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Pipelines are the engines that make Superpipe run. A pipeline is a series of steps chained together that acts on a dataframe. A pipeline takes an optional evaluation function that can run arbitrary Python code. Evaluation functions need to return booleans.

Pipeline statistics

A pipeline object has associated pipeline statistics.

Stat Description
score Accuracy score of the pipeline as defined by the evaluation function.
input_tokens Total number of input tokens used by the pipeline split out by model.
output_tokens Total number of output tokens used by the pipeline split out by model.
input_cost Total input cost of the pipeline split out by model.
output_cost Total output cost of the pipeline split out by model.
num_success Number of successful rows.
num_failure Number of unsuccessful rows.
total_latency Total latency of the pipeline.

Pipeline methods


pipeline.update_params() takes a parameters dictionary of steps and parameters. For example, to update the categorize pipeline to use GPT-4, we can call update_param and pass in the step name as the key, with a sub dictionary with model as the key.

  "categorize": {
    "model": models.gpt4


You can find the full code for this example in the comparing pipelines example. This is just the pipeline definition.

evaluate = lambda row: row['predicted_category'].lower() == row['category_new'].lower()

categorizer = pipeline.Pipeline([
], evaluation_fn=evaluate)