Update README with LanceDB examples
Browse files
README.md
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@@ -68,6 +68,18 @@ ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/va
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print(ds.count_rows(), ds.schema.names, ds.list_indices())
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```
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> **Tip — for production use, download locally first.**
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> ```bash
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> hf download lance-format/coco-detection-2017-lance --repo-type dataset --local-dir ./coco-detection-2017-lance
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@@ -112,6 +124,31 @@ busy = ds.scanner(
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).to_table().to_pylist()
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```
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## Visual similarity search
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```python
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@@ -129,6 +166,26 @@ neighbors = ds.scanner(
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).to_table().to_pylist()
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```
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## Why Lance?
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- One dataset carries images + boxes + categories + areas + embeddings + indices — no JSON sidecars.
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print(ds.count_rows(), ds.schema.names, ds.list_indices())
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```
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## Load with LanceDB
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These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the serverless vector database built on Lance, for simplified vector search and other queries.
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```python
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import lancedb
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db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
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tbl = db.open_table("val")
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print(f"LanceDB table opened with {len(tbl)} images")
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```
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> **Tip — for production use, download locally first.**
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> ```bash
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> hf download lance-format/coco-detection-2017-lance --repo-type dataset --local-dir ./coco-detection-2017-lance
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).to_table().to_pylist()
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```
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### Filter by classes with LanceDB
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```python
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import lancedb
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db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
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tbl = db.open_table("val")
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rows = (
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tbl.search()
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.where("array_has_all(categories_present, ['person', 'frisbee'])")
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.select(["image_id", "category_names"])
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.limit(10)
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.to_list()
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)
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busy = (
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tbl.search()
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.where("num_objects >= 5")
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.select(["image_id", "num_objects"])
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.limit(10)
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.to_list()
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)
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```
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## Visual similarity search
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```python
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).to_table().to_pylist()
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```
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### LanceDB visual similarity search
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```python
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import lancedb
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db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
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tbl = db.open_table("val")
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ref = tbl.search().limit(1).select(["image_emb"]).to_list()[0]
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query_embedding = ref["image_emb"]
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results = (
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tbl.search(query_embedding)
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.metric("cosine")
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.select(["image_id", "category_names"])
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.limit(5)
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.to_list()
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)
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```
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## Why Lance?
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- One dataset carries images + boxes + categories + areas + embeddings + indices — no JSON sidecars.
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