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Documentation Index

Fetch the complete documentation index at: https://docs.vectorshift.ai/llms.txt

Use this file to discover all available pages before exploring further.

The Pipeline class is the main entry point for building, saving, and running VectorShift pipelines from Python. Pipelines are graphs of nodes (LLMs, data loaders, transforms, knowledge-base readers, …) that you assemble with the fluent pipeline.add builder, then save() and run() against the platform. Recently the pipeline API gained background runs (start/stream/terminate), async variants of every method, and user/org-scoped sharing (share(user_id=...)).

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Quick start

from vectorshift import Pipeline

p = Pipeline.new(name="My pipeline")
inp = p.add.input(name="query", input_type="string")
llm = p.add.llm(provider="openai", model="gpt-4o", prompt=inp.text)
p.add.output(name="answer", value=llm.response)

p.save()
result = p.run({"query": "What is VectorShift?"})
print(result["outputs"]["answer"])

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Reference

Every public method, grouped by topic.

Examples

Runnable examples to copy-paste and adapt.