dierk
5a8985440e
- Fix logo aspect ratio (4.458:1) on title slide and all footer instances - Speaker slide: simplify 2026 career entry - Last slide: add website www.dl-cons.de alongside email - CLIP slide: mention larger model variants (ViT-L-14, ~1.7 GB) for higher precision - Slide 6: replace 'trees' with 'Bäume' for consistency Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
960 lines
48 KiB
Python
960 lines
48 KiB
Python
"""
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Generates "Vektoren in der Datenbank.pptx" — a LibreOffice-compatible presentation.
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Run from the project root: python3 make_presentation.py
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"""
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from pptx import Presentation
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from pptx.util import Inches, Pt, Emu
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from pptx.dml.color import RGBColor
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from pptx.enum.text import PP_ALIGN
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from pptx.oxml.ns import qn
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from pptx.oxml import parse_xml
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from lxml import etree
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import os
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import numpy as np
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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_A_NS = "http://schemas.openxmlformats.org/drawingml/2006/main"
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def OxmlElement(tag):
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local = tag.split(":")[1]
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return etree.fromstring(f'<a:{local} xmlns:a="{_A_NS}"/>')
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# ── Diagram generation (matplotlib → PNG → embedded in slide) ────────────────
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DIAG_BG = "#1e1e2e"
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DIAG_GRID = "#313244"
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DIAG_AXIS = "#6c7086"
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def _fig(w, h):
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fig, ax = plt.subplots(figsize=(w, h))
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fig.patch.set_facecolor(DIAG_BG)
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ax.set_facecolor(DIAG_BG)
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return fig, ax
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def _save(fig, name):
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path = os.path.join("diagrams", name)
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fig.savefig(path, dpi=150, bbox_inches="tight", facecolor=DIAG_BG)
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plt.close(fig)
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return path
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def diagram_s3_vectors():
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"""Slide 3: 2-D vector space with Hund / Katze / Auto."""
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fig, ax = _fig(5, 5)
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ax.set_xlim(-1.3, 1.3)
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ax.set_ylim(-1.3, 1.3)
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ax.set_aspect("equal")
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ax.grid(True, color=DIAG_GRID, linewidth=0.5, alpha=0.6)
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ax.axhline(0, color=DIAG_AXIS, linewidth=1)
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ax.axvline(0, color=DIAG_AXIS, linewidth=1)
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ax.set_xticks([]); ax.set_yticks([])
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for sp in ax.spines.values(): sp.set_visible(False)
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ax.text(1.27, 0.05, "x₁", color=DIAG_AXIS, fontsize=12)
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ax.text( 0.05, 1.27, "x₂", color=DIAG_AXIS, fontsize=12)
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vecs = [
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((0.91, 0.12), "#89b4fa", "Hund"),
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((0.87, 0.18), "#74c7ec", "Katze"),
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((-0.30, 0.90), "#f38ba8", "Auto"),
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]
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for (vx, vy), color, label in vecs:
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ax.annotate("", xy=(vx, vy), xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color=color, lw=2.5))
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ox, oy = 0.10, 0.07
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ax.text(vx + ox * np.sign(vx or 1),
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vy + oy * np.sign(vy or 1),
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label, color=color, fontsize=13, fontweight="bold")
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# Small arc: Hund ↔ Katze
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a1 = np.degrees(np.arctan2(0.12, 0.91))
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a2 = np.degrees(np.arctan2(0.18, 0.87))
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ax.add_patch(mpatches.Arc((0, 0), 0.32, 0.32, angle=0,
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theta1=min(a1, a2), theta2=max(a1, a2),
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color="#a6e3a1", lw=2))
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ax.text(0.22, -0.10, "klein", color="#a6e3a1", fontsize=10, ha="center")
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# Large arc: Hund ↔ Auto
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a3 = np.degrees(np.arctan2(0.90, -0.30))
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ax.add_patch(mpatches.Arc((0, 0), 0.52, 0.52, angle=0,
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theta1=a1, theta2=a3,
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color="#fab387", lw=2))
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ax.text(-0.35, 0.28, "groß", color="#fab387", fontsize=10)
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plt.tight_layout(pad=0.3)
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return _save(fig, "s3_vectors.png")
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def diagram_s4_flow():
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"""Slide 4: Semantic search pipeline as a flow diagram."""
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fig, ax = _fig(12, 1.9) # flat figure — matches slide aspect ratio
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ax.set_xlim(0, 12); ax.set_ylim(0, 1.9)
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ax.axis("off")
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steps = [
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(1.2, 'Text-Anfrage\n"Bäume"', "#89b4fa"),
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(3.6, "CLIP-Modell", "#cba6f7"),
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(6.0, "Vektor 512 floats", "#74c7ec"),
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(8.4, "Datenbank k-NN", "#f38ba8"),
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(10.8, "Ergebnisse\nnach Score", "#a6e3a1"),
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]
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for x, label, color in steps:
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box = mpatches.FancyBboxPatch((x - 1.05, 0.22), 2.1, 1.4,
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boxstyle="round,pad=0.1",
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facecolor="#313244", edgecolor=color, linewidth=2)
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ax.add_patch(box)
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ax.text(x, 0.92, label, ha="center", va="center",
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color=color, fontsize=13, fontweight="bold", multialignment="center",
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fontfamily="sans-serif")
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for i in range(len(steps) - 1):
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x1 = steps[i][0] + 1.05
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x2 = steps[i+1][0] - 1.05
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ax.annotate("", xy=(x2, 0.92), xytext=(x1, 0.92),
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arrowprops=dict(arrowstyle="->", color=DIAG_AXIS, lw=2.5))
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plt.tight_layout(pad=0.15)
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return _save(fig, "s4_flow.png")
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def diagram_s6_cosine():
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"""Slide 6: Two vectors with the cosine angle between them."""
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fig, ax = _fig(5, 4.5)
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ax.set_xlim(-0.2, 1.35); ax.set_ylim(-0.15, 1.35)
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ax.set_aspect("equal")
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ax.axis("off")
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vA = np.array([1.1, 0.25]) # image vector
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vB = np.array([0.55, 1.0 ]) # text vector
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for v, color, label, lpos in [
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(vA, "#89b4fa", "Bild-Vektor", (1.17, 0.08)),
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(vB, "#cba6f7", 'Text-Vektor\n"Bäume"', (0.56, 1.07)),
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]:
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ax.annotate("", xy=v, xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color=color, lw=3))
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ax.text(*lpos, label, color=color, fontsize=12,
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fontweight="bold", ha="center", multialignment="center")
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# Angle arc
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a1 = np.degrees(np.arctan2(vA[1], vA[0]))
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a2 = np.degrees(np.arctan2(vB[1], vB[0]))
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ax.add_patch(mpatches.Arc((0, 0), 0.45, 0.45, angle=0,
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theta1=a1, theta2=a2,
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color="#a6e3a1", lw=2.5))
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mid_angle = np.radians((a1 + a2) / 2)
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ax.text(0.28 * np.cos(mid_angle), 0.28 * np.sin(mid_angle),
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"θ", color="#a6e3a1", fontsize=16, fontweight="bold",
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ha="center", va="center")
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# Origin dot
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ax.plot(0, 0, "o", color=DIAG_AXIS, markersize=6)
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# Formula
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ax.text(0.58, -0.12,
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"Ähnlichkeit = 1 − cos(θ)",
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color="#cdd6f4", fontsize=11, ha="center",
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fontfamily="monospace")
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plt.tight_layout(pad=0.3)
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return _save(fig, "s6_cosine.png")
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def diagram_architecture():
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"""Architecture slide: 3 columns showing app server, database, and where CLIP runs."""
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CLIP_CLR = "#a6e3a1"
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# (x, db_name, color, port, clip_app, clip_db, db_tech, vec_embed_fn, foto_storage)
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COLS = [
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(2.3, "PostgreSQL 18", "#89b4fa", "Port 8000", True, False, "pgvector 0.8.2\nHNSW (Disk)", None, "Fotos: Dateipfad (Filesystem)"),
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(6.65, "Oracle 26ai\nVECTORS_USER", "#f38ba8", "Port 8001", True, False, "HNSW (SGA)", None, "Fotos: Dateipfad (Filesystem)"),
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(11.0, "Oracle 26ai\nVECTOR", "#cba6f7", "Port 8002", False, True, "HNSW (SGA)", "VECTOR_EMBEDDING()", "Fotos: BLOB (in Oracle)"),
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]
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BOX_H = 2.2 # all boxes same height
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DB_Y = 0.15 # database box bottom
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GAP = 0.60 # space between DB top and app server bottom
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APP_Y = DB_Y + BOX_H + GAP # = 2.95
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fig, ax = _fig(13.5, 6.5)
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ax.set_xlim(0, 13.5); ax.set_ylim(-0.8, 5.9)
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ax.axis("off")
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for x, db_name, color, port, clip_app, clip_db, db_tech, vec_fn, foto_storage in COLS:
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APP_TOP = APP_Y + BOX_H # = 5.15
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DB_TOP = DB_Y + BOX_H # = 2.35
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# ── Port label
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ax.text(x, APP_TOP + 0.28, port, ha="center", color=color,
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fontsize=13, fontweight="bold")
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# ── App server box
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.7, APP_Y), 3.4, BOX_H,
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boxstyle="round,pad=0.1", facecolor="#28293d", edgecolor=color, lw=2))
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ax.text(x, APP_TOP - 0.22, "App-Server (FastAPI)", ha="center",
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color=color, fontsize=11, fontweight="bold")
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if clip_app:
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.2, APP_Y + 0.10), 2.4, 0.75,
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boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=CLIP_CLR, lw=2))
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ax.text(x, APP_Y + 0.475, "CLIP-Modell\n(sentence-transformers)",
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ha="center", va="center", color=CLIP_CLR, fontsize=9.5, fontweight="bold",
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multialignment="center")
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.2, APP_Y + 0.95), 2.4, 0.42,
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boxstyle="round,pad=0.06", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
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linestyle="dashed"))
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ax.text(x, APP_Y + 1.16, foto_storage,
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ha="center", va="center", color=DIAG_AXIS, fontsize=9, style="italic")
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else:
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.2, APP_Y + 0.10), 2.4, 0.75,
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boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
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linestyle="dashed"))
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ax.text(x, APP_Y + 0.475, "kein CLIP",
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ha="center", va="center", color=DIAG_AXIS, fontsize=10, style="italic")
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# ── Arrow with comfortable gap
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ax.annotate("", xy=(x, DB_TOP + 0.05), xytext=(x, APP_Y - 0.05),
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arrowprops=dict(arrowstyle="->", color=DIAG_AXIS, lw=2))
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arrow_lbl = "Vektor (512 floats)" if clip_app else "Text-String"
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ax.text(x + 0.2, (DB_TOP + APP_Y) / 2, arrow_lbl, ha="left", va="center",
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color=DIAG_AXIS, fontsize=9, style="italic")
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# ── Database box
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.7, DB_Y), 3.4, BOX_H,
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boxstyle="round,pad=0.1", facecolor="#28293d", edgecolor=color, lw=2))
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if clip_db:
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.2, DB_Y + 0.10), 2.4, 0.72,
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boxstyle="round,pad=0.08", facecolor="#1e1e2e", edgecolor=CLIP_CLR, lw=2))
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ax.text(x, DB_Y + 0.46, "CLIP-Modell\n(ONNX, in Oracle)",
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ha="center", va="center", color=CLIP_CLR, fontsize=9.5, fontweight="bold",
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multialignment="center")
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ax.add_patch(mpatches.FancyBboxPatch(
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(x-1.2, DB_Y + 0.92), 2.4, 0.40,
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boxstyle="round,pad=0.06", facecolor="#1e1e2e", edgecolor=DIAG_AXIS, lw=1,
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linestyle="dashed"))
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ax.text(x, DB_Y + 1.12, foto_storage,
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ha="center", va="center", color=DIAG_AXIS, fontsize=9, style="italic")
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ax.text(x, DB_Y + 1.50, vec_fn,
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ha="center", color="#fab387", fontsize=10, fontweight="bold",
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fontfamily="monospace")
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ax.text(x, DB_Y + 1.72, "Oracle 26ai", ha="center", color=color,
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fontsize=11, fontweight="bold")
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ax.text(x, DB_Y + 1.92, "Schema: VECTOR", ha="center", color=color,
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fontsize=9)
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ax.text(x, DB_Y + 2.10, db_tech, ha="center", color=DIAG_AXIS,
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fontsize=9)
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else:
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# Split db_name → ["PostgreSQL 18"] or ["Oracle 26ai", "VECTORS_USER"]
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# Split db_tech → ["pgvector 0.8.2", "HNSW (Disk)"] or ["HNSW (SGA)"]
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name_parts = db_name.split("\n")
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tech_parts = db_tech.split("\n")
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hnsw = tech_parts[-1] # always last
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tech_extra = tech_parts[:-1] # e.g. ["pgvector 0.8.2"]
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# HNSW — same height across all three DB boxes
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ax.text(x, DB_Y + 2.10, hnsw, ha="center", color=DIAG_AXIS, fontsize=9)
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# Middle line: schema name or version info (matches "Schema: VECTOR" in col 3)
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if len(name_parts) > 1:
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mid_label = "Schema: " + name_parts[1]
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elif tech_extra:
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mid_label = tech_extra[0]
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else:
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mid_label = ""
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if mid_label:
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ax.text(x, DB_Y + 1.92, mid_label, ha="center", color=color, fontsize=9)
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# Main DB name (matches "Oracle 26ai" in col 3)
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ax.text(x, DB_Y + 1.72, name_parts[0], ha="center", color=color,
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fontsize=11, fontweight="bold")
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# ── Vertical separators
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for xsep in [4.5, 8.85]:
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ax.plot([xsep, xsep], [0.05, 5.55], color=DIAG_GRID, lw=1, linestyle="--")
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# ── Caption — separated from boxes, applies to all three columns
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ax.plot([0.3, 13.2], [-0.18, -0.18], color=DIAG_GRID, lw=1)
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ax.text(6.75, -0.5, "116 Street Fotos · CLIP ViT-B/32 · 512-dimensionale Vektoren",
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ha="center", va="center", color="#cdd6f4", fontsize=13, style="italic")
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plt.tight_layout(pad=0.2)
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return _save(fig, "architecture.png")
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# Generate diagrams up front
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os.makedirs("diagrams", exist_ok=True)
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DIAG_S3 = diagram_s3_vectors()
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DIAG_S4 = diagram_s4_flow()
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DIAG_S6 = diagram_s6_cosine()
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DIAG_ARCH = diagram_architecture()
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import copy
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# ── Colour palette (dark theme) ──────────────────────────────────────────────
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BG = RGBColor(0x1e, 0x1e, 0x2e) # slide background
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TITLE_CLR = RGBColor(0xcb, 0xd3, 0xff) # slide titles
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BODY_CLR = RGBColor(0xcd, 0xd6, 0xf4) # body text
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DIM_CLR = RGBColor(0x6c, 0x70, 0x86) # dimmed / captions
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ACCENT_PG = RGBColor(0x89, 0xb4, 0xfa) # pgvector blue
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ACCENT_ORA = RGBColor(0xf3, 0x8b, 0xa8) # Oracle red/pink
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ACCENT_IDB = RGBColor(0xcb, 0xa6, 0xf7) # in-DB purple
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ACCENT_GRN = RGBColor(0xa6, 0xe3, 0xa1) # green for highlights
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CODE_BG = RGBColor(0x31, 0x32, 0x44) # code block background
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CODE_CLR = RGBColor(0xa6, 0xe3, 0xa1) # code text
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W = Inches(13.33) # widescreen 16:9
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H = Inches(7.5)
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FONT = "Roboto"
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prs = Presentation()
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prs.slide_width = W
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prs.slide_height = H
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blank_layout = prs.slide_layouts[6] # completely blank
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LOGO_PATH = "/home/dierk/Bilder/Logo/Logo DLC Final.png"
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CONFERENCE = "Quest Data Minds Konferenz"
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EVENT_DATE = "28. Mai 2026"
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EVENT_CITY = "Köln"
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_slide_num = [0] # mutable counter so nested calls can increment it
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def add_slide(logo=True, footer=True):
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slide = prs.slides.add_slide(blank_layout)
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bg = slide.background
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fill = bg.fill
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fill.solid()
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fill.fore_color.rgb = BG
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if logo:
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slide.shapes.add_picture(LOGO_PATH,
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Inches(11.6), Inches(7.03), Inches(1.6), Inches(0.36))
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if footer:
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_slide_num[0] += 1
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# thin separator line
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sep = slide.shapes.add_shape(1, Inches(0.3), Inches(6.95), Inches(11.1), Pt(1))
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sep.fill.solid()
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sep.fill.fore_color.rgb = DIM_CLR
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sep.line.fill.background()
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# left: conference info
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txb(slide, f"{CONFERENCE} · {EVENT_CITY}, {EVENT_DATE}",
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Inches(0.3), Inches(7.02), Inches(9.5), Inches(0.35),
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size=11, color=DIM_CLR)
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# right: page number (before logo)
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txb(slide, str(_slide_num[0]),
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Inches(10.9), Inches(7.02), Inches(0.6), Inches(0.35),
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size=11, color=DIM_CLR, align=PP_ALIGN.RIGHT)
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return slide
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def txb(slide, text, x, y, w, h,
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size=24, bold=False, color=BODY_CLR,
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align=PP_ALIGN.LEFT, italic=False):
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box = slide.shapes.add_textbox(x, y, w, h)
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tf = box.text_frame
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tf.word_wrap = True
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p = tf.paragraphs[0]
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p.alignment = align
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run = p.add_run()
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run.text = text
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run.font.size = Pt(size)
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run.font.bold = bold
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run.font.italic = italic
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run.font.color.rgb = color
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run.font.name = FONT
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return box
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||
|
||
def title_slide_layout(slide, title, subtitle=None):
|
||
txb(slide, title,
|
||
Inches(1), Inches(2.8), Inches(11.33), Inches(1.2),
|
||
size=48, bold=True, color=TITLE_CLR, align=PP_ALIGN.CENTER)
|
||
if subtitle:
|
||
txb(slide, subtitle,
|
||
Inches(1), Inches(4.1), Inches(11.33), Inches(0.8),
|
||
size=24, color=DIM_CLR, align=PP_ALIGN.CENTER)
|
||
|
||
|
||
def section_header(slide, title, accent=ACCENT_PG):
|
||
"""Full-width coloured bar at top, then title."""
|
||
bar = slide.shapes.add_shape(
|
||
1, # MSO_SHAPE_TYPE.RECTANGLE
|
||
Inches(0), Inches(0), W, Inches(0.12)
|
||
)
|
||
bar.fill.solid()
|
||
bar.fill.fore_color.rgb = accent
|
||
bar.line.fill.background()
|
||
|
||
txb(slide, title,
|
||
Inches(0.5), Inches(0.2), Inches(12.33), Inches(0.8),
|
||
size=32, bold=True, color=TITLE_CLR)
|
||
|
||
|
||
def bullet_box(slide, items, x, y, w, h, size=20, color=BODY_CLR, indent=False):
|
||
box = slide.shapes.add_textbox(x, y, w, h)
|
||
tf = box.text_frame
|
||
tf.word_wrap = True
|
||
first = True
|
||
for item in items:
|
||
if first:
|
||
p = tf.paragraphs[0]
|
||
first = False
|
||
else:
|
||
p = tf.add_paragraph()
|
||
p.space_before = Pt(4)
|
||
run = p.add_run()
|
||
run.text = (" " if indent else "") + item
|
||
run.font.size = Pt(size)
|
||
run.font.color.rgb = color
|
||
run.font.name = FONT
|
||
|
||
|
||
def code_box(slide, code, x, y, w, h, size=13):
|
||
# Background rectangle (no text)
|
||
bg = slide.shapes.add_shape(1, x, y, w, h)
|
||
bg.fill.solid()
|
||
bg.fill.fore_color.rgb = CODE_BG
|
||
bg.line.color.rgb = RGBColor(0x58, 0x5b, 0x70)
|
||
bg.text_frame.text = ""
|
||
|
||
# Text box on top — textboxes have predictable left-aligned defaults
|
||
pad = Pt(7)
|
||
tb = slide.shapes.add_textbox(x + pad, y + pad, w - pad * 2, h - pad * 2)
|
||
tf = tb.text_frame
|
||
tf.word_wrap = False
|
||
tf.margin_left = Pt(0)
|
||
tf.margin_right = Pt(0)
|
||
tf.margin_top = Pt(0)
|
||
tf.margin_bottom = Pt(0)
|
||
|
||
first = True
|
||
for line in code.strip().split("\n"):
|
||
if first:
|
||
p = tf.paragraphs[0]
|
||
first = False
|
||
else:
|
||
p = tf.add_paragraph()
|
||
p.alignment = PP_ALIGN.LEFT
|
||
p.space_before = Pt(0)
|
||
p.space_after = Pt(0)
|
||
# Explicitly zero out left margin, hanging indent, and remove any bullet
|
||
pPr = p._p.get_or_add_pPr()
|
||
pPr.set("marL", "0")
|
||
pPr.set("indent", "0")
|
||
for tag in ("a:buClr","a:buClrTx","a:buFont","a:buFontTx","a:buChar","a:buAutoNum","a:buNone"):
|
||
for el in pPr.findall(qn(tag)):
|
||
pPr.remove(el)
|
||
pPr.append(OxmlElement("a:buNone"))
|
||
run = p.add_run()
|
||
run.text = line
|
||
run.font.size = Pt(size)
|
||
run.font.color.rgb = CODE_CLR
|
||
run.font.name = "Courier New"
|
||
|
||
|
||
def divider(slide, y, color=DIM_CLR):
|
||
line = slide.shapes.add_shape(1, Inches(0.5), y, Inches(12.33), Pt(1))
|
||
line.fill.solid()
|
||
line.fill.fore_color.rgb = color
|
||
line.line.fill.background()
|
||
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 1 — Titelfolie
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide(logo=False, footer=False) # title slide: custom layout
|
||
title_slide_layout(s,
|
||
"Vektoren in der Datenbank",
|
||
"Der VECTOR-Datentyp in Oracle 26ai und PostgreSQL")
|
||
# Conference details
|
||
txb(s, CONFERENCE,
|
||
Inches(1), Inches(5.0), Inches(11.33), Inches(0.5),
|
||
size=20, bold=True, color=ACCENT_PG, align=PP_ALIGN.CENTER)
|
||
txb(s, f"{EVENT_DATE} · {EVENT_CITY}",
|
||
Inches(1), Inches(5.5), Inches(11.33), Inches(0.45),
|
||
size=18, color=DIM_CLR, align=PP_ALIGN.CENTER)
|
||
# Larger centred logo
|
||
s.shapes.add_picture(LOGO_PATH, Inches(4.67), Inches(6.2), Inches(4.0), Inches(0.90))
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# 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", "Gründung 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)
|
||
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)
|
||
bullet_box(s, [
|
||
"01 Was ist ein Vektor?",
|
||
"02 Semantische Suche — jenseits von Schlüsselwörtern",
|
||
"03 Das CLIP-Modell",
|
||
"04 Ähnlichkeit messen: Cosinus-Distanz",
|
||
"05 PostgreSQL + pgvector",
|
||
"06 Oracle 26ai — nativer Vektor-Support",
|
||
"07 Oracle 26ai — Embedding in der Datenbank",
|
||
"08 Architektur der Demo",
|
||
"09 Demo",
|
||
"10 Vergleich & Fazit",
|
||
], Inches(1.5), Inches(1.3), Inches(10), Inches(5.5), size=20)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 3 — Was ist ein Vektor?
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Was ist ein Vektor?", ACCENT_PG)
|
||
bullet_box(s, [
|
||
"▸ Ein Vektor ist eine geordnete Liste von Zahlen: [0.12, -0.87, 0.44, …]",
|
||
"▸ Jede Zahl beschreibt eine Dimension im semantischen Raum",
|
||
"▸ Moderne KI-Modelle erzeugen Vektoren mit 512 bis 1536 Dimensionen",
|
||
"▸ Ähnliche Inhalte → ähnliche Vektoren → kleiner Abstand im Raum",
|
||
"▸ Texte, Bilder, Audio — alles lässt sich in denselben Vektorraum einbetten",
|
||
], Inches(0.8), Inches(1.3), Inches(7.2), Inches(4), size=20)
|
||
|
||
# 2-D vector diagram on the right
|
||
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(0.3), Inches(5.75), Inches(7.4), Inches(0.8),
|
||
size=26, bold=True, color=ACCENT_GRN)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 4 — Semantische Suche
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Semantische Suche — jenseits von Schlüsselwörtern", ACCENT_PG)
|
||
bullet_box(s, [
|
||
"Klassische Suche: \"Bäume\" findet nur Dokumente mit dem Wort \"Bäume\"",
|
||
"",
|
||
"Semantische Suche: \"Bäume\" findet Bilder von Wäldern, Parks, Natur —",
|
||
" ohne dass das Wort irgendwo steht",
|
||
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(2.2), size=20)
|
||
|
||
divider(s, Inches(3.7))
|
||
|
||
bullet_box(s, [
|
||
"▸ Text-Anfrage wird in denselben Vektorraum eingebettet wie die Bilder",
|
||
"▸ Datenbankabfrage: finde die k nächsten Nachbarn (k-NN)",
|
||
"▸ Ergebnis: Bilder nach semantischer Ähnlichkeit gerankt",
|
||
"▸ Kein manuelles Tagging, keine Metadaten nötig",
|
||
], Inches(0.8), Inches(3.9), Inches(11.5), Inches(1.1), size=20)
|
||
|
||
# Flow diagram
|
||
s.shapes.add_picture(DIAG_S4, Inches(0.5), Inches(5.1), Inches(12.3), Inches(1.75))
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 5 — CLIP-Modell
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Das CLIP-Modell (OpenAI)", ACCENT_IDB)
|
||
bullet_box(s, [
|
||
"CLIP = Contrastive Language–Image Pretraining",
|
||
"▸ Trainiert auf hunderten Millionen Bild-Text-Paaren",
|
||
"▸ Bildet sowohl Bilder als auch Text in denselben 512-dimensionalen Raum ab",
|
||
"▸ Modell: clip-ViT-B-32 (Vision Transformer, Patch-Größe 32×32)",
|
||
"▸ Quell-Gewichte: Hugging Face Hub (sentence-transformers/clip-ViT-B-32)",
|
||
], Inches(0.8), Inches(1.3), Inches(7.5), Inches(3.2), size=20)
|
||
|
||
code_box(s,
|
||
'from sentence_transformers import (\n SentenceTransformer)\n\nmodel = SentenceTransformer(\n "clip-ViT-B-32")\n\n# Bild einbetten\nvec = model.encode(image)\n# → 512 floats\n\n# Text einbetten\nvec = model.encode("Bäume")\n# → 512 floats, gleicher Raum!',
|
||
Inches(8.8), Inches(1.3), Inches(4.3), Inches(3.8), size=11)
|
||
|
||
txb(s, "Bild-Vektor und Text-Vektor zeigen in dieselbe Richtung,\nwenn Bild und Text inhaltlich übereinstimmen.",
|
||
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 — ~600 MB, automatischer Download beim ersten Start · Größere Varianten (ViT-L-14, ~1,7 GB) liefern höhere Präzision",
|
||
Inches(0.8), Inches(6.55), Inches(12.0), Inches(0.35),
|
||
size=11, color=DIM_CLR, italic=True)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 6 — Cosinus-Distanz
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Ähnlichkeit messen: Cosinus-Distanz", ACCENT_PG)
|
||
bullet_box(s, [
|
||
"▸ CLIP-Vektoren haben unterschiedliche Beträge — daher kein euklidischer Abstand",
|
||
"▸ Cosinus-Distanz misst nur den Winkel zwischen zwei Vektoren",
|
||
"▸ Cosinus-Distanz = 0 → identisch",
|
||
"▸ Cosinus-Distanz = 1 → völlig unähnlich",
|
||
"▸ Ähnlichkeitswert = 1 − Distanz → 1.0 = perfekte Übereinstimmung",
|
||
], Inches(0.8), Inches(1.3), Inches(7.5), Inches(3.5), size=20)
|
||
|
||
# Cosine diagram on the right
|
||
s.shapes.add_picture(DIAG_S6, Inches(8.0), Inches(1.1), Inches(5.1), Inches(3.7))
|
||
|
||
code_box(s,
|
||
"-- PostgreSQL\n1 - (embedding <=> query_vec)\n\n-- Oracle 26ai\n1 - VECTOR_DISTANCE(embedding, query_vec, COSINE)",
|
||
Inches(0.8), Inches(5.0), Inches(6.0), Inches(1.85), size=13)
|
||
|
||
txb(s, "In der Demo:\nScore 28 % = schwach\nScore 75 % = stark",
|
||
Inches(7.0), Inches(5.0), Inches(5.0), Inches(1.85),
|
||
size=18, color=ACCENT_GRN)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 7 — PostgreSQL + pgvector: Voraussetzungen
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "PostgreSQL + pgvector", ACCENT_PG)
|
||
|
||
txb(s, "Was wird benötigt?", Inches(0.8), Inches(1.3), Inches(11), Inches(0.5),
|
||
size=22, bold=True, color=ACCENT_PG)
|
||
bullet_box(s, [
|
||
"▸ PostgreSQL (ab Version 13)",
|
||
"▸ pgvector-Extension — docker image: pgvector/pgvector:pg18",
|
||
"▸ Extension aktivieren: CREATE EXTENSION vector;",
|
||
"▸ Python-Paket: psycopg2-binary",
|
||
"▸ KI-Bibliothek: sentence-transformers (auf dem Anwendungsserver)",
|
||
], Inches(0.8), Inches(1.9), Inches(11.5), Inches(2.5), size=20)
|
||
|
||
divider(s, Inches(4.6))
|
||
|
||
txb(s, "Schema & Index", Inches(0.8), Inches(4.5), Inches(11), Inches(0.5),
|
||
size=22, bold=True, color=ACCENT_PG)
|
||
code_box(s,
|
||
"CREATE TABLE images (\n id SERIAL PRIMARY KEY,\n filename TEXT NOT NULL UNIQUE,\n embedding vector(512) -- pgvector-Typ\n);\n\nCREATE INDEX ON images USING hnsw (embedding vector_cosine_ops);",
|
||
Inches(0.8), Inches(5.0), Inches(7.5), Inches(1.85), size=13)
|
||
|
||
bullet_box(s, [
|
||
"HNSW = Hierarchical Navigable Small World",
|
||
"Approximativer k-NN Index",
|
||
"Sehr schnell bei der Suche",
|
||
], Inches(8.8), Inches(5.0), Inches(4.3), Inches(1.85), size=18, color=DIM_CLR)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 8 — PostgreSQL: Suchanfrage
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "PostgreSQL: Suchanfrage", ACCENT_PG)
|
||
|
||
bullet_box(s, [
|
||
"1. Text-Anfrage mit CLIP in Python in einen Vektor umwandeln",
|
||
"2. Vektor an die SQL-Abfrage übergeben",
|
||
"3. PostgreSQL findet die ähnlichsten Bilder via HNSW-Index",
|
||
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(1.5), size=20)
|
||
|
||
code_box(s,
|
||
"# Python\nvec = model.encode(\"Bäume\") # → 512 floats\n\n# SQL\nSELECT filename,\n 1 - (embedding <=> %s::vector) AS score\nFROM images\nORDER BY embedding <=> %s::vector\nLIMIT 12;",
|
||
Inches(0.8), Inches(3.0), Inches(7.5), Inches(3.5), size=16)
|
||
|
||
bullet_box(s, [
|
||
"<=> Cosinus-Distanz-Operator",
|
||
"(pgvector-spezifisch)",
|
||
"",
|
||
"$1::vector expliziter Cast",
|
||
"erforderlich",
|
||
"",
|
||
"LIMIT statt FETCH FIRST",
|
||
], Inches(9.0), Inches(3.0), Inches(4.0), Inches(3.5), size=18, color=DIM_CLR)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 9 — Oracle 26ai: Nativer Support
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Oracle 26ai — nativer Vektor-Support", ACCENT_ORA)
|
||
|
||
txb(s, "Was wird benötigt?", Inches(0.8), Inches(1.3), Inches(11), Inches(0.5),
|
||
size=22, bold=True, color=ACCENT_ORA)
|
||
bullet_box(s, [
|
||
"▸ Oracle AI Database 26ai Free (oder Enterprise)",
|
||
"▸ Keine Extension nötig — Vektoren sind eingebaut",
|
||
"▸ Vector Memory Area im SGA konfigurieren (für HNSW-Index)",
|
||
"▸ Python-Paket: oracledb (Thin Mode — kein Oracle Client nötig)",
|
||
"▸ KI-Bibliothek: sentence-transformers (auf dem Anwendungsserver)",
|
||
], Inches(0.8), Inches(1.9), Inches(11.5), Inches(2.2), size=20)
|
||
|
||
divider(s, Inches(4.2))
|
||
|
||
txb(s, "Schema & Index", Inches(0.8), Inches(4.3), Inches(11), Inches(0.45),
|
||
size=20, bold=True, color=ACCENT_ORA)
|
||
code_box(s,
|
||
"CREATE TABLE images (\n id NUMBER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,\n filename VARCHAR2(255) NOT NULL UNIQUE,\n embedding VECTOR(512, FLOAT32) -- Typ + Dimension\n);\nCREATE VECTOR INDEX images_idx ON images(embedding)\n ORGANIZATION INMEMORY NEIGHBOR GRAPH\n WITH DISTANCE COSINE WITH TARGET ACCURACY 95;",
|
||
Inches(0.8), Inches(4.8), Inches(8.5), Inches(2.0), size=11)
|
||
|
||
bullet_box(s, [
|
||
"HNSW im SGA",
|
||
"(Vector Memory Area)",
|
||
"512 MB konfiguriert",
|
||
], Inches(9.8), Inches(4.8), Inches(3.3), Inches(2.0), size=17, color=DIM_CLR)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 10 — Oracle: Unterschiede zu pgvector
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Oracle vs. pgvector — Schema-Unterschiede", ACCENT_ORA)
|
||
|
||
rows = [
|
||
("Extension", "CREATE EXTENSION vector", "Eingebaut, keine Extension"),
|
||
("Vektor-Spalte", "vector(512) — nur Dimension", "VECTOR(512, FLOAT32) — Dim + Typ"),
|
||
("Primary Key", "SERIAL", "NUMBER GENERATED ALWAYS AS IDENTITY"),
|
||
("Text-Spalte", "TEXT (unbegrenzt)", "VARCHAR2(n) — Länge erforderlich"),
|
||
("HNSW-Syntax", "USING hnsw (...ops)", "ORGANIZATION INMEMORY NEIGHBOR GRAPH"),
|
||
("Genauigkeit", "Implizit via Index-Parameter", "WITH TARGET ACCURACY 95 (explizit)"),
|
||
("Speicher", "Kein Sonder-Speicher nötig", "vector_memory_size im SGA"),
|
||
("Abstand-Op", "<=> (Operator)", "VECTOR_DISTANCE(col, vec, COSINE)"),
|
||
("Top-N", "LIMIT n", "FETCH FIRST n ROWS ONLY"),
|
||
]
|
||
|
||
# Column header row
|
||
y = Inches(1.3)
|
||
hdr_bg = s.shapes.add_shape(1, Inches(0.3), y, Inches(12.7), Inches(0.55))
|
||
hdr_bg.fill.solid()
|
||
hdr_bg.fill.fore_color.rgb = RGBColor(0x18, 0x18, 0x28)
|
||
hdr_bg.line.fill.background()
|
||
txb(s, "Aspekt", Inches(0.4), y + Pt(6), Inches(2.2), Inches(0.5), size=14, bold=True, color=BODY_CLR)
|
||
txb(s, "PostgreSQL + pgvector",Inches(2.7), y + Pt(6), Inches(4.8), Inches(0.5), size=14, bold=True, color=ACCENT_PG)
|
||
txb(s, "Oracle 26ai", Inches(7.6), y + Pt(6), Inches(5.4), Inches(0.5), size=14, bold=True, color=ACCENT_ORA)
|
||
y += Inches(0.56)
|
||
|
||
for i, (aspect, pg, ora) in enumerate(rows):
|
||
bg_color = RGBColor(0x28, 0x29, 0x3d) if i % 2 == 0 else RGBColor(0x24, 0x25, 0x38)
|
||
row_bg = s.shapes.add_shape(1, Inches(0.3), y, Inches(12.7), Inches(0.52))
|
||
row_bg.fill.solid()
|
||
row_bg.fill.fore_color.rgb = bg_color
|
||
row_bg.line.fill.background()
|
||
|
||
txb(s, aspect, Inches(0.4), y + Pt(5), Inches(2.2), Inches(0.48), size=13, bold=True, color=DIM_CLR)
|
||
txb(s, pg, Inches(2.7), y + Pt(5), Inches(4.8), Inches(0.48), size=13, color=ACCENT_PG)
|
||
txb(s, ora, Inches(7.6), y + Pt(5), Inches(5.4), Inches(0.48), size=13, color=ACCENT_ORA)
|
||
y += Inches(0.53)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 11 — Oracle In-Database Embedding
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Oracle 26ai — Embedding in der Datenbank", ACCENT_IDB)
|
||
|
||
bullet_box(s, [
|
||
"▸ Oracle kann ONNX-Modelle direkt in die Datenbank laden",
|
||
" (ONNX = Open Neural Network Exchange)",
|
||
"▸ VECTOR_EMBEDDING() ruft das Modell innerhalb einer SQL-Abfrage auf",
|
||
"▸ Kein Python, keine KI-Bibliothek auf dem Anwendungsserver zur Laufzeit",
|
||
"▸ 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(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(3.8), Inches(7.5), Inches(3.0), size=11)
|
||
|
||
bullet_box(s, [
|
||
":q = reiner Text aus Python",
|
||
"",
|
||
"Oracle übernimmt:",
|
||
" • Tokenisierung",
|
||
" • ONNX-Inferenz",
|
||
" • Vektorsuche",
|
||
"",
|
||
"→ Architektur vereinfacht sich",
|
||
], Inches(9.0), Inches(3.8), Inches(4.0), Inches(3.0), size=16, color=DIM_CLR)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 12 — ONNX in Oracle: Besonderheit
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "ONNX in Oracle: Was zu beachten ist", ACCENT_IDB)
|
||
|
||
bullet_box(s, [
|
||
"Oracle's ONNX-Validator stellt strenge Anforderungen an das Modell-Graph:",
|
||
"",
|
||
"▸ input_ids darf nur in einem einzigen Gather-Knoten verwendet werden",
|
||
"▸ Standard-CLIP-Export verwendet input_ids auch in ArgMax → wird abgelehnt",
|
||
"",
|
||
"Lösung: CLIP_TXT mit CLS-Token-Pooling (Position 0) statt EOS-Token-Pooling",
|
||
"▸ Einfacherer ONNX-Graph, den Oracle akzeptiert",
|
||
"▸ Cosinus-Ähnlichkeit zwischen EOS- und CLS-Variante: ~0,70",
|
||
"▸ Modell muss beim Export entsprechend angepasst werden",
|
||
], Inches(0.8), Inches(1.3), Inches(11.5), Inches(3.8), size=19)
|
||
|
||
code_box(s,
|
||
"-- Modell laden (einmalig durch Administrator)\nEXEC DBMS_VECTOR.LOAD_ONNX_MODEL(\n 'VEC_DUMP', 'clip_txt.onnx', 'CLIP_TXT',\n JSON('{\"function\":\"embedding\",\"embeddingOutput\":\"output\",\n \"input\":{\"input\":[\"DATA\"]}}'));",
|
||
Inches(0.8), Inches(5.2), Inches(11.5), Inches(1.6), size=13)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 13 — Architektur: Wo wird CLIP berechnet?
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Architektur der Demo", ACCENT_GRN)
|
||
s.shapes.add_picture(DIAG_ARCH, Inches(0.3), Inches(1.1), Inches(12.73), Inches(5.7))
|
||
|
||
# Slide 15 — Demo-Hinweis
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Demo", ACCENT_GRN)
|
||
|
||
for url, label, color, y in [
|
||
("http://localhost:8000/ui/", "pgvector (blau)", ACCENT_PG, Inches(2.2)),
|
||
("http://localhost:8001/ui/", "Oracle 26ai (rot)", ACCENT_ORA, Inches(3.5)),
|
||
("http://localhost:8002/ui/", "Oracle In-DB (lila)",ACCENT_IDB, Inches(4.8)),
|
||
]:
|
||
txb(s, url, Inches(1.5), y, Inches(6), Inches(0.5), size=22, bold=True, color=color)
|
||
txb(s, label, Inches(7.8), y + Inches(0.05), Inches(4.5), Inches(0.5), size=20, color=DIM_CLR)
|
||
|
||
txb(s, "Suchbegriffe zum Ausprobieren:",
|
||
Inches(1.5), Inches(5.9), Inches(10), Inches(0.5), size=18, color=BODY_CLR)
|
||
txb(s, "Bäume · Wasser · Menschen · Gebäude · Himmel · Nacht · Autos",
|
||
Inches(1.5), Inches(6.3), Inches(10), Inches(0.6), size=20, bold=True, color=ACCENT_GRN)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 15 — Vergleich
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Vergleich", ACCENT_PG)
|
||
|
||
rows = [
|
||
("Merkmal", "PostgreSQL + pgvector", "Oracle · VECTORS_USER", "Oracle · VECTOR"),
|
||
("Fotos indiziert", "116", "116", "116"),
|
||
("Indizierungszeit", "Ø 12,1 Sek. (3 Läufe)", "Ø 12,1 Sek. (3 Läufe)", "Ø 13,6 Sek. (3 Läufe)"),
|
||
("Index-Typ", "HNSW (auf Disk)", "HNSW (im Speicher)", "HNSW (im Speicher)"),
|
||
("RAM-Bedarf", "Keiner", "512 MB SGA", "512 MB SGA"),
|
||
("CLIP zur Laufzeit", "Ja (Python)", "Ja (Python)", "Nein"),
|
||
("Embedding-Ort", "Python-Prozess", "Python-Prozess", "In der Datenbank"),
|
||
("VECTOR_EMBEDDING()", "—", "—", "Ja"),
|
||
("Extension nötig", "CREATE EXTENSION vector", "Nein", "Nein"),
|
||
]
|
||
|
||
y = Inches(1.3)
|
||
header = True
|
||
for row in rows:
|
||
bg_color = RGBColor(0x18, 0x18, 0x28) if header else (RGBColor(0x28, 0x29, 0x3d) if rows.index(row) % 2 == 0 else RGBColor(0x24, 0x25, 0x38))
|
||
row_bg = s.shapes.add_shape(1, Inches(0.3), y, Inches(12.7), Inches(0.52))
|
||
row_bg.fill.solid()
|
||
row_bg.fill.fore_color.rgb = bg_color
|
||
row_bg.line.fill.background()
|
||
|
||
colors = [DIM_CLR, ACCENT_PG, ACCENT_ORA, ACCENT_IDB] if header else [BODY_CLR, ACCENT_PG, ACCENT_ORA, ACCENT_IDB]
|
||
widths = [2.5, 3.0, 3.1, 3.1]
|
||
xs = [0.4, 2.9, 6.0, 9.15]
|
||
for j, (cell, col, w, x) in enumerate(zip(row, colors, widths, xs)):
|
||
txb(s, cell, Inches(x), y + Pt(4), Inches(w), Inches(0.48),
|
||
size=13, bold=header, color=col)
|
||
y += Inches(0.53)
|
||
header = False
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Slide 16 — Fazit
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
s = add_slide()
|
||
section_header(s, "Fazit", ACCENT_GRN)
|
||
|
||
bullet_box(s, [
|
||
"▸ Beide Datenbanken unterstützen Vektorsuche produktionsreif",
|
||
"▸ pgvector: einfach, leichtgewichtig, kein zusätzlicher Speicher nötig",
|
||
"▸ Oracle 26ai: vollständig integriert, kein Extension-Management",
|
||
"▸ 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",
|
||
"▸ 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))
|
||
|
||
txb(s, "Quellcode & Dokumentation",
|
||
Inches(0.8), Inches(5.2), Inches(11), Inches(0.5),
|
||
size=20, bold=True, color=BODY_CLR)
|
||
txb(s, "https://gitea.dl-cons.de/dierk/vector-search-demo",
|
||
Inches(0.8), Inches(5.7), Inches(11), Inches(0.5),
|
||
size=20, color=ACCENT_PG)
|
||
|
||
txb(s, "dierk.lenz@dl-cons.de · www.dl-cons.de",
|
||
Inches(0.8), Inches(6.2), Inches(11), Inches(0.4),
|
||
size=18, color=BODY_CLR)
|
||
|
||
txb(s, "Programmierung und Folien unterstützt durch Claude (Anthropic)",
|
||
Inches(0.8), Inches(6.55), Inches(11.33), Inches(0.35),
|
||
size=13, italic=True, color=DIM_CLR, align=PP_ALIGN.CENTER)
|
||
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# Save
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
OUT = "Vektoren in der Datenbank.pptx"
|
||
prs.save(OUT)
|
||
print(f"Saved: {OUT} ({prs.slides.__len__()} slides)")
|