow the [Docker Setup Guide](docker-setup.md) for instructions on running the pipeline using Docker Compose
+- This option handles all dependencies and environment setup automatically
+- Ideal for reproducible runs
 
-If you want to re-run just one stage, add `--no-deps` like this:
-```bash
-docker compose run --no-deps --build --rm search-and-analyze
-```
 
 ## Project Structure
 
 ```
 ├── README.md
 ├── requirements.txt
+├── Dockerfile
+├── docker-compose.yml
+├── notebook/
+│   ├── 01-mmrag-blog-quick-start.ipynb   # Jupyter notebook execution
 ├── src/
 │   ├── embedding_generator.py   # ImageBind wrapper
 │   ├── elastic_manager.py       # Elasticsearch operations
@@ -60,6 +58,7 @@ docker compose run --no-deps --build --rm search-and-analyze
     ├── audios/
     ├── texts/
     └── depths/
+
 ```
 
 ## Sample Data
diff --git a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-setup.md b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-setup.md
new file mode 100644
index 00000000..f049b93d
--- /dev/null
+++ b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-setup.md
@@ -0,0 +1,97 @@
+# Building a Multimodal RAG Pipeline with Elasticsearch: The Story of Gotham City
+
+This repository contains the code for implementing a Multimodal Retrieval-Augmented Generation (RAG) system using Elasticsearch. The system processes and analyzes different types of evidence (images, audio, text, and depth maps) to solve a crime in Gotham City.
+
+## Overview
+
+The pipeline demonstrates how to:
+- Generate unified embeddings for multiple modalities using ImageBind
+- Store and search vectors efficiently in Elasticsearch
+- Analyze evidence using GPT-4 to generate forensic reports
+
+## Prerequisites
+
+- A Docker runtime with 8GB+ free ram
+  - GPU is optional, but recommended
+- Elasticsearch cluster (cloud or local)
+- OpenAI API key - Setup an OpenAI account and create a [secret key](https://platform.openai.com/docs/quickstart)
+
+## Quick Start
+
+This example runs four stages as docker compose services:
+
+```mermaid
+graph TD
+    verify-file-structure --> generate-embeddings
+    generate-embeddings --> index-content
+    index-content --> search-and-analyze
+```
+
+First, copy [env.example](env.example) to `.env` and fill in values noted inside.
+
+
+Edit the `.env` file and fill in your credentials:
+
+```env
+# Elasticsearch Configuration
+ELASTICSEARCH_URL="https://your-elasticsearch-endpoint:443"
+ELASTICSEARCH_API_KEY="your-api-key"
+# If not using API key, uncomment these and fill them in:
+# ELASTICSEARCH_USER=elastic
+# ELASTICSEARCH_PASSWORD=elastic
+
+# OpenAI Configuration
+OPENAI_API_KEY="your-openai-api-key"
+```
+
+### 2. Configure Docker Resources
+
+The ImageBind model requires significant memory. Ensure Docker has enough resources:
+
+- **Memory**: At least 8GB (16GB recommended)
+- **Storage**: At least 10GB free space
+
+For Docker Desktop users:
+1. Open Docker Desktop settings
+2. Go to Resources > Advanced
+3. Increase memory allocation to at least 8GB
+4. Apply & Restart
+
+### 3. Running the Complete Pipeline
+
+To run the entire pipeline from file structure verification to evidence analysis:
+
+```bash
+docker compose run --build --rm search-and-analyze
+```
+
+This command will:
+1. Build the Docker image if needed
+2. Run each stage in sequence
+3. Cache the ImageBind model weights for future runs
+
+The first run will take longer as it builds the image and downloads model weights (~4.5GB).
+
+### 4. Running Individual Stages
+
+If you prefer to run each stage separately:
+
+```bash
+# File structure verification
+docker compose run --build --rm verify-file-structure
+
+# Generate embeddings
+docker compose run --build --rm generate-embeddings
+
+# Index content in Elasticsearch
+docker compose run --build --rm index-content
+
+# Search and analyze evidence
+docker compose run --build --rm search-and-analyze
+```
+
+To skip dependency checks when running a specific stage:
+
+```bash
+docker compose run --no-deps --build --rm search-and-analyze
+```