Smart Retail Assistant: Practical Analysis of a Retail Analytics Platform Based on Multi-Agent Architecture

Smart Retail Assistant is a retail analytics solution integrating FastAPI, MongoDB, RAG retrieval, and multi-agent systems. It covers core functions such as sales forecasting, document retrieval, and anomaly detection. As an open-source practical reference case, it demonstrates the implementation of AI technology in retail scenarios.

The original author/maintainer is iaditya8808, sourced from GitHub with the original title Smart-Retail-Assistant, released in May 2026. This project is designed specifically for the retail industry, integrating traditional sales data analysis with generative AI technology. Based on a multi-agent architecture, it provides proactive intent understanding and targeted analysis services, making it highly valuable for developers implementing AI in business scenarios.

The backend uses the FastAPI asynchronous framework to handle concurrent requests, and MongoDB to store unstructured retail data. The machine learning module integrates scikit-learn prediction models (supporting local/Azure ML inference) to achieve sales forecasting for the next week. It also automatically detects anomalies in sales data through algorithms, helping to identify operational issues.

RAG system workflow: Azure Document Intelligence parses PDFs → Azure OpenAI embedding model vectorizes → ChromaDB stores → semantic retrieval. The multi-agent architecture includes three agents: data analysis, document retrieval, and ML expert. The routing layer automatically assigns problem types to enhance answer professionalism.

The project provides rich RESTful API endpoints: /data-ingestion (data ingestion), /sales-data (sales query), /predict (forecasting), /search-documents (document search), /ask (general Q&A), /multi-agent (agent routing), /detect-anomalies (anomaly detection). The code structure is clearly layered: backend/ (FastAPI main code), agents/ (agent logic), ml/ (model scripts), rag/ (RAG implementation), etc.

For learners: It demonstrates RAG pipelines, multi-agent collaboration, MCP protocol application, and FastAPI best practices. For retail practitioners: It provides implementable intelligent transformation solutions to meet needs such as sales forecasting, knowledge bases, and intelligent customer service.

The project manages configurations via environment variables, supporting MongoDB (data storage), Azure OpenAI (LLM service), and Azure ML (cloud inference). It allows full local, full cloud, or hybrid deployment modes; developers need to configure flexibly according to their needs.

This project integrates mature technical components around real business needs. Its multi-agent architecture, RAG system, and modular code provide references for similar projects. As LLM technology evolves, intelligent retail analytics platforms will become more powerful, and this open-source project is a solid foundation for future development.