wehub-resource-sync
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
from deepeval.dataset import EvaluationDataset, Golden
|
|
from deepeval.metrics import AnswerRelevancyMetric
|
|
from deepeval.openai import OpenAI
|
|
from deepeval.tracing import observe
|
|
|
|
client = OpenAI()
|
|
|
|
|
|
@observe(type="llm", model="gpt-4.1")
|
|
def generate_response(input: str) -> str:
|
|
response = client.chat.completions.create(
|
|
model="gpt-4.1",
|
|
messages=[
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": input},
|
|
],
|
|
)
|
|
return response
|
|
|
|
|
|
response = generate_response("What is the weather in Tokyo?")
|
|
|
|
############################################
|
|
|
|
|
|
client = OpenAI()
|
|
|
|
|
|
@observe(type="llm", model="gpt-4.1")
|
|
def generate_response(input: str) -> str:
|
|
response = client.chat.completions.create(
|
|
model="gpt-4.1",
|
|
messages=[
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": input},
|
|
],
|
|
metrics=[AnswerRelevancyMetric()],
|
|
)
|
|
return response
|
|
|
|
|
|
# Create goldens
|
|
goldens = [
|
|
Golden(input="What is the weather in Bogotá, Colombia?"),
|
|
Golden(input="What is the weather in Paris, France?"),
|
|
]
|
|
dataset = EvaluationDataset(goldens=goldens)
|
|
|
|
# Run component-level evaluation
|
|
for golden in dataset.evals_iterator():
|
|
generate_response(golden.input)
|