Data Flow Orchestration Best Practices
LazyLLM offers three core solutions for the data flow challenges of complex AI applications:
- Pipeline Pipeline Mode: By
pipeline()Create linear processing streams where the output of each step is automatically the input to the next one - Parallel mode: Use
parallel()Simultaneous execution of multiple tasks for model parallelism or data enhancement scenarios - Diverter: Implement conditional branch routing to support dynamic decision processes
Implementation Example:
from lazyllm import pipeline, parallel
# 构建文本处理流水线
flow = pipeline(
preprocess=lambda x: x.strip(),
infer=parallel(
sentiment=analyze_sentiment,
entities=extract_entities
)
)
print(flow(" Hello world! "))The key advantage is:
- Automatic handling of data type conversions
- built-in error retry mechanism
- Visual logs show data flow status
Improve development efficiency by more than 3 times compared to manual implementation.
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 Improve Data Flow Management for Complex AI Applications with LazyLLM's Flow Feature?
