ai-fashion-house 0.1.9

AI Fashion House, A multi agent adk application for fashion design

AI Fashion House

A project built for the ADK Hackathon with Google Cloud, AI Fashion House is a multi-agent system designed to assist with design inspiration, fashion image generation, and cinematic runway video creation.

What is AI Fashion House?

AI Fashion House is an AI-powered fashion design assistant that transforms expressive or abstract user prompts into rich visual content. Built on a modular, multi-agent architecture, it automates the entire creative pipeline—from concept interpretation to high-fidelity visual generation—by coordinating a set of intelligent, specialized agents.

How It Works

The system relies on a multi-agent framework, where each agent handles a specific step in the creative process. These agents operate asynchronously, enabling a flexible and dynamic design workflow:

1. Input Analysis Interprets user input to identify themes, fashion concepts, and stylistic cues.

2. Visual Reference Retrieval The met_rag_agent agent searches the Metropolitan Museum of Art's open-access archive (over 500,000 images) to retrieve relevant historical references.

   • BigQuery RAG: Performs semantic retrieval using Retrieval-Augmented Generation with BigQuery.
   • GenAI Embeddings: Embeds captions using the text-embedding-005 model for similarity comparison.
   • Gemini Multimodal Analysis: Processes both images and text to extract stylistic and structural fashion details.

3. Internet Search Expansion The search_agent agent uses Google Search Grounding to retrieve contemporary fashion references from the web.

4. Style Prompt Generation The promp_writer_agent & fashion_design agents organize visual data using a sequential pattern and combines it via an aggregator assistant to produce a detailed, fashion-specific prompt.

5. Artifact Creation and Orchestration The marketing_agent agent uses the style prompt to generate visual outputs:

   • Imagen 3 is used to produce high-quality fashion images.
   • Veo 3 generates stylized runway videos.

Target Audience

AI Fashion House is designed for:

• Fashion designers exploring new creative directions
• Educators and students in fashion design programs
• Archivists and curators seeking to combine history with generative AI
• Creators and developers interested in visual storytelling and AI-powered prototyping

Technology Stack

This project integrates:

• Google Cloud (Vertex AI, BigQuery, Cloud Storage)
• Gemini API and GenAI text/image embedding models
• Imagen 3 and Veo 3 for advanced image and video synthesis
• A modular, multi-agent orchestration system

Multi-Agent Architecture

Each step of the workflow is managed by a dedicated agent:

1. Input Analysis
2. Visual Reference Retrieval (met_rag agent)
   • BigQuery-based semantic search
   • Embedding generation and filtering
   • Multimodal image analysis
3. Web Search (research_agent agent)
4. Prompt Generation (fashion_design agent and aggregator)
5. Visual and Video Generation (marketing_agent agent using Imagen 3 and Veo 4)

Installation

Create and Activate A Virtual Environment with Python 11.0 or Higher

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Package

pip install ai-fashion-house

Configure Environment Variables to run the application

Create a .env file in the root directory with the following content:

GOOGLE_GENAI_USE_VERTEXAI=1
GOOGLE_API_KEY=<your_google_api_key>
GOOGLE_CLOUD_PROJECT=<your_google_cloud_project_id>
GOOGLE_CLOUD_LOCATION=us-central1

# RAG settings
BIGQUERY_DATASET_ID=met_data
BIGQUERY_CONNECTION_ID=met_data_conn
BIGQUERY_REGION=US

# Embeddings and captioning models
BIGQUERY_EMBEDDINGS_MODEL_ID=embeddings_model
BIGQUERY_EMBEDDINGS_MODEL=text-embedding-005
BIGQUERY_CAPTIONING_MODEL_ID=gemini_model
BIGQUERY_CAPTIONING_MODEL=gemini-2.0-flash
BIGQUERY_TABLE_ID=fashion_ai_met
BIGQUERY_VECTOR_INDEX_ID=met_data_index

VEO2_MODEL_ID=veo-3.0-generate-preview
IMAGEN_MODEL_ID=imagen-4.0-generate-preview-06-06

MEDIA_FILES_BUCKET_GCS_URI=gs://myfiles2025

Set Up MET RAG(Retrieval-Augmented Generation)

To simplify the installation process, you can use the setup-rag command to automatically configure the MET RAG (Retrieval-Augmented Generation) environment on GCP BigQuery. This command sets up the required dataset, connection, and vector index for the MET RAG agent. In case the automated setup fails or you prefer manual deployment, we’ve also included the necessary BigQuery SQL scripts in the scripts/ folder.

ai-fashion-house setup-rag

Run the Application

ai-fashion-house start

Open your browser and navigate to:

http://localhost:8080

to access the AI Fashion House interface.

🤝 Contributing

Contributions are welcome and appreciated! To contribute:

1. Fork this repository.
2. Create a new branch for your feature or fix.
3. Commit your changes with clear messages.
4. Push to your forked repository.
5. Open a Pull Request (PR) to the main branch with a description of your changes and any relevant context.

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🛠️ Running the Project Locally

1. Start the Backend

Run the backend server from the root directory:

ai-fashion-house start --reload

💡 Use the --reload flag to enable hot-reloading during development.

2. Start the React Frontend

Open a new terminal, navigate to the ui directory, and run:

cd ui
npm install
npm run dev

Then open your browser and navigate to:

http://localhost:5173

3. Build for Production

To generate the production build of the frontend:

npm run build

⚛️ The UI is a React.js app using Vite.