diff --git a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/.dockerignore b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/.dockerignore
deleted file mode 100644
index c93b7331..00000000
--- a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/.dockerignore
+++ /dev/null
@@ -1,8 +0,0 @@
-# Ignore everything
-**
-
-# Allow specific files and directories
-!requirements.txt
-!data/
-!src/
-!stages/
diff --git a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/Dockerfile b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/Dockerfile
deleted file mode 100644
index 2b22ab0d..00000000
--- a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/Dockerfile
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@@ -1,36 +0,0 @@
-# Use non-slim image due to OS dependencies of python packages. This gives us
-# git, build-essential, libglib2 (opencv) and gomp (torchaudio).
-FROM python:3.12
-
-COPY /requirements.txt .
-
-# Our python requirements have some OS dependencies beyond the base layer:
-#
-# * imagebind pulls in cartopy which has OS dependencies on geos and proj
-# * opencv has a runtime OS dependency on libgl1-mesa-glx
-#
-# The dev dependencies are installed temporarily to compile the wheels.
-# We leave the only the runtime dependencies, to keep the image smaller.
-RUN apt-get update && \
-    # install build and runtime dependencies
-    apt-get install -y --no-install-recommends \
-        libgeos-dev \
-        libproj-dev \
-        libgeos-c1v5 \
-        libproj25 \
-        libgl1-mesa-glx && \
-    # Install everything except xformers first
-    grep -v "\bxformers\b" requirements.txt > /tmp/r.txt && pip install -r /tmp/r.txt && \
-    # Now, install xformers, as it should be able to see torch now
-    grep "\bxformers\b" requirements.txt > /tmp/r.txt && pip install -r /tmp/r.txt && \
-    # remove build dependencies
-    apt-get purge -y libgeos-dev libproj-dev && \
-    apt-get autoremove -y && \
-    rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-RUN mkdir -p ./data ./src ./stages
-COPY ./data ./data
-COPY ./src ./src
-COPY ./stages ./stages
-
diff --git a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-compose.yml b/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-compose.yml
deleted file mode 100644
index fd08c664..00000000
--- a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-compose.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-name: gotham-city-crime-analysis
-
-services:
-  verify-file-structure:
-    build:
-      context: .
-    container_name: verify-file-structure
-    restart: 'no'  # no need to re-verify file structure
-    env_file:
-      - .env
-    command: python stages/01-stage/files_check.py
-    extra_hosts:  # send localhost traffic to the docker host, e.g. your laptop
-        - "localhost:host-gateway"
-
-  generate-embeddings:
-    depends_on:
-      verify-file-structure:
-        condition: service_completed_successfully
-    build:
-      context: .
-    container_name: generate-embeddings
-    restart: 'no'  # no need to re-generate embeddings
-    env_file:
-      - .env
-    command: python stages/02-stage/test_embedding_generation.py
-    extra_hosts:  # send localhost traffic to the docker host, e.g. your laptop
-        - "localhost:host-gateway"
-    volumes:
-      - torch-checkpoints:/root/.cache/torch/checkpoints/
-
-  index-content:
-    depends_on:
-      generate-embeddings:
-        condition: service_completed_successfully
-    build:
-      context: .
-    container_name: index-content
-    restart: 'no'  # no need to re-verify file structure
-    env_file:
-      - .env
-    command: python stages/03-stage/index_all_modalities.py
-    extra_hosts:  # send localhost traffic to the docker host, e.g. your laptop
-        - "localhost:host-gateway"
-
-  search-and-analyze:
-    depends_on:
-      index-content:
-        condition: service_completed_successfully
-    build:
-      context: .
-    container_name: search-and-analyze
-    restart: 'no'  # no need to re-verify file structure
-    env_file:
-      - .env
-    command: python stages/04-stage/rag_crime_analyze.py
-    extra_hosts:  # send localhost traffic to the docker host, e.g. your laptop
-        - "localhost:host-gateway"
-
-volumes:
-  # Avoid re-downloading a >4GB model checkpoint
-  torch-checkpoints:
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
deleted file mode 100644
index f049b93d..00000000
--- a/supporting-blog-content/building-multimodal-rag-with-elasticsearch-gotham/docker-setup.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# 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
-```