Teaching Assistant - Your Instant Personal Assistant for Uploaded Books

Welcome to Teaching Assistant, a powerful tool that transforms your uploaded books into an instant personal assistant. The application utilizes Google Generative AI Embeddings, FAISS for vector database storage, and the Gemini-Pro model. Text splitting is achieved through the Recursive Character Text Splitter.

Project Overview

Teaching Assistant is designed to provide instant assistance based on the content of uploaded books. The integration of advanced technologies allows for efficient text processing and retrieval.

Project Architecture

How it Works

1. Google Generative AI Embeddings: The application leverages Google Generative AI Embeddings for processing the text data extracted from uploaded books.

2. Vector Database (FAISS): FAISS is used to efficiently store and retrieve embeddings, enhancing the overall performance of the personal assistant.

3. Model: Gemini-Pro, a sophisticated model, is employed for in-depth analysis and understanding of the book content.

Getting Started

Prerequisites

Ensure you have Docker installed on your machine.

Two Ways to Install

1. Using Pre-built Docker Image from Docker Hub:

Pull the Docker Image

docker pull vanshpatel/teaching_assistant:1.0.0

Run the Docker Container

docker run -p 8501:8501 --env-file .env vanshpatel/teaching-assistant:1.0.0

Access the Teaching Assistant in your web browser at http://localhost:8501.

2. Clone Repository, Build, and Run:

Clone the Repository

git clone https://github.com/vanshpatelx/learningAssistant

Navigate to the Project Directory

cd learningAssistant

Build the Docker Image

docker build -t teaching-assistant .

Run the Docker Container

docker run -p 8501:8501 --env-file .env teaching-assistant

Access the Teaching Assistant in your web browser at http://localhost:8501.