Overview

A pipeline is a workflow designed to execute multiple tasks in a predefined sequence, providing full control and flexibility. They are powerful solutions for deterministic automation flows.

Key Features

• Full control and flexibility: Manage every aspect of your pipeline, from the sequence of tasks and agents to the specifics of input processing. Implement conditional logic and branching to enhance reliability and adaptability.
• Parallel execution: Run multiple processes concurrently to maximize efficiency and reduce execution time.
• Modularity and extensibility: Independently develop and test each node within the pipeline, ensuring flexibility and ease of updates. Extend functionality seamlessly by adding tasks without altering the deployed endpoint.
• Asset swapping: Effortlessly replace assets within nodes to trial different models or utilities, maintaining the pipeline structure intact.
• Batching: Process inputs in batches to boost efficiency and throughput. Explore batching strategies here.
• Reusability: Built pipelines can be reused within different agents, serving as a versatile tool for various applications.

Components

• Input Node: Accepts input data to the pipeline. The accepted data types and formats are determined by the node connected to the input node.
• Output Node: Provides pipeline output.
• Model Node: Processes data based on the populated asset e.g., a translation asset would translate input data, a scarper asset will scrape the input url.
• Script Node: Executes custom python code.
• Decision Node: Applies logical conditions to direct the flow within the pipeline.
• Router Node: Distributes incoming data to different paths based on predefined rules.
• Metric Node: Uses metric assets to evaluate input data based on reference data.

Create a pipeline

This example pipeline takes text input, translates it into another language, and performs sentiment analysis on the translated text. The pipeline has:

• 1 input node: Accepts text input.
• 2 processing nodes: Performs translation and sentiment analysis.
• 2 output nodes: Outputs the translated text and its sentiment analysis results.

from aixplain.factories.pipeline_factory import PipelineFactory
from aixplain.modules.pipeline.designer import Input

pipeline = PipelineFactory.init("Multi Input-Output Pipeline")
text_input_node = Input(data="text_input", data_types=["TEXT"], pipeline=pipeline)

TRANSLATION_ASSET_ID = '60ddefbe8d38c51c5885f98a'
translation_node = pipeline.translation(asset_id=TRANSLATION_ASSET_ID)

SENTIMENT_ASSET_ID = '61728750720b09325cbcdc36'
sentiment_node = pipeline.sentiment_analysis(asset_id=SENTIMENT_ASSET_ID)

text_input_node.link(translation_node, 'input', 'text')
translation_node.link(sentiment_node, 'data', 'text')

translated_output_node = translation_node.use_output('data')
sentiment_output_node = sentiment_node.use_output('data')

pipeline.save()
outputs = pipeline.run({
    'text_input': 'This is example text to translate.'
})

print(outputs)

Guide to Building the Example Pipeline

Initialize Pipeline

from aixplain.factories.pipeline_factory import PipelineFactory

pipeline = PipelineFactory.init("Multi Input-Output Pipeline")

Add input and output nodes

Input Nodes

Define the input node for text input.

from aixplain.modules.pipeline.designer import Input

text_input_node = Input(data="text_input", data_types=["TEXT"], pipeline=pipeline)

Output Nodes

Add nodes to output the translated text and sentiment analysis results.

translated_output_node = translation_node.use_output('data')
sentiment_output_node = sentiment_node.use_output('data')

Add Processing Nodes

Add translation and sentiment analysis nodes to perform the required tasks.

TRANSLATION_ASSET_ID = '60ddefbe8d38c51c5885f98a'
translation_node = pipeline.translation(asset_id=TRANSLATION_ASSET_ID)

SENTIMENT_ASSET_ID = '61728750720b09325cbcdc36'
sentiment_node = pipeline.sentiment_analysis(asset_id=SENTIMENT_ASSET_ID)

Link Nodes

Link all nodes ensuring that the data types of connected nodes are compatible. The specific input and output parameters of each node are determined by the asset ID linked to the node.

View Node Parameters

Inspect the parameters of the nodes for debugging or configuration.

print(translation_node.inputs)
print(translation_node.outputs)

Connect the nodes to pass data between them.

text_input_node.link(translation_node, 'input', 'text')
translation_node.link(sentiment_node, 'data', 'text')

Validate Pipeline

Ensure the pipeline is valid:

• Starts with at least one input node.
• Ends with at least one output node.
• All nodes are fully connected in sequence.

pipeline.validate()

Save and Run

Save the pipeline configuration and execute it with input data.

pipeline.save()
outputs = pipeline.run({
    'text_input': 'This is example text to translate.'
})

print(outputs)

Debug Pipeline

To gain full visibility into the intermediate results within your pipeline, attach output nodes to key intermediate nodes. This allows you to inspect the data processed at various stages, making it easier to debug or analyze the pipeline's behavior.

Update Pipeline

pipeline.update(
    pipeline=updated_pipeline_dict,          # Pass the updated pipeline as a Python dictionary
    save_as_asset=True,                     # Save as asset
    name="Draft Pipeline Name"               # Optional: New name for the draft pipeline
)

Pipeline Parameters

Covers the parameters used in the create method to define a pipeline's identity, functionality, and configuration.

Parameter	Type	Default	Description
id	str	Auto-generated	The unique identifier for the pipeline.
name	str	[required]	The name of the pipeline, serving as its primary identifier.
description	str	None	A brief description of the pipeline's role and purpose.
supplier	str	config.TEAM_API_KEY	Name of the aiXplain team owning the pipeline, derived from the api_key.
version	str	1.0	Specifies the version of the pipeline.
license	str	None	License for using the pipeline, defining its usage rights and restrictions.
privacy	enum	Private	Determines the visibility and accessibility of the pipeline. Learn more about asset privacy.
cost	float	Calculated at runtime	The cost of executing the pipeline, calculated in credits based on usage (e.g., data volume).
api_key	str	Derived from team configuration	The API key of the team owning the pipeline, required for authentication.
url	str	Generated from backend configuration	The endpoint URL of the pipeline.
additional_info	dict	{'nodes': [], 'links': [], 'instance': None}	Additional details about the pipeline, including nodes and their connections.
nodes	list	[]	List of nodes representing individual steps or operations within the pipeline.
links	list	[]	Connections between nodes, defining the data flow within the pipeline.

Pipeline Methods

Covers the core methods for creating, configuring, and managing pipelines.

Function	Description
init	Creates a new pipeline object with a specified name.
input	Adds an input node to the pipeline.
output	Adds an output node to the pipeline.
adding an Asset Node	Adds an asset node for a specific functionality, such as speech recognition.
script	Incorporates a custom script node into the pipeline using a local Python file.
link	Links the outputs and inputs of two nodes within the pipeline.
save	Saves the configured pipeline as an asset for future use.
run	Executes the pipeline with specified input data.
delete	Deletes the pipeline configuration from the platform.