Coding Challenge-3: Natural Language Queries Against a Structural Database

This repo is forked from Onuralp Soylemez's (@cx0) repo: https://github.com/cx0/chatGPT-for-genetics

The goal of this coding challenge is to build a function that takes a natural language instruction or a question, and returns an appropriate response using using Open Targets API endpoints Open Targets Platform GraphQL.

You may use Onuralp's scripts as starting point, or you can write it from scratch.

Tasks:

1. Handle single step queries e.g. "What are the targets of vorinostat?", "Find drugs that are used for treating ulcerative colitis." etc.

2. 2-step queries e.g. "Which diseases are associated with the genes targetted by fasudil?", "Show all the diseases that have at least 5 pathways associated with Alzheimer"

Expectations:

• You can build the solution on Jupyter notebook, but we prefer to see as a CLI functionality
• The response should list the queried entities, no extra paragraphs or text.
• We will test the solution on a set of held out instructions and questions (10 cases for each task).
• You may need an OpenAI account for OpenAI api or a similar LLM API access.