Simple Chat Bot Using Open Source LLM Model

This repository contains a simple chatbot application developed using Streamlit and an open-source LLM model. The chatbot is designed to interact with users and provide helpful responses to their queries.

Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Code Overview
• Environment Variables
• Results

Features

• User-friendly interface built with Streamlit.
• Integration with the Ollama open-source LLM model.
• Adjustable parameters for response generation (temperature and max tokens).
• Environment variable management for sensitive information.

Technologies Used

• Streamlit - For creating the web application.
• OpenAI - For language model integration.
• LangChain - For handling prompt templates and output parsing.
• Ollama - Open-source LLM model.
• Python - Programming language used.

Installation

1. Clone the repository:

   git clone https://github.com/Lonishubh48/simple-chat-bot.git
   cd simple-chat-bot

2. Create a virtual environment and activate it:

     conda create -p <your env_name> python==<version>
     conda activate <your env_name>

3. Install the required packages: create a requirements.txt file and keep all the required library

      pip install -r requirements.txt

4. Set up environment variables. Create a .env file in the root directory of the project and add your Langchain API key:

      LANGCHAIN_API_KEY="************"
      LANGCHAIN_PROJECT="************"
      GEN_API_KEY="***********"
      HF_TOKEN="**************"

Usage

To run the application, execute the following command in your terminal: bash streamlit run app.py

Code Overview

The main logic of the chatbot is implemented in app.py. Here’s a brief overview of the code:

• Imports: Necessary libraries are imported, including Streamlit and LangChain.
• Environment Variables: Loaded using dotenv to manage sensitive information.
• Prompt Template: A template is created to structure the interaction between the user and the chatbot.
• Response Generation: A function that takes user input and generates a response using the selected LLM model.
• Streamlit Interface: The user interface is built with Streamlit, allowing users to input questions and receive answers.

Environment Variables

Make sure to set the following environment variables in your .env file:

   LANGCHAIN_API_KEY="************"
   LANGCHAIN_PROJECT="************"
   GEN_API_KEY="***********"
   HF_TOKEN="**************"

Results

Here are some examples of the chatbot's responses:

• User: hi

  • Assistant: Hello! How can i assit you today.

• User: What is generative ai ?.

  • Assistant: Generative Al refers to a category of artificial intelligence systems that can generate new content, such as text, images, or music..