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Presentation refinements: VECTOR datatype context, diagram fixes, stronger messaging

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- Title slide: updated subtitle to mention VECTOR datatype
- New slide 2: explain VECTOR as new native datatype, state presentation goals
- Slide 3 diagram: fix 'groß' label position (off y-axis), 'Bild-Vektor' label clear of arrow
- Slide 3: enlarge central message "Vektoren machen Ähnlichkeit berechenbar." (size 26)
- Last slide: add bullet on VECTOR as welcome addition making AI embeddings first-class

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Dierk
2026-05-20 11:38:11 +02:00
parent 9116533f03
commit 64ee10c182
2 changed files with 105 additions and 42 deletions
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make_presentation.py
+105 -42
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@@ -83,7 +83,7 @@ def diagram_s3_vectors():
ax.add_patch(mpatches.Arc((0, 0), 0.52, 0.52, angle=0,
theta1=a1, theta2=a3,
color="#fab387", lw=2))
ax.text(-0.10, 0.34, "groß", color="#fab387", fontsize=10)
ax.text(-0.35, 0.28, "groß", color="#fab387", fontsize=10)
plt.tight_layout(pad=0.3)
return _save(fig, "s3_vectors.png")
@@ -132,7 +132,7 @@ def diagram_s6_cosine():
vB = np.array([0.55, 1.0 ]) # text vector
for v, color, label, lpos in [
(vA, "#89b4fa", "Bild-Vektor", (1.12, 0.18)),
(vA, "#89b4fa", "Bild-Vektor", (1.17, 0.08)),
(vB, "#cba6f7", 'Text-Vektor\n"Bäume"', (0.56, 1.07)),
]:
ax.annotate("", xy=v, xytext=(0, 0),
@@ -167,82 +167,120 @@ def diagram_s6_cosine():
def diagram_architecture():
"""Architecture slide: 3 columns showing app server, database, and where CLIP runs."""
CLIP_CLR = "#a6e3a1"
# (x, db_name, color, port, clip_app, clip_db, db_tech, vec_embed_fn)
# (x, db_name, color, port, clip_app, clip_db, db_tech, vec_embed_fn, foto_storage)
COLS = [
(2.3, "PostgreSQL 18", "#89b4fa", "Port 8000", True, False, "pgvector 0.8.2\nHNSW (Disk)", None),
(6.65, "Oracle 26ai\nVECTORS_USER", "#f38ba8", "Port 8001", True, False, "HNSW (SGA)", None),
(11.0, "Oracle 26ai\nVECTOR", "#cba6f7", "Port 8002", False, True, "HNSW (SGA)", "VECTOR_EMBEDDING()"),
(2.3, "PostgreSQL 18", "#89b4fa", "Port 8000", True, False, "pgvector 0.8.2\nHNSW (Disk)", None, "Fotos: Dateipfad (Filesystem)"),
(6.65, "Oracle 26ai\nVECTORS_USER", "#f38ba8", "Port 8001", True, False, "HNSW (SGA)", None, "Fotos: Dateipfad (Filesystem)"),
(11.0, "Oracle 26ai\nVECTOR", "#cba6f7", "Port 8002", False, True, "HNSW (SGA)", "VECTOR_EMBEDDING()", "Fotos: BLOB (in Oracle)"),
]
BOX_H = 2.2 # all boxes same height
DB_Y = 0.15 # database box bottom
GAP = 0.60 # space between DB top and app server bottom
APP_Y = DB_Y + BOX_H + GAP # = 2.95
fig, ax = _fig(13.5, 6.5)
ax.set_xlim(0, 13.5); ax.set_ylim(-0.8, 6.0)
ax.set_xlim(0, 13.5); ax.set_ylim(-0.8, 5.9)
ax.axis("off")
for x, db_name, color, port, clip_app, clip_db, db_tech, vec_fn in COLS:
# ── Column title + port
ax.text(x, 5.78, port, ha="center", color=color, fontsize=13, fontweight="bold")
for x, db_name, color, port, clip_app, clip_db, db_tech, vec_fn, foto_storage in COLS:
APP_TOP = APP_Y + BOX_H # = 5.15
DB_TOP = DB_Y + BOX_H # = 2.35
# ── Port label
ax.text(x, APP_TOP + 0.28, port, ha="center", color=color,
fontsize=13, fontweight="bold")
# ── App server box
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.7, 3.7), 3.4, 1.85,
(x-1.7, APP_Y), 3.4, BOX_H,
boxstyle="round,pad=0.1", facecolor="#28293d", edgecolor=color, lw=2))
ax.text(x, 5.38, "App-Server (FastAPI)", ha="center",
ax.text(x, APP_TOP - 0.22, "App-Server (FastAPI)", ha="center",
color=color, fontsize=11, fontweight="bold")
if clip_app:
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.2, 3.78), 2.4, 0.82,
(x-1.2, APP_Y + 0.10), 2.4, 0.75,
boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=CLIP_CLR, lw=2))
ax.text(x, 4.19, "CLIP-Modell\n(sentence-transformers)",
ax.text(x, APP_Y + 0.475, "CLIP-Modell\n(sentence-transformers)",
ha="center", va="center", color=CLIP_CLR, fontsize=9.5, fontweight="bold",
multialignment="center")
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.2, APP_Y + 0.95), 2.4, 0.42,
boxstyle="round,pad=0.06", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
linestyle="dashed"))
ax.text(x, APP_Y + 1.16, foto_storage,
ha="center", va="center", color=DIAG_AXIS, fontsize=9, style="italic")
else:
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.2, 3.78), 2.4, 0.82,
(x-1.2, APP_Y + 0.10), 2.4, 0.75,
boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
linestyle="dashed"))
ax.text(x, 4.19, "kein CLIP",
ax.text(x, APP_Y + 0.475, "kein CLIP",
ha="center", va="center", color=DIAG_AXIS, fontsize=10, style="italic")
# ── Arrow + what is sent
ax.annotate("", xy=(x, 3.05), xytext=(x, 3.65),
# ── Arrow with comfortable gap
ax.annotate("", xy=(x, DB_TOP + 0.05), xytext=(x, APP_Y - 0.05),
arrowprops=dict(arrowstyle="->", color=DIAG_AXIS, lw=2))
arrow_lbl = "Vektor (512 floats)" if clip_app else "Text-String"
ax.text(x, 3.35, arrow_lbl, ha="center", va="center",
ax.text(x + 0.2, (DB_TOP + APP_Y) / 2, arrow_lbl, ha="left", va="center",
color=DIAG_AXIS, fontsize=9, style="italic")
# ── Database box
db_h = 2.8 if clip_db else 1.9
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.7, 0.15), 3.4, db_h,
(x-1.7, DB_Y), 3.4, BOX_H,
boxstyle="round,pad=0.1", facecolor="#28293d", edgecolor=color, lw=2))
if clip_db:
# CLIP ONNX box inside DB
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.2, 0.25), 2.4, 0.82,
(x-1.2, DB_Y + 0.10), 2.4, 0.72,
boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=CLIP_CLR, lw=2))
ax.text(x, 0.66, "CLIP-Modell\n(ONNX, in Oracle)",
ax.text(x, DB_Y + 0.46, "CLIP-Modell\n(ONNX, in Oracle)",
ha="center", va="center", color=CLIP_CLR, fontsize=9.5, fontweight="bold",
multialignment="center")
# VECTOR_EMBEDDING() label
ax.text(x, 1.22, vec_fn,
ax.add_patch(mpatches.FancyBboxPatch(
(x-1.2, DB_Y + 0.92), 2.4, 0.40,
boxstyle="round,pad=0.06", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
linestyle="dashed"))
ax.text(x, DB_Y + 1.12, foto_storage,
ha="center", va="center", color=DIAG_AXIS, fontsize=9, style="italic")
ax.text(x, DB_Y + 1.50, vec_fn,
ha="center", color="#fab387", fontsize=10, fontweight="bold",
fontfamily="monospace")
# DB name
ax.text(x, 1.65, db_name, ha="center", color=color,
ax.text(x, DB_Y + 1.72, "Oracle 26ai", ha="center", color=color,
fontsize=11, fontweight="bold")
ax.text(x, 2.35, db_tech, ha="center", color=DIAG_AXIS,
fontsize=9, multialignment="center")
ax.text(x, DB_Y + 1.92, "Schema: VECTOR", ha="center", color=color,
fontsize=9)
ax.text(x, DB_Y + 2.10, db_tech, ha="center", color=DIAG_AXIS,
fontsize=9)
else:
ax.text(x, 1.5, db_name, ha="center", color=color,
# Split db_name → ["PostgreSQL 18"] or ["Oracle 26ai", "VECTORS_USER"]
# Split db_tech → ["pgvector 0.8.2", "HNSW (Disk)"] or ["HNSW (SGA)"]
name_parts = db_name.split("\n")
tech_parts = db_tech.split("\n")
hnsw = tech_parts[-1] # always last
tech_extra = tech_parts[:-1] # e.g. ["pgvector 0.8.2"]
# HNSW — same height across all three DB boxes
ax.text(x, DB_Y + 2.10, hnsw, ha="center", color=DIAG_AXIS, fontsize=9)
# Middle line: schema name or version info (matches "Schema: VECTOR" in col 3)
if len(name_parts) > 1:
mid_label = "Schema: " + name_parts[1]
elif tech_extra:
mid_label = tech_extra[0]
else:
mid_label = ""
if mid_label:
ax.text(x, DB_Y + 1.92, mid_label, ha="center", color=color, fontsize=9)
# Main DB name (matches "Oracle 26ai" in col 3)
ax.text(x, DB_Y + 1.72, name_parts[0], ha="center", color=color,
fontsize=11, fontweight="bold")
ax.text(x, 0.72, db_tech, ha="center", color=DIAG_AXIS,
fontsize=9, multialignment="center")
# ── Vertical separators
for xsep in [4.5, 8.85]:
ax.plot([xsep, xsep], [0.05, 5.9], color=DIAG_GRID, lw=1, linestyle="--")
ax.plot([xsep, xsep], [0.05, 5.55], color=DIAG_GRID, lw=1, linestyle="--")
# ── Caption — separated from boxes, applies to all three columns
ax.plot([0.3, 13.2], [-0.18, -0.18], color=DIAG_GRID, lw=1)
@@ -437,7 +475,7 @@ def divider(slide, y, color=DIM_CLR):
s = add_slide(logo=False, footer=False) # title slide: custom layout
title_slide_layout(s,
"Vektoren in der Datenbank",
"Semantische Bildsuche mit PostgreSQL/pgvector und Oracle 26ai")
"Der VECTOR-Datentyp in Oracle 26ai und PostgreSQL")
# Conference details
txb(s, CONFERENCE,
Inches(1), Inches(5.0), Inches(11.33), Inches(0.5),
@@ -449,7 +487,30 @@ 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 — Agenda
# Slide 2 — Motivation: Der VECTOR-Datentyp
# ════════════════════════════════════════════════════════════════════════════
s = add_slide()
section_header(s, "Der VECTOR-Datentyp", ACCENT_PG)
bullet_box(s, [
"▸ VECTOR ist ein neuer nativer Datentyp in Oracle AI Database 26ai und PostgreSQL (pgvector)",
"▸ Ermöglicht das Speichern hochdimensionaler Vektoren direkt in der Datenbank",
"▸ Bringt optimierte Suchoperatoren und Indizes für Ähnlichkeitssuche (k-NN) mit",
"▸ Macht KI-Embeddings zu einem First-Class-Citizen in relationalen Datenbanken",
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(2.2), size=22)
divider(s, Inches(3.7))
txb(s, "Ziel dieses Vortrags", Inches(0.8), Inches(3.85), Inches(11.5), Inches(0.5),
size=22, bold=True, color=ACCENT_PG)
bullet_box(s, [
"▸ Den VECTOR-Datentyp erklären — was er ist, wie er funktioniert",
"▸ Gemeinsamkeiten und Unterschiede zwischen Oracle 26ai und PostgreSQL/pgvector zeigen",
"▸ Eine konkrete Demo: semantische Bildsuche mit 116 Street-Fotos",
"▸ Drei Ansätze vergleichen: pgvector, Oracle (Python-Embedding), Oracle (In-Database-Embedding)",
], Inches(0.8), Inches(4.4), Inches(11.5), Inches(2.3), size=20)
# ════════════════════════════════════════════════════════════════════════════
# Slide 3 — Agenda
# ════════════════════════════════════════════════════════════════════════════
s = add_slide()
section_header(s, "Agenda", ACCENT_PG)
@@ -483,8 +544,8 @@ bullet_box(s, [
s.shapes.add_picture(DIAG_S3, Inches(7.8), Inches(1.1), Inches(5.3), Inches(5.3))
txb(s, "Vektoren machen Ähnlichkeit berechenbar.",
Inches(0.8), Inches(5.8), Inches(6.8), Inches(0.7),
size=22, bold=True, color=ACCENT_GRN)
Inches(0.3), Inches(5.75), Inches(7.4), Inches(0.8),
size=26, bold=True, color=ACCENT_GRN)
# ════════════════════════════════════════════════════════════════════════════
# Slide 4 — Semantische Suche
@@ -696,11 +757,11 @@ bullet_box(s, [
"▸ Der Text-String ist der einzige Parameter aus Python",
"▸ Schema: VECTOR — Tabelle: FOTO_VEKTOR — Bilder als BLOB gespeichert",
"▸ HNSW-Index auf FOTO_VEKTOR (wie in Schema VECTORS_USER)",
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(3.0), size=19)
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(2.4), size=16)
code_box(s,
"-- Gesamte Logik in einem SQL-Statement\nSELECT filename,\n 1 - VECTOR_DISTANCE(\n foto_vek,\n VECTOR_EMBEDDING(CLIP_TXT USING :q AS data),\n COSINE\n ) AS score\nFROM VECTOR.FOTO_VEKTOR\nORDER BY VECTOR_DISTANCE(\n foto_vek,\n VECTOR_EMBEDDING(CLIP_TXT USING :q AS data), COSINE)\nFETCH FIRST 12 ROWS ONLY;",
Inches(0.8), Inches(3.6), Inches(7.5), Inches(3.3), size=13)
Inches(0.8), Inches(3.8), Inches(7.5), Inches(3.0), size=11)
bullet_box(s, [
":q = reiner Text aus Python",
@@ -711,7 +772,7 @@ bullet_box(s, [
" • Vektorsuche",
"",
"→ Architektur vereinfacht sich",
], Inches(9.0), Inches(3.6), Inches(4.0), Inches(3.4), size=18, color=DIM_CLR)
], Inches(9.0), Inches(3.8), Inches(4.0), Inches(3.0), size=16, color=DIM_CLR)
# ════════════════════════════════════════════════════════════════════════════
# Slide 12 — ONNX in Oracle: Besonderheit
@@ -809,7 +870,9 @@ bullet_box(s, [
"▸ Oracle In-DB Embedding: Architektur ohne ML-Laufzeit im App-Server",
"▸ CLIP ermöglicht Bildersuche per Freitext — ohne Tagging oder Metadaten",
"▸ HNSW liefert schnelle approximative k-NN-Suche in beiden Datenbanken",
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(3.5), size=21)
"▸ VECTOR ist eine sehr willkommene Erweiterung — relationale Datenbanken",
" nutzen damit KI-Embeddings als First-Class-Citizen",
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(4.2), size=21)
divider(s, Inches(5.1))