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Python Library Assistant Powered by Sanity Structured Content

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Sanity Challenge Path One Submission

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content

What I Built

I developed an AI-grounded Python Library Assistant that queries structured documentation stored directly in Sanity's Content Lake. Ungrounded AI models frequently hallucinate parameter names, syntax, and method signatures. This project solves that problem by grounding technical advice for libraries like Pandas and Scikit-learn directly in verified, structured records stored in Sanity.

Demo

The assistant queries Sanity Content Lake via targeted GROQ queries and retrieves verified syntax and best practices:

Topic: Machine Learning Pipelines
Reference: Use sklearn.pipeline.Pipeline to sequentially apply a list of transforms and a final estimator. Prevents data leakage between training and testing sets.
Best Practice: Only call fit() on the training data, and use transform() or predict() on the test split.
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Code

How I Used Sanity

  • Content Lake as Grounded Knowledge: Ingested verified technical documentation, method syntax, and best practices as structured JSON documents directly into Sanity's production dataset.
  • Targeted GROQ Retrieval: Used GROQ queries against Sanity's API to fetch the exact context needed for each library without unnecessary token overhead.
  • Zero-Dependency Integration: Built using Python's standard library (urllib, json), demonstrating that Sanity Content Lake can seamlessly power any lightweight developer tool or CLI agent.

Sanity Project Details

  • Project ID: 8b2bupco
  • Dataset: production

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