# Gem - Your Shopping Agent

**Gem** is an intelligent, friendly, and personalized AI shopping agent designed to help you find the perfect products on Amazon. Engage in a natural conversation, and Gem will guide you through the shopping process with smart recommendations, advanced filtering, and a personalized touch.

This project is built using the **[Agno](https://github.com/agno-agi/agno)** framework, which provides a production-ready runtime called **AgentOS** to manage and serve the AI agent securely.

## Frontend UI

This repository contains the **backend service** for the Gem Agent. The official chat interface for interacting with the agent is available in a separate repository.

➡️ **[GemAI Frontend Repository](https://github.com/aasherkamal216/GemAI_Frontend)**

## Features

-   **Conversational Search:** Simply chat with Gem about what you're looking for. No more rigid keyword searches.
-   **Visual Search:** Upload images and let Gem find similar products on Amazon.
-   **Advanced Filtering:** Effortlessly narrow down results by price range, category, ratings, best-sellers, and active deals.
-   **Intelligent Reranking:** Uses Cohere's state-of-the-art reranking model to sort products based on true relevance to your query, not just keywords.
-   **Personalized Experience:** Gem remembers your preferences across conversations, providing a tailored shopping journey every time.
-   **Secure & Scalable:** Built on **AgentOS**, which runs as a secure FastAPI application within your own infrastructure.
-   **Dockerized:** Fully containerized for easy, consistent, and reproducible deployment anywhere.

## How It Works

The agent follows a sophisticated workflow to deliver highly relevant product recommendations:

1.  **User Interaction**: A user sends a request (e.g., "I need running shoes under $100") from the frontend to the secure AgentOS API endpoint.
2.  **Agent Processing**: The request is routed to the `shopping-agent`, which is powered by **Google Gemini**. The agent analyzes the user's intent based on its core instructions (`app/prompts.py`).
3.  **Tool Execution**: The agent identifies the need to search for products and calls the `fetch_products` tool.
4.  **Product Search**: The tool queries a **RapidAPI Amazon Search** endpoint to retrieve a list of candidate products.
5.  **Relevance Reranking**: The initial results are passed to the **Cohere Rerank** model. This step is crucial, as it re-orders the products to prioritize those that are most semantically relevant to the user's specific query.
6.  **Memory & Context**: The agent uses a **Groq-powered Memory Manager** with a **Redis** backend to recall user preferences (e.g., "user prefers Nike") and maintain conversation history.
7.  **Response Generation**: The Gemini model synthesizes the reranked product data and its memory to generate a friendly, conversational response, highlighting the best options and asking clarifying questions.

## Tech Stack

-   **Framework:** [Agno](https://github.com/agno-agi/agno)
-   **Core LLM:** Google Gemini (`gemini-2.5-flash`)
-   **Memory Manager LLM:** Groq (`openai/gpt-oss-120b`)
-   **Reranking Model:** Cohere (`rerank-v3.5`)
-   **Backend Server:** FastAPI (via AgentOS)
-   **Database:** Redis (for session history and agentic memory)
-   **Containerization:** Docker
-   **Product Search API:** Amazon Search via RapidAPI