{"id":29613788,"url":"https://github.com/barathkumarpm/ecomm_rag-llm_recommender","last_synced_at":"2025-07-20T22:37:15.370Z","repository":{"id":304955890,"uuid":"1020687799","full_name":"BarathKumarpm/Ecomm_RAG-LLM_recommender","owner":"BarathKumarpm","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-16T08:56:07.000Z","size":18,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-17T12:07:31.490Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BarathKumarpm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-16T08:42:08.000Z","updated_at":"2025-07-16T08:56:10.000Z","dependencies_parsed_at":"2025-07-17T16:21:13.054Z","dependency_job_id":"64bb445f-6db7-4e83-8d09-bdf6c00b1a50","html_url":"https://github.com/BarathKumarpm/Ecomm_RAG-LLM_recommender","commit_stats":null,"previous_names":["barathkumarpm/ecomm_rag-llm_recommender"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/BarathKumarpm/Ecomm_RAG-LLM_recommender","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BarathKumarpm%2FEcomm_RAG-LLM_recommender","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BarathKumarpm%2FEcomm_RAG-LLM_recommender/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BarathKumarpm%2FEcomm_RAG-LLM_recommender/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BarathKumarpm%2FEcomm_RAG-LLM_recommender/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BarathKumarpm","download_url":"https://codeload.github.com/BarathKumarpm/Ecomm_RAG-LLM_recommender/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BarathKumarpm%2FEcomm_RAG-LLM_recommender/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266210975,"owners_count":23893346,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-07-20T22:37:13.346Z","updated_at":"2025-07-20T22:37:15.354Z","avatar_url":"https://github.com/BarathKumarpm.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ecomm_RAG-LLM_recommender\n# 🛍️ Personalized Recommendation System using RAG and LLMs\n\nA 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.\n\n---\n\n## 📌 Problem Statement\n\nTraditional search and recommendation systems face significant limitations:\n\n- ❌ Inability to understand nuanced natural language queries\n- ❌ Poor contextual awareness (e.g., fashion styles, seasonal trends)\n- ❌ Generic, one-size-fits-all recommendations\n- ❌ Limited personalization, leading to lower user engagement\n\nThis 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.\n\n---\n\n## 🎯 Objectives\n\n- ✅ **Store a diverse product dataset** by converting product data into **vector embeddings** and saving them in an efficient **Vector Store Index**.\n- ✅ **Enable natural language understanding** using LLMs to interpret nuanced user queries and preferences.\n- ✅ **Build a RAG-based system** where:\n  - **Retrievers** fetch the most relevant results from the vector store.\n  - **LLM-based synthesizers** generate human-like responses and personalized suggestions.\n- ✅ **Develop domain-specific chatbots** using dedicated knowledge bases and vector indexes tailored for different businesses or product categories.\n\n---\n\n## 🔭 Scope of the Project\n\n### 📦 1. Vector-Based Product Representation\n- Embed all product metadata into vector form using transformer-based models (e.g., SentenceTransformers).\n- Store these vectors in a scalable **vector store** (e.g., FAISS, Chroma, Weaviate).\n\n### 💬 2. Custom Chatbot Frameworks\n- Create modular chatbots backed by **custom vector stores** containing domain-specific product data.\n- Support for **multi-brand**, **multi-category**, or **multi-platform** deployments.\n\n### 🧠 3. Intelligent Recommendations via RAG\n- Combine a retriever + generator pipeline to power:\n  - Personalized recommendations\n  - Dynamic response generation\n  - Multi-turn user interaction\n\n---\n\n## 🧠 Technologies Used\n\n- `Python 3.x`\n- `LlamaIndex` (for vector store, RAG, LLM orchestration)\n- `Hugging Face Transformers` (for embeddings or optional LLM)\n- `OpenAI`, `Gemini`, `Mistral`, or local LLMs (LLM backends)\n- `Gradio` or `Streamlit` (for interactive UI)\n---\n\n## 🚀 Example Use Cases\n\n- 🛒 Online shopping platforms (fashion, electronics, etc.)\n- 🤖 Brand-specific customer service bots\n- 📚 Domain-specific product explainers\n- 💡 Chatbot assistants for recommendation-based marketing\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarathkumarpm%2Fecomm_rag-llm_recommender","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbarathkumarpm%2Fecomm_rag-llm_recommender","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarathkumarpm%2Fecomm_rag-llm_recommender/lists"}