Files
wehub-resource-sync e768098d0e
tools_continuous_delivery / Private PyPI non-main branch release (push) Has been skipped
tools_continuous_delivery / Private PyPI main branch release (push) Failing after 2m42s
Publish Promptflow Doc / Build (push) Has been cancelled
Publish Promptflow Doc / Deploy (push) Has been cancelled
Flake8 Lint / flake8 (push) Has been cancelled
Spell check CI / Spell_Check (push) Has been cancelled
chore: import upstream snapshot with attribution
2026-07-13 13:39:52 +08:00
..

Phase 1 — Audit & Map

Before writing any MAF code, understand what you have. This phase is diagnostic — no code changes, no deployments.

Step 1a: Export your flow structure

Run this from the Prompt Flow CLI to get a full YAML representation of your flow's nodes, types, and wiring:

# Exports flow.dag.yaml into ./flow_export/
pf flow export --source <your-flow-directory> --output ./flow_export

Open flow_export/flow.dag.yaml. It lists every node with:

  • type: llm, python, or prompt
  • inputs: what data each node receives
  • outputs: what it passes downstream

This YAML is your migration blueprint. Keep it open while working through Phase 2.

Step 1b: Map each node to its MAF equivalent

See node-mapping.md for the full table.

The core mental model:

  • Every node → one Executor class with a @handler method
  • The flow graph → a WorkflowBuilder chain with .add_edge() calls
  • Connections (credentials) → environment variables in .env

Checklist before moving to Phase 2

  • flow.dag.yaml exported and reviewed
  • Every node has a mapped MAF equivalent (see node-mapping.md)
  • .env file populated (copy .env.example from repo root)
  • You know which samples in phase-2-rebuild match your flow's patterns