Documentation Index
Fetch the complete documentation index at: https://docs.vectorshift.ai/llms.txt
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Creates a simple LLM pipeline, starts it in the background,
polls for status, and retrieves the result.
from vectorshift.pipeline import Pipeline
# Create a standalone LLM pipeline
PIPELINE_NAME = "bg_run_example"
try:
pipeline = Pipeline.fetch(name=PIPELINE_NAME)
print(f"Pipeline fetched: id={pipeline.id}, branch_id={pipeline.branch_id}")
except Exception as e:
print(f"Error fetching pipeline: {e}")
pipeline = Pipeline.new(name=PIPELINE_NAME)
print(f"Pipeline created: id={pipeline.id}, branch_id={pipeline.branch_id}")
inp = pipeline.add(name="input_0", id="input_0").input(input_type="string")
llm = pipeline.add(name="llm", id="llm").llm(
provider="openai", model="gpt-4o", prompt=inp.text
)
out = pipeline.add(name="output_0", id="output_0").output(
output_type="string", value=llm.response
)
pipeline.save(deploy=True)
# Start the pipeline in the background
handler = pipeline.start(
inputs={"input_0": "Tell me a fun fact about space in 50 words."}
)
print("Pipeline started in background")
print(f" Task ID: {handler.task_id}")
print(f" Trace ID: {handler.trace_id}")
# Check status manually
status = handler.run_status()
print(f" Status: {status['status']}")
# Block until the run completes and outputs are available
result = handler.result(poll_interval=2.0, timeout=120.0)
print(f" Final status: {result['status']}")
print(f" Outputs: {result.get('result')}")
print(f" Pipeline ID (to be deleted): {pipeline.id}")
pipeline.delete()
print(" Pipeline deleted")
Source: examples/pipelines/background_run.py in the SDK repo.