from openlayer import Openlayer

# Let's say we want to stream the following row, which represents a model prediction:
data = {
  "user_query": "what's the meaning of life?",
  "output": "42",
  "tokens": 7,
  "cost": 0.02,
  "timestamp": 1620000000
}

# Prepare the config for the data, which depends on your project's task type. In this
# case, we have an LLM project:
from openlayer.types.inference_pipelines import data_stream_params

config = data_stream_params.ConfigLlmData(
    input_variable_names=["user_query"],
    output_column_name="output",
    num_of_token_column_name="tokens",
    cost_column_name="cost",
    timestamp_column_name="timestamp",
    prompt=[{"role": "user", "content": "{{ user_query }}"}],
)

client = Openlayer()
data_stream_response = client.inference_pipelines.data.stream(
    inference_pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    rows=[data],
    config=config,
)

{
  "success": true
}