Langchain Demo with Gemma Model

This repository demonstrates how to build a simple question-answering application using the Langchain framework and Gemma model. The application allows users to input a question, and it provides a response generated using the Ollama Llama2 model.

Features

• Streamlit Framework: Interactive web interface to input questions and display responses.
• Langchain Integration: Uses Langchain prompts to manage the conversation flow.
• Ollama Model: Utilizes the gemma2:2b model for generating intelligent answers.
• Langsmith Tracking: Tracks the prompt and responses for logging and debugging.

Getting Started

Prerequisites

• Python 3.8+
• Ollama Model setup
• Install dependencies using pip:

pip install langchain-community langchain-core streamlit python-dotenv

Environment Variables

Create a .env file in the root of your project and set the following variables:

LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_PROJECT=your_project_name

Run the Application

To run the application, simply execute:

streamlit run app.py

You can then open the application in your browser at http://localhost:8501 and start interacting with the model.

Code Overview

• Langchain API Key: The API key is loaded using dotenv for Langsmith tracking.
• Prompt Template: The conversation is structured using a Langchain ChatPromptTemplate, allowing the user to ask a question and receive a response.
• LLM (Large Language Model): The Ollama model (gemma2:2b) is used to generate answers to user questions.
• Streamlit Input: The user inputs a question through a simple text field, and the response is displayed in real-time.

Future Enhancements

• Add more models to switch between different LLMs.
• Implement additional Langchain tools for better user interaction.
• Add more complex prompt management for better responses.

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