Guide to Build a Math Problem Solver Chat App with LangChain

This article demonstrates building a math problem-solving chat app using LangChain, Gemma 9b, Llama 3.2 Vision, and Streamlit. The app solves both text and image-based math problems, providing step-by-step solutions.

Key Features & Functionality:

The application leverages the strengths of several technologies:

• Gemma 9b (text processing): Acts as the core problem-solving engine for text-based math problems.
• Llama 3.2 Vision (image processing): Handles image-based problems, converting them into a format usable by Gemma 9b.
• LangChain: Orchestrates the interaction between the user interface, Gemma 9b, Llama 3.2 Vision, and external tools like a calculator and Wikipedia.
• Streamlit: Provides a user-friendly web interface for inputting problems (text or image) and viewing solutions.

The app's workflow involves:

1. Environment Setup: Setting up Python, creating a virtual environment, and installing necessary libraries. This includes obtaining a Groq API key for accessing the LLMs.
2. Model Initialization: Loading the Gemma 9b and Llama 3.2 Vision models via the Groq API.
3. Tool Integration: Integrating tools like a calculator and Wikipedia access within the LangChain framework to enhance problem-solving capabilities.
4. User Interface (Streamlit): Creating a web app with separate sections for text and image-based problem input.
5. Problem Processing: The app processes user input, routing text to Gemma 9b and images to Llama 3.2 Vision. LangChain manages the flow and integrates the external tools as needed.
6. Solution Generation & Display: The app presents solutions in a clear, step-by-step manner.

Technical Details & Implementation:

The article details the code for setting up the environment, installing dependencies, loading environment variables, initializing the LLMs, and integrating tools. It provides code snippets for handling both text and image-based queries, including base64 encoding for image uploads. A flow diagram visually represents the application's architecture. The article also addresses ethical considerations, such as preventing cheating.

Example Outputs:

The article includes examples of both text and image-based problem inputs and their corresponding outputs, showcasing the app's functionality.

Conclusion & Further Learning:

The article concludes by highlighting the key takeaways, emphasizing the power of combining these AI technologies for educational applications. A FAQ section addresses common questions about the technologies used. The complete code is available on GitHub (link provided in the original article).

Note: The image URLs are placeholders and need to be replaced with the actual image URLs from the original article. The images are kept in the same order as the original.

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