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breakpilot-pwa/docs-site/services/ki-daten-pipeline/index.html
BreakPilot Dev 557305db5d
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feat: Add Academy, Whistleblower, Incidents SDK modules, pitch-deck, blog and CI/CD config 
- Academy, Whistleblower, Incidents frontend pages with API proxies and types
- Vendor compliance API proxy route
- Go backend handlers and models for all new SDK modules
- Investor pitch-deck app with interactive slides
- Blog section with DSGVO, AI Act, NIS2, glossary articles
- MkDocs documentation site
- CI/CD pipelines (Woodpecker, GitHub Actions), security scanning config
- Planning and implementation documentation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-13 21:12:16 +01:00

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OCR-Labeling (/api/v1/ocr-label/)
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RAG Pipeline (/api/ai/rag-pipeline)
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<h1 id="ki-daten-pipeline">KI-Daten-Pipeline<a class="headerlink" href="#ki-daten-pipeline" title="Permanent link">&para;</a></h1>
<p>Die KI-Daten-Pipeline ist ein zusammenhaengendes System aus drei Modulen, das den Datenfluss von der Erfassung bis zur semantischen Suche abbildet.</p>
<h2 id="uebersicht">Uebersicht<a class="headerlink" href="#uebersicht" title="Permanent link">&para;</a></h2>
<pre class="mermaid"><code>flowchart LR
subgraph OCR["OCR-Labeling"]
A[Klausur-Scans] --&gt; B[OCR Erkennung]
B --&gt; C[Ground Truth Labels]
end
subgraph RAG["RAG Pipeline"]
D[PDF Dokumente] --&gt; E[Text-Extraktion]
E --&gt; F[Chunking]
F --&gt; G[Embedding]
end
subgraph SEARCH["Daten &amp; RAG"]
H[Qdrant Collections]
I[Semantische Suche]
end
C --&gt;|Export| D
G --&gt;|Indexierung| H
H --&gt; I
I --&gt;|Ergebnisse| J[Klausur-Korrektur]</code></pre>
<h2 id="module">Module<a class="headerlink" href="#module" title="Permanent link">&para;</a></h2>
<table>
<thead>
<tr>
<th>Modul</th>
<th>Pfad</th>
<th>Funktion</th>
<th>Backend</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>OCR-Labeling</strong></td>
<td><code>/ai/ocr-labeling</code></td>
<td>Ground Truth fuer Handschrift-OCR</td>
<td>klausur-service:8086</td>
</tr>
<tr>
<td><strong>RAG Pipeline</strong></td>
<td><code>/ai/rag-pipeline</code></td>
<td>Dokument-Indexierung</td>
<td>klausur-service:8086</td>
</tr>
<tr>
<td><strong>Daten &amp; RAG</strong></td>
<td><code>/ai/rag</code></td>
<td>Vektor-Suche &amp; Collection-Mapping</td>
<td>klausur-service:8086</td>
</tr>
</tbody>
</table>
<h2 id="datenfluss">Datenfluss<a class="headerlink" href="#datenfluss" title="Permanent link">&para;</a></h2>
<h3 id="1-ocr-labeling-eingabe">1. OCR-Labeling (Eingabe)<a class="headerlink" href="#1-ocr-labeling-eingabe" title="Permanent link">&para;</a></h3>
<p>Das OCR-Labeling-Modul erfasst Ground Truth Daten fuer das Training von Handschrift-Erkennungsmodellen:</p>
<ul>
<li><strong>Upload</strong>: Klausur-Scans (PDF/Bilder) werden hochgeladen</li>
<li><strong>OCR-Verarbeitung</strong>: Mehrere OCR-Modelle erkennen den Text</li>
<li><code>llama3.2-vision:11b</code> - Vision LLM (beste Qualitaet)</li>
<li><code>trocr</code> - Microsoft Transformer (schnell)</li>
<li><code>paddleocr</code> - PaddleOCR + LLM (4x schneller)</li>
<li><code>donut</code> - Document Understanding (strukturiert)</li>
<li><strong>Labeling</strong>: Manuelles Pruefen und Korrigieren der OCR-Ergebnisse</li>
<li><strong>Export</strong>: Gelabelte Daten koennen exportiert werden fuer:</li>
<li>TrOCR Fine-Tuning</li>
<li>Llama Vision Fine-Tuning</li>
<li>Generic JSON</li>
</ul>
<h3 id="2-rag-pipeline-verarbeitung">2. RAG Pipeline (Verarbeitung)<a class="headerlink" href="#2-rag-pipeline-verarbeitung" title="Permanent link">&para;</a></h3>
<p>Die RAG Pipeline verarbeitet Dokumente und macht sie suchbar:</p>
<pre class="mermaid"><code>flowchart TD
A[Datenquellen] --&gt; B[OCR/Text-Extraktion]
B --&gt; C[Chunking]
C --&gt; D[Embedding]
D --&gt; E[Qdrant Indexierung]
subgraph sources["Datenquellen"]
S1[NiBiS PDFs]
S2[Eigene EH]
S3[Rechtskorpus]
S4[Schulordnungen]
end</code></pre>
<p><strong>Verarbeitungsschritte:</strong></p>
<ol>
<li><strong>Dokumentenextraktion</strong>: PDFs und Bilder werden per OCR in Text umgewandelt</li>
<li><strong>Chunking</strong>: Lange Texte werden in Abschnitte aufgeteilt</li>
<li>Chunk-Groesse: 1000 Zeichen</li>
<li>Ueberlappung: 200 Zeichen</li>
<li><strong>Embedding</strong>: Jeder Chunk wird in einen Vektor umgewandelt</li>
<li>Modell: <code>text-embedding-3-small</code></li>
<li>Dimensionen: 1536</li>
<li><strong>Indexierung</strong>: Vektoren werden in Qdrant gespeichert</li>
</ol>
<h3 id="3-daten-rag-ausgabe">3. Daten &amp; RAG (Ausgabe)<a class="headerlink" href="#3-daten-rag-ausgabe" title="Permanent link">&para;</a></h3>
<p>Das Daten &amp; RAG Modul ermoeglicht die Verwaltung und Suche:</p>
<ul>
<li><strong>Collection-Uebersicht</strong>: Status aller Qdrant Collections</li>
<li><strong>Semantische Suche</strong>: Fragen werden in Vektoren umgewandelt und aehnliche Dokumente gefunden</li>
<li><strong>Regulierungs-Mapping</strong>: Zeigt welche Regulierungen indexiert sind</li>
</ul>
<h2 id="qdrant-collections">Qdrant Collections<a class="headerlink" href="#qdrant-collections" title="Permanent link">&para;</a></h2>
<table>
<thead>
<tr>
<th>Collection</th>
<th>Inhalt</th>
<th>Status</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>bp_nibis_eh</code></td>
<td>Offizielle NiBiS Erwartungshorizonte</td>
<td>Aktiv</td>
</tr>
<tr>
<td><code>bp_eh</code></td>
<td>Benutzerdefinierte Erwartungshorizonte</td>
<td>Aktiv</td>
</tr>
<tr>
<td><code>bp_schulordnungen</code></td>
<td>Schulordnungen aller Bundeslaender</td>
<td>In Arbeit</td>
</tr>
<tr>
<td><code>bp_legal_corpus</code></td>
<td>Rechtskorpus (DSGVO, AI Act, BSI, etc.)</td>
<td>Aktiv</td>
</tr>
</tbody>
</table>
<h2 id="technische-architektur">Technische Architektur<a class="headerlink" href="#technische-architektur" title="Permanent link">&para;</a></h2>
<h3 id="services">Services<a class="headerlink" href="#services" title="Permanent link">&para;</a></h3>
<pre class="mermaid"><code>graph TB
subgraph Frontend["Admin-v2 (Next.js)"]
F1["/ai/ocr-labeling"]
F2["/ai/rag-pipeline"]
F3["/ai/rag"]
end
subgraph Backend["klausur-service (Python)"]
B1[OCR Endpoints]
B2[Indexierungs-Jobs]
B3[Such-API]
end
subgraph Storage["Datenbanken"]
D1[(PostgreSQL)]
D2[(Qdrant)]
D3[(MinIO)]
end
F1 --&gt; B1
F2 --&gt; B2
F3 --&gt; B3
B1 --&gt; D1
B1 --&gt; D3
B2 --&gt; D2
B3 --&gt; D2</code></pre>
<h3 id="backend-endpunkte">Backend-Endpunkte<a class="headerlink" href="#backend-endpunkte" title="Permanent link">&para;</a></h3>
<h4 id="ocr-labeling-apiv1ocr-label">OCR-Labeling (<code>/api/v1/ocr-label/</code>)<a class="headerlink" href="#ocr-labeling-apiv1ocr-label" title="Permanent link">&para;</a></h4>
<table>
<thead>
<tr>
<th>Endpoint</th>
<th>Methode</th>
<th>Beschreibung</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>/sessions</code></td>
<td>GET/POST</td>
<td>Session-Verwaltung</td>
</tr>
<tr>
<td><code>/sessions/{id}/upload</code></td>
<td>POST</td>
<td>Bilder hochladen</td>
</tr>
<tr>
<td><code>/queue</code></td>
<td>GET</td>
<td>Labeling-Queue</td>
</tr>
<tr>
<td><code>/confirm</code></td>
<td>POST</td>
<td>OCR bestaetigen</td>
</tr>
<tr>
<td><code>/correct</code></td>
<td>POST</td>
<td>OCR korrigieren</td>
</tr>
<tr>
<td><code>/skip</code></td>
<td>POST</td>
<td>Item ueberspringen</td>
</tr>
<tr>
<td><code>/stats</code></td>
<td>GET</td>
<td>Statistiken</td>
</tr>
<tr>
<td><code>/export</code></td>
<td>POST</td>
<td>Trainingsdaten exportieren</td>
</tr>
</tbody>
</table>
<h4 id="rag-pipeline-apiairag-pipeline">RAG Pipeline (<code>/api/ai/rag-pipeline</code>)<a class="headerlink" href="#rag-pipeline-apiairag-pipeline" title="Permanent link">&para;</a></h4>
<table>
<thead>
<tr>
<th>Action</th>
<th>Beschreibung</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>jobs</code></td>
<td>Indexierungs-Jobs auflisten</td>
</tr>
<tr>
<td><code>dataset-stats</code></td>
<td>Datensatz-Statistiken</td>
</tr>
<tr>
<td><code>create-job</code></td>
<td>Neue Indexierung starten</td>
</tr>
<tr>
<td><code>pause</code></td>
<td>Job pausieren</td>
</tr>
<tr>
<td><code>resume</code></td>
<td>Job fortsetzen</td>
</tr>
<tr>
<td><code>cancel</code></td>
<td>Job abbrechen</td>
</tr>
</tbody>
</table>
<h4 id="legal-corpus-apilegal-corpus">Legal Corpus (<code>/api/legal-corpus/</code>)<a class="headerlink" href="#legal-corpus-apilegal-corpus" title="Permanent link">&para;</a></h4>
<table>
<thead>
<tr>
<th>Endpoint</th>
<th>Beschreibung</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>/status</code></td>
<td>Collection-Status</td>
</tr>
<tr>
<td><code>/search</code></td>
<td>Semantische Suche</td>
</tr>
<tr>
<td><code>/ingest</code></td>
<td>Dokumente indexieren</td>
</tr>
</tbody>
</table>
<h2 id="integration-mit-klausur-korrektur">Integration mit Klausur-Korrektur<a class="headerlink" href="#integration-mit-klausur-korrektur" title="Permanent link">&para;</a></h2>
<p>Die KI-Daten-Pipeline liefert Erwartungshorizont-Vorschlaege fuer die Klausur-Korrektur:</p>
<pre class="mermaid"><code>sequenceDiagram
participant L as Lehrer
participant K as Klausur-Korrektur
participant R as RAG-Suche
participant Q as Qdrant
L-&gt;&gt;K: Schueler-Antwort pruefen
K-&gt;&gt;R: EH-Vorschlaege laden
R-&gt;&gt;Q: Semantische Suche
Q-&gt;&gt;R: Top-k Chunks
R-&gt;&gt;K: Relevante EH-Passagen
K-&gt;&gt;L: Bewertungsvorschlaege</code></pre>
<h2 id="deployment">Deployment<a class="headerlink" href="#deployment" title="Permanent link">&para;</a></h2>
<p>Die Module werden als Teil des admin-v2 Containers deployed:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># 1. Sync</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>rsync<span class="w"> </span>-avz<span class="w"> </span>--delete<span class="w"> </span>--exclude<span class="w"> </span><span class="s1">&#39;node_modules&#39;</span><span class="w"> </span>--exclude<span class="w"> </span><span class="s1">&#39;.next&#39;</span><span class="w"> </span>--exclude<span class="w"> </span><span class="s1">&#39;.git&#39;</span><span class="w"> </span><span class="se">\</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="w"> </span>/Users/benjaminadmin/Projekte/breakpilot-pwa/admin-v2/<span class="w"> </span><span class="se">\</span>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="w"> </span>macmini:/Users/benjaminadmin/Projekte/breakpilot-pwa/admin-v2/
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="c1"># 2. Build &amp; Deploy</span>
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>ssh<span class="w"> </span>macmini<span class="w"> </span><span class="s2">&quot;/usr/local/bin/docker compose \</span>
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="s2"> -f /Users/benjaminadmin/Projekte/breakpilot-pwa/docker-compose.yml \</span>
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="s2"> build --no-cache admin-v2 &amp;&amp; \</span>
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="s2"> /usr/local/bin/docker compose \</span>
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="s2"> -f /Users/benjaminadmin/Projekte/breakpilot-pwa/docker-compose.yml \</span>
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="s2"> up -d admin-v2&quot;</span>
</code></pre></div>
<h2 id="verwandte-dokumentation">Verwandte Dokumentation<a class="headerlink" href="#verwandte-dokumentation" title="Permanent link">&para;</a></h2>
<ul>
<li><a href="../klausur-service/OCR-Labeling-Spec/">OCR Labeling Spezifikation</a></li>
<li><a href="../klausur-service/RAG-Admin-Spec/">RAG Admin Spezifikation</a></li>
<li><a href="../klausur-service/NiBiS-Ingestion-Pipeline/">NiBiS Ingestion Pipeline</a></li>
<li><a href="../../architecture/multi-agent/">Multi-Agent Architektur</a></li>
</ul>
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