InquisiBot leverages the power of natural language processing (NLP) and the Google Search Language Model (LLM) to create an intelligent chatbot capable of answering a wide range of queries related to science, general knowledge, and more. The chatbot is designed to provide insightful and accurate responses by harnessing the capabilities of cutting-edge language models.

• Google Search LLM Integration: Harness the capabilities of Google Search Language Model to fetch relevant information and provide accurate answers to user queries.

• Natural Language Processing (NLP): Utilize advanced NLP techniques to understand and process user input, allowing for a conversational and user-friendly experience.

• Wide Range of Topics: InquisiBot is trained to answer questions spanning various domains, including science, technology, general knowledge, and more.

• Colab Implementation: The project is implemented as a Google Colab notebook, making it easy to run and experiment with in a cloud-based environment.

1. Open Colab Notebook: Click on the InquisiBot.ipynb to open the notebook in Google Colab.

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2. Run the Notebook: Once the Colab notebook is open, run the cells sequentially to execute the code and interact with InquisiBot.

3. Experiment and Contribute: Feel free to experiment with the code, and if you have suggestions or improvements, consider contributing back to the project.

If you're interested in contributing to InquisiBot, feel free to submit pull requests, report issues, or suggest improvements. Your contributions are highly appreciated!