Frameworks & Platforms
Ragas
Evaluate LLM apps with Ragas and TokenLab
Overview
Ragas can evaluate TokenLab-backed applications by passing an OpenAI-compatible AsyncOpenAI client into llm_factory.
Installation
pip install "ragas==0.4.3" "langchain-community<0.4" openaiEnvironment
export TOKENLAB_API_KEY="sk-your-tokenlab-key"Example Evaluator
import os
from openai import AsyncOpenAI
from ragas.llms import llm_factory
from ragas.metrics import DiscreteMetric
client = AsyncOpenAI(
api_key=os.environ["TOKENLAB_API_KEY"],
base_url="https://api.tokenlab.sh/v1",
)
llm = llm_factory("claude-sonnet-5", client=client)
metric = DiscreteMetric(
name="summary_accuracy",
allowed_values=["accurate", "inaccurate"],
prompt="Evaluate whether the response is accurate. Answer only accurate or inaccurate.\n\nResponse: {response}",
)Use llm in Ragas metrics and testsets the same way you would use an OpenAI SDK-backed model.
Endpoint Notes
This Ragas setup uses the OpenAI SDK. Responses, Anthropic Messages, and Gemini require an evaluation client that supports the corresponding format.