Add speaker intro slide with photo, career timeline, and company info

Slide 2: Dierk Lenz — photo (correct 2:3 ratio), career timeline 1983–2026,
Oracle expertise, co-authored books. No footer, logo top-right.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -439,6 +439,15 @@ pip3 install -r pgvector-demo/backend/requirements.txt --break-system-packages
 pip3 install -r oravector-demo/backend/requirements.txt --break-system-packages
 ```
 
+**CLIP model** — not included in the repository. It is downloaded automatically from
+Hugging Face Hub on first use (~600 MB, cached in `~/.cache/huggingface/hub/`):
+
+> `sentence-transformers/clip-ViT-B-32`
+> https://huggingface.co/sentence-transformers/clip-ViT-B-32
+
+No manual download is required — `sentence-transformers` handles this transparently
+when `index_images.py` or a backend is started for the first time.
+
 ### 1. PostgreSQL
 
 **Start the container:**

make_presentation.py

@@ -487,7 +487,62 @@ txb(s, f"{EVENT_DATE}  ·  {EVENT_CITY}",
 s.shapes.add_picture(LOGO_PATH, Inches(4.67), Inches(6.1), Inches(4.0), Inches(1.06))
 
 # ════════════════════════════════════════════════════════════════════════════
-# Slide 2 — Motivation: Der VECTOR-Datentyp
+# Slide 2 — Über den Referenten
+# ════════════════════════════════════════════════════════════════════════════
+s = add_slide(logo=False, footer=False)
+section_header(s, "Über den Referenten", ACCENT_PG)
+
+# Logo top-right — below the accent bar (y=0.14), correct 4.46:1 ratio
+s.shapes.add_picture(LOGO_PATH, Inches(9.63), Inches(0.22), Inches(3.5), Inches(0.785))
+
+# Photo — correct 2:3 ratio (3471×5206 px), dark bg blends with slide theme
+s.shapes.add_picture("/home/dierk/Bilder/Porträt Pro Neg 2.jpg",
+    Inches(0.4), Inches(1.1), Inches(3.4), Inches(5.1))
+
+# Name + title
+txb(s, "Dierk Lenz",
+    Inches(4.3), Inches(1.2), Inches(8.7), Inches(0.7),
+    size=32, bold=True, color=TITLE_CLR)
+txb(s, "Inhaber & Geschäftsführer  ·  Dierk Lenz Consulting GmbH",
+    Inches(4.3), Inches(1.9), Inches(8.7), Inches(0.45),
+    size=18, color=ACCENT_PG)
+
+# Dividers starting after the photo (x=4.2, not cutting into photo)
+for div_y in [Inches(2.45), Inches(4.65)]:
+    ln = s.shapes.add_shape(1, Inches(4.2), div_y, Inches(8.83), Pt(1))
+    ln.fill.solid(); ln.fill.fore_color.rgb = RGBColor(0x44, 0x47, 0x5a)
+    ln.line.fill.background()
+
+# Career timeline — year in accent, description in body colour
+for y_pos, year, desc in [
+    (2.60, "1983 – 1989", "Informatik-Studium, RWTH Aachen"),
+    (3.10, "1989 – 1995", "Senior Systemberater, Oracle Deutschland, Düsseldorf"),
+    (3.60, "1995 / 1996", "Co-Gründer Herrmann & Lenz (GbR & GmbH)"),
+    (4.10, "2026",        "Gründer & Geschäftsführer, Dierk Lenz Consulting GmbH"),
+]:
+    txb(s, year, Inches(4.3), Inches(y_pos), Inches(1.7), Inches(0.45),
+        size=15, bold=True, color=ACCENT_PG)
+    txb(s, desc, Inches(6.1), Inches(y_pos), Inches(6.9), Inches(0.45),
+        size=15, color=BODY_CLR)
+
+# Oracle expertise
+txb(s, "Oracle Database von Version 6 bis Oracle AI Database 26ai",
+    Inches(4.3), Inches(4.78), Inches(8.7), Inches(0.4),
+    size=16, bold=True, color=ACCENT_ORA)
+txb(s, "Schulungen  ·  Workshops  ·  Vorträge  ·  Projekte",
+    Inches(4.3), Inches(5.22), Inches(8.7), Inches(0.4),
+    size=15, color=BODY_CLR)
+
+# Books
+txb(s, "Co-Autor:",
+    Inches(4.3), Inches(5.7), Inches(1.3), Inches(0.35),
+    size=13, bold=True, color=DIM_CLR)
+txb(s, "Oracle 7.3  ·  Oracle8 für den DBA  ·  Oracle 9i für den DBA  ·  Oracle 10g für den DBA  ·  Oracle 11g R2 für den DBA",
+    Inches(5.65), Inches(5.7), Inches(7.35), Inches(0.35),
+    size=13, color=DIM_CLR, italic=True)
+
+# ════════════════════════════════════════════════════════════════════════════
+# Slide 3 — Motivation: Der VECTOR-Datentyp
 # ════════════════════════════════════════════════════════════════════════════
 s = add_slide()
 section_header(s, "Der VECTOR-Datentyp", ACCENT_PG)
@@ -592,6 +647,10 @@ txb(s, "Bild-Vektor und Text-Vektor zeigen in dieselbe Richtung,\nwenn Bild und
     Inches(0.8), Inches(5.0), Inches(11.5), Inches(1.0),
     size=18, italic=True, color=ACCENT_IDB)
 
+txb(s, "🔗  huggingface.co/sentence-transformers/clip-ViT-B-32  — wird beim ersten Start automatisch heruntergeladen (~600 MB)",
+    Inches(0.8), Inches(6.55), Inches(12.0), Inches(0.35),
+    size=11, color=DIM_CLR, italic=True)
+
 # ════════════════════════════════════════════════════════════════════════════
 # Slide 6 — Cosinus-Distanz
 # ════════════════════════════════════════════════════════════════════════════

oravector-demo/backend/embedder.py

@@ -6,6 +6,9 @@ _model = None
 def _get_model():
     # Lazy load: the CLIP model is ~600 MB and takes several seconds to initialise.
     # Loading on first call avoids the cost at import time and during indexing warmup.
+    # Downloaded automatically from Hugging Face Hub on first use:
+    # https://huggingface.co/sentence-transformers/clip-ViT-B-32
+    # Cached in ~/.cache/huggingface/hub/
     global _model
     if _model is None:
         _model = SentenceTransformer("clip-ViT-B-32")

oravector-demo/frontend/indb/index.html

@@ -147,18 +147,18 @@
 
   <div class="search-area">
     <div class="search-row">
-      <input id="query" type="text" placeholder="Search photos, e.g. trees, water, night…" />
-      <button class="search-btn" onclick="doSearch()">Search</button>
+      <input id="query" type="text" placeholder="Fotos suchen, z.B. Bäume, Wasser, Nacht…" />
+      <button class="search-btn" onclick="doSearch()">Suchen</button>
     </div>
     <div class="chips">
-      <span class="chip" onclick="setQuery('trees')">trees</span>
-      <span class="chip" onclick="setQuery('water')">water</span>
-      <span class="chip" onclick="setQuery('people')">people</span>
-      <span class="chip" onclick="setQuery('buildings')">buildings</span>
-      <span class="chip" onclick="setQuery('sky')">sky</span>
-      <span class="chip" onclick="setQuery('street')">street</span>
-      <span class="chip" onclick="setQuery('night')">night</span>
-      <span class="chip" onclick="setQuery('cars')">cars</span>
+      <span class="chip" onclick="setQuery('Bäume')">Bäume</span>
+      <span class="chip" onclick="setQuery('Wasser')">Wasser</span>
+      <span class="chip" onclick="setQuery('Menschen')">Menschen</span>
+      <span class="chip" onclick="setQuery('Gebäude')">Gebäude</span>
+      <span class="chip" onclick="setQuery('Himmel')">Himmel</span>
+      <span class="chip" onclick="setQuery('Straße')">Straße</span>
+      <span class="chip" onclick="setQuery('Nacht')">Nacht</span>
+      <span class="chip" onclick="setQuery('Autos')">Autos</span>
     </div>
   </div>
 

oravector-demo/frontend/index.html

@@ -147,18 +147,18 @@
 
   <div class="search-area">
     <div class="search-row">
-      <input id="query" type="text" placeholder="Search photos, e.g. trees, water, night…" />
-      <button class="search-btn" onclick="doSearch()">Search</button>
+      <input id="query" type="text" placeholder="Fotos suchen, z.B. Bäume, Wasser, Nacht…" />
+      <button class="search-btn" onclick="doSearch()">Suchen</button>
     </div>
     <div class="chips">
-      <span class="chip" onclick="setQuery('trees')">trees</span>
-      <span class="chip" onclick="setQuery('water')">water</span>
-      <span class="chip" onclick="setQuery('people')">people</span>
-      <span class="chip" onclick="setQuery('buildings')">buildings</span>
-      <span class="chip" onclick="setQuery('sky')">sky</span>
-      <span class="chip" onclick="setQuery('street')">street</span>
-      <span class="chip" onclick="setQuery('night')">night</span>
-      <span class="chip" onclick="setQuery('cars')">cars</span>
+      <span class="chip" onclick="setQuery('Bäume')">Bäume</span>
+      <span class="chip" onclick="setQuery('Wasser')">Wasser</span>
+      <span class="chip" onclick="setQuery('Menschen')">Menschen</span>
+      <span class="chip" onclick="setQuery('Gebäude')">Gebäude</span>
+      <span class="chip" onclick="setQuery('Himmel')">Himmel</span>
+      <span class="chip" onclick="setQuery('Straße')">Straße</span>
+      <span class="chip" onclick="setQuery('Nacht')">Nacht</span>
+      <span class="chip" onclick="setQuery('Autos')">Autos</span>
     </div>
   </div>
 

pgvector-demo/backend/embedder.py

@@ -6,6 +6,9 @@ _model = None
 def _get_model():
     # Lazy load: the CLIP model is ~600 MB and takes several seconds to initialise.
     # Loading on first call avoids the cost at import time and during indexing warmup.
+    # Downloaded automatically from Hugging Face Hub on first use:
+    # https://huggingface.co/sentence-transformers/clip-ViT-B-32
+    # Cached in ~/.cache/huggingface/hub/
     global _model
     if _model is None:
         _model = SentenceTransformer("clip-ViT-B-32")

pgvector-demo/frontend/index.html

@@ -147,18 +147,18 @@
 
   <div class="search-area">
     <div class="search-row">
-      <input id="query" type="text" placeholder="Search photos, e.g. trees, water, night…" />
-      <button class="search-btn" onclick="doSearch()">Search</button>
+      <input id="query" type="text" placeholder="Fotos suchen, z.B. Bäume, Wasser, Nacht…" />
+      <button class="search-btn" onclick="doSearch()">Suchen</button>
     </div>
     <div class="chips">
-      <span class="chip" onclick="setQuery('trees')">trees</span>
-      <span class="chip" onclick="setQuery('water')">water</span>
-      <span class="chip" onclick="setQuery('people')">people</span>
-      <span class="chip" onclick="setQuery('buildings')">buildings</span>
-      <span class="chip" onclick="setQuery('sky')">sky</span>
-      <span class="chip" onclick="setQuery('street')">street</span>
-      <span class="chip" onclick="setQuery('night')">night</span>
-      <span class="chip" onclick="setQuery('cars')">cars</span>
+      <span class="chip" onclick="setQuery('Bäume')">Bäume</span>
+      <span class="chip" onclick="setQuery('Wasser')">Wasser</span>
+      <span class="chip" onclick="setQuery('Menschen')">Menschen</span>
+      <span class="chip" onclick="setQuery('Gebäude')">Gebäude</span>
+      <span class="chip" onclick="setQuery('Himmel')">Himmel</span>
+      <span class="chip" onclick="setQuery('Straße')">Straße</span>
+      <span class="chip" onclick="setQuery('Nacht')">Nacht</span>
+      <span class="chip" onclick="setQuery('Autos')">Autos</span>
     </div>
   </div>