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Add these nodes with the pipeline builder: pipeline.add(name="...").<node>(...). Each entry lists the node’s configuration parameters. See the Pipeline reference for add, run, and lifecycle methods.

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browser_extension — Browser Extension

Run a VectorShift workflow using the current page captured by the VectorShift chrome extension as input.
Platform docs: Browser Extension
pipeline.add(name="node").browser_extension()
Parameters
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page_content
str
default:"''"
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page_html
str
default:"''"
​
page_urls
list[str]
default:"[]"
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screenshot
AcceptsImage
default:"{}"
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show_chrome_extension
bool
default:"True"
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url
str
default:"''"

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input — Input

Pass data of different types into your workflow
Platform docs: Input
pipeline.add(name="node").input()
Parameters
​
input_type
str
default:"'string'"
Raw Text
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use_default_value
bool
default:"False"
Set default value to be used if no value is provided
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description
str
default:"''"
The input description. If pipeline is used as a tool in an agent, the description will be passed to the agent to help the agent know how to fill this input.
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default_value
AcceptsAgent | AcceptsAudio | AcceptsDataframe | AcceptsFile | AcceptsFileList | AcceptsImage | AcceptsKnowledgeBase | AcceptsPipeline | AcceptsTable | AcceptsTimestamp | ListType | bool | float | int | list[AcceptsFileList] | list[list[str]] | list[str] | str
default:"{}"
The default value to be used if no value is provided
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dataframe_type
str
default:"'table'"
The type of dataframe to be used One of: csv, dataframe_file, json, md, sql, table
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file_parser
str
default:"'default'"
The processing model with which the document will be processed. Default processing model includes standard document parsing / OCR. Llamaparse will allow for ability to read documents with complex features (e.g., tables, charts, etc.). Llamaparse will be charged at 0.3 cents per page. Textract for most advanced data extraction and will be charged at 1.5 cents per page. Reducto enables rich structured parsing. One of: contextual_ai, default, docling, llama_parse, mistral_ocr, reducto, textract

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output — Output

Output data of different types from your workflow.
Platform docs: Output
pipeline.add(name="node").output(value=...)
Parameters
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output_type
str
default:"'string'"
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description
str
default:"''"
The output description. If pipeline is used as a tool in an agent, the description will be passed to the agent to help the agent know how to fill this output.
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format_output
bool
default:"True"
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value
AcceptsAudio | AcceptsDataframe | AcceptsFile | AcceptsFileList | AcceptsImage | AcceptsStream | AcceptsTimestamp | bool | float | int | str
required
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dataframe_type
str
default:"'table'"
The type of dataframe to be used One of: csv, dataframe_file, json, md, sql, table

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start_flag — Start a conversation

Start a conversation
Platform docs: Start a conversation
pipeline.add(name="node").start_flag()

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sticky_note — sticky_note

Platform docs: sticky_note
pipeline.add(name="node").sticky_note()
Parameters
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text
str
default:"''"