This project demonstrates building and exploring knowledge graphs (KGs) using either of the two models: Rebel(seq2seq) and Gemini(Google LLM).

• KG Construction: Build KGs using either:
  • Provided Text: Define your own text for analysis.
  • Gemini-Generated Text: Let Gemini generate text for KG construction.
• Model Selection: Choose between Rebel and Gemini for relation extraction.
• Querying: Perform various KG queries:
  • Keyword search
  • Semantic BFS (breadth-first search)
  • Semantic document search based on embeddings
• Visualization: Convert the KG to a network graph for visual exploration (requires pyvis library).

1. Access the Colab Notebook: [Link to your Colab notebook here]
2. Install Libraries: Follow the instructions in the notebook to install required libraries (e.g., rebel-api, pyvis).
3. Obtain API Keys: If using Rebel, acquire an API key from https://huggingface.co/Babelscape/rebel-large and set the REBEL_API_KEY environment variable in Colab. For Gemini, obtain your gemini api key and set the GOOGLE_API_KEY.
4. Run Cells: Execute the notebook cells to construct, query, and visualize your KG.

• Refer to the notebook for detailed explanations and customization options.
• Experiment with different text inputs, models, and query types to explore your data.
• Consider installing pyvis locally for offline visualization.

This project provides a starting point for building and interacting with KGs using LLMs in Google Colab. Feel free to adapt and extend it for your specific needs.