🛍️ LLM-Based E-Commerce Fashion Recommender

AI-powered fashion recommendation system leveraging LLMs, embeddings, and retrieval techniques to deliver personalized shopping experiences.

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🚀 Project Overview

This project is a Retrieval-Augmented Generation (RAG) chatbot designed for fashion e-commerce. It provides personalized recommendations, answers product queries, and enhances user engagement using state-of-the-art LLMs and vector-based retrieval.

Built with FastAPI, FAISS, ChromaDB, LangChain, Ollama, and Streamlit, this system efficiently indexes a 30K-product fashion dataset and serves real-time recommendations.

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✨ Features

✅ AI-Powered Fashion Recommendations – Get smart and personalized product suggestions.
✅ Hybrid Retrieval (FAISS + BM25 + ChromaDB) – Multi-modal search for better results.
✅ LLM-Driven Q&A – Handles customer queries with real-time responses.
✅ Cross-Encoder Reranking – Improves retrieval accuracy.
✅ Self-Querying Retriever – Converts queries into structured filters.
✅ Streamlit Chatbot UI – A modern, user-friendly interface.
✅ FastAPI Backend – A scalable API serving the recommender.
✅ Dockerized Deployment – Runs seamlessly in containers.

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🏗️ Tech Stack

Category	Tools Used
Programming	Python 3.12
LLM Models	GPT-4o-mini, Llama 3.2:3B, Ollama
Vector Search	FAISS, ChromaDB
Retrieval & Ranking	BM25, LangChain
Backend	FastAPI, Pydantic, Loguru
Frontend	Streamlit
Deployment	Docker, Docker Compose
Data Handling	Pandas, Numpy, Kaggle API

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🔧 Setup & Installation

1️⃣ Prerequisites

• Python 3.12+
• Docker & Docker Compose
• Ollama installed on your machine

2️⃣ Clone the Repository

git clone https://github.com/amine-akrout/llm-based-recommender.git
cd llm-based-recommender

3️⃣ Set Up Environment

Copy the example .env file and configure necessary credentials:

cp .env.example .env

Modify the .env file to include your Kaggle API key, OpenAI API key, and other configurations.

4️⃣ Run the Application

🐳 With Docker Compose (Recommended)

docker-compose up --build

Or

Make docker-start

🏗️ Manual Setup (Local Environment) using Makefile

Make install-python # Install Python
Make install # Install dependencies
Make indexing # Index the dataset
Make retriever # Create the retriever
Make app # Start the FastAPI app
Make ui # Start the Streamlit UI

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📡 API Endpoints

The FastAPI backend exposes multiple endpoints. After running the API, visit Swagger Docs:

🔗 Swagger UI: http://localhost:8000/docs
🔗 Redoc: http://localhost:8000/redoc

Method	Endpoint	Description
POST	/recommend/	Get fashion product recommendations
GET	/health	Check API health status

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🖥️ Streamlit Chatbot UI

🔗 Access the UI at: http://localhost:8501

The chatbot interface allows users to ask for product recommendations, filter results, and get AI-powered responses.

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📊 Data & Indexing

The recommender is built using a 30K-product e-commerce dataset indexed with FAISS, BM25, and ChromaDB.

Indexing Pipeline:

1. Download Dataset – Uses Kaggle API
2. Preprocess Data – Cleans and structures the dataset
3. Generate Embeddings – Vectorizes product descriptions
4. Store in FAISS & BM25 – Hybrid retrieval for fast search

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🔄 Recommendation Flow

The chatbot's recommendation process follows a structured LangGraph workflow:

1. Check Topic: Determines if the query is relevant to fashion.
2. Self-Query Retrieve: Extracts relevant product information.
3. Ranker: If retrieval returns empty, BM25 & FAISS rank results.
4. RAG Recommender: Uses LLM to generate the final recommendation.

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📺 Demo

🛠️ Project Structure

📦 llm-based-recommender
├── 📂 src
│   ├── 📂 api                 # FastAPI Backend
│   ├── 📂 indexing            # FAISS, BM25, Chroma Indexing
│   ├── 📂 retriever           # Query Processing
│   ├── 📂 recommender         # Core LLM-based Recommender
│   ├── 📂 ui                  # Streamlit Chatbot
│   ├── config.py              # App Configuration
├── 📄 docker-compose.yml      # Docker Services
├── 📄 Dockerfile              # API Containerization
├── 📄 pyproject.toml          # Project Dependencies
├── 📄 requirements.txt        # Python Packages
├── 📄 .env.example            # Environment Variables

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🎯 Future Improvements

🔹 Fine-tune LLM for better recommendations
🔹 Improve UI/UX with product images
🔹 Add multi-language support
🔹 Deploy to AWS/GCP

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🏆 Contributing

Contributions are welcome! If you’d like to contribute:

1️⃣ Fork the repo
2️⃣ Create a new branch
3️⃣ Commit your changes
4️⃣ Submit a pull request

For major changes, please open an issue first to discuss what you’d like to change.

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⭐ Star This Repo!

If you find this project useful, don’t forget to ⭐ star the repository! 🚀✨

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📩 Contact

👤 Amine
💼 LinkedIn

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