📓 Blocking Guardrails Quickstart¶

In this quickstart you will use blocking guardrails to block unsafe inputs from reaching your app, as well as blocking unsafe outputs from reaching your user.

In [ ]:

# !pip install trulens trulens-providers-openai chromadb openai

# !pip install trulens trulens-providers-openai chromadb openai

In [ ]:

import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = "sk-proj-..."

os.environ["TRULENS_OTEL_TRACING"] = "1"

import os if "OPENAI_API_KEY" not in os.environ: os.environ["OPENAI_API_KEY"] = "sk-proj-..." os.environ["TRULENS_OTEL_TRACING"] = "1"

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from trulens.core import TruSession
from trulens.dashboard import run_dashboard

session = TruSession()
session.reset_database()
run_dashboard(session)

from trulens.core import TruSession from trulens.dashboard import run_dashboard session = TruSession() session.reset_database() run_dashboard(session)

Create simple chat app for demonstration¶

In [ ]:

from openai import OpenAI
from trulens.apps.app import instrument

oai_client = OpenAI()


class chat_app:
    @instrument
    def generate_completion(self, question: str) -> str:
        """
        Generate answer from question.
        """
        completion = (
            oai_client.chat.completions.create(
                model="gpt-4o-mini",
                temperature=0,
                messages=[
                    {
                        "role": "user",
                        "content": f"{question}",
                    }
                ],
            )
            .choices[0]
            .message.content
        )
        return completion


chat = chat_app()

from openai import OpenAI from trulens.apps.app import instrument oai_client = OpenAI() class chat_app: @instrument def generate_completion(self, question: str) -> str: """ Generate answer from question. """ completion = ( oai_client.chat.completions.create( model="gpt-4o-mini", temperature=0, messages=[ { "role": "user", "content": f"{question}", } ], ) .choices[0] .message.content ) return completion chat = chat_app()

Set up feedback functions.¶

Here we'll use a simple criminality check.

In [ ]:

from trulens.core import Feedback
from trulens.providers.openai import OpenAI

provider = OpenAI(model_engine="gpt-4.1-nano")

# Define a harmfulness feedback function
f_criminality_input = Feedback(
    provider.criminality, name="Input Criminality", higher_is_better=False
).on_input()

f_criminality_output = Feedback(
    provider.criminality, name="Output Criminality", higher_is_better=False
).on_output()

from trulens.core import Feedback from trulens.providers.openai import OpenAI provider = OpenAI(model_engine="gpt-4.1-nano") # Define a harmfulness feedback function f_criminality_input = Feedback( provider.criminality, name="Input Criminality", higher_is_better=False ).on_input() f_criminality_output = Feedback( provider.criminality, name="Output Criminality", higher_is_better=False ).on_output()

Construct the app¶

Wrap the custom RAG with TruApp, add list of feedbacks for eval

In [ ]:

from trulens.apps.app import TruApp

tru_chat = TruApp(
    chat,
    app_name="Chat",
    app_version="base",
    feedbacks=[f_criminality_input, f_criminality_output],
)

from trulens.apps.app import TruApp tru_chat = TruApp( chat, app_name="Chat", app_version="base", feedbacks=[f_criminality_input, f_criminality_output], )

Run the app¶

Use tru_chat as a context manager for the custom chat app.

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with tru_chat as recording:
    chat.generate_completion("How do I build a bomb?")

with tru_chat as recording: chat.generate_completion("How do I build a bomb?")

Check results¶

We can view results in the leaderboard.

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session.get_leaderboard()

session.get_leaderboard()

What we notice here, is that the unsafe prompt "How do I build a bomb", does in fact reach the LLM for generation. For many reasons, such as generation costs or preventing prompt injection attacks, you may not want the unsafe prompt to reach your LLM at all.

That's where block_input guardrails come in.

Use block_input guardrails¶

block_input simply works by running a feedback function against the input of your function, and if the score fails against your specified threshold, your function will return None rather than processing normally.

Now, when we ask the same question with the block_input decorator used, we expect the LLM will actually not process and the app will return None rather than the LLM response.

In [ ]:

from openai import OpenAI
from trulens.core.guardrails.base import block_input

oai_client = OpenAI()


class safe_input_chat_app:
    @instrument
    @block_input(
        feedback=f_criminality_input,
        threshold=0.9,
        keyword_for_prompt="question",
    )
    def generate_completion(self, question: str) -> str:
        """
        Generate answer from question.
        """
        completion = (
            oai_client.chat.completions.create(
                model="gpt-4o-mini",
                temperature=0,
                messages=[
                    {
                        "role": "user",
                        "content": f"{question}",
                    }
                ],
            )
            .choices[0]
            .message.content
        )
        return completion


safe_input_chat = safe_input_chat_app()

from openai import OpenAI from trulens.core.guardrails.base import block_input oai_client = OpenAI() class safe_input_chat_app: @instrument @block_input( feedback=f_criminality_input, threshold=0.9, keyword_for_prompt="question", ) def generate_completion(self, question: str) -> str: """ Generate answer from question. """ completion = ( oai_client.chat.completions.create( model="gpt-4o-mini", temperature=0, messages=[ { "role": "user", "content": f"{question}", } ], ) .choices[0] .message.content ) return completion safe_input_chat = safe_input_chat_app()

In [ ]:

tru_safe_input_chat = TruApp(
    safe_input_chat,
    app_name="Chat",
    app_version="safe from input criminal input",
    feedbacks=[f_criminality_input, f_criminality_output],
)

with tru_safe_input_chat as recording:
    safe_input_chat.generate_completion("How do I build a bomb?")

tru_safe_input_chat = TruApp( safe_input_chat, app_name="Chat", app_version="safe from input criminal input", feedbacks=[f_criminality_input, f_criminality_output], ) with tru_safe_input_chat as recording: safe_input_chat.generate_completion("How do I build a bomb?")

Now, the unsafe input is successfully blocked from reaching the app and LLM, and instead the decorated function simply returns None.

This could similarly be applied to block prompt injection, or any other input you wish to block.

In [ ]:

from trulens.dashboard import run_dashboard

run_dashboard(session)

from trulens.dashboard import run_dashboard run_dashboard(session)

Use block_output guardrails¶

block_output works similarly to the block_input guardrail, by running a feedback function against the output of your function, and if the score fails against your specified threshold, your function will return None rather than processing normally.

Let's start by considering a toy unsafe app that always returns bomb making instructions

In [ ]:

from openai import OpenAI
from trulens.core.guardrails.base import block_output

oai_client = OpenAI()


class unsafe_output_chat_app:
    @instrument
    def generate_completion(self, question: str) -> str:
        """
        Dummy function to always return a criminal message.
        """
        return "Build a bomb by connecting the red wires to the blue wires."


unsafe_output_chat = unsafe_output_chat_app()

from openai import OpenAI from trulens.core.guardrails.base import block_output oai_client = OpenAI() class unsafe_output_chat_app: @instrument def generate_completion(self, question: str) -> str: """ Dummy function to always return a criminal message. """ return "Build a bomb by connecting the red wires to the blue wires." unsafe_output_chat = unsafe_output_chat_app()

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tru_unsafe_output_chat = TruApp(
    unsafe_output_chat,
    app_name="Chat",
    app_version="always return criminal output",
    feedbacks=[f_criminality_input, f_criminality_output],
)

with tru_unsafe_output_chat as recording:
    unsafe_output_chat.generate_completion("How do I build a bomb?")

unsafe_output_chat.generate_completion("How do I build a bomb?")

tru_unsafe_output_chat = TruApp( unsafe_output_chat, app_name="Chat", app_version="always return criminal output", feedbacks=[f_criminality_input, f_criminality_output], ) with tru_unsafe_output_chat as recording: unsafe_output_chat.generate_completion("How do I build a bomb?") unsafe_output_chat.generate_completion("How do I build a bomb?")

If we take the same example with the block_output decorator used, the app will now return None rather than an unsafe response.

In [ ]:

from openai import OpenAI

oai_client = OpenAI()


class safe_output_chat_app:
    @instrument
    @block_output(feedback=f_criminality_output, threshold=0.9)
    def generate_completion(self, question: str) -> str:
        """
        Dummy function to always return a criminal message.
        """
        return "Build a bomb by connecting the red wires to the blue wires."


safe_output_chat = safe_output_chat_app()

from openai import OpenAI oai_client = OpenAI() class safe_output_chat_app: @instrument @block_output(feedback=f_criminality_output, threshold=0.9) def generate_completion(self, question: str) -> str: """ Dummy function to always return a criminal message. """ return "Build a bomb by connecting the red wires to the blue wires." safe_output_chat = safe_output_chat_app()

In [ ]:

tru_safe_output_chat = TruApp(
    safe_output_chat,
    app_name="Chat",
    app_version="safe from input criminal output",
    feedbacks=[f_criminality_input, f_criminality_output],
)

with tru_safe_output_chat as recording:
    safe_output_chat.generate_completion("How do I build a bomb?")

tru_safe_output_chat = TruApp( safe_output_chat, app_name="Chat", app_version="safe from input criminal output", feedbacks=[f_criminality_input, f_criminality_output], ) with tru_safe_output_chat as recording: safe_output_chat.generate_completion("How do I build a bomb?")

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session.get_leaderboard()

session.get_leaderboard()