README.md

metadata

title: AI Chatbot with Memory
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
python_version: 3.9
pinned: false
license: apache-2.0
short_description: An intelligent chatbot with conversation memory capabilities
tags:
  - chatbot
  - ai
  - conversational-ai
  - memory
  - nlp

AI Chatbot with LangGraph and Streamlit

A powerful AI chatbot application built with LangGraph, LangChain, and Streamlit that supports multiple LLM providers including Groq and OpenAI.

🚀 Features

• Multi-LLM Support: Choose between Groq and OpenAI models
• Interactive Chat Interface: Clean Streamlit-based chat UI
• Persistent Chat History: Conversations are maintained throughout the session
• Configurable Models: Easy model selection and configuration
• Graph-Based Architecture: Built with LangGraph for scalable conversation flows
• Real-time Responses: Streaming responses from AI models

🛠️ Tech Stack

• Frontend: Streamlit
• AI Framework: LangChain + LangGraph
• LLM Providers:
  • Groq (Llama, Gemma models)
  • OpenAI (GPT-4o, GPT-4o-mini)
• State Management: LangGraph State
• Configuration: ConfigParser

📦 Installation

Prerequisites

• Python 3.8+
• pip or conda

Setup

1. Clone the repository

   git clone <your-repo-url>
   cd AI-News
   

2. Create a virtual environment

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

3. Install dependencies

   pip install -r requirements.txt
   

4. Set up API Keys

   You have two options:

   Option A: Environment Variables (Recommended)

   export GROQ_API_KEY="your_groq_api_key_here"
   export OPENAI_API_KEY="your_openai_api_key_here"
   

   Option B: Enter in UI

   • Leave environment variables empty
   • Enter API keys directly in the Streamlit sidebar

🔑 Getting API Keys

Groq API Key

1. Visit Groq Console
2. Sign up or log in
3. Navigate to API Keys section
4. Create a new API key

OpenAI API Key

1. Visit OpenAI Platform
2. Sign up or log in
3. Navigate to API Keys section
4. Create a new API key

🚀 Usage

1. Start the application

   streamlit run app.py
   

2. Access the app

   • Open your browser to https://huggingface.co/spaces/bpratik/Chatbot

3. Configure the chatbot

   • Select your preferred LLM provider (Groq or OpenAI)
   • Choose a model from the dropdown
   • Enter your API key (if not set as environment variable)
   • Select a use case

4. Start chatting

   • Type your message in the chat input at the bottom
   • Press Enter to send
   • View responses in the chat interface

📁 Project Structure

AI-News/
├── app.py                      # Main application entry point
├── requirements.txt            # Python dependencies
├── src/
│   ├── __init__.py
│   ├── main.py                 # Core application logic
│   ├── graph/
│   │   ├── __init__.py
│   │   └── graph_builder.py    # LangGraph state graph builder
│   ├── llms/
│   │   ├── __init__.py
│   │   ├── groq.py            # Groq LLM integration
│   │   └── openai.py          # OpenAI LLM integration
│   ├── nodes/
│   │   ├── __init__.py
│   │   └── basic_chatbot.py   # Chatbot node implementation
│   ├── state/
│   │   ├── __init__.py
│   │   └── state.py           # LangGraph state definition
│   ├── ui/
│   │   ├── __init__.py
│   │   ├── config.ini         # UI configuration
│   │   ├── config.py          # Configuration loader
│   │   ├── display_results.py # Results display component
│   │   └── load.py            # UI loader
│   ├── tools/
│   │   └── __init__.py
│   └── vectorstore/
       └── __init__.py

⚙️ Configuration

The application can be configured through src/ui/config.ini:

[DEFAULT]
Title = Basic Chatbot
USE_CASE = Basic Chatbot, Chatbot with Web Search
LLM_options = Groq, OpenAI
GROQ_MODEL = meta-llama/llama-4-scout-17b-16e-instruct, gemma2-9b-it, meta-llama/llama-4-maverick-17b-128e-instruct
OPENAI_MODEL = gpt-4o, gpt-4o-mini

🔧 Available Models

Groq Models

• meta-llama/llama-4-scout-17b-16e-instruct
• gemma2-9b-it
• meta-llama/llama-4-maverick-17b-128e-instruct

OpenAI Models

• gpt-4o
• gpt-4o-mini

🐛 Troubleshooting

Common Issues

1. API Key Errors

   • Ensure your API key is valid and has sufficient credits
   • Check if the API key is properly set in environment variables or entered in UI

2. Import Errors

   • Make sure all dependencies are installed: pip install -r requirements.txt
   • Verify you're running from the correct directory

3. Model Not Found

   • Check if the model name in config.ini matches the provider's available models
   • Ensure your API key has access to the selected model

4. Streamlit Issues

   • Clear Streamlit cache: streamlit cache clear
   • Restart the application

Error Messages

• "Failed to initialize the model": Check API key and model availability
• "No use case selected": Select a use case from the sidebar dropdown
• "Graph must have an entrypoint": This indicates a configuration issue - restart the app

🚧 Future Enhancements

• Memory/History Implementation: Add persistent conversation memory using LangChain's built-in memory features
• Web Search Integration: Implement web search capabilities for the chatbot
• File Upload Support: Allow users to upload and chat about documents
• Multiple Conversation Sessions: Support for multiple concurrent chat sessions
• Custom Model Integration: Support for additional LLM providers
• Chat Export: Export conversation history to various formats