Fashion Assistant (FastAPI + React + LangChain + Groq)

Backend

• Python FastAPI app with LangChain memory-enabled agent using Groq (langchain_groq).
• Endpoint: POST /api/chat with JSON { session_id, message, user_profile? }.

Setup

1. Create and activate venv

python -m venv .venv
.venv\\Scripts\\activate

2. Install deps

pip install -r requirements.txt

3. Set environment variables (PowerShell)

$env:GROQ_API_KEY = "<your_groq_api_key>"
$env:GROQ_MODEL = "llama-3.1-70b-versatile"
$env:GROQ_TEMPERATURE = "0.3"

4. Run server

uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

Frontend

• Vite + React single-page chat UI.

Setup

cd frontend
npm install
npm run dev

• Optional: change API base via VITE_API_BASE env (defaults to http://localhost:8000/api).

Notes

• Memory is per session_id in-memory on the server (ephemeral). Persisted stores (Redis/Postgres) can be added later.
• Groq model defaults to llama-3.1-70b-versatile. Adjust via env.