Skip to Content
SDKModels

Models

Models are core components of the SDK that enable you to create and manage test sets for Gen AI applications. Models also play a crucial role in the evaluation process, serving as LLM judges to assess and validate outputs.

Using the get_model function

The easiest way to use models is through the get_model function. When called without any arguments, this function returns the default Rhesis model.

from rhesis.sdk.models import get_model
 
model = get_model()

To use a different provider, you can pass the provider name as an argument. This will use the default model for that provider.

from rhesis.sdk.models import get_model
 
# Use default Gemini model
model = get_model("gemini")

Supported providers:

  • gemini - Google’s AI models with multimodal support
  • openai - OpenAI models including GPT-4
  • huggingface - Open-source models from Hugging Face
  • ollama - Local model execution using Ollama
  • rhesis - Models served by Rhesis

To use a specific model, provide its name in the format provider/model_name:

from rhesis.sdk.models import get_model
 
model = get_model("gemini/gemini-2.0-flash")

The above code is equivalent to:

from rhesis.sdk.models import get_model
 
model = get_model(provider="gemini", model_name="gemini-2.0-flash")

Direct import

Alternatively, you can access models by importing the model class directly. When you provide a model name as an argument, that specific model will be used. If no model name is provided, the default model for that provider will be used.

from rhesis.sdk.models import GeminiLLM, OllamaLLM
 
# Use default Ollama model
ollama_model = OllamaLLM()
 
# Use specific Gemini model
gemini_model = GeminiLLM('gemini-2.0-flash')

Generate content with models

All models share a consistent interface. The primary function is generate, which accepts:

  • prompt: The text prompt for generation
  • schema: (optional) A Pydantic schema defining the structure of the generated text

Generate text using prompt only:

# Use default Rhesis model
model = get_model()
output = model.generate(prompt="What is the capital of France?")
# Output: "The capital of France is Paris."

Generate structured output using schemas:

from pydantic import BaseModel
from rhesis.sdk.models import get_model
 
class City(BaseModel):
    name: str
    population: int
 
class CityResponse(BaseModel):
    biggest_cities: list[City]
 
# Use default Rhesis model
model = get_model()
output = model.generate(
    prompt="The list of 5 biggest cities in Germany?",
    schema=CityResponse
)

Using models with synthesizers and metrics

Models become most useful when combined with synthesizers and metrics:

from rhesis.sdk.models import get_model
from rhesis.sdk.synthesizers import PromptSynthesizer
from rhesis.sdk.metrics import RhesisPromptMetricNumeric
 
# With synthesizers
model = get_model("gemini")
synthesizer = PromptSynthesizer(
    prompt="Generate tests for the car selling chatbot",
    model=model
)
 
# With metrics
metric = RhesisPromptMetricNumeric(
    name="answer_quality_evaluator",
    evaluation_prompt="Evaluate the answer for accuracy, completeness, clarity, and relevance.",
    model="gemini"
)
Installation & SetupMetrics