Lars/mitai-jinkendo
1
0
Fork 0
Code Issues 33 Pull Requests Actions Packages Projects Releases 5 Wiki Activity
439 Commits 2 Branches 16 Tags 15 MiB
ce4666a535
Commit Graph

3 Commits

Author SHA1 Message Date
Lars
1f8791f4dd feat: Phase 2 - Normalisierung + Workflow Executor
All checks were successful
Deploy Development / deploy (push) Successful in 44s
Details
Build Test / lint-backend (push) Successful in 0s
Details
Build Test / build-frontend (push) Successful in 14s
Details 
Backend:
- normalization_engine.py (200 Zeilen): Synonym-Mapping, 5 Statuswerte
  * normalize_decision_signal(): Kaskade (exact → case → synonym → invalid)
  * apply_synonym_mapping(): DB-basierte Synonyme (case-insensitive)
  * normalize_all_signals(): Batch-Processing gegen Katalog
  * load_question_catalog(): Lädt normalization_rules aus DB
- workflow_executor.py (440 Zeilen): Sequenzielle Workflow-Ausführung
  * execute_workflow(): Traversiert DAG in topologischer Reihenfolge
  * execute_node(): Führt analysis nodes aus (start/end = no-op)
  * aggregate_results(): Kombiniert analysis_core + normalized_signals
  * save_execution_state(): Persistiert in workflow_executions
- workflow_models.py: Erweitert um Phase 2 Models
  * SignalStatus Enum (valid, normalized, unclear, invalid, not_decidable)
  * NormalizedSignal (question_type, raw_value, normalized_value, status)
  * NodeExecutionState (node_id, status, analysis_core, normalized_signals)
  * ExecutionResult (execution_id, workflow_id, status, node_states, aggregated_result)
- workflow_engine.py: Neue Funktion get_execution_order()
  * Flattened topological sort für sequenzielle Execution
  * Phase 7: Wird zu levels (parallele Execution)
- prompt_executor.py: execute_workflow_prompt() Implementierung
  * Ruft workflow_executor.execute_workflow() auf
  * Konvertiert ExecutionResult zu API-Response
- routers/workflows.py (230 Zeilen): Workflow Execution API
  * POST /api/workflows/{id}/execute (mit enable_debug)
  * GET /api/workflows/executions/{id} (lädt gespeicherten State)
  * GET /api/workflows (listet alle aktiven Workflows)
  * GET /api/workflows/{id} (lädt einzelnen Workflow mit Graph)
- main.py: Router-Registrierung (workflows.router)

Tests:
- test_phase2_normalization.py (17 Tests): Alle Normalisierungs-Szenarien
  * Exact match, case-insensitive, synonym mapping, invalid, whitespace
  * Batch-Normalisierung, not_in_catalog, mixed validity
- test_phase2_workflow_executor.py (10 Tests): Executor + Aggregation
  * aggregate_results mit verschiedenen Konstellationen
  * execute_node für start/end/analysis/unknown
  * Integration mit question_augmenter + result_container_parser

Alle 27 Unit-Tests bestanden.

version: 0.9k (backend)
module:  workflow 0.3.0

Konzept: .claude/task/Workflow_engine_prompting_engine/anforderungsanalyse_umsetzungsplan.md (Phase 2)
 2026-04-03 21:20:23 +02:00
Lars
ca562b7130 feat: Phase 1 - Fragenergänzung + Strukturierter Container
All checks were successful
Deploy Development / deploy (push) Successful in 49s
Details
Build Test / lint-backend (push) Successful in 0s
Details
Build Test / build-frontend (push) Successful in 14s
Details 
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)
 2026-04-03 18:02:25 +02:00
Lars
b5be6e21a5 feat: Phase 0 - Workflow Engine Foundation
All checks were successful
Deploy Development / deploy (push) Successful in 50s
Details
Build Test / lint-backend (push) Successful in 0s
Details
Build Test / build-frontend (push) Successful in 13s
Details 
Backend:
- DB-Migration 034: workflow_definitions, workflow_question_catalog, workflow_executions
- ai_prompts.question_augmentations JSONB-Spalte (Hybridmodell: Prompt-Defaults)
- 6 Grundtypen Fragenergänzungen mit Normalisierungsregeln (Seed-Daten)
- Pydantic-Modelle (16 Models, 11 Enums) in workflow_models.py
- Workflow-Engine: Graph-Parsing, Topologische Sortierung, DAG-Validierung
- Dispatcher-Erweiterung type='workflow' (Stub für Phase 1-3)
- Adjacency Lists, Erreichbarkeits-Prüfungen, Zyklen-Erkennung

Testing:
- 22 Unit-Tests (alle bestanden): Graph-Parsing, Validierung, Topologische Sortierung
- Fixtures: simple_valid_graph, parallel_graph, branching_graph

Version:
- APP_VERSION 0.9i
- DB_SCHEMA_VERSION 20260403
- Module: workflow 0.1.0

Anforderungsanalyse: .claude/task/Workflow_engine_prompting_engine/anforderungsanalyse_umsetzungsplan.md
Konzept-Basis: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-03 16:55:51 +02:00