Custom Blocklists with Azure Content Safety¶

In this cookbook, we'll create a custom blocklist to block Gen Alpha slang from text content using Azure Content Safety and any-guardrail.

What You'll Learn¶

• How to create a custom blocklist with Azure Content Safety
• How to add terms to a blocklist
• How to validate text against your blocklist
• How to process large documents (classic literature in this instance) through the guardrail

Prerequisites¶

• Python 3.11+
• Azure account with a Content Safety resource
• CONTENT_SAFETY_KEY and CONTENT_SAFETY_ENDPOINT from your Azure portal

Setup¶

Install the required packages and configure your Azure credentials.

In [ ]:

%pip install gutenbergpy --quiet
%pip install 'any-guardrail[azure-content-safety]' --quiet

import os
from getpass import getpass


def ensure_env_var(name: str) -> None:
    """Prompt for an environment variable if not already set."""
    if name not in os.environ:
        print(f"{name} not found in environment!")
        value = getpass(f"Please enter your {name}: ")
        os.environ[name] = value
        print(f"{name} set for this session!")
    else:
        print(f"{name} found in environment.")


for var in ["CONTENT_SAFETY_KEY", "CONTENT_SAFETY_ENDPOINT"]:
    ensure_env_var(var)

%pip install gutenbergpy --quiet %pip install 'any-guardrail[azure-content-safety]' --quiet import os from getpass import getpass def ensure_env_var(name: str) -> None: """Prompt for an environment variable if not already set.""" if name not in os.environ: print(f"{name} not found in environment!") value = getpass(f"Please enter your {name}: ") os.environ[name] = value print(f"{name} set for this session!") else: print(f"{name} found in environment.") for var in ["CONTENT_SAFETY_KEY", "CONTENT_SAFETY_ENDPOINT"]: ensure_env_var(var)

Create a Blocklist¶

Initialize the guardrail and create a new blocklist. Here we're creating one for Gen Alpha slang terms.

In [ ]:

from any_guardrail import AnyGuardrail, GuardrailName

guardrail = AnyGuardrail.create(GuardrailName.AZURE_CONTENT_SAFETY)

blocklist_name = "GenAlphaSlang"
blocklist_description = "List of gen alpha words"

guardrail.create_or_update_blocklist(
    blocklist_name=blocklist_name,
    blocklist_description=blocklist_description,
)

from any_guardrail import AnyGuardrail, GuardrailName guardrail = AnyGuardrail.create(GuardrailName.AZURE_CONTENT_SAFETY) blocklist_name = "GenAlphaSlang" blocklist_description = "List of gen alpha words" guardrail.create_or_update_blocklist( blocklist_name=blocklist_name, blocklist_description=blocklist_description, )

Add Terms to the Blocklist¶

Add the specific terms you want to filter. These can be individual words or phrases.

In [ ]:

blocklist_terms = [
    "Skibidi",
    "Rizz",
    "Sigma",
    "Gyatt",
    "Brain Rot",
    "Fanum Tax",
    "Ohio",
    "Mewing",
    "Aura",
    "Sigma",
    "Crash Out",
    "Delulu",
    "Glaze",
    "Mog",
    "Pookie",
    "Opp",
    "Slay",
]
guardrail.add_blocklist_items(blocklist_name=blocklist_name, blocklist_terms=blocklist_terms)

blocklist_terms = [ "Skibidi", "Rizz", "Sigma", "Gyatt", "Brain Rot", "Fanum Tax", "Ohio", "Mewing", "Aura", "Sigma", "Crash Out", "Delulu", "Glaze", "Mog", "Pookie", "Opp", "Slay", ] guardrail.add_blocklist_items(blocklist_name=blocklist_name, blocklist_terms=blocklist_terms)

Validate Text¶

Test the blocklist against sample text. The guardrail returns valid=True if no blocked terms are found, and valid=False with details about which terms were matched.

In [ ]:

# Pass
text = "Hello, how are you?"
result = guardrail.validate(text)
print(f"Text: {text} \nEvaluation result:{result} ")

# Fail - contains a term from the block list
text = "The startup pitch was all delulu with no solulu"
result = guardrail.validate(text)
print(f"Text: {text} \nEvaluation result:{result} ")

# Pass text = "Hello, how are you?" result = guardrail.validate(text) print(f"Text: {text} \nEvaluation result:{result} ") # Fail - contains a term from the block list text = "The startup pitch was all delulu with no solulu" result = guardrail.validate(text) print(f"Text: {text} \nEvaluation result:{result} ")

Below, test against a classic novel - Anne of Green Gables from Project Gutenberg. This demonstrates that literature from 1908 contains no Gen Alpha slang (as expected!).

In [ ]:

import gutenbergpy.textget

# Get a book by its Gutenberg ID (e.g., 45 for Anne of Green Gables)
# raw_book = gutenbergpy.textget.get_text_by_id(2701)
raw_book = gutenbergpy.textget.get_text_by_id(45)
# Strip headers and footers automatically
clean_book = gutenbergpy.textget.strip_headers(raw_book)
chunks = [clean_book[i : i + 7000] for i in range(0, len(clean_book), 7000)]
result = guardrail.validate(chunks[0])

print(f"Result: {result}")

import gutenbergpy.textget # Get a book by its Gutenberg ID (e.g., 45 for Anne of Green Gables) # raw_book = gutenbergpy.textget.get_text_by_id(2701) raw_book = gutenbergpy.textget.get_text_by_id(45) # Strip headers and footers automatically clean_book = gutenbergpy.textget.strip_headers(raw_book) chunks = [clean_book[i : i + 7000] for i in range(0, len(clean_book), 7000)] result = guardrail.validate(chunks[0]) print(f"Result: {result}")

Next Steps¶

• Try adding your own terms to the blocklist
• You can use blocklists to filter competitor brand names, profanity, or domain-specific terms
• Combine blocklist filtering with other Azure Content Safety features (hate, violence, etc.)

For more information, see the Azure Content Safety blocklist documentation.