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TechDaddy: RAG ChatBot for college assistance based on Mixtral-8x7b an Open-source LLM

Project Overview

TechDaddy is an AI chatbot built using a Retrieval-Augmented Generation (RAG) pipeline to assist college students by providing accurate and contextually relevant answers to their queries. Leveraging LangChain, HuggingFace, and open-source LLM and sentence-transformer embedding model like Mixtral-8x7b and all-minilm respectively. TechDaddy is trained on all available data of the college to address various student needs.

Project Goals and Objectives

  • Answer common questions that freshmen might have.
  • Provide information about courses and academic structures.
  • Offer office timings of faculty members.
  • Guide students on where to obtain forms for facilities like the swimming pool.
  • Explain the organizational hierarchy within the college.
  • Provide roadmaps for various tech fields.

Target Audience

The primary users of TechDaddy are college students, especially freshmen. Faculty and administrative staff can also benefit from the chatbot by quickly responding to student queries.

Features and Functionality

  • Q&A for Freshmen: Clears doubts and answers frequently asked questions by new students.
  • Course Information: Provides detailed information about courses, including syllabus, structure, and prerequisites.
  • Faculty Office Timings: Displays the office hours of faculty members.
  • Form Locations: Guides users on where to obtain various forms, such as those for the swimming pool.
  • College Hierarchy: Explains the organizational structure of the college.
  • Tech Roadmaps: Provides guidance and roadmaps for different technology fields.

Technologies Used

  • Streamlit: For creating the web interface.
  • LangChain: To build the RAG pipeline.
  • Groq: For using the ChatGroq LLM.
  • HuggingFace: For integrating open-source LLMs.
  • Python: For overall development.
  • FAISS: For vector similarity search.
  • dotenv: For managing environment variables.
  • PyPDFLoader: For loading PDF documents.

Expected Outcomes

  • Enhanced Student Experience: Freshmen will have a smoother transition into college life with easy access to information.
  • Efficient Information Dissemination: Reduces the workload on administrative staff by automating responses to common queries.
  • Resource Accessibility: Students will easily find resources and forms they need, improving their overall college experience.

Setup and Installation

  1. Clone the Repository:

    git clone https://github.com/aayushkanjani/TechDaddy.git
cd TechDaddy

  2. Create and Activate Virtual Environment:

    python -m venv venv
# On Windows
.\venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

  3. Install Dependencies:

    pip install -r requirements.txt

  4. Set Up Environment Variables:

    • Create a .env file in the project root and add your Groq API key: 
      GROQ_API_KEY=your_groq_api_key_here

  5. Run the Application:

    streamlit run app.py

Contact Information