AI Assistant for Website

Overview

The AI Assistant for Website is a Flask-based application integrated with OpenAI APIs, providing an interactive chatbot experience. This project demonstrates the ability to create a full-stack application, including a backend developed with Python and Flask, and a dynamic frontend using HTML, CSS, and JavaScript. The chatbot leverages advanced AI functionalities to engage users with intelligent responses based on their queries.

Technical Features

Backend

• Flask Application: Utilized Flask, a lightweight WSGI web application framework, to handle HTTP requests and serve dynamic content.
• OpenAI API Integration: Integrated OpenAI’s powerful language models to generate responses, ensuring the chatbot can handle complex queries with contextually appropriate answers.
• Vector Search with Qdrant: Implemented a vector search mechanism using Qdrant, which optimizes the retrieval of relevant information from a corpus of text, enhancing the chatbot's ability to provide accurate answers.
• Custom Middleware Functions: Developed middleware to preprocess user queries and manage response content, demonstrating capabilities in crafting efficient data processing pipelines.
• Rate Limiting and User Session Management: Applied rate limiting to manage request frequency and used in-memory storage to track user interactions, showcasing skills in developing robust and scalable applications.

Frontend

• Interactive Chat Interface: Built an interactive user interface with HTML, CSS, and JavaScript, providing users with a seamless and engaging interaction experience.
• JavaScript Enhancements: Implemented AJAX calls for dynamic data fetching without reloading the webpage, improving user experience with asynchronous JavaScript.
• CSS for Responsive Design: Crafted a responsive design using CSS, ensuring the application is accessible and visually appealing across different devices and screen sizes.

Deployment

• Replit Hosting: Deployed the application on Replit, demonstrating familiarity with cloud-based hosting environments and continuous deployment practices.

Skills Demonstrated

• Python Programming: Proficient in Python, with a focus on backend development using Flask.
• Frontend Development: Skills in HTML, CSS, and JavaScript, capable of building and styling interactive web interfaces.
• API Integration: Experienced in integrating and utilizing third-party APIs, specifically OpenAI, to enhance application functionalities.
• Data Handling: Ability to handle and process data efficiently, evident from the implementation of custom middleware and integration with vector databases.
• Problem Solving: Strong problem-solving skills, capable of designing solutions to complex software requirements.

Setup and Installation

To set up and run the BotBuddy AI Assistant locally, follow these steps:

Prerequisites

• Python 3.8 or higher
• pip for Python package installation
• An active internet connection for accessing external APIs

Installation Steps

1. Clone the Repository:

   git clone https://github.com/yourusername/botbuddy.git
   cd botbuddy
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Set Environment Variables: You'll need to set up the following environment variables:

   • QDRANT_HOST
   • QDRANT_API_KEY
   • QDRANT_COLLECTION
   • OPENAI_API_KEY

   These can be set in your shell or through a .env file using a library like python-dotenv.

4. Initialize the Database: Run the Python script provided in the repository to set up your Qdrant vector database with the necessary configuration.

5. Run the Application:

   python main.py
   

   This will start the Flask server on http://localhost:8080.

6. Accessing the Web Interface: Open your browser and navigate to http://localhost:8080 to interact with the AI assistant.

Author

• Ricasco - Feel free to connect with me on LinkedIn

License

This project is licensed under the MIT License - see the LICENSE.md file for details.