Legal Entity Extraction - Example 2

[fayvor]

#15

• Add notebook for entity extraction from NH caselaw
• Add entities to graph using entity names as node keys
• Add entities to vector db using short description
• Resolve entities to entity names using vector search
• Retrieve case from graph to answer question
• Use senate election challenge case as an example

PR Checklist

Model Interaction

• Flexible LLM platform support The platform should be easily switchable. Use LangChain or LlamaIndex.
• Use prompt guide corresponding to the model For example for Granite 3.x Language Models

Data

• Example data: Follow the example data guidance.

Notebook requirements

• Notebook outputs cleared: Ensure all notebook outputs are cleared.
• Automated testing: Add the recipe to the automated tests as described here
• Test in Google Colab:
  • Test that it works in Google Colab (Python 3.10.12).
  • Colab has its own package set and Python version, so ensure compatibility.
• Test locally:
  • Ensure the code works in a fresh Python virtual environment (venv).
• Standard access to secrets and variables Include !pip install git+https://github.com/ibm-granite-community/utils in the first code cell in order to make get_env_var available to accessing secrets and variables in the recipe.

Incoming References

• README.md updates:
  • Add a link to the recipe in the Table of Contents (ToC).
  • Include a Colab button after that link.

GitHub

• Commits signed: All commits must be GPG or SSH signed.
• DCO Compliance: Developer Certificate of Origin (DCO) applies to the code, documentation, and any example data provided. Ensure commits are signed off.