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Build Runner

Generate Python code to run a pipe with all necessary imports and example inputs.

Usage

From a bundle

pipelex build runner bundle <PATH> [OPTIONS]

Arguments:

  • PATH - Path to a .mthds bundle file or a pipeline directory

Options:

  • --pipe - Pipe code to use (optional if the .mthds declares a main_pipe)
  • --output, -o - Path to save the generated Python file (defaults to target's directory)
  • --library-dirs, -L - Directories to search for pipe definitions. Can be specified multiple times.

From an installed method

pipelex build runner method <NAME> [OPTIONS]

Arguments:

  • NAME - Name of the installed method

Options:

  • --pipe - Pipe code (overrides method's main_pipe)
  • --output, -o - Path to save the generated Python file
  • --library-dirs, -L - Directories to search for pipe definitions. Can be specified multiple times.

Examples

Generate runner from a bundle (uses main_pipe):

pipelex build runner bundle my_bundle.mthds

Generate runner from a pipeline directory:

pipelex build runner bundle pipeline_01/

Specify which pipe to use from a bundle:

pipelex build runner bundle my_bundle.mthds --pipe my_pipe

With additional library directories:

pipelex build runner bundle my_bundle.mthds -L ./shared_pipes/ -L ./common/

Custom output path:

pipelex build runner bundle my_bundle.mthds --output custom_runner.py

What Gets Generated

The generated Python file includes:

  1. All necessary imports - Imports for Pipelex execution, content types, and any custom structures
  2. Generated Pydantic structures - Models for your concepts (also generated separately)
  3. Input memory setup - Example input values based on the pipe's input types
  4. Pipeline execution - Async function that executes the pipeline
  5. Output handling - Code to extract and display the results
  6. Main execution block - Pipelex initialization and asyncio setup

Input Type Handling

  • Native concepts (Text, Image, Document, etc.) - Automatically generates appropriate content objects
  • Custom concepts - Recursively generates the structure with example values
  • Structured content - Creates example data matching the concept's fields

Using the Generated Runner

After generating the runner file:

  1. Open the generated Python file
  2. Review and customize the example input values
  3. Run the script: python results/run_{pipe_code}.py
  4. Iterate and adjust as needed