141df021c1
2 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
| de5b8cbf15 |
fix: CRITICAL - Use question ID (not type) for LLM communication
ROOT ARCHITECTURAL CHANGE:
Multiple questions with same type are now supported!
Problem:
- question_augmenter used q.type as LLM key
- If two questions had type="unsicherheit":
- LLM saw duplicate keys: "- unsicherheit: [ja/nein]"
- Could only answer one
- Signals were ambiguous
Solution:
- Use question.id as LLM key (unique by design)
- Keep type for normalization logic
- Map id → type internally
Backend question_augmenter.py:
- format_question_list() now uses q.id as key
- Format: "- **q21**: [ja/nein] # Question text"
- Question text as comment for LLM context
Backend workflow_executor.py:
- Removed type→id mapping (no longer needed)
- decision_signals now keyed by id (from LLM)
- Build id→type catalog for normalization
- NormalizedSignal.question_type stores id (not type!)
- End Node template: signal_{id} directly available
Flow:
1. Questions sent to LLM: "- q21: [ja/nein] # Ist Protein unsicher?"
2. LLM answers: "- q21: nein"
3. Normalization: id→type lookup for spectrum/rules
4. Template: {{ node_4.signal_q21 }} = "nein"
Example (TWO unsicherheit questions):
Questions:
- q21: type=unsicherheit, question="Ist Protein unsicher?"
- q22: type=unsicherheit, question="Ist Energie unsicher?"
LLM Prompt:
```
## Entscheidungsfragen
- **q21**: [ja/nein] # Ist Protein unsicher?
- **q22**: [ja/nein] # Ist Energie unsicher?
```
LLM Response:
```
- q21: nein
- q22: ja
```
Template:
```
{{ node_4.signal_q21 }} → "nein"
{{ node_4.signal_q22 }} → "ja"
```
BREAKING CHANGE:
- Old workflows with decision_signals keyed by type will break
- Need to re-execute workflows after update
Issue: Cannot have multiple questions with same type
Version: 0.9p (workflow module)
Part 3: End Node Template Engine - ARCHITECTURAL FIX
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
|
|||
| ca562b7130 |
feat: Phase 1 - Fragenergänzung + Strukturierter Container
Backend: - question_augmenter.py (290 Zeilen): Hybrid-Modell für Fragenergänzungen * merge_question_augmentations(): Knotengebundene Fragen überschreiben Prompt-Defaults * augment_prompt_with_questions(): Markdown-formatierte Fragenergänzung * parse_question_augmentations_from_jsonb(): JSONB → QuestionAugmentation[] - result_container_parser.py (250 Zeilen): Markdown-Sektionen-Parsing * parse_result_container(): Extrahiert Analysekern, Entscheidungsanteil, Begründungsanker * validate_decision_signal(): Normalisierung gegen answer_spectrum * Fallback-Parsing bei unstrukturierten Antworten - routers/workflow_questions.py (236 Zeilen): CRUD für workflow_question_catalog * GET /api/workflow/questions (mit active_only Filter) * POST/PUT/DELETE (Admin only, Soft Delete) - prompt_executor.py: Integration in execute_base_prompt() * Fragenergänzung vor LLM-Call (wenn node_questions oder catalog vorhanden) * Result-Container-Parsing nach LLM-Response - main.py: Router-Registrierung (workflow_questions) Tests: - test_phase1_question_augmenter.py (8 Tests): Hybrid-Modell, Formatierung, JSONB-Parsing - test_phase1_result_container_parser.py (17 Tests): Sektion-Extraktion, Decision-Parsing, Validierung Alle 25 Unit-Tests bestanden. version: 0.9j (backend) module: workflow 0.2.0 Konzept: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md (Phase 1) |
