Modular Expansion Technology Solutions
LazyLLM offers three ways to integrate existing algorithms:
- Function registration mechanism: By
@registerDecorator transforms Python functions into callable smartbody modules - Bash command encapsulation: support for direct invocation of command-line tools, suitable for integrating algorithms not implemented in Python
- API Service Access: Configure URL endpoints for external services via config.yaml
Specific operational steps:
- Adding decorators to Python functions:
@register(name='my_alg')
def custom_algorithm(input):
return input * 2 - Call the registration name in Flow:
flow = pipeline(my_alg, other_modules) - or referenced through the deployment configuration file
Caveats:
- Inputs and outputs need to be compatible with JSON serialization.
- Complex dependencies are recommended to be packaged with a Docker image
- Performance-critical modules can be enabled
@register(parallel=True)
The method reduces the integration time of established algorithms from days to 2 hours.
This answer comes from the articleLazyLLM: Shangtang's open source low-code development tool for building multi-intelligence body applicationsThe
May not be reproduced without permission:AI productivity tools " How to achieve fast integration of established algorithms with multi-intelligent body systems through LazyLLM?
