Smart Device Guide

Overview

This project predicts the price of a device (laptop or mobile phone) based on user-provided specifications. After predicting the price, it offers tailored recommendations within the user's budget and suggests devices that closely match the user's requirements.

Additionally, a chatbot is integrated to provide users with flexible assistance and support by fetching relevant information from the database.

Demo:

pyprik

PyPi LINK : https://pypi.org/project/pyprik/

Install the Package

pip install pyprik

Retrieval-Augmented Generation (RAG)

In this project, i implemented a Retrieval-Augmented Generation (RAG) chatbot to provide users with a conversational interface for enhanced flexibility and assistance. RAG combines the strengths of retrieval-based models and generative models to deliver accurate and contextually relevant responses. It retrieves relevant information from a knowledge base or document store and uses a generative model to craft coherent responses based on the retrieved content.

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RAG in the Chatbot

1. Enhanced User Assistance:
   The chatbot fetches relevant device specifications and recommendations from a database, allowing users to make informed decisions based on real-time data.

2. Conversational Recommendations:
   It provides tailored suggestions for devices that fit the user's budget and preferences through natural, conversational interactions.

3. Dynamic Knowledge Integration:
   By converting the dataset into a document format and using a vector database, the chatbot can retrieve relevant information and generate accurate responses even as the dataset updates.

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Steps

• Data Preprocessing and Conversion:

  • The data was transformed into document format to ensure compatibility with retrieval systems.
  • Each document represents a structured segment of the knowledge base, making it easy to query and retrieve relevant information.

• Embedding Generation:

  • We used Hugging Face embeddings to convert textual data into high-dimensional vector representations.
  • These embeddings capture the semantic meaning of the text, allowing for efficient similarity searches.

• Vector Database:

  • AstraDB (powered by Cassandra) was employed as the vector database.
  • This ensures scalable, high-performance storage and retrieval of vectorized documents. The vector database allows efficient querying for similar documents based on the user's input.

• Model Integration with GROQ API:

  • To generate conversational responses, the chatbot leverages a pre-trained generative model accessed via the GROQ API.
  • The generative model takes the retrieved documents and crafts contextually appropriate and coherent responses.

• Conversational Chatbot:

  • A fully conversational chatbot was developed to handle dynamic queries from users.
  • The chatbot seamlessly integrates RAG to retrieve and generate responses, providing personalized assistance and enhancing user engagement.

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Create a Conda Environment

conda create -p <env_name> python=3.10 -y

Activate Your Conda Environment

conda activate <env_path>

• If activating on bash terminal use this command:

source activate ./<env_name>

• Else, use:

conda activate <env_path>

Install Dependencies from requirements.txt

pip install -r requirements.txt

Create a .env File to Store Your Environment Variables

GROQ_API_KEY=""
ASTRA_DB_API_ENDPOINT=""
ASTRA_DB_APPLICATION_TOKEN=""
ASTRA_DB_KEYSPACE=""
HF_TOKEN=""

Download and Install Docker

1. Download Docker from here.
2. Follow the installation instructions based on your operating system.

Build and Run Docker Image Locally

1. Build the Docker Image

After installing Docker, navigate to your project directory and run the following command to build the Docker image:

docker build -t <appName>:latest .

This command will build the Docker image with the tag appName.

2. Run the Docker Container

Once the image is built, run the Docker container using the following command:

docker run -d -p 8501:8501 --ipc="host" --name=product_guide <appName>:latest

This will start the Streamlit application in a detached mode (-d) and map port 8501 on your local machine to port 8501 in the container.

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