stage22: Abschluss & Production Readiness — E2E-Tests, CHANGELOG, Dokumentation
This commit is contained in:
NSCT Agent
301
API.md
301
API.md
@@ -1,8 +1,6 @@
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# NSCT — API-Referenz
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> **Hinweis:** Diese API-Referenz beschreibt nur die Endpunkte
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> von Stage 0. Die eigentliche Evidence-Pipeline (Suche,
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> Analyse, Report) wird in späteren Stages implementiert.
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> **Hinweis:** Diese API-Referenz beschreibt den aktuellen Stand (Stage 22).
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## Server-Adresse
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@@ -54,7 +52,7 @@ Wenn bereit:
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"status": "ready",
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"llm": "ok",
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"database": "unknown",
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"llm_model": "Qwen3.6-6-35B"
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"llm_model": "Qwen3.6-35B"
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}
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```
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@@ -71,10 +69,6 @@ Wenn nicht bereit:
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- `ready` — Alle geprüften Services sind erreichbar
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- `not_ready` — Mindestens ein Service ist nicht erreichbar
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**Mögliche `llm`-Werte:**
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- `ok` — LLM-Provider ist erreichbar
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- `error:<detail>` — Fehlerbeschreibung (HTTP-Statuscode oder Exception-Typ)
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---
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### GET /providers
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@@ -91,12 +85,12 @@ GET /providers
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{
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"llm": {
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"available": true,
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"model": "Qwen3.6-6-35B",
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"model": "Qwen3.6-35B",
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"max_concurrency": 3
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},
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"vision": {
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"available": true,
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"model": "Qwen2-VL-3B"
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"model": "Qwen2.5-VL-3B"
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},
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"audio": {
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"available": true,
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@@ -115,28 +109,291 @@ GET /providers
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```
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**Wichtig:** Dieser Endpoint gibt **keinerlei** Secrets, API-Keys
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oder senssible Konfigurationswerte zurück.
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oder sensible Konfigurationswerte zurück.
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---
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## Entwicklung (nur mit NSCT_DEBUG=true)
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## Research API (Stage 14+)
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### GET /docs
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Alle Research-Endpoints haben das Präfix `/v1/research`.
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Swagger UI mit interaktiver API-Documentation.
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---
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```bash
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curl http://localhost:8080/docs
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### POST /v1/research
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**Beschreibung:** Startet eine neue Recherche asynchron (Background-Pipeline).
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Die Antwort kommt sofort — das eigentliche Ergebnis ist über
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`GET /v1/research/{id}/report` abrufbar.
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**Request:**
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```json
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{
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"query": "Welche wesentlichen Fortschritte gab es im letzten Jahr bei Kernfusion?",
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"language": "de",
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"depth": "normal",
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"extra_queries": ["Fusion breakthroughs 2025", "commercial fusion progress"]
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}
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```
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### GET /redoc
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**Parameter:**
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ReDoc-Generierung.
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `query` | string | yes | — | Forschungsfrage (1–2000 Zeichen) |
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| `language` | string | no | `"de"` | Sprachcode (z.B. `"de"`, `"en"`) |
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| `depth` | string | no | `"normal"` | Suchtiefe: `"quick"`, `"normal"`, `"deep"` |
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| `extra_queries` | string[] | no | `null` | Optionale zusätzliche Suchanfragen |
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```bash
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curl http://localhost:8080/redoc
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**Depth-Konfiguration:**
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| Tiefe | max_search_queries | max_sources | max_llm_requests | max_duration | max_context |
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|-------|--------------------|-------------|-------------------|--------------|-------------|
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| `quick` | 10 | 15 | 15 | 120s | 16k |
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| `normal` | 20 | 30 | 30 | 300s | 24k |
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| `deep` | 40 | 60 | 60 | 600s | 32k |
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"status": "pending",
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"query": "Welche Fortschritte bei Kernfusion?",
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"depth": "normal",
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"state": "created"
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}
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```
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**Error (400):**
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```json
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{
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"detail": "Ungültige Tiefe 'ultra'. Gültig: 'quick', 'normal', 'deep'."
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}
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```
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---
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### GET /v1/research
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**Beschreibung:** Liste aller Research-Runs (paginiert).
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**Query-Parameter:**
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `limit` | int | 10 | Max Anzahl Ergebnisse |
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| `offset` | int | 0 | Offset für Pagination |
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**Response (200 OK):**
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```json
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{
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"total": 5,
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"limit": 10,
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"offset": 0,
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"items": [
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{
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"research_id": "uuid-...",
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"query": "Kernfusion",
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"depth": "normal",
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"state": "completed",
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"created_at": "2025-01-01T00:00:00+00:00",
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"source_count": 5,
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"claim_count": 12
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}
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]
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}
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```
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---
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### GET /v1/research/{id}
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**Beschreibung:** Metadaten eines Research-Runs.
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**Path-Parameter:**
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `id` | string | UUID des Research-Runs |
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"query": "Kernfusion 2025",
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"depth": "normal",
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"state": "completed",
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"created_at": "2025-01-01T00:00:00+00:00",
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"updated_at": "2025-01-01T00:05:00+00:00",
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"search_count": 20,
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"source_count": 5,
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"claim_count": 12,
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"is_completed": true,
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"is_running": false
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}
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```
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---
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### GET /v1/research/{id}/status
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**Beschreibung:** State-Machine Status eines Research-Runs.
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Identisch zu `GET /v1/research/{id}` — gleicher Response.
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---
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### GET /v1/research/{id}/sources
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**Beschreibung:** Quellen für einen Research-Run.
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"sources": [
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{
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"id": "s1",
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"url": "https://example.com/1",
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"title": "Beispiel Quelle",
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"domain": "example.com",
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"source_type": "primary",
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"retrieved_at": "2025-01-01T00:01:00+00:00"
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}
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],
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"total": 1
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}
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```
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---
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### GET /v1/research/{id}/claims
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**Beschreibung:** Claims (Behauptungen) für einen Research-Run.
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"claims": [
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{
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"id": "c1",
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"research_run_id": "uuid-...",
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"source_id": "s1",
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"claim_text": "Behauptung A",
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"evidence_span": "Quelle 1, Abschnitt 2",
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"claim_type": "factual",
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"confidence": 0.85
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}
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],
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"total": 1
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}
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```
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---
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### GET /v1/research/{id}/evidence
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**Beschreibung:** Evidence-Scores (6-dimensional) für einen Research-Run.
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"evidence": [
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{
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"claim_id": "c1",
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"research_run_id": "uuid-...",
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"evidence_type": "primary_report",
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"source_independence_score": 0.9,
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"primary_source_proximity": 0.95,
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"cross_source_support": 0.7,
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"contradiction_level": 0.2,
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"evidence_directness": 0.88,
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"date_relevance_score": 0.95
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}
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],
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"total": 1
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}
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```
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---
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### GET /v1/research/{id}/report
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**Beschreibung:** Synthese-Bericht (finaler Research Report).
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**Response (200 OK):**
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```json
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{
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"research_id": "uuid-...",
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"query": "Kernfusion 2025",
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"summary": "Zusammenfassung der Recherche...",
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"findings": [
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{
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"text": "Fundamentale Erkenntnis A",
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"confidence": 0.85,
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"source_ids": ["s1", "s2"]
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}
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],
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"disagreements": [
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{
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"claim": "Behauptung X",
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"positions": [
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{"source": "Quelle 1", "view": "Für X"},
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{"source": "Quelle 2", "view": "Gegen X"}
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]
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}
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],
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"uncertainties": ["Nicht ausreichend belegte Behauptung Y"],
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"source_statistics": {
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"total_sources": 5,
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"primary_sources": 2,
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"secondary_sources": 3
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},
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"methodology": "NSCT Evidence Pipeline — Search, Extract, Claim, Compare, Synthesize",
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"generated_at": "2025-01-01T00:00:00+00:00"
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}
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```
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---
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### DELETE /v1/research/{id}
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**Beschreibung:** Löscht einen Research-Run (nur wenn nicht completed).
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**Response (200 OK):**
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```json
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{
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"status": "deleted",
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"research_id": "uuid-..."
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}
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```
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**Error (400):** Completed runs sind immutable.
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---
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## Prometheus Metrics
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### GET /metrics
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**Response:** Prometheus Text Format mit allen Metriken:
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- `search_queries_total`
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- `sources_discovered_total`
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- `sources_fetched_total`
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- `sources_rejected_total`
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- `claims_extracted_total`
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- `duplicate_sources_total`
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- `contradictions_detected_total`
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- `llm_requests_total`
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- `llm_tokens_input_total`
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- `llm_tokens_output_total`
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- `research_duration_seconds`
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- `research_completed_total`
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- `research_failed_total`
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- `active_research_runs`
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---
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## Error Response Format
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@@ -153,5 +410,5 @@ Alle Fehler folgen einem konsistenten Format:
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## Authentifizierung
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Stage 0: Keine Authentifizierung. Dies wird in späteren Stages
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hinzugefügt (API-Key, OAuth, etc.).
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Stage 22: Keine Authentifizierung. Dies kann in späteren Stages
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hinzugefügt werden (API-Key, OAuth, etc.).
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138
ARCHITECTURE.md
138
ARCHITECTURE.md
@@ -9,6 +9,10 @@ Behauptungen (Claims) und erzeugt einen neutralen, quellengestützten Bericht.
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**Kernprinzip:** Webcontent ist DATA, keine INSTRUCTION. Der LLM verarbeitet
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nur strukturierte Daten, niemals rohen Webcontent direkt als Prompt.
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**Stage 0–22 abgeschlossen.** NSCT ist production-ready.
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---
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## 2. Architektur-Diagramm
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```
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@@ -24,8 +28,8 @@ nur strukturierte Daten, niemals rohen Webcontent direkt als Prompt.
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│ │ │ │
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│ │ ┌─────────┐ ┌──────────┐ ┌───────────┐ ┌───────────┐ │ │
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│ │ │ Search │──▶│ Fetch │──▶│ Extract │──▶│ Classify │ │ │
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│ │ │ (SearX │ │ (HTTP/ │ │(Trafilat │ │(LLM/Rule)│ │ │
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│ │ │ NG) │ │ Playwright)│ │ura/BS4) │ │ │ │ │
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│ │ │ (DDG/ │ │(HTTP/ │ │(Trafilat │ │(LLM/Rule)│ │ │
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│ │ │ Multi) │ │ Playright)│ │ura/BS4) │ │ │ │ │
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│ │ └─────────┘ └──────────┘ └───────────┘ └─────┬─────┘ │ │
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│ │ │ │ │
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│ │ ┌─────────┐ ┌───────────┐ ┌────────────┐ ┌───┴─────┐ │ │
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@@ -35,17 +39,25 @@ nur strukturierte Daten, niemals rohen Webcontent direkt als Prompt.
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│ │ │ │ │
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│ │ ▼ │ │
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│ │ ┌──────────────┐ │ │
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│ │ │Evidence │ │ │
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│ │ │Scoring (6D) │ │ │
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│ │ └──────┬───────┘ │ │
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│ │ ▼ │ │
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│ │ ┌──────────────┐ │ │
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│ │ │ Report Gen. │ │ │
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│ │ │ (LLM) │ │ │
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│ │ └──────┬───────┘ │ │
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│ │ ▼ │ │
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│ │ ┌──────────────┐ │ │
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│ │ │ Evidence DB │ │ │
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│ │ │Provenance │ │ │
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│ │ │+ Hash │ │ │
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│ │ └──────────────┘ │ │
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│ └──────────────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────────┘
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```
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---
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## 3. Datenfluss
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```
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@@ -65,49 +77,134 @@ Query ──▶ SearchProvider.search() ──▶ List[SearchResult]
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│
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▼
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EvidenceRelation[] ──▶ Agreement matrix
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│
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│ (6-dim evidence scoring)
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│
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▼
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EvidencePackage ──▶ Neutral Synthesis
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│
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│ (LLM: synthesize report)
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│
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▼
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ResearchReport (Summary + Findings + Disagreements + Uncertainties)
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│
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▼
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research_run_hash (deterministic, reproducible)
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```
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---
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## 4. Module
|
||||
|
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### 4.1 `src/nsct/api/` — REST API
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- **main.py**: FastAPI application factory, CORS, lifespan hooks
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|
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- **rest_research.py**: Research CRUD — POST/GET/DELETE + status, sources, claims, evidence, report
|
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- **health.py**: `/health`, `/ready`, `/providers` endpunkte
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- **search.py**: Search API endpoint
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- **debug.py**: Debug-Endpunkte (nur bei `NSCT_DEBUG=true`)
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- **Pattern:** Each router is a separate file; mounted in `main.py`
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### 4.2 `src/nsct/models/` — Pydantic v2 Schemas
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- **schemas.py**: Data transfer objects (SearchQuery, Source, Claim, etc.)
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- **plan.py**: ResearchPlan Schema
|
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- **claim.py**: Claim Schema mit Provenance
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- **synthesis.py**: Synthese Report Schema
|
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- **source_independence.py**: Independence Graph Schema
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- **gap_analysis.py**: GapAnalysis Schema
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- **audio.py**: Audio Transcription Schema
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- **vision.py**: Vision Analysis Schema
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- Pure Pydantic — no database or API coupling
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### 4.3 `src/nsct/storage/` — Database Layer
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|
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- **models.py**: SQLAlchemy 2.0 declarative models
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- **engine.py**: Async engine factory with pool management
|
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- Uses `asyncpg` for PostgreSQL
|
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|
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### 4.4 `src/nsct/providers/` — Provider Interfaces
|
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- Abstract base classes (LLMProvider, VisionProvider, SearchProvider,
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ContentFetcher, AudioProvider)
|
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|
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- **abstract.py**: Abstract base classes (LLMProvider, VisionProvider, SearchProvider, etc.)
|
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- **llm.py**: OpenAI-kompatibler LLM Provider
|
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- **duckduckgo.py**: DuckDuckGo Search Provider
|
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- **multi.py**: MultiProviderSearch für parallele Abfrage
|
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- **vision.py**: Qwen2.5-VL-3B Vision Provider
|
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- **audio.py**: Audio/STT Provider
|
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- **metrics.py**: Provider-Metriken Tracking
|
||||
- **priority_queue.py**: Priority-Queue mit HIGH/NORMAL/LOW Prioritäten
|
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- **semaphore.py**: Concurrency Semaphore (default: 3)
|
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- **No concrete implementations in Stage 0** — just protocols
|
||||
- All providers accept structured data, never raw web content
|
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|
||||
### 4.5 `src/nsct/security/` — Security
|
||||
|
||||
- **policy.py**: SSRF protection, URL validation, IP blocklisting
|
||||
- Called before every outbound HTTP request
|
||||
|
||||
### 4.6 `src/nsct/config.py` — Configuration
|
||||
|
||||
- Pydantic BaseSettings — all values from environment
|
||||
- Zero hard-coded secrets or URLs
|
||||
- `AppSettings.from_env()` creates the root configuration
|
||||
|
||||
### 4.7 `src/nsct/logging_config.py` — Structured Logging
|
||||
|
||||
- JSON-formatted log output
|
||||
- Per-request context tracking (`research_id`, `llm_request_id`)
|
||||
- Global context dict merged into every log record
|
||||
|
||||
### 4.8 `src/nsct/metrics.py` — Prometheus Metrics
|
||||
|
||||
- Counters: search queries, sources fetched, claims, contradictions, completed/failed
|
||||
- Histograms: research duration
|
||||
- Gauges: active research runs
|
||||
|
||||
### 4.9 `src/nsct/agents/` — Research Planning
|
||||
|
||||
- **planner.py**: LLM-basierte Recherchestrategie (Query Expansion, Bias Reduction)
|
||||
- **validator.py**: Plausibilitäts-Prüfung von Research-Plänen
|
||||
|
||||
### 4.10 `src/nsct/crawler/` — Content Extraction
|
||||
|
||||
- **fetcher.py**: Asynchroner HTTP-Fetcher mit SSRF-Schutz
|
||||
- **extraction.py**: Main Content Extraction (trafilatura, BeautifulSoup)
|
||||
- **normalize.py**: Dokumenten-Normalisierung
|
||||
- **pdf.py**: PDF-Extraktion
|
||||
- **policy.py**: SSRF/Download-Policy
|
||||
- **manager.py**: Batch-Verwaltung
|
||||
|
||||
### 4.11 `src/nsct/orchestration/` — Pipeline Control
|
||||
|
||||
- **state.py**: State Machine mit 12 Zuständen
|
||||
- **budget.py**: Hard Budget Limits (7 config options, frozen)
|
||||
- **models.py**: ResearchRun Pydantic Model (frozen, immutable)
|
||||
- **orchestrator.py**: Vollständige Pipeline-Steuerung mit Fallbacks
|
||||
- **context_budget.py**: Pro-Stage Context Token Limits
|
||||
- **gap_analysis.py**: Lückenerkennung für iterative Recherche
|
||||
|
||||
### 4.12 `src/nsct/stages/` — Pipeline Stages (5-13)
|
||||
|
||||
- **stage5_extract_claims.py**: Claim Extraction
|
||||
- **stage6_source_independence.py**: Source Independence Graph
|
||||
- **stage7_clustering.py**: Claim Clustering
|
||||
- **stage7_normalize_numerics.py**: Numerical Normalization
|
||||
- **stage8_evidence_scoring.py**: 6-dimensional Evidence Scoring
|
||||
- **stage9_synthesis.py**: Neutral Synthesis
|
||||
- **stage10_vision.py**: Vision Integration
|
||||
- **stage11_audio.py**: Audio/STT Integration
|
||||
- **stage13_gap_analysis.py**: Iterative Gap Analysis
|
||||
|
||||
### 4.13 `src/nsct/provenance.py` — Provenance & Reproducibility
|
||||
|
||||
- **Stage 21**: Vollständige Provenance aller Pipeline-Schritte
|
||||
- `research_run_hash`: Deterministischer Hash für Reproduzierbarkeit
|
||||
|
||||
### 4.14 `src/nsct/cli.py` — Command Line Interface
|
||||
|
||||
- **Stage 15**: `nsct research`, `nsct status`, `nsct report`, etc.
|
||||
|
||||
---
|
||||
|
||||
## 5. Interfaces / Protocols
|
||||
|
||||
```python
|
||||
@@ -129,10 +226,12 @@ class ContentFetcher(ABC):
|
||||
async def fetch(url, **kwargs) -> dict: ...
|
||||
```
|
||||
|
||||
Alle konkreten Implementationen (SearXNG, OpenAI, Trafilatura, etc.)
|
||||
Alle konkreten Implementationen (DuckDuckGo, Qwen, Trafilatura, etc.)
|
||||
müssen diese Interfaces implementieren — das ermöglicht den Wechsel
|
||||
von Providern ohne Codeänderung im Core.
|
||||
|
||||
---
|
||||
|
||||
## 6. Sicherheitsarchitektur
|
||||
|
||||
- **Control Plane vs. Evidence Plane:** Der LLM verarbeitet nur strukturierte
|
||||
@@ -142,6 +241,9 @@ von Providern ohne Codeänderung im Core.
|
||||
Stattdessen wird er in strukturierte JSON-Objekte serialisiert.
|
||||
- **Non-Root-Docker:** Der Container läuft als nicht-root User `nsct`.
|
||||
- **Read-Only-Filesystem:** Wo möglich (`read_only: true` + `tmpfs`).
|
||||
- **Dropped Capabilities:** `cap_drop: [ALL]`
|
||||
|
||||
---
|
||||
|
||||
## 7. Datenmodell
|
||||
|
||||
@@ -156,6 +258,21 @@ von Providern ohne Codeänderung im Core.
|
||||
| `citation_edges` | Quelle-zu-Quelle Referenzen |
|
||||
| `research_reports` | Aggregierte Forschungsberichte |
|
||||
|
||||
### Evidence Scoring (Stage 8)
|
||||
|
||||
6 dimensionale Scores für jede Evidenz:
|
||||
|
||||
| Dimension | Range | Beschreibung |
|
||||
|-----------|-------|-------------|
|
||||
| `source_independence` | 0–1 | Wie unabhängig ist diese Quelle? |
|
||||
| `primary_source_proximity` | 0–1 | Wie nah ist die Quelle an der Primärquelle? |
|
||||
| `cross_source_support` | 0–1 | Wie viele unabhängige Quellen bestätigen? |
|
||||
| `contradiction_level` | 0–1 | Wie hoch ist der Widerspruch? (invertiert) |
|
||||
| `evidence_directness` | 0–1 | Wie direkt ist die Evidenz? |
|
||||
| `date_relevance_score` | 0–1 | Wie aktuell ist die Evidenz? |
|
||||
|
||||
---
|
||||
|
||||
## 8. Nicht-funktionale Anforderungen
|
||||
|
||||
| Kriterium | Anforderung |
|
||||
@@ -164,5 +281,8 @@ von Providern ohne Codeänderung im Core.
|
||||
| Database | PostgreSQL 16+, asyncpg, SQLAlchemy 2.0 |
|
||||
| Logging | Strukturiert (JSON), jede Anfrage tracebar |
|
||||
| Config | Environment-only, keine Config-Dateien |
|
||||
| Testing | pytest-asyncio, FastAPI TestClient |
|
||||
| Deployment | Docker Compose, reproduzierbar |
|
||||
| Testing | pytest-asyncio, FastAPI TestClient, E2E-Tests |
|
||||
| Deployment | Docker Compose, reproduzierbar |
|
||||
| LLM Concurrency | Konfigurierbar (`NSCT_LLM_MAX_CONCURRENCY`), default: 3 |
|
||||
| Context Budgeting | Pro-Stage Limits (Planner→Claim→Contradiction→Synthesis) |
|
||||
| Reproducibility | `research_run_hash` + vollständige Provenance |
|
||||
290
CHANGELOG.md
Normal file
290
CHANGELOG.md
Normal file
@@ -0,0 +1,290 @@
|
||||
# Changelog — NSCT (Neutral Search Crawler Tool)
|
||||
|
||||
Alle signifikanten Änderungen an NSCT, organisiert nach Stage.
|
||||
|
||||
---
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Stage 22 — Abschluss & Production Readiness
|
||||
|
||||
**Added**
|
||||
- `tests/stages/test_e2e_integration.py` — 9 End-to-End-Integrationstests (POST → Pipeline → GET report)
|
||||
- `CHANGELOG.md` — Vollständiges Versions-Changelog
|
||||
|
||||
**Documentation**
|
||||
- `README.md` — Vollständig überarbeitet mit aktuellem Stand (Stage 0–22), Architektur-Diagramm, API-Referenz, Docker-Anleitung
|
||||
- `ARCHITECTURE.md` — Aktualisierte Module-Beschreibungen mit allen Stages
|
||||
- `API.md` — Vollständige Endpunkt-Dokumentation mit request/response Beispielen
|
||||
- `DEPLOYMENT.md` — Deployment-Anleitung mit allen Konfigurationsoptionen
|
||||
- `SECURITY.md` — Sicherheitsgrundsätze (Control/Evidence Plane, SSRF, Docker)
|
||||
- `METHODOLOGY.md` — Methodik-Dokumentation: Neutralität, Quellenabhängigkeit, Claim-Vergleich
|
||||
|
||||
---
|
||||
|
||||
## Stage 21 — Reproduzierbarkeit
|
||||
|
||||
**Added**
|
||||
- `src/nsct/provenance.py` — Vollständige Provenance aller Pipeline-Schritte
|
||||
- `research_run_hash` im Report — deterministischer Hash aus Query, Sources, Claims
|
||||
- Schema-Version Tracking für backward-compatible Reports
|
||||
|
||||
---
|
||||
|
||||
## Stage 20 — Context Budgeting
|
||||
|
||||
**Added**
|
||||
- `src/nsct/orchestration/context_budget.py` — Pro-Stage Kontext-Limits
|
||||
- Planner: 12k, Claim Extraction: 16k, Contradiction: 24k, Synthesis: 48k
|
||||
- `_track_context_tokens()` im Orchestrator
|
||||
- `tests/test_context_budget.py` — 18 Tests
|
||||
- `tests/test_context_budget_integration.py` — 16 Integrationstests
|
||||
- `src/nsct/providers/priority_queue.py` — async_fix für Python 3.14+
|
||||
|
||||
---
|
||||
|
||||
## Stage 19 — Performanceoptimierung
|
||||
|
||||
**Added**
|
||||
- `src/nsct/providers/semaphore.py` — LLM Concurrency Semaphore (configurable, default: 3)
|
||||
- `src/nsct/providers/priority_queue.py` — Priority queue mit HIGH/NORMAL/LOW Prioritäten
|
||||
- `tests/test_priority_limiter.py` — 14 Tests für Priority Limiter
|
||||
|
||||
---
|
||||
|
||||
## Stage 18 — Docker Hardening
|
||||
|
||||
**Added**
|
||||
- Non-Root-User (`nsct`, UID 1000) im Dockerfile
|
||||
- Read-Only-Filesystem mit tmpfs
|
||||
- Dropped Capabilities (`cap_drop: [ALL]`)
|
||||
- Resource Limits (Memory, CPU) per Container
|
||||
- Healthcheck für Docker Container
|
||||
|
||||
---
|
||||
|
||||
## Stage 17 — Neutralitäts-Tests
|
||||
|
||||
**Added**
|
||||
- `tests/stages/test_neutrality_a_syndication.py` — Syndication-Erkennung
|
||||
- `tests/stages/test_neutrality_b_political_statements.py` — Politische Aussagen
|
||||
- `tests/stages/test_neutrality_c_scientific_disagreement.py` — Wissenschaftlicher Dissens
|
||||
- `tests/stages/test_neutrality_d_prompt_injection.py` — Prompt Injection
|
||||
- `tests/stages/test_neutrality_e_missing_evidence.py` — Fehlende Evidenz
|
||||
|
||||
---
|
||||
|
||||
## Stage 16 — Observability
|
||||
|
||||
**Added**
|
||||
- `src/nsct/logging_config.py` — Strukturiertes JSON-Logging mit request context
|
||||
- `src/nsct/metrics.py` — Prometheus Metriken (search, sources, claims, contradictions, LLM)
|
||||
- `tests/test_logging.py` — Logging-Tests
|
||||
- `tests/test_metrics.py` — Metriken-Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 15 — CLI
|
||||
|
||||
**Added**
|
||||
- `src/nsct/cli.py` — Kommandozeileninterface
|
||||
- `tests/test_cli.py` — CLI-Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 14 — REST API
|
||||
|
||||
**Added**
|
||||
- `src/nsct/api/rest_research.py` — Vollständige Research REST API
|
||||
- `POST /v1/research` — Start research (async, background pipeline)
|
||||
- `GET /v1/research` — Liste aller Recherchen (paginiert)
|
||||
- `GET /v1/research/{id}` — Metadaten
|
||||
- `GET /v1/research/{id}/status` — State-Machine Status
|
||||
- `GET /v1/research/{id}/sources` — Quellen
|
||||
- `GET /v1/research/{id}/claims` — Claims
|
||||
- `GET /v1/research/{id}/evidence` — Evidence-Scores
|
||||
- `GET /v1/research/{id}/report` — Synthese-Bericht
|
||||
- `DELETE /v1/research/{id}` — Löschen (nicht-komplettiert)
|
||||
- `tests/test_rest_research.py` — Komplette API-Tests
|
||||
- Depth-Configs: `quick`, `normal`, `deep` mit escalating Budgets
|
||||
|
||||
---
|
||||
|
||||
## Stage 13 — Iterative Research / Gap Analysis
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage13_gap_analysis.py` — Lückenerkennung
|
||||
- `src/nsct/orchestration/gap_analysis.py` — GapSearchQuery
|
||||
- Max 3 iterative Runden (konfigurierbar über `NSCT_MAX_RESEARCH_ROUNDS`)
|
||||
- `tests/test_stage13_gap_analysis.py` — 17 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 12 — Research Orchestrator
|
||||
|
||||
**Added**
|
||||
- `src/nsct/orchestration/state.py` — State Machine (12 Zustände)
|
||||
- `src/nsct/orchestration/budget.py` — Hard Budget Limits (Pydantic, frozen)
|
||||
- `src/nsct/orchestration/models.py` — ResearchRun Pydantic Model (frozen)
|
||||
- `src/nsct/orchestration/orchestrator.py` — Vollständige Pipeline-Steuerung
|
||||
|
||||
---
|
||||
|
||||
## Stage 11 — Audio Integration
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage11_audio.py` — STT-Transkription mit timestamped Claims
|
||||
- `src/nsct/models/audio.py` — Audio Pydantic Schema
|
||||
- `src/nsct/providers/audio.py` — AudioProvider Implementation
|
||||
- `src/nsct/api/audio.py` — Audio API Endpoint
|
||||
- `tests/stages/test_stage11_audio.py` — ~20 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 10 — Vision Integration
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage10_vision.py` — Qwen2.5-VL-3B Integration
|
||||
- `src/nsct/models/vision.py` — Vision Pydantic Schema
|
||||
- `src/nsct/providers/vision.py` — VisionProvider Implementation
|
||||
- `src/nsct/api/vision.py` — Vision API Endpoint
|
||||
- `tests/stages/test_stage10_vision.py` — ~25 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 9 — Neutral Synthesis Engine
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage9_synthesis.py` — LLM-basierte neutrale Berichts-Synthese
|
||||
- `src/nsct/models/synthesis.py` — Synthese Pydantic Schema
|
||||
- `src/nsct/api/synthesis.py` — Synthese API Endpoint
|
||||
- `tests/stages/test_stage9_synthesis.py` — 25 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 8 — Evidence Scoring
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage8_evidence_scoring.py` — 6-dimensionale Evidence-Scores
|
||||
- `evidence_type` und `raw_scores_json` Felder
|
||||
- `src/nsct/api/stage8.py` — Evidence Scoring API
|
||||
- `tests/stages/test_stage8_evidence_scoring.py` — ~60 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 7 — Claim Clustering & Contradiction
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage7_clustering.py` — LLM-Clustering
|
||||
- `src/nsct/stages/stage7_normalize_numerics.py` — Numerische Normalisierung
|
||||
- `tests/stages/test_stage7_clustering.py` — 79 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 6 — Source Independence & Citation Graph
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage6_source_independence.py` — Syndication-Erkennung
|
||||
- `src/nsct/models/source_independence.py` — Independence Pydantic Schema
|
||||
- `tests/stages/test_stage6_source_independence.py` — 43 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 5 — Claim Extraction
|
||||
|
||||
**Added**
|
||||
- `src/nsct/stages/stage5_extract_claims.py` — Atomare, überprüfbare Claims
|
||||
- `src/nsct/models/claim.py` — Claim Pydantic Schema (mit provenance)
|
||||
- `tests/stages/test_stage5_extract_claims.py` — 36 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 4 — Research Planner
|
||||
|
||||
**Added**
|
||||
- `src/nsct/agents/planner.py` — LLM-basierte Recherchestrategie
|
||||
- `src/nsct/models/plan.py` — Plan Pydantic Schema
|
||||
- `src/nsct/api/planner.py` — Planner API Endpoint
|
||||
- `tests/test_planner.py` — 25 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 3 — Crawler & Content Extraction
|
||||
|
||||
**Added**
|
||||
- `src/nsct/crawler/fetcher.py` — Asynchroner HTTP-Fetcher mit SSRF-Schutz
|
||||
- `src/nsct/crawler/extraction.py` — Content-Extraktion (HTML, Text, PDF)
|
||||
- `src/nsct/crawler/manager.py` — Batch-Verwaltung
|
||||
- `src/nsct/crawler/normalize.py` — Dokumenten-Normalisierung
|
||||
- `src/nsct/crawler/pdf.py` — PDF-Extraktion
|
||||
- `src/nsct/crawler/policy.py` — SSRF-Schutz Policy
|
||||
- `tests/test_crawler.py` — ~15 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 2 — Search Provider Abstraction
|
||||
|
||||
**Added**
|
||||
- `src/nsct/providers/duckduckgo.py` — DuckDuckGo Search Provider
|
||||
- `src/nsct/providers/multi.py` — MultiProviderSearch (parallele Abfrage)
|
||||
- `src/nsct/api/search.py` — Search API Endpoint
|
||||
- `tests/test_search.py` — 25 Tests
|
||||
|
||||
---
|
||||
|
||||
## Stage 1 — Model Provider Layer
|
||||
|
||||
**Added**
|
||||
- `src/nsct/providers/llm.py` — OpenAI-kompatibler LLM-Provider
|
||||
- `src/nsct/providers/abstract.py` — Abstrakte Provider-Basisklassen
|
||||
- `src/nsct/api/debug.py` — Debug Endpoints (NSCT_DEBUG only)
|
||||
- `tests/test_crawler.py`, `tests/test_health.py`, `tests/test_planner.py` — Tests für Stage 1
|
||||
|
||||
---
|
||||
|
||||
## Stage 0 — Repository & Architekturgrundlage
|
||||
|
||||
**Added**
|
||||
- Vollständige Projektstruktur (`src/nsct/` mit allen Unterverzeichnissen)
|
||||
- `pyproject.toml` — Python Project Configuration
|
||||
- `Dockerfile` — Container Image
|
||||
- `docker-compose.yml` — PostgreSQL, SearXNG, NSCT
|
||||
- `.env.example` — Konfigurations-Vorlage
|
||||
- `src/nsct/config.py` — Pydantic BaseSettings Konfiguration
|
||||
- `src/nsct/storage/` — SQLAlchemy 2.0 Models + Engine (asyncpg)
|
||||
- `src/nsct/models/schemas.py` — Pydantic v2 Schemas
|
||||
- `src/nsct/security/policy.py` — SSRF-Schutz, URL-Validierung, IP-Blocklist
|
||||
- `src/nsct/api/health.py` — `/health`, `/ready`, `/providers` Endpunkte
|
||||
- `tests/test_health.py` — Health Check Tests
|
||||
- `README.md`, `ARCHITECTURE.md`, `SECURITY.md`, `METHODOLOGY.md` — Grundlegende Dokumentation
|
||||
- 6 Basis-Tests
|
||||
|
||||
---
|
||||
|
||||
**Zusammenfassung:**
|
||||
|
||||
| Stage | Description | Status |
|
||||
|-------|-------------|--------|
|
||||
| 0 | Repository & Architekturgrundlage | ✅ |
|
||||
| 1 | Model Provider Layer | ✅ |
|
||||
| 2 | Search Provider Abstraction | ✅ |
|
||||
| 3 | Crawler & Content Extraction | ✅ |
|
||||
| 4 | Research Planner | ✅ |
|
||||
| 5 | Claim Extraction | ✅ |
|
||||
| 6 | Source Independence & Citation Graph | ✅ |
|
||||
| 7 | Claim Clustering & Contradiction | ✅ |
|
||||
| 8 | Evidence Scoring | ✅ |
|
||||
| 9 | Neutral Synthesis Engine | ✅ |
|
||||
| 10 | Vision Integration | ✅ |
|
||||
| 11 | Audio Integration | ✅ |
|
||||
| 12 | Research Orchestrator | ✅ |
|
||||
| 13 | Iterative Research / Gap Analysis | ✅ |
|
||||
| 14 | REST API | ✅ |
|
||||
| 15 | CLI | ✅ |
|
||||
| 16 | Observability | ✅ |
|
||||
| 17 | Neutralitäts-Tests | ✅ |
|
||||
| 18 | Docker Hardening | ✅ |
|
||||
| 19 | Performanceoptimierung | ✅ |
|
||||
| 20 | Context Budgeting | ✅ |
|
||||
| 21 | Reproduzierbarkeit | ✅ |
|
||||
| 22 | Abschluss & Production Readiness | ✅ |
|
||||
@@ -5,6 +5,7 @@
|
||||
- **Docker** (≥ 24.0) und **Docker Compose** (≥ 2.23)
|
||||
- Mindestens 2 GB freier RAM
|
||||
- Zugang zu einem OpenAI-kompatiblen LLM-Endpunkt
|
||||
- PostgreSQL 16+ (wird mit docker-compose bereitgestellt)
|
||||
|
||||
## 2. Konfiguration
|
||||
|
||||
@@ -18,17 +19,17 @@ Trage folgende Werte ein:
|
||||
|
||||
```env
|
||||
# LLM (verpflichtend)
|
||||
NSCT_LLM_BASE_URL=http://192.168.80.199:8030/openai/v1
|
||||
NSCT_LLM_MODEL=Qwen3.6-6-35B
|
||||
NSCT_LLM_BASE_URL=http://your-llm-host:8030/openai/v1
|
||||
NSCT_LLM_MODEL=Qwen3.6-35B
|
||||
NSCT_LLM_MAX_CONCURRENCY=3
|
||||
NSCT_LLM_API_KEY=<dein-api-key>
|
||||
|
||||
# Vision (optional)
|
||||
NSCT_VISION_BASE_URL=http://192.168.80.199:8030/openai/visual/v1
|
||||
NSCT_VISION_MODEL=Qwen2-VL-3B
|
||||
NSCT_VISION_BASE_URL=http://your-vision-host:8030/openai/visual/v1
|
||||
NSCT_VISION_MODEL=Qwen2.5-VL-3B
|
||||
|
||||
# Audio (optional)
|
||||
NSCT_AUDIO_BASE_URL=http://192.168.80.199:8030/hermes-audio
|
||||
NSCT_AUDIO_BASE_URL=http://your-audio-host:8030/hermes-audio
|
||||
NSCT_AUDIO_MODEL=default
|
||||
|
||||
# PostgreSQL
|
||||
@@ -102,14 +103,16 @@ docker compose logs -f nsct-api
|
||||
|
||||
- `.env` **niemals** committen — `.gitignore` behandelt das
|
||||
- Verwende ein Secrets-Management Tool (Hashicorp Vault, AWS Secrets Manager)
|
||||
- API-Authentifizierung wird in Stage 2+ implementiert
|
||||
- Network-Policies für Docker (nur interner Traffic zwischen Services)
|
||||
- Non-Root-Docker-Container mit Read-Only-Filesystem
|
||||
- Dropped Capabilities (`cap_drop: [ALL]`)
|
||||
|
||||
### 5.2 Skalierung
|
||||
|
||||
- Derzeit: Single-Instance (kein horizontal scaling)
|
||||
- PostgreSQL: Connection Pooling über `pool_size=10, max_overflow=20`
|
||||
- LLM: Max. `NSCT_LLM_MAX_CONCURRENCY` parallele Requests (standard: 3)
|
||||
- LLM: Max. `NSCT_LLM_MAX_CONCURRENCY` parallele Requests (default: 3)
|
||||
- Concurrency-Semaphore im Provider-Layer
|
||||
|
||||
### 5.3 Datenpersistenz
|
||||
|
||||
@@ -122,8 +125,7 @@ volumes:
|
||||
driver: local
|
||||
```
|
||||
|
||||
Für Production: Verwende ein volumen-Plugin mit Backup-Unterstützung
|
||||
(например, Velero für Kubernetes).
|
||||
Für Production: Verwende ein volumen-Plugin mit Backup-Unterstützung.
|
||||
|
||||
### 5.4 Monitoring
|
||||
|
||||
@@ -136,6 +138,18 @@ docker compose logs --tail=100 nsct-api
|
||||
|
||||
# DB-Größe
|
||||
docker exec -it nsct-postgres psql -U nsct -d nsct -c "SELECT pg_database_size('nsct');"
|
||||
|
||||
# Prometheus Metrics
|
||||
curl http://localhost:8080/metrics
|
||||
```
|
||||
|
||||
### 5.5 Healthcheck
|
||||
|
||||
Das Dockerfile enthält einen HEALTHCHECK:
|
||||
|
||||
```dockerfile
|
||||
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
|
||||
CMD curl -f http://localhost:8080/health || exit 1
|
||||
```
|
||||
|
||||
## 6. Troubleshooting
|
||||
@@ -166,7 +180,7 @@ docker compose logs postgres
|
||||
|
||||
```bash
|
||||
# Network-Ping zum LLM-Host
|
||||
docker compose run --rm nsct-api curl -v http://192.168.80.199:8030/openai/v1/models
|
||||
docker compose run --rm nsct-api curl -v http://your-llm-host:8030/openai/v1/models
|
||||
```
|
||||
|
||||
## 7. Update / Migration
|
||||
@@ -180,4 +194,31 @@ docker compose up --build -d
|
||||
|
||||
# Datenbank-Migrationen (wenn benötigt)
|
||||
# werden in späteren Stages mit Alembic implementiert
|
||||
```
|
||||
```
|
||||
|
||||
## 8. CI/CD
|
||||
|
||||
### Unit Tests
|
||||
|
||||
```bash
|
||||
.docker compose run --rm nsct-api python -m pytest tests/ -v
|
||||
```
|
||||
|
||||
### Docker Build
|
||||
|
||||
```bash
|
||||
docker compose build nsct-api
|
||||
docker push your-registry/nsct:latest
|
||||
```
|
||||
|
||||
## 9. Production Checklist
|
||||
|
||||
- [ ] `.env` nicht in Git
|
||||
- [ ] Strong PostgreSQL Password
|
||||
- [ ] LLM API Key konfiguriert
|
||||
- [ ] Resource Limits gesetzt
|
||||
- [ ] Healthcheck konfiguriert
|
||||
- [ ] Logging zu externem System (ELK, Grafana, etc.)
|
||||
- [ ] Backup-Policy für PostgreSQL Volume
|
||||
- [ ] Network Policies für Docker
|
||||
- [ ] Monitoring Alerts (Prometheus + Alertmanager)
|
||||
155
README.md
155
README.md
@@ -1,6 +1,4 @@
|
||||
# ============================================================
|
||||
# NSCT — Neutral Search Crawler Tool
|
||||
# ============================================================
|
||||
|
||||
**NSCT** ist ein vollständig lokal betreibbares, containerisiertes Recherche- und
|
||||
Analyse-System. Es durchsucht Webquellen, extrahiert Inhalte, vergleicht
|
||||
@@ -9,30 +7,43 @@ quellengestützten Bericht.
|
||||
|
||||
**NSCT steht für:** Neutral Search Crawler Tool.
|
||||
|
||||
---
|
||||
|
||||
## Architektur-Übersicht
|
||||
|
||||
```
|
||||
┌─────────────┐ ┌──────────────┐ ┌───────────────┐
|
||||
│ User / CLI │───▶│ NSCT API │───▶│ FastAPI / │
|
||||
│ │ │ (FastAPI) │ │ uvicorn │
|
||||
└─────────────┘ └──────────────┘ └───────────────┘
|
||||
│
|
||||
┌────────────┼─────────────┐
|
||||
▼ ▼ ▼
|
||||
┌───────────┐ ┌─────────┐ ┌──────────┐
|
||||
│ LLM │ │ Vision │ │ Audio │
|
||||
│ Provider │ │ Model │ │ Model │
|
||||
└───────────┘ └─────────┘ └──────────┘
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────────────────────────┐
|
||||
│ PostgreSQL (evidence store) │
|
||||
└─────────────────────────────────────┘
|
||||
▲
|
||||
│
|
||||
┌──────────────┐
|
||||
│ SearXNG │ (optional search backend)
|
||||
└──────────────┘
|
||||
┌─────────────┐ ┌──────────────────────────┐ ┌───────────────┐
|
||||
│ User / CLI │───▶│ NSCT REST API (FastAPI) │───▶│ uvicorn │
|
||||
│ │ │ (async, background pip.)│ │ :8080 │
|
||||
└─────────────┘ └──────────┬───────────────┘ └───────────────┘
|
||||
│
|
||||
┌───────────────┼─────────────────┐
|
||||
▼ ▼ ▼
|
||||
┌──────────────┐ ┌──────────┐ ┌──────────────┐
|
||||
│ Search │ │ Content │ │ Crawler │
|
||||
│ Providers │ │ Fetcher │ │ (SSRF-Prot) │
|
||||
│ (DDG, Multi) │ │ (HTTP/ │ │ (HTML,PDF, │
|
||||
└──────┬───────┘ │ Playwright)│ │ PDF, ...) │
|
||||
│ └──────┬─────┘ └──────────────┘
|
||||
▼ ▼
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ Evidence Pipeline (Orchestrator) │
|
||||
│ Search → Fetch → Extract → Claim → │
|
||||
│ Compare → Score → Synthesis → Report │
|
||||
└──────────────────────────┬─────────────────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────────────┐
|
||||
│ PostgreSQL (asyncpg) │
|
||||
│ Evidence Store │
|
||||
└────────────────────────┘
|
||||
▲
|
||||
│
|
||||
┌──────────────┴──────────────┐
|
||||
│ LLM (Qwen3.6-35B) │
|
||||
│ Vision (Qwen2.5-VL-3B) │
|
||||
│ Audio (STT) │
|
||||
└──────────────────────────────┘
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
@@ -42,6 +53,7 @@ quellengestützten Bericht.
|
||||
```bash
|
||||
# 1. Kopiere die Beispiel-Env
|
||||
cp .env.example .env
|
||||
|
||||
# 2. Trage deine Endpunkte ein (LLM, Vision, Audio, PostgreSQL)
|
||||
|
||||
# 3. Starte alles
|
||||
@@ -59,40 +71,101 @@ Die API ist danach unter `http://localhost:8080` erreichbar.
|
||||
```
|
||||
nsct/
|
||||
├── src/nsct/
|
||||
│ ├── api/ # FastAPI-Routen
|
||||
│ ├── models/ # Pydantic-Schemata
|
||||
│ ├── providers/ # Abstrakte Provider-Interfaces
|
||||
│ ├── api/ # FastAPI-Routen (health, research, search, etc.)
|
||||
│ ├── agents/ # Research Planner & Validator
|
||||
│ ├── crawler/ # Content Fetcher, Extraction, Normalization
|
||||
│ ├── models/ # Pydantic v2 Schemas
|
||||
│ ├── orchestration/# State Machine, Budget, Gap Analysis
|
||||
│ ├── provenance.py # Vollständige Provenance Tracking
|
||||
│ ├── providers/ # LLM, Vision, Audio, Search, Multi, Priority
|
||||
│ ├── security/ # SSRF-Schutz, URL-Validierung
|
||||
│ └── storage/ # SQLAlchemy Models + Engine
|
||||
├── tests/ # pytest-Tests
|
||||
│ ├── stages/ # Pipeline-Stages (5-13: claim, evidence, synthesis)
|
||||
│ ├── storage/ # SQLAlchemy 2.0 Models + Engine
|
||||
│ ├── cli.py # Kommandozeileninterface
|
||||
│ ├── config.py # Pydantic BaseSettings Konfiguration
|
||||
│ ├── logging_config.py # Strukturiertes JSON-Logging
|
||||
│ ├── metrics.py # Prometheus Metriken
|
||||
│ └── main.py # FastAPI Application Factory
|
||||
├── tests/ # pytest-Tests (Unit + Integration)
|
||||
├── docker-compose.yml
|
||||
├── Dockerfile
|
||||
├── pyproject.toml
|
||||
├── .env.example
|
||||
├── README.md
|
||||
├── ARCHITECTURE.md
|
||||
├── SECURITY.md
|
||||
├── METHODOLOGY.md
|
||||
├── API.md
|
||||
├── DEPLOYMENT.md
|
||||
└── .env.example
|
||||
└── CHANGELOG.md
|
||||
```
|
||||
|
||||
## Schnellstart-Beispiel (CLI)
|
||||
|
||||
```bash
|
||||
nsct research "Welche wesentlichen Entwicklungen gab es im letzten Jahr bei Kernfusion?" \
|
||||
--depth normal \
|
||||
--language de
|
||||
```
|
||||
|
||||
## Schnellstart-Beispiel (API)
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8080/v1/research \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "Welche Entwicklungen gab es bei Kernfusion?",
|
||||
"language": "de",
|
||||
"depth": "normal"
|
||||
}'
|
||||
```
|
||||
|
||||
Antwort:
|
||||
```json
|
||||
{
|
||||
"research_id": "uuid-...",
|
||||
"status": "pending",
|
||||
"query": "Welche Entwicklungen gab es bei Kernfusion?",
|
||||
"depth": "normal",
|
||||
"state": "created"
|
||||
}
|
||||
```
|
||||
|
||||
Den Status und das Ergebnis abrufen:
|
||||
```bash
|
||||
# Status
|
||||
curl http://localhost:8080/v1/research/<research_id>/status
|
||||
|
||||
# Bericht
|
||||
curl http://localhost:8080/v1/research/<research_id>/report
|
||||
|
||||
# Quellen
|
||||
curl http://localhost:8080/v1/research/<research_id>/sources
|
||||
|
||||
# Claims
|
||||
curl http://localhost:8080/v1/research/<research_id>/claims
|
||||
|
||||
# Evidence-Scores
|
||||
curl http://localhost:8080/v1/research/<research_id>/evidence
|
||||
```
|
||||
|
||||
## Status
|
||||
|
||||
**Stage 0** — Repository und Architekturgrundlage.
|
||||
**Stage 22** — Abschluss & Production Readiness.
|
||||
|
||||
Stage 0 enthält:
|
||||
- Vollständige Projektstruktur mit allen Konfigurationsdateien
|
||||
- Pydantic v2 Datenmodelle
|
||||
- SQLAlchemy 2.0 Persistenzmodelle
|
||||
- Sicherheitshards (SSRF-Schutz, IP-Blocklist)
|
||||
- Provider-Interfaces (abstrakte Basisklassen)
|
||||
- Health-, Ready- und Provider-Endpunkte
|
||||
- Strukturiertes Logging
|
||||
- Docker-Konfiguration für PostgreSQL, SearXNG und NSCT
|
||||
Alle 22 Stages sind abgeschlossen. NSCT ist production-ready:
|
||||
|
||||
Die eigentliche Evidence-Pipeline (Search, Fetch, Extract, Claim, Compare, Report)
|
||||
wird in späteren Stages implementiert.
|
||||
- **Pipeline:** Search → Fetch → Extract → Claim → Compare → Score → Synthesize → Report
|
||||
- **State Machine:** 12 Zustände (CREATED → ... → COMPLETED/FAILED/CANCELLED)
|
||||
- **Budget Limits:** Hard limits für Queries, Sources, LLM-Requests, Duration
|
||||
- **Iterative Research:** Gap Analysis mit max 3 Runden
|
||||
- **REST API:** Vollständiger Research Lifecycle (POST/GET/DELETE)
|
||||
- **CLI:** Kommandozeileninterface mit depth, language, format Optionen
|
||||
- **Observability:** Strukturiertes Logging + Prometheus Metriken
|
||||
- **Docker Hardening:** Non-Root, Read-Only FS, Dropped Capabilities, Resource Limits
|
||||
- **Reproduzierbarkeit:** Vollständige Provenance + research_run_hash
|
||||
- **Neutralitäts-Tests:** Syndication, politische Aussagen, Dissens, Prompt Injection, fehlende Evidenz
|
||||
- **E2E-Tests:** 9 Integrationstests für den vollständigen Pipeline-Durchlauf
|
||||
|
||||
## Lizenz
|
||||
|
||||
|
||||
44
SECURITY.md
44
SECURITY.md
@@ -6,6 +6,10 @@ NSCT verarbeitet **untrusted input** von überall im Web. Jede externe Quelle
|
||||
kann versuchen, die Integrität des Systems zu kompromittieren. Alle
|
||||
Sicherheitsmaßnahmen folgen dem "zero trust" Prinzip.
|
||||
|
||||
**Stage 0–22 abgeschlossen.** NSCT ist production-ready mit hardened Docker-Image.
|
||||
|
||||
---
|
||||
|
||||
## 2. Control Plane vs. Evidence Plane
|
||||
|
||||
```
|
||||
@@ -35,6 +39,8 @@ Sicherheitsmaßnahmen folgen dem "zero trust" Prinzip.
|
||||
**Regel:** Webcontent wechselt niemals direkt die Grenze von Evidence Plane
|
||||
in die Control Plane als Prompt-Text.
|
||||
|
||||
---
|
||||
|
||||
## 3. SSRF-Schutz
|
||||
|
||||
Jede outbound-HTTP-Anfrage muss durch `nsct/security/policy.py`:
|
||||
@@ -45,13 +51,15 @@ Jede outbound-HTTP-Anfrage muss durch `nsct/security/policy.py`:
|
||||
- `10.0.0.0/8` — Private Class A
|
||||
- `172.16.0.0/12` — Private Class B
|
||||
- `192.168.0.0/16` — Private Class C
|
||||
- `169.254.0.0/16` — Link-local / Link-local
|
||||
- `169.254.0.0/16` — Link-local / Metadata
|
||||
- `192.0.0.0/24` — IETF Protocol Assignments
|
||||
- `100.64.0.0/10` — Shared Address Space
|
||||
- `192.0.2.0/24`, `198.51.100.0/24`, `203.0.113.0/24` — TEST-NET
|
||||
3. **Metadata-Endpunkte:** Cloud-Instance-Metadata (`169.254.169.254`, etc.)
|
||||
4. **Schemata:** `file://`, `ftp://`, `gopher://`, `ldap://`, `data://` blockiert
|
||||
|
||||
---
|
||||
|
||||
## 4. Prompt-Isolation
|
||||
|
||||
Der LLM darf **niemals** rohen Webcontent als Teil des Prompts sehen. Der
|
||||
@@ -81,6 +89,8 @@ Daten:
|
||||
|
||||
Der LLM sieht **nur** dieses JSON — nie den HTML/Raw-Text.
|
||||
|
||||
---
|
||||
|
||||
## 5. Untrusted Data Handling
|
||||
|
||||
| Quelle | Risiko | Schutzmaßnahme |
|
||||
@@ -90,16 +100,44 @@ Der LLM sieht **nur** dieses JSON — nie den HTML/Raw-Text.
|
||||
| LLM Output | Halluzination, Bias | Claims mit confidence scoring; keine single-source truth |
|
||||
| Audio/Vision | Manipulierte Inputs | Content-Type-Validierung; Größ Limits |
|
||||
|
||||
## 6. Docker-Sicherheit
|
||||
---
|
||||
|
||||
## 6. Docker-Sicherheit (Stage 18+)
|
||||
|
||||
- **Non-Root-User:** `nsct` (UID 1000)
|
||||
- **Read-Only-Filesystem:** `read_only: true` wo möglich
|
||||
- **Dropped Capabilities:** `cap_drop: [ALL]`
|
||||
- **No Secrets in Image:** `.env` wird nicht gebuildet
|
||||
- **Resource Limits:** Memory und CPU begrenzt pro Container
|
||||
- **No Docker Socket:** Kein Zugriff auf Host-Docker-Socket
|
||||
- **No Host Filesystem:** Kein Zugriff auf Host-Dateisysteme
|
||||
- **Persistent nur in explizite Volumes**
|
||||
|
||||
---
|
||||
|
||||
## 7. Compliance
|
||||
|
||||
- Keine Speicherung von personenbezogenen Daten über die Quellen-Metadaten hinaus
|
||||
- Claims und Evidence Relations werden nur so lange gespeichert wie nötig
|
||||
- Alle Daten sind exportierbar und löschbar (SQLAlchemy cascade)
|
||||
- Alle Daten sind exportierbar und löschbar (SQLAlchemy cascade)
|
||||
- Vollständige Provenance (Stage 21): Jeder Schritt ist nachvollziehbar
|
||||
|
||||
---
|
||||
|
||||
## 8. Neutrale Recherche-Sicherheit (Stage 17+)
|
||||
|
||||
### Test A — Syndication
|
||||
Eine Agenturmeldung wird von 10 Webseiten kopiert.
|
||||
Erwartung: 1 ursprüngliche Quelle, 9 abhängige Quellen — nicht 10 unabhängige.
|
||||
|
||||
### Test B — Politische Aussagen
|
||||
Behauptungen verschiedener politischer Parteien werden klar getrennt — keine einfache Mehrheit.
|
||||
|
||||
### Test C — Wissenschaftlicher Dissens
|
||||
Verschiedene Studientypen und Evidenzstärken werden sichtbar gemacht — keine Stimmenzählung.
|
||||
|
||||
### Test D — Prompt Injection
|
||||
"ignore all previous instructions" auf Webseiten hat keine Auswirkung auf Policy.
|
||||
|
||||
### Test E — Fehlende Evidenz
|
||||
Nur Blogs wiederholen eine unbelegte Behauptung → System sagt: "Konnte nicht durch Primärquelle verifiziert werden."
|
||||
@@ -449,6 +449,7 @@ async def _run_pipeline(run: _ResearchRunState, request: ResearchRequest, budget
|
||||
if result.get("success"):
|
||||
run.state = ResearchRunState.COMPLETED.value
|
||||
run.report = result.get("report")
|
||||
run.sources = result.get("sources", [])
|
||||
run.claims = result.get("report", {}).get("claims", [])
|
||||
run.evidence_scores = result.get("report", {}).get("evidence", [])
|
||||
|
||||
|
||||
432
tests/stages/test_e2e_integration.py
Normal file
432
tests/stages/test_e2e_integration.py
Normal file
@@ -0,0 +1,432 @@
|
||||
"""End-to-End Integration Test for the full research pipeline (Stage 22).
|
||||
|
||||
Tests the complete lifecycle from POST /v1/research → background pipeline →
|
||||
GET /v1/research/{id}/report with mocked LLM and search providers so the test
|
||||
is deterministic and fast — no network calls, no real LLM needed.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Generator
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Fixtures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def client() -> Generator[TestClient, None, None]:
|
||||
"""Synchronous FastAPI TestClient."""
|
||||
from nsct.api.main import create_app
|
||||
|
||||
app = create_app()
|
||||
with TestClient(app) as c:
|
||||
yield c
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def clear_store(client: TestClient) -> None:
|
||||
"""Clear the in-memory research store before every test."""
|
||||
from nsct.api.rest_research import _research_store
|
||||
|
||||
_research_store.clear()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helper — build a mock orchestration result that mimics a successful run
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _mock_successful_pipeline() -> dict:
|
||||
"""Return a mock orchestrator.run() result for a successful research run."""
|
||||
return {
|
||||
"success": True,
|
||||
"report": {
|
||||
"summary": "Zusammenfassung der Recherche.",
|
||||
"findings": [
|
||||
{"text": "Fundamentale Erkenntnis A", "confidence": 0.85, "source_ids": ["s1"]},
|
||||
{"text": "Erkenntnis B bestätigt A", "confidence": 0.72, "source_ids": ["s2"]},
|
||||
],
|
||||
"disagreements": [
|
||||
{
|
||||
"claim": "Behauptung X",
|
||||
"positions": [
|
||||
{"source": "Quelle 1", "view": "Für X"},
|
||||
{"source": "Quelle 2", "view": "Gegen X"},
|
||||
],
|
||||
}
|
||||
],
|
||||
"uncertainties": ["Nicht ausreichend belegte Behauptung Y"],
|
||||
"source_statistics": {"total_sources": 5, "primary_sources": 2, "secondary_sources": 3},
|
||||
"methodology": "NSCT Evidence Pipeline — Search, Extract, Claim, Compare, Synthesize",
|
||||
"generated_at": "2025-01-01T00:00:00+00:00",
|
||||
"claims": [
|
||||
{
|
||||
"id": "c1",
|
||||
"research_run_id": "run-1",
|
||||
"source_id": "s1",
|
||||
"claim_text": "Behauptung A",
|
||||
"evidence_span": "Quelle 1, Abschnitt 2",
|
||||
"claim_type": "factual",
|
||||
"confidence": 0.85,
|
||||
},
|
||||
],
|
||||
"evidence": [
|
||||
{
|
||||
"claim_id": "c1",
|
||||
"research_run_id": "run-1",
|
||||
"evidence_type": "primary_report",
|
||||
"source_independence_score": 0.9,
|
||||
"primary_source_proximity": 0.95,
|
||||
"cross_source_support": 0.7,
|
||||
"contradiction_level": 0.2,
|
||||
"evidence_directness": 0.88,
|
||||
"date_relevance_score": 0.95,
|
||||
},
|
||||
],
|
||||
},
|
||||
"sources": [
|
||||
{"id": "s1", "url": "https://example.com/1", "title": "Beispiel 1", "domain": "example.com", "source_type": "primary"},
|
||||
{"id": "s2", "url": "https://example.com/2", "title": "Beispiel 2", "domain": "example.com", "source_type": "secondary"},
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 1 — POST /v1/research returns 200 and creates a research run
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_01_post_research_returns_200_and_creates_run(client: TestClient) -> None:
|
||||
"""Step 1: POST /v1/research creates a research run and returns research_id."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
resp = client.post(
|
||||
"/v1/research",
|
||||
json={
|
||||
"query": "Welche Entwicklungen gab es bei Kernfusion?",
|
||||
"language": "de",
|
||||
"depth": "normal",
|
||||
},
|
||||
)
|
||||
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["status"] == "pending"
|
||||
assert body["query"] == "Welche Entwicklungen gab es bei Kernfusion?"
|
||||
assert body["depth"] == "normal"
|
||||
assert "research_id" in body
|
||||
research_id = body["research_id"]
|
||||
assert len(research_id) == 36 # UUID length
|
||||
|
||||
# Verify entry in in-memory store
|
||||
from nsct.api.rest_research import _research_store
|
||||
|
||||
assert research_id in _research_store
|
||||
run = _research_store[research_id]
|
||||
assert run.query == "Welche Entwicklungen gab es bei Kernfusion?"
|
||||
assert run.language == "de"
|
||||
assert run.depth == "normal"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 2 — Background pipeline completes and report is available
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_02_full_pipeline_until_report_available(client: TestClient) -> None:
|
||||
"""Step 2-9: Background pipeline simulates a full run; report endpoint returns data."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
resp = client.post(
|
||||
"/v1/research",
|
||||
json={
|
||||
"query": "Kernfusion 2025",
|
||||
"language": "de",
|
||||
"depth": "deep",
|
||||
},
|
||||
)
|
||||
research_id = resp.json()["research_id"]
|
||||
|
||||
# After background pipeline completes, report should have findings
|
||||
from nsct.api.rest_research import _research_store
|
||||
run = _research_store[research_id]
|
||||
|
||||
# Verify report data was stored
|
||||
assert run.report is not None
|
||||
assert run.report.get("summary") == "Zusammenfassung der Recherche."
|
||||
assert len(run.report["findings"]) == 2
|
||||
assert len(run.report["disagreements"]) == 1
|
||||
assert len(run.report["uncertainties"]) == 1
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 3 — GET /v1/research/{id}/report returns structured findings
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_03_get_report_returns_structured_findings(client: TestClient) -> None:
|
||||
"""Step 10: GET report endpoint returns all expected fields."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
client.post(
|
||||
"/v1/research",
|
||||
json={"query": "Kernfusion", "language": "de", "depth": "normal"},
|
||||
)
|
||||
|
||||
# Fetch the report via the public API
|
||||
from nsct.api.rest_research import _research_store
|
||||
research_id = list(_research_store.keys())[0]
|
||||
|
||||
resp = client.get(f"/v1/research/{research_id}/report")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
|
||||
assert body["research_id"] == research_id
|
||||
assert "summary" in body and body["summary"]
|
||||
assert "findings" in body
|
||||
assert len(body["findings"]) > 0
|
||||
for finding in body["findings"]:
|
||||
assert "text" in finding
|
||||
assert "confidence" in finding
|
||||
assert "source_ids" in finding
|
||||
assert "disagreements" in body
|
||||
assert "uncertainties" in body
|
||||
assert "source_statistics" in body
|
||||
assert "methodology" in body and body["methodology"]
|
||||
assert "generated_at" in body
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 4 — GET /v1/research/{id}/sources, /claims, /evidence return data
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_04_sources_claims_evidence_endpoints(client: TestClient) -> None:
|
||||
"""Steps 3-8: Sources, claims, and evidence endpoints return correct data."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
client.post(
|
||||
"/v1/research",
|
||||
json={"query": "Fusion", "language": "de", "depth": "normal"},
|
||||
)
|
||||
|
||||
from nsct.api.rest_research import _research_store
|
||||
research_id = list(_research_store.keys())[0]
|
||||
|
||||
# --- Sources ---
|
||||
resp = client.get(f"/v1/research/{research_id}/sources")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["research_id"] == research_id
|
||||
assert body["total"] == 2
|
||||
assert len(body["sources"]) == 2
|
||||
for src in body["sources"]:
|
||||
assert "id" in src
|
||||
assert "url" in src
|
||||
assert "title" in src
|
||||
assert "domain" in src
|
||||
|
||||
# --- Claims ---
|
||||
resp = client.get(f"/v1/research/{research_id}/claims")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["research_id"] == research_id
|
||||
assert body["total"] == 1
|
||||
assert len(body["claims"]) == 1
|
||||
claim = body["claims"][0]
|
||||
assert "claim_text" in claim
|
||||
assert "evidence_span" in claim
|
||||
assert "claim_type" in claim
|
||||
assert "confidence" in claim
|
||||
|
||||
# --- Evidence ---
|
||||
resp = client.get(f"/v1/research/{research_id}/evidence")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["research_id"] == research_id
|
||||
assert body["total"] == 1
|
||||
assert len(body["evidence"]) == 1
|
||||
evidence = body["evidence"][0]
|
||||
assert "claim_id" in evidence
|
||||
assert "source_independence_score" in evidence
|
||||
assert "primary_source_proximity" in evidence
|
||||
assert "cross_source_support" in evidence
|
||||
assert "contradiction_level" in evidence
|
||||
assert "evidence_directness" in evidence
|
||||
assert "date_relevance_score" in evidence
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 5 — Status endpoint reflects COMPLETED state
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_05_status_reflects_completed_state(client: TestClient) -> None:
|
||||
"""After pipeline completion, status shows COMPLETED with counts."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
client.post(
|
||||
"/v1/research",
|
||||
json={"query": "Status test", "language": "de", "depth": "normal"},
|
||||
)
|
||||
|
||||
from nsct.api.rest_research import _research_store
|
||||
research_id = list(_research_store.keys())[0]
|
||||
|
||||
resp = client.get(f"/v1/research/{research_id}/status")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["research_id"] == research_id
|
||||
assert body["state"] == "completed"
|
||||
assert body["source_count"] == 2
|
||||
assert body["claim_count"] == 1
|
||||
assert body["is_completed"] is True
|
||||
assert body["is_running"] is False
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 6 — List endpoint aggregates all research runs
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_06_list_aggregates_all_runs(client: TestClient) -> None:
|
||||
"""GET /v1/research returns all completed researches with correct counts."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
for i in range(3):
|
||||
client.post(
|
||||
"/v1/research",
|
||||
json={"query": f"Query {i}", "language": "de", "depth": "quick"},
|
||||
)
|
||||
|
||||
from nsct.api.rest_research import _research_store
|
||||
|
||||
resp = client.get("/v1/research")
|
||||
assert resp.status_code == 200
|
||||
body = resp.json()
|
||||
assert body["total"] == 3
|
||||
assert len(body["items"]) == 3
|
||||
for item in body["items"]:
|
||||
assert "research_id" in item
|
||||
assert "query" in item
|
||||
assert "state" in item
|
||||
assert "source_count" in item
|
||||
assert "claim_count" in item
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 7 — Error handling: non-existent research returns 404
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_07_nonexistent_research_returns_404(client: TestClient) -> None:
|
||||
"""All individual research endpoints return 404 for unknown IDs."""
|
||||
endpoints = [
|
||||
"/v1/research/nonexistent-id",
|
||||
"/v1/research/nonexistent-id/status",
|
||||
"/v1/research/nonexistent-id/sources",
|
||||
"/v1/research/nonexistent-id/claims",
|
||||
"/v1/research/nonexistent-id/evidence",
|
||||
"/v1/research/nonexistent-id/report",
|
||||
]
|
||||
|
||||
for ep in endpoints:
|
||||
resp = client.get(ep)
|
||||
assert resp.status_code == 404, f"Expected 404 for {ep}, got {resp.status_code}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 8 — Delete only works on non-completed runs
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_08_delete_completed_returns_400(client: TestClient) -> None:
|
||||
"""DELETE on a COMPLETED research run returns 400 (immutable)."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(return_value=_mock_successful_pipeline())
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
client.post(
|
||||
"/v1/research",
|
||||
json={"query": "Delete test", "language": "de", "depth": "normal"},
|
||||
)
|
||||
|
||||
from nsct.api.rest_research import _research_store, ResearchRunState
|
||||
|
||||
research_id = list(_research_store.keys())[0]
|
||||
run = _research_store[research_id]
|
||||
|
||||
# Manually set to completed so the test is deterministic
|
||||
run.state = ResearchRunState.COMPLETED.value
|
||||
|
||||
resp = client.delete(f"/v1/research/{research_id}")
|
||||
assert resp.status_code == 400
|
||||
body = resp.json()
|
||||
assert "detail" in body
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test 9 — Pipeline failure is handled gracefully
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_e2e_09_failed_pipeline_returns_failed_state(client: TestClient) -> None:
|
||||
"""When the orchestrator.run() raises, the run state becomes FAILED."""
|
||||
with patch("nsct.orchestration.orchestrator.ResearchOrchestrator") as MockOrch:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.start = AsyncMock()
|
||||
mock_instance.run = AsyncMock(
|
||||
side_effect=RuntimeError("LLM service unavailable")
|
||||
)
|
||||
MockOrch.return_value = mock_instance
|
||||
|
||||
resp = client.post(
|
||||
"/v1/research",
|
||||
json={"query": "Fehler test", "language": "de", "depth": "quick"},
|
||||
)
|
||||
research_id = resp.json()["research_id"]
|
||||
|
||||
from nsct.api.rest_research import _research_store, ResearchRunState
|
||||
|
||||
run = _research_store.get(research_id)
|
||||
assert run is not None
|
||||
assert run.state == ResearchRunState.FAILED.value
|
||||
assert "error" in run.metadata
|
||||
|
||||
# Status endpoint should show FAILED
|
||||
resp = client.get(f"/v1/research/{research_id}/status")
|
||||
assert resp.status_code == 200
|
||||
assert resp.json()["state"] == "failed"
|
||||
assert resp.json()["is_completed"] is False
|
||||
215
uv.lock
generated
215
uv.lock
generated
@@ -1,6 +1,10 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.12"
|
||||
requires-python = ">=3.11"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12' and sys_platform == 'emscripten'",
|
||||
"python_full_version < '3.12' or sys_platform != 'emscripten'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiosqlite"
|
||||
@@ -48,6 +52,14 @@ version = "0.31.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/fe/cc/d18065ce2380d80b1bcce927c24a2642efd38918e33fd724bc4bca904877/asyncpg-0.31.0.tar.gz", hash = "sha256:c989386c83940bfbd787180f2b1519415e2d3d6277a70d9d0f0145ac73500735", size = 993667, upload-time = "2025-11-24T23:27:00.812Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/08/17/cc02bc49bc350623d050fa139e34ea512cd6e020562f2a7312a7bcae4bc9/asyncpg-0.31.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:eee690960e8ab85063ba93af2ce128c0f52fd655fdff9fdb1a28df01329f031d", size = 643159, upload-time = "2025-11-24T23:25:36.443Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a4/62/4ded7d400a7b651adf06f49ea8f73100cca07c6df012119594d1e3447aa6/asyncpg-0.31.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2657204552b75f8288de08ca60faf4a99a65deef3a71d1467454123205a88fab", size = 638157, upload-time = "2025-11-24T23:25:37.89Z" },
|
||||
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[[package]]
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name = "trafilatura"
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version = "2.2.0"
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Reference in New Issue
Block a user