ai-fashion-house 0.1.11

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

AI Fashion House

AI Fashion House is a project created for the ADK Hackathon with Google Cloud. It's a modular, multi-agent system that transforms fashion design ideas into beautiful visuals. It automates the entire creative pipeline — from finding design inspirations to generating fashion images and then cinematic runway videos — by coordinating a set of specialized, intelligent agents. It's built with ADK and various Google Cloud tech.

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 designn references.

   • BigQuery RAG: Performs semantic retrieval using Retrieval-Augmented Generation (RAG) with BigQuery.
   • GenAI Embeddings: Embeds captions using the text-embedding-005 model for similarity comparison (image search).
   • 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.
   • Gemini writes social media posts.

Target Audience

• Fashion designers seeking design inspirations and showcase their designs visually
• Educators or students in fashion design education
• Archivists or curators seeking to combine design history with generative AI
• Creators and developers interested in visual storytelling and AI-powered prototyping

Technology Stack

• Agent Development Kit (ADK)
• 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

Prerequisites:

1. Google Cloud SDK (gcloud CLI) installed for authentication.

   • Terminal command: gcloud init and choose the project ID.
   • Set a default login: gcloud auth application-default login.

2. Access to Google Cloud: BigQuery, Gemini, Imagen 4, Veo 3 (public preview).

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://your-bucket-name>

Note: you will need to update .env with your own:

• Google API key (get it from Google AI Studio)
• Google Cloud project id
• Google Cloud bucket for storing generated images and videos

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 web UI 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.