SQL Playground: Customer Sentiment Analysis

This example demonstrates using Monty for a task that cannot be solved with a single SQL query: analyzing customer purchase data (CSV) and correlating it with their social media sentiment (JSON tweets).

Data is from https://github.com/mafudge/datasets.

Why This Example is Interesting

1. Cross-format data joining: CSV customer data must join with JSON tweets via Twitter handle - requires programmatic data wrangling
2. Loop-based external calls: Sentiment analysis for each tweet happens in a loop - with JSON tool calling this would flood the context window with 50+ results
3. In-sandbox computation: Averages, correlation, and aggregation happen in Python - no need for the LLM to do mental math
4. Variable iteration: Different customers have different numbers of tweets - code handles this naturally
5. File sandboxing: Uses OSAccess to mount the data files, so the code can read them and nothing else
6. Type checking: Validates LLM-generated code against type stubs before execution

To run

uv run python examples/sql_playground/main.py