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File size: 1,785 Bytes
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image_embed.py — CLIP embeddings via fastembed (ONNX / onnxruntime, NO torch).
CLIP puts images and text in ONE shared vector space, so the same collection can
be searched by an uploaded photo (vision encoder) OR by text (text encoder).
fastembed runs on onnxruntime, whose signed DLLs pass Windows App Control —
unlike torch-based CLIP.
Models (512-dim, aligned):
images: Qdrant/clip-ViT-B-32-vision
text : Qdrant/clip-ViT-B-32-text
"""
IMG_MODEL = "Qdrant/clip-ViT-B-32-vision"
TXT_MODEL = "Qdrant/clip-ViT-B-32-text"
_img = None
_txt = None
def get_image_model():
global _img
if _img is None:
from fastembed import ImageEmbedding
_img = ImageEmbedding(model_name=IMG_MODEL)
return _img
def get_text_model():
global _txt
if _txt is None:
from fastembed import TextEmbedding
_txt = TextEmbedding(model_name=TXT_MODEL)
return _txt
def embed_images(paths):
"""List of image file paths -> list of 512-dim vectors (as python lists)."""
return [v.tolist() for v in get_image_model().embed(paths)]
def embed_texts(texts):
"""List of strings -> list of 512-dim CLIP-text vectors (as python lists)."""
return [v.tolist() for v in get_text_model().embed(texts)]
def embed_image_dataurl(data_url):
"""Embed a base64 data-URL image (from the web upload). Returns one vector."""
import base64
import os
import tempfile
if "," in data_url:
data_url = data_url.split(",", 1)[1]
raw = base64.b64decode(data_url)
fd, path = tempfile.mkstemp(suffix=".jpg")
try:
with os.fdopen(fd, "wb") as fh:
fh.write(raw)
return embed_images([path])[0]
finally:
try:
os.remove(path)
except OSError:
pass
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