Ryzosphere AI Scraper (Internship PoC)

A proof-of-concept data pipeline demonstrating an AI-native approach to scraping, structuring, and validating agricultural market data.

Application Context

I built this sandbox project specifically for my Summer 2026 CS Intern application at The Ryzosphere.

To align with your mission of building data infrastructure for the food-and-agriculture ecosystem, this project simulates the exact workflow outlined in the job description: extracting unstructured agricultural data, mapping it to a ground-truth taxonomy using an LLM agent, and safely writing the validated output to a local staging environment.

Key Features & Alignment

• Responsible Scraping: Utilizes a custom User-Agent and algorithmic rate limiting (time.sleep) to respect target servers, avoiding the overwhelming of local agricultural websites.
• Asset-Mapping Taxonomy: Replaces brittle HTML parsing rules with Google's Gemini 3.5 Flash model to intelligently interpret scraped content and force it into a strict, predefined schema.json format.
• Safe Staging: Validates the LLM response against the schema and writes all validated results to a local staging_output/ directory—never to live systems.

Example Output

When the pipeline runs, it fetches the raw market information, hands it to the AI agent for structuring, and saves the validated JSON.

Console Output

Fetching data from [https://en.wikipedia.org/wiki/Union_Square_Greenmarket](https://en.wikipedia.org/wiki/Union_Square_Greenmarket)...
Respecting rate limits: Sleeping for 2 seconds before request...
Successfully fetched the page!

Handing raw text over to the AI Agent for structuring...

=== THE FINAL STRUCTURED DATA ===
{
  "market_name": "Union Square",
  "location": "Union Square Park, Manhattan, New York City"
}

Success! Validated data written to staging_output/market_data_v1.json