# Build a Chatbot with Custom Spike Prime Knowledge

This repository provides a step-by-step guide on how to build a custom chatbot that leverages **Spike Prime knowledge** and serves as a starting point to create a personalized knowledge base for GPT models. From gathering raw documents (such as web pages) to processing them into JSON, this project will teach you how to design, structure, and fine-tune chatbot responses using **prompt engineering** techniques. 

The goal is to empower both technical and non-technical users to build custom chatbots using **OpenAI** or **Azure AI Studio**.


## 🚀 Features

- **Custom Knowledge Base Creation:** Learn how to collect raw documents (e.g., web pages) and transform them into structured JSON data.
- **Python Data Processing:** Use Python scripts to parse and process raw documents efficiently.
- **Prompt Engineering Techniques:** Master how to control chatbot responses using the **system message**.
- **Non-Technical Friendly:** Simple enough for non-technical users to deploy a chatbot on **OpenAI** or **Azure AI Studio**.
- **Continuous Improvement:** Keep building, tuning, and updating the chatbot’s data to improve performance over time.

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## 🔧 Requirements

- **Python 3.x** installed on your system
- Basic knowledge of Markdown and JSON formatting

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## 📦 Installation

1. **Clone the Repository:**
   ```bash
   git clone https://github.com/yourusername/spike-prime-chatbot.git
   cd spike-prime-chatbot

2. Install Dependencies: Make sure you have the necessary Python packages installed:

   pip install -r requirements.txt

3. Prepare Your Data:

   • Collect raw documents (web pages, PDFs, etc.).
   • Use the provided Python script (parse_documents.py) to convert them into structured JSON.

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🛠 Usage

1. Processing Documents: Use the following command to parse and structure your documents:

   python parse_documents.py --input <your_raw_documents> --output data.json

2. Configure Prompt Engineering: Modify the system_message parameter in the chatbot’s configuration to customize the way responses are generated.

3. Deploy on OpenAI or Azure AI Studio:

   • Use OpenAI: Create a new chatbot, upload your custom data, and set your prompt configurations.
   • Use Azure AI Studio: Follow the instructions to integrate your chatbot and JSON data into Azure’s platform.

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💡 Prompt Engineering Tips

• Set the system message: This is crucial for controlling the tone, scope, and behavior of the chatbot.

  {
    "system_message": "You are a helpful assistant specialized in Spike Prime knowledge."
  }

• Experiment with instructions: Adjust the prompt to refine the chatbot's responses based on user needs.

• Iterate on feedback: Regularly update the knowledge base with new information to continuously improve the chatbot's effectiveness.

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🤝 Contributing

Contributions are welcome! If you'd like to improve the project or add new features, please:

1. Fork the repository.
2. Create a new branch (feature-branch-name).
3. Commit your changes.
4. Open a Pull Request.

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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📢 Acknowledgments

This project is inspired by the idea of using custom knowledge bases to enhance GPT models and make them more relevant to niche topics. Thank you to the contributors and community members for continuous improvements!

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Start building your custom Spike Prime chatbot today 🚀! The sky's the limit when it comes to tuning and improving your knowledge base for personalized experiences.

This `README.md` provides a clear overview of the project, instructions, and guidelines for users. It is structured to be accessible to non-technical users while offering flexibility for continuous development.