dierk
66f7db40b0
Three FastAPI backends comparing PostgreSQL/pgvector and Oracle 26ai for semantic image search using CLIP embeddings: Python-side embedding for both databases, plus Oracle in-database embedding via VECTOR_EMBEDDING(CLIP_TXT). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import os
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from fastapi import FastAPI, Query
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse
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from dotenv import load_dotenv
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from db_oracle import get_connection_indb
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load_dotenv()
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PHOTOS_DIR = os.getenv("PHOTOS_DIR")
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app = FastAPI()
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
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@app.get("/search")
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def search(q: str = Query(...), limit: int = Query(12)):
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conn = get_connection_indb()
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cur = conn.cursor()
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cur.execute(
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"""
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SELECT filename,
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1 - VECTOR_DISTANCE(
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foto_vek,
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VECTOR_EMBEDDING(CLIP_TXT USING :q AS data),
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COSINE
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) AS score
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FROM VECTOR.FOTO_VEKTOR
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ORDER BY VECTOR_DISTANCE(
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foto_vek,
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VECTOR_EMBEDDING(CLIP_TXT USING :q AS data),
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COSINE
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)
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FETCH FIRST :lim ROWS ONLY
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""",
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{"q": q, "lim": limit},
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)
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rows = cur.fetchall()
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cur.close()
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conn.close()
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return [{"filename": r[0], "score": round(r[1], 4)} for r in rows]
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@app.get("/stats")
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def stats():
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conn = get_connection_indb()
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cur = conn.cursor()
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cur.execute("SELECT COUNT(*) FROM VECTOR.FOTO_VEKTOR")
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count = cur.fetchone()[0]
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cur.close()
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conn.close()
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return {"count": count}
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@app.get("/photos/{filename}")
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def get_photo(filename: str):
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path = os.path.join(PHOTOS_DIR, filename)
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return FileResponse(path, media_type="image/jpeg")
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