Streamlit Chatbot

A Streamlit-based chatbot application utilizing the Groq API and Langchain for conversational AI.

Description

This project features a chatbot application built with Streamlit. It integrates the Groq API and Langchain to provide real-time conversational responses. The chatbot is designed to retain conversation history and offer relevant answers based on previous interactions.

Features

• Interactive chatbot interface using Streamlit
• Real-time conversation with a language model
• Conversation history management
• Configurable LLM parameters for focused responses

chat.mp4

Technical Specifications

Language Model

• Model Name: llama-3.1-70b-versatile
• API Provider: Groq
• Temperature: 0.7
  • Description: Controls the randomness of responses. A lower value results in more focused and deterministic responses.
• Max Tokens: 512
  • Description: Limits the number of tokens (words or characters) in the generated response. Ensures quicker responses.
• Timeout: 10 seconds
  • Description: Sets the maximum time to wait for a response from the API before timing out.
• Max Retries: 1
  • Description: Number of times to retry the request in case of failure.

Streamlit Setup

• Framework: Streamlit
• Purpose: Provides the interactive web interface for the chatbot application.
• Features:
  • Real-time chat interface.
  • User-friendly input and display components.

Memory Management

• Memory Type: ConversationSummaryMemory
• Description: Stores the conversation history and summarizes past interactions to maintain context and continuity in the conversation.

Dependencies

• Streamlit: Used to create the interactive web interface.
• python-dotenv: Loads environment variables from a .env file.
• langchain_groq: Provides integration with Groq's language model API.

Installation and Setup

1. Clone the Repository:

   git clone https://github.com/DhushanthanK/streamlit-chatbot.git
   cd streamlit-chatbot

2. Create a Virtual Environment (Optional):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies:

   Create a requirements.txt file with the following content:

   streamlit
   python-dotenv
   langchain_groq

   Install the dependencies:

   pip install -r requirements.txt

4. Configure Environment Variables:

   Create a .env file in the project directory with the following content:

   groq_API_KEY=your_groq_api_key_here

   Replace your_groq_api_key_here with your actual Groq API key.

5. Run the Application:

   streamlit run chat.py

   This will start the Streamlit server, and you can access the chatbot application in your web browser at http://localhost:8501.

Usage

• Open the application in your web browser.
• Type your message in the input box and press Enter to start a conversation.
• The chatbot will respond based on previous interactions, and the conversation history will be managed automatically.

License

• License Type: MIT License
• Details: See the LICENSE file for more information.

Additional Information

• Free to Use: The application and its code are freely available for use and modification under the MIT License.
• Language Model: The application uses Groq's language model, which is versatile and can be configured to suit various conversational needs.