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---
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:
```env
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:
```bash
pip install -r requirements.txt
```
### 2. Execution
Launch the FastAPI server:
```bash
python main.py
```
The API will be available at `http://localhost:7860`.
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