ChatBot

Our goal here is to design and implement a conversational chatbot based on a Large Language Model (LLM) with the ability to preserve and utilize conversation history. Unlike a simple chatbot that only responds to the most recent input, our system maintains awareness of the entire dialogue context, enabling it to provide more coherent, context-aware, and human-like responses. This feature is crucial in creating natural and intelligent interactions where users may refer to earlier parts of the conversation.
Find detailed explanations of any of the sections in notebook

7B-size chatbot

Context aware chat

• Initialization → Loads model and tokenizer.
• User Input → Reads message from console.
• History Update → Maintains all previous exchanges in conversation_history.
• Context Management → Before generating, the history may be trimmed using manage_context_window.
• Input Preparation → Combines history + current user input into tokenized format.
• Response Generation → Calls the model to produce a reply.
• Decoding → Converts raw tokens to human-readable response.
• Display + Save → Prints the bot reply and updates history for the next turn.

Retrieval-Augmented Generation (RAG)

Retrieved data from user's pdf

• RAG System Initialization

  • initialize rag if possible
  • if PDF exists at PDF PATH then
    • Extract text using
    • Split into chunks
    • Initialize embedding model
    • Generate embeddings and normalize
    • Create FAISS index
    • Add embeddings to index
    • Set rag enabled

• Information Retrieval Process

  • if rag enabled AND index exists AND chunks not empty then
    • Encode query
    • Search index
    • Format results with reference numbering
    • Return formatted system message

WebIntegration

Web search sample

• Preserve original user input for display
• if web search enabled AND needed then
  • search results= searchexa(user input)
  • Update data_state.webdata

Interactive Game

20Q game view

• Initialization
  • Import ValidatorModel.
  • Instantiate validator_model = ValidatorModel().
• Game Start
  • Prompt the user (or system input) to confirm that the user intends to play.
• Questioning Phase
  • Generate a yes/no question about the secret word (e.g., "Is this an animal?").
  • Submit the question: validator_model.validate_question(question).
  • Store the response.
• Guessing Phase
  • Use accumulated answers to form a hypothesis about the secret word.
  • Submit the guess: validator_model.validate_guess(guess).
  • If correct, terminate with success.
• Termination
  • If the model cannot guess correctly within 20 questions, terminate and mark as unsuccessful.

User Interface with Gradio

The interface implements a multi-tab architecture with sophisticated state management and real-time monitoring.

Tabs and resources