README.md

metadata

title: MerchFlow AI
emoji: 👟
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false

MerchFlow AI

MerchFlow AI is a high-performance, multi-agent orchestration system designed to automate the generation of premium e-commerce product listings. By synergizing Computer Vision, Retrieval Augmented Generation (RAG), and Large Language Models (LLMs), it transforms raw product images into SEO-optimized market-ready content.

🏗️ Architecture Flow

The system employs a sophisticated event-driven architecture orchestrated by FastAPI:

1. 👁️ Visual Agent (Gemini 1.5)

   • Function: Zero-shot image analysis.
   • Process: Extracts high-fidelity visual attributes including dominant color palettes, stylistic classifications, and granular item types.
   • Engine: Google Gemini 1.5 Flash (Vision).

2. 🧠 Memory Agent (Pinecone)

   • Function: Semantic Search & RAG.
   • Process: Vectorizes visual tags to query a high-dimensional index, retrieving historically high-performing SEO keywords and market trends relevant to the product.
   • Engine: Pinecone Vector Database.

3. ✍️ Writer Agent (Llama 3)

   • Function: Creative Synthesis.
   • Process: Fuses visual data with retrieved market intelligence to generate persuasive, conversion-focused title, description, and feature bullets.
   • Engine: Meta Llama 3 (via Groq Cloud).

4. ⚙️ Orchestrator (FastAPI)

   • Function: Async Pipeline Management.
   • Process: Handles non-blocking agent execution, error propagation, and API lifecycle management.

5. 🔗 Post-Processing (n8n)

   • Function: Automation Webhook.
   • Process: Triggers downstream workflows (database storage, Shopify API integration) via secure webhooks upon successful generation.

🚀 Complete Setup

To run this system locally, ensure you have the following environment variables configured in your .env file:

GEMINI_API_KEY=your_gemini_key
GROQ_API_KEY=your_groq_key
PINECONE_API_KEY=your_pinecone_key
N8N_WEBHOOK_URL=your_n8n_webhook_url

⚡ Quick Start

1. Installation

Install the required dependencies using pip:

pip install -r requirements.txt

2. Execution

Launch the FastAPI server:

python main.py

The API will be available at http://localhost:7860.