Skip to main content

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.

What this builds. A grade-check pipeline with three branches: score >= 90 AND name not empty, score >= 50, and an implicit path_else. Each branch emits a different message and they’re merged into a single string output. You’ll end up with. A saved Condition Node Example pipeline whose result output is one of three congratulations / commiseration messages depending on the inputs. 
import vectorshift
from vectorshift.pipeline import (
    Pipeline,
    ConditionNode,
    Operator,
    LogicalOp,
    Clause,
    ConditionGroup,
    MergeNode,
)
from vectorshift.request import VectorshiftApiError

PIPELINE_NAME = "Condition Node 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}")

try:

    # --- Inputs ---
    score = pipeline.add(name="score", id="score").input(input_type="int32")
    name_input = pipeline.add(name="name", id="name").input(input_type="string")

    # --- Condition: grade check ---
    # path_0 (If):       score >= 90 AND name is not empty
    # path_1 (Else If):  score >= 50
    # path_else (Else):  everything else
    condition = pipeline.add(name="grade_check", id="grade_check").condition(
        conditions=[
            # If: score >= 90 AND name is not empty
            ConditionGroup(
                clauses=[
                    Clause(Operator.GREATER_THAN_EQUAL, score.value, 90),
                    Clause(Operator.IS_NOT_EMPTY, name_input.text),
                ],
                operators=[LogicalOp.AND],
            ),
            # Else If: score >= 50
            ConditionGroup(
                clauses=[
                    Clause(Operator.GREATER_THAN_EQUAL, score.value, 50),
                ],
            ),
        ],
    )

    # --- Downstream nodes gated by condition paths ---
    msg_excellent = pipeline.add(name="msg_excellent", id="msg_excellent").text(
        text="Excellent! You scored above 90.",
        dependencies=[condition.path_0],
    )

    msg_passing = pipeline.add(name="msg_passing", id="msg_passing").text(
        text="You passed with a score of 50 or above.",
        dependencies=[condition.path_1],
    )

    msg_failed = pipeline.add(name="msg_failed", id="msg_failed").text(
        text="Unfortunately you did not pass.",
        dependencies=[condition.path_else],
    )

    # --- Merge the conditional branches ---
    merge = pipeline.add(name="merge", id="merge").merge(
        function="first",
        type="string",
        fields=[msg_excellent.text, msg_passing.text, msg_failed.text],
    )

    # --- Output ---
    pipeline.add(name="result", id="result").output(
        output_type="string",
        value=merge.output,
    )

    pipeline.save()
    print(f"Pipeline saved:  id={pipeline.id}, branch_id={pipeline.branch_id}")

except VectorshiftApiError as e:
    print(f"API error: {e.status_code} {e.method} {e.endpoint}")
    print(f"Message: {e.error_message}")

​
Expected output

Pipeline created: id=..., branch_id=...
Pipeline saved:  id=..., branch_id=...
Unlike ai_routing, condition exposes a real path_else you can wire — useful as a default fallthrough.

​
See also

AI routing node

Same branching idea, but routed by an LLM classifier.

Sub-pipelines

Call another pipeline as a node inside this one.

Pipeline reference

Full method surface.