d-e-e-k-11/amazon_product

Retail Product Knowledge Assistant (RAG Model)

This project builds a Retrieval-Augmented Generation (RAG) model using retail product data (Kindle reviews and details). It uses a vector database to store product information and an LLM to answer questions naturally.

Tech Stack

• LLM: Google Gemini (via langchain-google-genai)
• Embeddings: HuggingFace (all-MiniLM-L6-v2)
• Vector Store: ChromaDB
• Framework: LangChain

Setup

1. API Key: Add your Google Gemini API Key to the .env file:

   GOOGLE_API_KEY=your_actual_key_here
   

2. Build Knowledge Base: Run the following command to process the data and build the vector database:

   python main.py
   

   (It will automatically detect if the database needs to be built).

Usage

Run main.py and ask questions about the products, such as:

• "Which Kindle model has the best resolution?"
• "What do users say about the battery life of the Paperwhite?"
• "Is the Kindle Voyage worth the extra money?"

Files

• 7817_1.csv: Raw product data.
• preprocess.py: Cleans and formats data into JSON.
• rag_model.py: Contains the logic for the RAG pipeline.
• main.py: Interactive CLI for user queries.
• chroma_db/: Directory where the vector store is persisted.