How to build a local AI proxy to redact PII before LLMs

The tutorial outlines a method for using cloud-based language models (LLMs) while maintaining data privacy, particularly focusing on personally identifiable information (PII). It introduces a local AI proxy pattern that acts as an intermediary between the user's application and the cloud LLM, allowing for the inspection and redaction of sensitive data using a combination of a local small language model (SLM), specifically GLiNER, and regex patterns. This approach sanitizes prompts by replacing sensitive elements with placeholders before sending them to the cloud model and then restoring the original data in the model's response. The process, demonstrated through Node.js and Express, involves setting up a local NER model for detecting PII, creating a request context for managing token mappings, and developing a test suite to prevent PII leaks. The tutorial also emphasizes the importance of balancing privacy with the need for high-quality output from cloud models, and it provides guidance on deploying the setup using Docker while considering operational constraints and potential limitations.

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