[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-80796":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":12,"openIssues":14,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":14,"stars7d":14,"stars30d":14,"stars90d":14,"forks30d":14,"starsTrendScore":14,"compositeScore":15,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":16,"fork":16,"defaultBranch":17,"hasWiki":18,"hasPages":16,"topics":19,"createdAt":10,"pushedAt":10,"updatedAt":20,"readmeContent":21,"aiSummary":22,"trendingCount":14,"starSnapshotCount":14,"syncStatus":23,"lastSyncTime":24,"discoverSource":25},80796,"retail-ai-intelligence-platform","Noopur17\u002Fretail-ai-intelligence-platform","Noopur17","A modular AI platform for Enterprise-inspired Retail AI platform with RAG, semantic search, recommendation systems, OpenAI workflows, and vector retrieval architecture.tail intelligence, combining recommendation systems, fraud detection, customer analytics, and operational intelligence into one production-inspired architecture.","",null,"Python",38,33,0,4.59,false,"main",true,[],"2026-06-12 02:04:06","# 🛒 Retail AI Intelligence Platform\n\nAn enterprise-inspired AI platform for intelligent commerce systems, semantic retail search, recommendation workflows, and Retrieval-Augmented Generation (RAG).\n\nDesigned to demonstrate how modern AI systems can power large-scale retail ecosystems across grocery, electronics, fashion, home, and general merchandise commerce platforms.\n\n![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.11-blue)\n![FastAPI](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFastAPI-Microservices-green)\n![React](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FReact-Frontend-61DAFB)\n![OpenAI](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpenAI-RAG-orange)\n![ChromaDB](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FChromaDB-Vector_Search-purple)\n![Docker](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocker-Containerized-2496ED)\n![Retail AI](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDomain-Retail_AI-black)\n\n---\n\n# 🚀 Vision\n\nModern commerce platforms require more than isolated machine learning models.\n\nThey require intelligent systems capable of:\n\n- Understanding customer behavior\n- Delivering personalized recommendations\n- Generating AI-powered product content\n- Retrieving retail knowledge semantically\n- Assisting merchandising workflows\n- Powering intelligent commerce experiences\n\nThis project demonstrates how multiple AI services can work together inside a scalable Retail AI platform architecture.\n\n---\n\n# ✨ Platform Highlights\n\n- 🧠 Retail AI RAG Assistant\n- 🤖 Recommendation Intelligence Engine\n- ✍️ AI-Powered Content Generation\n- 🔎 Semantic Commerce Search\n- 🗂️ ChromaDB Vector Database Integration\n- ⚡ OpenAI Embeddings & Retrieval\n- 🐳 Dockerized Microservices Platform\n- 🛍️ Retail Knowledge Base Workflows\n- 📊 Enterprise Retail Intelligence Architecture\n- 🚀 React + FastAPI Production-Inspired Stack\n\n---\n\n# 📚 Documentation\n\n| Document | Description |\n|---|---|\n| [Platform Architecture](docs\u002Farchitecture\u002Fplatform-architecture.md) | High-level Retail AI platform architecture |\n| [RAG Architecture](docs\u002Farchitecture\u002Frag-architecture.md) | Semantic retrieval and vector search workflows |\n| [Service Architecture](docs\u002Farchitecture\u002Fservice-architecture.md) | FastAPI microservice interactions |\n| [Dataset Documentation](docs\u002Fdatasets\u002Fretail-ai-knowledge-base.md) | Retail AI dataset design and schema |\n| [Local Development](docs\u002Fsetup\u002Flocal-development.md) | Local setup and development workflow |\n| [Roadmap](docs\u002Froadmap.md) | Future platform direction |\n| [Research Alignment](docs\u002Fresearch-alignment.md) | AI engineering and research areas |\n\n---\n\n# 🏗️ Platform Architecture\n\n## Visual Architecture Diagram\n\n```mermaid\nflowchart TB\n    UI[React + Vite Frontend Dashboard]\n\n    UI --> REC[Recommendation Service\u003Cbr\u002F>FastAPI :8001]\n    UI --> CONTENT[Content Intelligence Service\u003Cbr\u002F>FastAPI :8002]\n    UI --> RAG[Retail AI RAG Assistant\u003Cbr\u002F>FastAPI :8003]\n\n    REC --> PRODUCT[(Retail Product Catalog Dataset)]\n    CONTENT --> OPENAI[OpenAI API]\n    RAG --> CSV[(Retail AI Knowledge Base CSV)]\n    RAG --> CHROMA[(ChromaDB Vector Store)]\n    RAG --> OPENAI\n\n    CSV --> INGEST[CSV Ingestion Pipeline]\n    INGEST --> CHUNK[Chunking]\n    CHUNK --> EMBED[OpenAI Embeddings]\n    EMBED --> CHROMA\n    CHROMA --> RETRIEVE[Semantic Retrieval]\n    RETRIEVE --> ANSWER[AI Retail Answer Generation]\n    ANSWER --> UI\n\n    subgraph Services[FastAPI Microservices]\n        REC\n        CONTENT\n        RAG\n    end\n\n    subgraph DataLayer[AI and Data Layer]\n        OPENAI\n        CHROMA\n        PRODUCT\n        CSV\n    end\n```\n\n```text\nRetail AI Intelligence Platform\n│\n├── Frontend (React + Vite)\n│\n├── Recommendation Intelligence Service\n│   ├── Product similarity search\n│   ├── Recommendation scoring\n│   ├── Category-aware discovery\n│   └── Retail recommendation workflows\n│\n├── Content Intelligence Service\n│   ├── OpenAI-powered product content\n│   ├── SEO metadata generation\n│   ├── Product merchandising workflows\n│   └── Retail content intelligence\n│\n├── Retail AI RAG Assistant Service\n│   ├── Retail knowledge ingestion\n│   ├── OpenAI embeddings\n│   ├── ChromaDB vector storage\n│   ├── Semantic retrieval\n│   ├── AI-powered retail Q&A\n│   └── Commerce intelligence workflows\n│\n├── Customer Analytics Service (Planned)\n│\n└── Log Intelligence Service (Planned)\n````\n\n---\n\n# 🧠 Retail AI RAG Workflow\n\n```text\nRetail Knowledge Base\n        ↓\nDocument Ingestion\n        ↓\nChunking Pipeline\n        ↓\nOpenAI Embeddings\n        ↓\nChromaDB Vector Store\n        ↓\nSemantic Retrieval\n        ↓\nContext Injection\n        ↓\nLLM Response Generation\n        ↓\nRetail AI Assistant\n```\n\n---\n\n# 🔌 Platform Services\n\n| Service                      | Description                           | Port |\n| ---------------------------- | ------------------------------------- | ---- |\n| Frontend Dashboard           | Enterprise retail AI interface        | 5173 |\n| Recommendation Service       | Recommendation intelligence workflows | 8001 |\n| Content Intelligence Service | AI-powered product content generation | 8002 |\n| Retail AI RAG Assistant      | Semantic retail retrieval & AI Q&A    | 8003 |\n\n---\n\n# 🧩 Core AI Services\n\n## 🛒 Recommendation Intelligence Service\n\nAI-powered retail recommendation workflows for product discovery and similarity search.\n\n### Features\n\n* Product similarity scoring\n* Category-aware recommendations\n* Semantic recommendation workflows\n* Retail product discovery\n* Recommendation ranking engine\n\n---\n\n## ✍️ Content Intelligence Service\n\nGenerative AI workflows for retail product content and merchandising systems.\n\n### Features\n\n* AI-generated product titles\n* Product descriptions\n* SEO metadata generation\n* Bullet point generation\n* Merchandising content workflows\n* OpenAI-powered content systems\n\n---\n\n## 🧠 Retail AI RAG Assistant Service\n\nA Retrieval-Augmented Generation (RAG) service designed for intelligent commerce retrieval workflows.\n\n### Features\n\n* Retail knowledge ingestion\n* OpenAI embeddings\n* ChromaDB vector search\n* Semantic retrieval\n* AI-powered retail Q&A\n* Retail merchandising intelligence\n* Commerce knowledge workflows\n* RAG-ready retrieval pipelines\n\n---\n\n## 📊 Customer Analytics Service *(Planned)*\n\nFuture customer intelligence workflows.\n\n### Planned Features\n\n* Customer segmentation\n* Behavioral intelligence\n* Engagement analysis\n* AI-powered customer insights\n* Retail analytics workflows\n\n---\n\n## ⚙️ Log Intelligence Service *(Planned)*\n\nOperational AI workflows for monitoring and intelligence systems.\n\n### Planned Features\n\n* AI-assisted log analysis\n* Operational intelligence\n* Intelligent monitoring workflows\n* Production issue insights\n\n---\n\n# 🤖 AI Capabilities\n\nThis platform explores practical AI applications for modern commerce systems.\n\n## Supported Workflows\n\n* Retrieval-Augmented Generation (RAG)\n* Recommendation systems\n* Semantic vector search\n* AI-powered content generation\n* Retail intelligence workflows\n* Product discovery systems\n* Semantic commerce retrieval\n* AI merchandising assistants\n* OpenAI embedding pipelines\n\n---\n\n# 📊 Retail AI Knowledge Base Dataset\n\nThis platform is connected with the Kaggle dataset:\n\n## 🧠 Retail AI Intelligence Knowledge Base\n\nA large-scale AI-ready dataset designed for:\n\n* Semantic retrieval\n* Recommendation systems\n* RAG workflows\n* Retail AI assistants\n* Commerce intelligence systems\n\n### Dataset Features\n\n* 100K+ retail intelligence records\n* Multi-category retail coverage\n* AI use case mappings\n* Semantic retrieval tags\n* Merchandising strategies\n* Customer segment intelligence\n\n---\n\n# 📓 Premium Kaggle Notebook\n\nThe project also includes a premium Kaggle notebook focused on:\n\n* RAG workflows\n* Semantic retrieval\n* Retail AI intelligence\n* Recommendation analysis\n* Commerce AI insights\n* AI-ready dataset engineering\n\n---\n\n# 🐳 Dockerized Architecture\n\nRun the entire platform locally using Docker Compose.\n\n## Start Platform\n\n```bash\ndocker compose up --build\n```\n\n---\n\n## Service URLs\n\n| Service                  | URL                                                      |\n| ------------------------ | -------------------------------------------------------- |\n| Frontend Dashboard       | [http:\u002F\u002Flocalhost:5173](http:\u002F\u002Flocalhost:5173)           |\n| Recommendation API       | [http:\u002F\u002Flocalhost:8001\u002Fdocs](http:\u002F\u002Flocalhost:8001\u002Fdocs) |\n| Content Intelligence API | [http:\u002F\u002Flocalhost:8002\u002Fdocs](http:\u002F\u002Flocalhost:8002\u002Fdocs) |\n| Retail AI RAG API        | [http:\u002F\u002Flocalhost:8003\u002Fdocs](http:\u002F\u002Flocalhost:8003\u002Fdocs) |\n\n---\n\n# 🐳 Docker Hub Images\n\n## Frontend\n\n```text\nhttps:\u002F\u002Fhub.docker.com\u002Fr\u002Fnoopur17\u002Fretail-ai-frontend\n```\n\n## Recommendation Service\n\n```text\nhttps:\u002F\u002Fhub.docker.com\u002Fr\u002Fnoopur17\u002Fretail-recommendation-service\n```\n\n## Content Intelligence Service\n\n```text\nhttps:\u002F\u002Fhub.docker.com\u002Fr\u002Fnoopur17\u002Fretail-content-intelligence-service\n```\n\n---\n\n# 🖼️ Demo Screenshots\n\n## 🛒 Retail AI Dashboard\n\n![Dashboard](docs\u002Fscreenshots\u002Fdashboard.png)\n\n---\n\n## 🤖 Recommendation Intelligence\n\n![Recommendations](docs\u002Fscreenshots\u002Frecommendation.png)\n\n---\n\n## ✍️ Content Intelligence\n\n![Content AI](docs\u002Fscreenshots\u002Fcontent-ai.png)\n\n---\n\n## 🧠 Retail AI RAG Assistant\n\n![RAG Assistant](docs\u002Fscreenshots\u002Frag-assistant.png)\n\n---\n\n## ⚙️ FastAPI Swagger APIs\n\n![Swagger](docs\u002Fscreenshots\u002Fswagger.png)\n\n---\n\n# 🛠️ Tech Stack\n\n## Frontend\n\n* React\n* Vite\n* JavaScript\n\n## Backend\n\n* FastAPI\n* Python\n* REST APIs\n\n## AI \u002F ML\n\n* OpenAI\n* ChromaDB\n* Scikit-learn\n* Pandas\n* Vector Embeddings\n\n## Infrastructure\n\n* Docker\n* Docker Compose\n* Docker Hub\n\n---\n\n# 📂 Project Structure\n\n```text\nretail-ai-intelligence-platform\u002F\n│\n├── docs\u002F\n│   └── screenshots\u002F\n│\n├── frontend\u002F\n│\n├── services\u002F\n│   ├── recommendation-service\u002F\n│   ├── content-intelligence-service\u002F\n│   ├── rag-assistant-service\u002F\n│   ├── customer-analytics-service\u002F\n│   └── log-intelligence-service\u002F\n│\n├── datasets\u002F\n│\n├── notebooks\u002F\n│\n└── docker-compose.yml\n```\n\n---\n\n# 🔌 API Documentation\n\n## Recommendation Service\n\n```text\nhttp:\u002F\u002Flocalhost:8001\u002Fdocs\n```\n\n## Content Intelligence Service\n\n```text\nhttp:\u002F\u002Flocalhost:8002\u002Fdocs\n```\n\n## Retail AI RAG Assistant Service\n\n```text\nhttp:\u002F\u002Flocalhost:8003\u002Fdocs\n```\n\n---\n\n# 🧪 Local Development\n\n## Recommendation Service\n\n```bash\ncd services\u002Frecommendation-service\npython -m uvicorn app.main:app --reload --port 8001\n```\n\n---\n\n## Content Intelligence Service\n\n```bash\ncd services\u002Fcontent-intelligence-service\npython -m uvicorn app.main:app --reload --port 8002\n```\n\n---\n\n## Retail AI RAG Assistant Service\n\n```bash\ncd services\u002Frag-assistant-service\n\npython3 -m venv venv\nsource venv\u002Fbin\u002Factivate\n\npython -m pip install -r requirements.txt\n\nexport OPENAI_API_KEY=\"your_api_key_here\"\n\npython -m uvicorn app.main:app --reload --port 8003\n```\n\n---\n\n## Frontend\n\n```bash\ncd frontend\u002Ffrontend\n\nnpm install\nnpm run dev\n```\n\n---\n\n# 🔬 Research & Engineering Areas\n\nThis project explores practical applications of:\n\n* Recommendation systems\n* Retrieval-Augmented Generation (RAG)\n* Semantic search systems\n* Retail intelligence workflows\n* Commerce AI systems\n* Generative AI applications\n* Intelligent retrieval pipelines\n* Enterprise AI platform engineering\n\n---\n\n# 🛣️ Platform Roadmap\n\n## Completed\n\n* [x] Recommendation Intelligence API\n* [x] Content Intelligence Service\n* [x] OpenAI Integration\n* [x] Retail AI RAG Assistant\n* [x] ChromaDB Vector Search\n* [x] Dockerized Platform\n* [x] Enterprise-style React Dashboard\n* [x] Kaggle Retail AI Dataset\n* [x] Premium Kaggle Notebook\n\n---\n\n## Planned\n\n* [ ] Frontend RAG Chat Integration\n* [ ] Customer Analytics Service\n* [ ] Customer Review Ingestion\n* [ ] Retail Analytics Dashboard\n* [ ] Conversation Memory\n* [ ] AI Shopping Assistant\n* [ ] Recommendation Feedback Loop\n* [ ] End-to-End Retail AI Simulation\n\n---\n\n# 👩‍💻 Author\n\n## Noopur Bhatt\n\nAI & Full-Stack Engineer focused on:\n\n* Retail AI Systems\n* Retrieval-Augmented Generation (RAG)\n* Recommendation Workflows\n* Generative AI Applications\n* Intelligent Commerce Platforms\n* Semantic Retrieval Systems\n* Scalable AI Services\n\n---\n\n# ⭐ Future Vision\n\nThe long-term vision of this project is to evolve into a production-inspired Retail AI ecosystem demonstrating how:\n\n* recommendation systems,\n* generative AI,\n* semantic retrieval,\n* vector search,\n* intelligent merchandising,\n* and commerce AI workflows\n\ncan work together inside modern enterprise retail platforms.\n","零售AI智能平台是一个面向企业的模块化AI系统，集成了推荐系统、欺诈检测、客户分析和运营智能等功能。其核心技术包括基于检索的生成（RAG）、语义搜索、OpenAI工作流以及向量检索架构，并使用Python开发，结合了FastAPI微服务、React前端界面和ChromaDB矢量数据库等技术。该平台适用于大型零售生态系统，如杂货、电子产品、时尚、家居及一般商品电商平台，旨在通过智能化手段提升客户体验与业务效率。",2,"2026-06-11 04:02:23","CREATED_QUERY"]