Streaming LLM Using Gemini

This project integrates Google's Gemini model into a conversational interface built with Streamlit. The application streams responses from the language model based on the user's input, creating a dynamic, interactive chatbot. This chatbot is powered by LangChain and provides real-time, context-aware responses.

Features

• Real-Time Streaming: The application streams AI responses as the user interacts, offering a dynamic and responsive experience.
• Conversational Memory: The chatbot maintains a history of conversations, allowing it to offer more contextually aware and meaningful responses.
• Google Gemini Integration: Uses the ChatGoogleGenerativeAI from LangChain to interact with Google's Gemini language model for natural language processing.
• Customizable Templates: The chatbot uses a customizable prompt template to ensure the AI responds in a friendly, helpful, and concise manner.

Requirements

To run this application locally, you will need the following dependencies:

• Python 3.7 or later
• Streamlit
• LangChain
• Google Generative AI (Gemini)
• dotenv

Install Dependencies

1. Clone the repository:

   git clone https://github.com/JonathanJourney99/Streaming-LLM.git

2. Install required libraries:

   pip install -r requirements.txt

3. Set up the environment variables:

   • Create a .env file in the root of your project directory.
   • Add your API keys and credentials for LangChain and Google Generative AI.

   Example .env file:

   GOOGLE_API_KEY=your_google_api_key
   

4. Ensure that the Google Gemini API is accessible and the langchain_google_genai library is configured correctly.

How to Run

1. After setting up the environment and installing dependencies, run the application with:

   streamlit run app.py

2. Open the web application in your browser at http://localhost:8501.

3. Start interacting with the chatbot by typing your queries in the input box.

How It Works

1. Initialization: The application uses Streamlit to create an interface for the user to interact with. The ChatGoogleGenerativeAI model is initialized with the gemini-1.5-flash model.

2. Chat History: The conversation history is stored in st.session_state.chat_history, which is passed to the model to provide context-aware responses.

3. Streaming Responses: When the user inputs a message, the model generates a response using the LangChain flow. The response is streamed and displayed dynamically, offering a smooth user experience.

4. Dynamic Prompts: The ChatPromptTemplate is used to build dynamic prompts with user input and previous chat history to ensure context is maintained throughout the conversation.

Example Interaction

1. User: "Hello, how are you?"
2. Assistant: "Hi! I'm here to help. How can I assist you today?"

The assistant responds based on the previous interactions and maintains the context throughout the conversation.

Code Explanation

Key Parts of the Code:

• model_response function: This function constructs a LangChain pipeline that takes the user's input and chat history, generates a response using the Gemini model, and outputs it.

• Streamlit Setup: The app uses st.session_state to store and update the conversation history. It renders the conversation between the human and the AI using st.chat_message.

• Conversation History: Each message, whether from the user or the assistant, is appended to st.session_state.chat_history to maintain the chat history, which is used to generate contextually aware responses.

Troubleshooting

Issue: Missing or Invalid API Keys

• Ensure that your .env file contains valid API keys for the Google Gemini model and other necessary configurations.

Issue: No response from the assistant

• Ensure your internet connection is stable and that the API keys are valid.

Contributing

If you'd like to contribute to this project, feel free to fork the repository and submit a pull request with your proposed changes. Please make sure to follow the code style and write tests for any new features or changes.

License

This project is licensed under the MIT License