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Kacper Łukawski
2026-01-22 14:39:14 +01:00
parent 3d5fa5da35
commit d51e2b40b2
3 changed files with 5 additions and 4 deletions
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qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
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@@ -164,7 +164,7 @@ from scipy.cluster.vq import kmeans2
# Embed a document image
image_path = "images/financial-report.png"
embeddings = list(model.embed_images([image_path]))[0]
embeddings = list(model.embed_image([image_path]))[0]
def hierarchical_pool(embeddings: np.ndarray, k: int) -> np.ndarray:
"""Pool embeddings using k-means clustering."""
@@ -187,7 +187,7 @@ for k in [16, 32, 64, 128]:
## What's Next
You've learned two complementary strategies for reducing the number of vectors per document:
This lesson covered two complementary strategies for reducing the number of vectors per document:
- **Row/column pooling**: Exploits spatial structure in image embeddings for a fixed reduction (32x for ColPali)
- **Hierarchical pooling**: Content-adaptive clustering that works for any multi-vector representation