AI Guard Response Tracing in Python

An example Python app demonstrating how to integrate Pangea's AI Guard service into a Python app to monitor and sanitize LLM generations.

Prerequisites

• Python v3.12 or greater.
• pip v24.2 or uv v0.4.29.
• A Pangea account with AI Guard enabled.
• An OpenAI API key.

Setup

git clone https://github.com/pangeacyber/python-aig-response-tracing.git
cd python-aig-response-tracing

If using pip:

python -m venv .venv
source .venv/bin/activate
pip install .

Or, if using uv:

uv sync
source .venv/bin/activate

Then the app can be executed with:

python aig_response_tracing.py "What is MFA?"

Usage

Usage: aig_response_tracing.py [OPTIONS] PROMPT

Options:
  --model TEXT           OpenAI model.  [default: gpt-4o-mini; required]
  --ai-guard-token TEXT  Pangea AI Guard API token. May also be set via the
                         `PANGEA_AI_GUARD_TOKEN` environment variable.
                         [required]
  --pangea-domain TEXT   Pangea API domain. May also be set via the
                         `PANGEA_DOMAIN` environment variable.  [default:
                         aws.us.pangea.cloud; required]
  --openai-api-key TEXT  OpenAI API key. May also be set via the
                         `OPENAI_API_KEY` environment variable.  [required]
  --help                 Show this message and exit.

Example

AI Guard will protect against leaking credentials like Pangea API tokens. The easiest way to demonstrate this would be to have the LLM repeat a given (fake) API token:

python aig_response_tracing.py "Echo 'pts_testtesttesttesttesttesttesttest' back."

The output after AI Guard is:

************************************

Audit logs can be viewed at the Secure Audit Log Viewer.