https://github.com/barathkumarpm/ecomm_rag-llm_recommender
https://github.com/barathkumarpm/ecomm_rag-llm_recommender
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/barathkumarpm/ecomm_rag-llm_recommender
- Owner: BarathKumarpm
- Created: 2025-07-16T08:42:08.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-16T08:56:07.000Z (11 months ago)
- Last Synced: 2025-07-17T12:07:31.490Z (11 months ago)
- Language: Jupyter Notebook
- Size: 17.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Ecomm_RAG-LLM_recommender
# 🛍️ Personalized Recommendation System using RAG and LLMs
A powerful, intelligent e-commerce recommendation system that leverages **Retrieval-Augmented Generation (RAG)** and **Large Language Models (LLMs)** to deliver highly relevant, context-aware product suggestions. This system enhances user experience by understanding complex, natural language queries and matching them to relevant items in a diverse product dataset.
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## 📌 Problem Statement
Traditional search and recommendation systems face significant limitations:
- ❌ Inability to understand nuanced natural language queries
- ❌ Poor contextual awareness (e.g., fashion styles, seasonal trends)
- ❌ Generic, one-size-fits-all recommendations
- ❌ Limited personalization, leading to lower user engagement
This project aims to overcome these issues by creating an intelligent, multimodal recommendation engine using **RAG**, **LLMs**, and **vector embeddings** — improving both accuracy and user satisfaction in online shopping environments.
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## 🎯 Objectives
- ✅ **Store a diverse product dataset** by converting product data into **vector embeddings** and saving them in an efficient **Vector Store Index**.
- ✅ **Enable natural language understanding** using LLMs to interpret nuanced user queries and preferences.
- ✅ **Build a RAG-based system** where:
- **Retrievers** fetch the most relevant results from the vector store.
- **LLM-based synthesizers** generate human-like responses and personalized suggestions.
- ✅ **Develop domain-specific chatbots** using dedicated knowledge bases and vector indexes tailored for different businesses or product categories.
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## 🔭 Scope of the Project
### 📦 1. Vector-Based Product Representation
- Embed all product metadata into vector form using transformer-based models (e.g., SentenceTransformers).
- Store these vectors in a scalable **vector store** (e.g., FAISS, Chroma, Weaviate).
### 💬 2. Custom Chatbot Frameworks
- Create modular chatbots backed by **custom vector stores** containing domain-specific product data.
- Support for **multi-brand**, **multi-category**, or **multi-platform** deployments.
### 🧠 3. Intelligent Recommendations via RAG
- Combine a retriever + generator pipeline to power:
- Personalized recommendations
- Dynamic response generation
- Multi-turn user interaction
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## 🧠 Technologies Used
- `Python 3.x`
- `LlamaIndex` (for vector store, RAG, LLM orchestration)
- `Hugging Face Transformers` (for embeddings or optional LLM)
- `OpenAI`, `Gemini`, `Mistral`, or local LLMs (LLM backends)
- `Gradio` or `Streamlit` (for interactive UI)
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## 🚀 Example Use Cases
- 🛒 Online shopping platforms (fashion, electronics, etc.)
- 🤖 Brand-specific customer service bots
- 📚 Domain-specific product explainers
- 💡 Chatbot assistants for recommendation-based marketing
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