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Documentation Index

Fetch the complete documentation index at: https://snowglobe.so/docs/llms.txt

Use this file to discover all available pages before exploring further.

Guardrails has support for 100+ LLMs through its integration with LiteLLM. This integration allows the Guardrails call API to use the same clean interface that LiteLLM and OpenAI use. To interact with a model, set the desired LLM API KEY and specify the model with the model property.

​
OpenAI

​
Basic usage

from guardrails import Guard
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPEN_AI_API_KEY"

guard = Guard()

result = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="gpt-4o",
)

print(f"{result.validated_output}")

​
Streaming

from guardrails import Guard
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPEN_AI_API_KEY"

guard = Guard()

stream_chunk_generator = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="gpt-4o",
    stream=True,
)

for chunk in stream_chunk_generator:
    print(f"{chunk.validated_output}")

​
Tools/Function calling

from pydantic import BaseModel, Field
from typing import List
from guardrails import Guard
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPEN_AI_API_KEY"

class Fruit(BaseModel):
    name: str
    color: str

class Basket(BaseModel):
    fruits: List[Fruit]
    
guard = Guard.for_pydantic(Basket)

result = guard(
    messages=[{"role":"user", "content":"Generate a basket of 5 fruits"}],
    model="gpt-4o",
    tools=guard.json_function_calling_tool([]),
    tool_choice="required",
)

print(f"{result.validated_output}")

​
Anthropic

​
Basic usage

from guardrails import Guard
import os

guard = Guard()

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

result = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="claude-3-opus-20240229"
)

print(f"{result.validated_output}")

​
Streaming

from guardrails import Guard
import os

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

guard = Guard()

stream_chunk_generator = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="claude-3-opus-20240229",
    stream=True,
)

for chunk in stream_chunk_generator:
    print(f"{chunk.validated_output}")

​
Azure OpenAI

​
Basic usage

from guardrails import Guard
import os

os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

guard = Guard()

result = guard(
    model="azure/<your_deployment_name>",
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
)

print(f"{result.validated_output}")

​
Streaming

from guardrails import Guard
import os

os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

guard = Guard()

stream_chunk_generator = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="azure/<your_deployment_name>", 
    stream=True
)

for chunk in stream_chunk_generator:
    print(f"{chunk.validated_output}")

​
Tools/Function calling

from pydantic import BaseModel, Field
from typing import List
from guardrails import Guard
import os

os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

class Fruit(BaseModel):
    name: str
    color: str

class Basket(BaseModel):
    fruits: List[Fruit]
    
guard = Guard.for_pydantic(Basket)

result = guard(
    messages=[{"role":"user", "content":"Generate a basket of 5 fruits"}],
    model="azure/<your_deployment_name>", 
    tools=guard.add_json_function_calling_tool([]),
    tool_choice="required",
)

print(f"{result.validated_output}")

​
Gemini

​
Basic usage

from guardrails import Guard
import os

os.environ['GEMINI_API_KEY'] = ""
guard = Guard()

result = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="gemini/gemini-pro"
)

print(f"{result.validated_output}")

​
Streaming

from guardrails import Guard
import os

os.environ['GEMINI_API_KEY'] = ""
guard = Guard()

stream_chunk_generator = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="gemini/gemini-pro",
    stream=True
)

for chunk in stream_chunk_generator:
    print(f"{chunk.validated_output}")

​
Databricks

​
Basic usage

from guardrails import Guard
import os

os.environ["DATABRICKS_API_KEY"] = "" # your databricks key
os.environ["DATABRICKS_API_BASE"] = "" # e.g.: https://abc-123ab12a-1234.cloud.databricks.com

guard = Guard()

result = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="databricks/databricks-dbrx-instruct",
)

print(f"{result.validated_output}")

​
Streaming

from guardrails import Guard
import os

os.environ["DATABRICKS_API_KEY"] = "" # your databricks key
os.environ["DATABRICKS_API_BASE"] = "" # e.g.: https://abc-123ab12a-1234.cloud.databricks.com

guard = Guard()

stream_chunk_generator = guard(
    messages=[{"role":"user", "content":"How many moons does Jupiter have?"}],
    model="databricks/databricks-dbrx-instruct",
    stream=True,
)

for chunk in stream_chunk_generator:
    print(f"{chunk.validated_output}")

​
Other LLMs

Over 100 LLMs are supported through our LiteLLM integration, including:
  • Anthropic
  • AWS Bedrock
  • Anyscale
  • Huggingface
  • Mistral
  • Predibase
  • Fireworks
Find your LLM in LiteLLM’s documentation. Then, follow those same steps and set the same environment variables they guide you to use, but invoke a Guard object instead of the litellm object. Guardrails will wire through the arguments to litellm, run the Guarding process, and return a validated outcome.

​
Custom LLM wrappers

If you’re using an LLM that isn’t natively supported by Guardrails and you don’t want to use LiteLLM, you can build a custom LLM API wrapper. Create a function that accepts a positional argument for the prompt as a string and any other arguments as keyword args. The function should return the output of the LLM API as a string. Install ProfanityFree from hub: 
guardrails hub install hub://guardrails/profanity_free

from guardrails import Guard
from guardrails.hub import ProfanityFree

# Create a Guard class
guard = Guard().use(ProfanityFree())

# Function that takes the prompt as a string and returns the LLM output as string
def my_llm_api(
    *,
    **kwargs
) -> str:
    """Custom LLM API wrapper.

    At least one of messages should be provided.

    Args:
        **kwargs: Any additional arguments to be passed to the LLM API

    Returns:
        str: The output of the LLM API
    """
    messages = kwargs.pop("messages", [])
    updated_messages = some_message_processing(messages)
    # Call your LLM API here
    # What you pass to the llm will depend on what arguments it accepts.
    llm_output = some_llm(updated_messages, **kwargs)

    return llm_output

# Wrap your LLM API call
validated_response = guard(
    my_llm_api,
    messages=[{"role":"user","content":"Can you generate a list of 10 things that are not food?"}],
    **kwargs,
)