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zenodflow
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Jan 19, 2025
examples/tasks/assemble/assemble.py
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| mappings = llmint.map(source_df, dest_df) | ||
| output = assemble(source_df, mappings) | ||
| combined_df = pd.concat([dest_df, output], axis=0) | ||
| print("Combined:", combined_df, sep="\n") |
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This is a very neat / nice example for schema alignment for static datasets (known records with fixed size).
Could we have perhaps another example demonstrating this in the message broker setting?
E.g., having a continuous sending data source and the assembled data flow - once "in stalled" to the data path - able to process incoming data.
zenodflow
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Jan 21, 2025
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One change: ensure the assemble method reflects the intended semantics, i.e., generating the dataflow that translates incoming data records. E.g., throw a nested function. In next PRs we should support native/non-python dataflow.
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Mapclass for mapping info to be more intuitive to useNOTE: requires code from Libem #111 to function