MNNIT-Chatbot

Overview

The MNNIT Chatbot is a conversational AI system designed to provide accurate and context-aware responses using a fallback Retrieval-Augmented Generation (RAG) mechanism. It integrates cutting-edge AI tools and frameworks to leverage both cloud-based and local AI models for an optimal user experience. This project is built specifically for use cases involving MNNIT-related information, sourced from two provided PDFs.

---

Key Features

• Hybrid Query Mechanism: Queries first interact with a cloud-based AI model (OpenAI GPT-4-o-mini API). If the response is unsatisfactory, the system falls back to a local Llama 2-7B model using a RAG mechanism.
• PDF Integration: Extracts, chunks, and embeds information from two provided MNNIT PDFs.
• Pinecone Vector Database: Stores embeddings for efficient retrieval during query processing.
• LangChain Integration: Builds robust prompt templates and retrieval-based question-answering (QA) workflows.
• Local LLM Deployment: Uses the Llama 2-7B model (quantized) via CTransformers for resource-efficient local execution.
• Flask Web Application: Provides a user-friendly interface for interaction and deployment.

---

Process Diagram

---

Technical Workflow

1. PDF Processing:

   • Two PDFs containing MNNIT-specific information are ingested.
   • Text is extracted and divided into manageable chunks.
   • Embeddings are created for these chunks using a pre-trained model.

2. Vector Database:

   • Generated embeddings are stored in the Pinecone vector database.
   • Pinecone enables efficient similarity searches to fetch the most relevant chunks during queries.

3. Query Processing:

   • User queries are first sent to a cloud-based AI model (e.g., GPT-4-o-mini API).
   • If the response quality is deemed unsatisfactory, the fallback RAG mechanism is activated.
   • The fallback mechanism performs:
     • Contextual Retrieval: Relevant information is retrieved from Pinecone.
     • Local LLM Execution: The Llama 2-7B model processes the retrieved context to generate a refined response.

4. Prompt Engineering:

   • LangChain is used to structure prompts for both the cloud-based and local models.
   • A custom prompt template ensures alignment with the chatbot’s context and objectives.

5. Deployment:

   • The Flask framework serves as the frontend interface for interacting with the chatbot.
   • End-users can query the chatbot via a web-based application.

---

Prerequisites

• Python 3.8
• Conda (for environment management)
• Access to APIs (OpenAI GPT-4-o-mini)
• Pre-downloaded Llama 2-7B Model:
  • File: llama-2-7b-chat.ggmlv3.q4_0.bin
  • Download Link

---

Installation

1. Clone the repository:

   git clone https://github.com/samudraneel05/MNNIT-Chatbot.git
   cd MNNIT-Chatbot

2. Run the following commands one by one:

conda create -n mnnitchatbot python=3.8 -y

conda activate mnnitchatbot

pip install -r requirements.txt