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The guard object is the main interface for Guardrails. It can be used without configuration for string-based LLM apps, and accepts a pydantic object for structured data usecases. The Guard is then used to run the Guardrails AI engine. It is the object that wraps LLM calls, orchestrates validation, and keeps track of call history.

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How it Works

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Two main flows

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Call

After initializing a guard, you can invoke it using a model name and messages for your prompt, similarly to how you would invoke a regular call to an LLM SDK. Guardrails will call the LLM and then validate the output against the guardrails you’ve configured. The output will be returned as a GuardResponse object, which contains the raw LLM output, the validated output, and whether or not validation was successful. 
from guardrails import Guard
import os

# Set your openai API key here
# os.environ["OPENAI_API_KEY"] = [YOUR API KEY]

guard = Guard()

res = guard(
    model="gpt-3.5-turbo",
    messages=[{
        "role": "user",
        "content": "How do I make a cake?"
    }]
)

print(res.raw_llm_output)
print(res.validated_output)
print(res.validation_passed)

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parse

If you would rather call the LLM yourself, or at least make the first call yourself, you can use Guard.parse to apply your RAIL specification to the LLM output as a post process. You can also allow Guardrails to make re-asks to the LLM by specifying the num_reasks argument, or keep it purely as a post-processor by setting it to zero. Guard.parse returns the same fields as __call__. Calling Guard.parse as a post-processor: 
import openai
from guardrails import Guard

guard = Guard()

output = openai.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{
        "role": "user",
        "content": "How do I make a cake?"
    }]
).choices[0].message.content

res = guard.parse(
    llm_output=output
)

print(res.validated_output) # Validated output
Calling Guard.parse with reasks: 
import openai
from guardrails import Guard

guard = Guard()

output = openai.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{
        "role": "user",
        "content": "How do I make a cake?"
    }]
).choices[0].message.content

res = guard.parse(
    llm_output=output,
    model="gpt-3.5-turbo",
    num_reasks=1
)

print(res.validated_output) # Validated output
print(guard.history.last.reasks) # A list of reasks

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Error Handling and Retries

Guardrails is designed to account for different types of errors that can occur when calling the LLM, and it is also designed to emit helpful errors when validations fail. Read more about error handling and retries here.