142 lines
4.2 KiB
Plaintext
142 lines
4.2 KiB
Plaintext
@startuml workflow-marker-extraction
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!theme plain
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title Workflow Marker Extraction with AI Normalization
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start
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:Discussion file staged\n(feature.discussion.md,\ndesign.discussion.md, etc);
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:workflow.py reads file content;
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partition "Two-Tier Extraction" {
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:Call extract_structured_basic()\nSimple fallback parsing;
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note right
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**Fallback: Simple Line-Start Matching**
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Only matches explicit markers at line start:
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- DECISION: text
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- QUESTION: text
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- Q: text
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- ACTION: text
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- TODO: text
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- ASSIGNED: text
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- DONE: text
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Uses case-insensitive startswith() matching.
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Handles strictly-formatted discussions.
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end note
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:Store fallback results\n(decisions, questions, actions, mentions);
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:Call agents.normalize_discussion()\nAI-powered extraction;
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partition "AI Normalization (agents.py)" {
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:Build prompt for AI model;
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note right
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**AI Prompt:**
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"Extract structured information from discussion.
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Return JSON with: votes, questions, decisions,
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action_items, mentions"
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Supports natural conversation like:
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"I'm making a decision here - we'll use X"
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"Does anyone know if we need Y?"
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"@Sarah can you check Z?"
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end note
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:Execute command chain\n(claude → codex → gemini);
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if (AI returned valid JSON?) then (yes)
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:Parse JSON response;
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:Extract structured data:\n- votes\n- questions\n- decisions\n- action_items\n- mentions;
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:Override fallback results\nwith AI results;
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note right
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**AI advantages:**
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- Handles embedded markers
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- Understands context
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- Extracts from natural language
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- No strict formatting required
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end note
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else (no - AI failed or unavailable)
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:Use fallback results only;
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note right
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**Fallback activated when:**
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- All providers fail
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- Invalid JSON response
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- agents.py import fails
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- API rate limits hit
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end note
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endif
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}
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}
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partition "Generate Summary Sections" {
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:Format Decisions section:\n- Group by participant\n- Number sequentially\n- Include rationale if present;
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:Format Open Questions section:\n- List unanswered questions\n- Track by participant\n- Mark status (OPEN/PARTIAL);
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:Format Action Items section:\n- Group by status (TODO/ASSIGNED/DONE)\n- Show assignees\n- Link to requesters;
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:Format Awaiting Replies section:\n- Group by @mentioned person\n- Show context of request\n- Track unresolved mentions;
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:Format Votes section:\n- Count by value (READY/CHANGES/REJECT)\n- List latest vote per participant\n- Exclude AI votes if configured;
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:Format Timeline section:\n- Chronological order (newest first)\n- Include status changes\n- Summarize key events;
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}
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:Update marker blocks in .sum.md;
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note right
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<!-- SUMMARY:DECISIONS START -->
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...
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<!-- SUMMARY:DECISIONS END -->
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end note
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:Stage updated .sum.md file;
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stop
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legend bottom
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**Example Input (natural conversation):**
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Name: Rob
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I've been thinking about the timeline. I'm making a decision here -
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we'll build the upload system first. Does anyone know if we need real-time
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preview? @Sarah can you research Unity Asset Store API?
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VOTE: READY
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**AI Normalization Output (JSON):**
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{
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"votes": [{"participant": "Rob", "vote": "READY"}],
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"decisions": [{"participant": "Rob",
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"decision": "build the upload system first"}],
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"questions": [{"participant": "Rob",
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"question": "if we need real-time preview"}],
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"action_items": [{"participant": "Rob", "action": "research Unity API",
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"assignee": "Sarah"}],
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"mentions": [{"from": "Rob", "to": "Sarah"}]
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}
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**Fallback Only Matches:**
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DECISION: We'll build upload first
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QUESTION: Do we need real-time preview?
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ACTION: @Sarah research Unity API
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endlegend
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note right
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**Architecture Benefits:**
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✓ Participants write naturally
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✓ No strict formatting rules
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✓ AI handles understanding
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✓ Simple code for fallback
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✓ Resilient (multi-provider chain)
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✓ Cost-effective (fast models)
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**Files:**
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- automation/agents.py (AI normalization)
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- automation/workflow.py (fallback + orchestration)
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- automation/patcher.py (provider chain execution)
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end note
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@enduml
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