@@ -0,0 +1,614 @@
"""
Generates " Vektoren in der Datenbank.pptx " — a LibreOffice-compatible presentation.
Run from the project root: python3 make_presentation.py
"""
from pptx import Presentation
from pptx . util import Inches , Pt , Emu
from pptx . dml . color import RGBColor
from pptx . enum . text import PP_ALIGN
from pptx . oxml . ns import qn
from pptx . oxml import parse_xml
from lxml import etree
_A_NS = " http://schemas.openxmlformats.org/drawingml/2006/main "
def OxmlElement ( tag ) :
local = tag . split ( " : " ) [ 1 ]
return etree . fromstring ( f ' <a: { local } xmlns:a= " { _A_NS } " /> ' )
import copy
# ── Colour palette (dark theme) ──────────────────────────────────────────────
BG = RGBColor ( 0x1e , 0x1e , 0x2e ) # slide background
TITLE_CLR = RGBColor ( 0xcb , 0xd3 , 0xff ) # slide titles
BODY_CLR = RGBColor ( 0xcd , 0xd6 , 0xf4 ) # body text
DIM_CLR = RGBColor ( 0x6c , 0x70 , 0x86 ) # dimmed / captions
ACCENT_PG = RGBColor ( 0x89 , 0xb4 , 0xfa ) # pgvector blue
ACCENT_ORA = RGBColor ( 0xf3 , 0x8b , 0xa8 ) # Oracle red/pink
ACCENT_IDB = RGBColor ( 0xcb , 0xa6 , 0xf7 ) # in-DB purple
ACCENT_GRN = RGBColor ( 0xa6 , 0xe3 , 0xa1 ) # green for highlights
CODE_BG = RGBColor ( 0x31 , 0x32 , 0x44 ) # code block background
CODE_CLR = RGBColor ( 0xa6 , 0xe3 , 0xa1 ) # code text
W = Inches ( 13.33 ) # widescreen 16:9
H = Inches ( 7.5 )
FONT = " Roboto "
prs = Presentation ( )
prs . slide_width = W
prs . slide_height = H
blank_layout = prs . slide_layouts [ 6 ] # completely blank
LOGO_PATH = " /home/dierk/Bilder/Logo/Logo DLC Final.png "
CONFERENCE = " Quest Data Minds Konferenz "
EVENT_DATE = " 28. Mai 2026 "
EVENT_CITY = " Köln "
_slide_num = [ 0 ] # mutable counter so nested calls can increment it
def add_slide ( logo = True , footer = True ) :
slide = prs . slides . add_slide ( blank_layout )
bg = slide . background
fill = bg . fill
fill . solid ( )
fill . fore_color . rgb = BG
if logo :
slide . shapes . add_picture ( LOGO_PATH ,
Inches ( 11.6 ) , Inches ( 7.0 ) , Inches ( 1.6 ) , Inches ( 0.42 ) )
if footer :
_slide_num [ 0 ] + = 1
# thin separator line
sep = slide . shapes . add_shape ( 1 , Inches ( 0.3 ) , Inches ( 6.95 ) , Inches ( 11.1 ) , Pt ( 1 ) )
sep . fill . solid ( )
sep . fill . fore_color . rgb = DIM_CLR
sep . line . fill . background ( )
# left: conference info
txb ( slide , f " { CONFERENCE } · { EVENT_CITY } , { EVENT_DATE } " ,
Inches ( 0.3 ) , Inches ( 7.02 ) , Inches ( 9.5 ) , Inches ( 0.35 ) ,
size = 11 , color = DIM_CLR )
# right: page number (before logo)
txb ( slide , str ( _slide_num [ 0 ] ) ,
Inches ( 10.9 ) , Inches ( 7.02 ) , Inches ( 0.6 ) , Inches ( 0.35 ) ,
size = 11 , color = DIM_CLR , align = PP_ALIGN . RIGHT )
return slide
def txb ( slide , text , x , y , w , h ,
size = 24 , bold = False , color = BODY_CLR ,
align = PP_ALIGN . LEFT , italic = False ) :
box = slide . shapes . add_textbox ( x , y , w , h )
tf = box . text_frame
tf . word_wrap = True
p = tf . paragraphs [ 0 ]
p . alignment = align
run = p . add_run ( )
run . text = text
run . font . size = Pt ( size )
run . font . bold = bold
run . font . italic = italic
run . font . color . rgb = color
run . font . name = FONT
return box
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 " ,
" Semantische Bildsuche mit PostgreSQL/pgvector und Oracle 26ai " )
# 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 ( 6.1 ) , Inches ( 4.0 ) , Inches ( 1.06 ) )
# ════════════════════════════════════════════════════════════════════════════
# Slide 2 — 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 ( 7.5 ) , Inches ( 4 ) , size = 20 )
code_box ( s , ' # 4-dimensionaler Beispielvektor \n vec_hund = [0.91, 0.12, -0.44, 0.72] \n vec_katze = [0.87, 0.18, -0.39, 0.68] \n # ähnlich! Abstand ≈ 0.04 \n vec_auto = [-0.3, -0.82, 0.91, -0.11] \n # weit entfernt ' ,
Inches ( 8.8 ) , Inches ( 1.5 ) , Inches ( 4.3 ) , Inches ( 2.6 ) , size = 12 )
txb ( s , " Vektoren machen Ähnlichkeit berechenbar. " ,
Inches ( 0.8 ) , Inches ( 5.8 ) , Inches ( 11 ) , Inches ( 0.7 ) ,
size = 22 , 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: \" trees \" findet nur Dokumente mit dem Wort \" trees \" " ,
" " ,
" Semantische Suche: \" trees \" 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 ( 2.8 ) , size = 20 )
# ════════════════════════════════════════════════════════════════════════════
# Slide 5 — CLIP-Modell
# ════════════════════════════════════════════════════════════════════════════
s = add_slide ( )
section_header ( s , " Das CLIP-Modell (OpenAI) " , ACCENT_IDB )
bullet_box ( s , [
" CLIP = Contrastive Language– " ,
" ▸ 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× " ,
" ▸ 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 \n model = SentenceTransformer( \n " clip-ViT-B-32 " ) \n \n # Bild einbetten \n vec = model.encode(image) \n # → 512 floats \n \n # Text einbetten \n vec = 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, \n wenn Bild und Text inhaltlich übereinstimmen. " ,
Inches ( 0.8 ) , Inches ( 5.0 ) , Inches ( 11.5 ) , Inches ( 1.0 ) ,
size = 18 , italic = True , color = ACCENT_IDB )
# ════════════════════════════════════════════════════════════════════════════
# 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 − " ,
] , Inches ( 0.8 ) , Inches ( 1.3 ) , Inches ( 8.5 ) , Inches ( 3.5 ) , size = 20 )
code_box ( s ,
" -- PostgreSQL \n 1 - (embedding <=> query_vec) \n \n -- Oracle 26ai \n 1 - VECTOR_DISTANCE(embedding, query_vec, COSINE) " ,
Inches ( 0.8 ) , Inches ( 5.0 ) , Inches ( 6.0 ) , Inches ( 1.9 ) , size = 13 )
txb ( s , " In der Demo: \n Score 28 % = schwache Übereinstimmung \n Score 75 % = starke Übereinstimmung " ,
Inches ( 7.5 ) , Inches ( 5.0 ) , Inches ( 5.0 ) , Inches ( 2.0 ) ,
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 \n CREATE 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 \n vec = model.encode( \" Bäume \" ) # → 512 floats \n \n # SQL \n SELECT filename, \n 1 - (embedding <=> %s ::vector) AS score \n FROM images \n ORDER BY embedding <=> %s ::vector \n LIMIT 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 ); \n CREATE 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 " ,
" ▸ 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 " ,
] , Inches ( 0.8 ) , Inches ( 1.3 ) , Inches ( 11.5 ) , Inches ( 2.2 ) , size = 20 )
code_box ( s ,
" -- Gesamte Logik in einem SQL-Statement \n SELECT filename, \n 1 - VECTOR_DISTANCE( \n foto_vek, \n VECTOR_EMBEDDING(CLIP_TXT USING :q AS data), \n COSINE \n ) AS score \n FROM VECTOR.FOTO_VEKTOR \n ORDER BY VECTOR_DISTANCE( \n foto_vek, \n VECTOR_EMBEDDING(CLIP_TXT USING :q AS data), COSINE) \n FETCH FIRST 12 ROWS ONLY; " ,
Inches ( 0.8 ) , Inches ( 3.6 ) , Inches ( 7.5 ) , Inches ( 3.3 ) , size = 13 )
bullet_box ( s , [
" :q = reiner Text aus Python " ,
" " ,
" Oracle übernimmt: " ,
" • Tokenisierung " ,
" • ONNX-Inferenz " ,
" • Vektorsuche " ,
" " ,
" → Architektur vereinfacht sich " ,
] , Inches ( 9.0 ) , Inches ( 3.6 ) , Inches ( 4.0 ) , Inches ( 3.4 ) , size = 18 , 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) \n EXEC 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 der Demo
# ════════════════════════════════════════════════════════════════════════════
s = add_slide ( )
section_header ( s , " Architektur der Demo " , ACCENT_GRN )
# Three columns
for i , ( label , port , color ) in enumerate ( [
( " pgvector " , " Port 8000 " , ACCENT_PG ) ,
( " Oracle 26ai \n (Python) " , " Port 8001 " , ACCENT_ORA ) ,
( " Oracle 26ai \n (In-DB) " , " Port 8002 " , ACCENT_IDB ) ,
] ) :
x = Inches ( 0.5 + i * 4.27 )
# Box
box = s . shapes . add_shape ( 1 , x , Inches ( 1.3 ) , Inches ( 3.8 ) , Inches ( 4.8 ) )
box . fill . solid ( )
box . fill . fore_color . rgb = RGBColor ( 0x28 , 0x29 , 0x3d )
box . line . color . rgb = color
txb ( s , label , x + Inches ( 0.1 ) , Inches ( 1.4 ) , Inches ( 3.6 ) , Inches ( 0.8 ) ,
size = 22 , bold = True , color = color , align = PP_ALIGN . CENTER )
txb ( s , port , x + Inches ( 0.1 ) , Inches ( 2.1 ) , Inches ( 3.6 ) , Inches ( 0.4 ) ,
size = 16 , color = DIM_CLR , align = PP_ALIGN . CENTER )
items = {
" pgvector " : [ " Browser /ui/ " , " FastAPI " , " CLIP (Python) " , " PostgreSQL 18 " , " pgvector 0.8.2 " ] ,
" Oracle 26ai \n (Python) " : [ " Browser /ui/ " , " FastAPI " , " CLIP (Python) " , " Oracle 26ai " , " HNSW (SGA) " ] ,
" Oracle 26ai \n (In-DB) " : [ " Browser /ui/ " , " FastAPI " , " (kein CLIP) " , " Oracle 26ai " , " VECTOR_EMBEDDING() " ] ,
} [ label ]
for j , item in enumerate ( items ) :
txb ( s , " ▸ " + item , x + Inches ( 0.2 ) , Inches ( 2.65 + j * 0.52 ) , Inches ( 3.5 ) , Inches ( 0.48 ) ,
size = 16 , color = BODY_CLR )
txb ( s , " 116 Street Fotos · CLIP ViT-B/32 · 512-dimensionale Vektoren " ,
Inches ( 0.5 ) , Inches ( 6.6 ) , Inches ( 12.33 ) , Inches ( 0.3 ) ,
size = 16 , color = DIM_CLR , align = PP_ALIGN . CENTER )
# ════════════════════════════════════════════════════════════════════════════
# Slide 14 — 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 26ai (Python) " , " Oracle 26ai (In-DB) " ) ,
( " Fotos indiziert " , " 116 " , " 116 " , " 116 " ) ,
( " Indizierungszeit " , " ~26 Sek. (CPU) " , " ~16 Sek. (CPU) " , " — (separat) " ) ,
( " Index-Typ " , " HNSW (auf Disk) " , " HNSW (im Speicher) " , " Full Table Scan " ) ,
( " 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 " ,
] , Inches ( 0.8 ) , Inches ( 1.3 ) , Inches ( 11.5 ) , Inches ( 3.5 ) , 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 , " 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) " )
dierk