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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- feature-extraction
+- image-classification
+language:
+- en
+tags:
+- laion
+- clip
+- vision-language
+- lance
+pretty_name: laion-1m-lance
+size_categories:
+- 1M<n<10M
+---
+# LAION-Subset (Lance Format)
+
+A Lance dataset of LAION image-text corpus (~1M rows) with inline JPEG bytes, CLIP embeddings (`img_emb`), and full metadata available directly from the Hub: `hf://datasets/lance-format/laion-1m/data/train.lance`.
+
+
+## Why Lance?
+
+Lance is an open-source format designed for multimodal AI data, offering significant advantages over traditional formats for modern AI workloads.
+
+- **Blazing Fast Random Access**: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
+- **Native Multimodal Support**: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
+- **Efficient Data Evolution**: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
+- **Versatile Querying**: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
+
+## Quick Start
+
+### Load with `datasets.load_dataset`
+
+```python
+import datasets
+
+hf_ds = datasets.load_dataset(
+    "lance-format/laion-1m",
+    split="train",
+    streaming=True
+)
+# Take first three rows and print captions
+for row in hf_ds.take(3):
+    print(row["caption"])
+```
+
+### Load with Lance
+
+Use Lance for ANN search, image export, and any operation that needs the vector index or binary blobs:
+
+```python
+import lance
+
+ds = lance.dataset("hf://datasets/lance-format/laion-1m/data/train.lance")
+print(ds.count_rows())
+```
+
+### Load with LanceDB
+
+These tables can also be consumed by [LanceDB](https://docs.lancedb.com/), the multimodal lakehouse for AI (built on top of Lance).
+LanceDB provides several convenience APIs for search, index creation and data updates on top of the Lance format.
+
+```python
+import lancedb
+
+db = lancedb.connect("hf://datasets/lance-format/laion-subset/data")
+tbl = db.open_table("train")
+print(f"LanceDB table opened with {len(tbl)} image-text pairs")
+```
+
+> **⚠️ HuggingFace Streaming Note**
+> 
+> **You may hit rate limits on HuggingFace's free tier.** For best performance and to avoid rate limits, pass a token for an account with a
+> Pro, Teams or Enterprise subscription (which come with much higher rate limits), or download the dataset locally:
+>
+> ```bash
+> # Download once
+> huggingface-cli download lance-format/laion-1m --repo-type dataset --local-dir ./laion`
+>
+> # Then load locally
+> ds = lance.dataset("./laion")
+> ```
+> 
+> Streaming is recommended only for quick exploration and testing.
+
+### Inspecting Existing Indices
+
+This dataset comes with a built in vector (IVF) index for image embeddings. You can inspect the prebuilt indices on the dataset:
+
+```python
+import lance
+
+dataset = lance.dataset("hf://datasets/lance-format/laion-1m/data/train.lance")
+
+# List all indices
+indices = dataset.list_indices()
+print(indices)
+```
+
+### Create New Index
+
+While this dataset comes with pre-built indices, you can also create your own custom indices if needed. For example:
+
+```python
+# ds is a local Lance dataset
+ds.create_index(
+    "img_emb",
+    index_type="IVF_PQ",
+    num_partitions=256,
+    num_sub_vectors=96,
+    replace=True,
+)
+```
+
+```python
+# ds is a local Lance dataset
+ds.create_fts_index("caption")
+```
+
+## Native Support for Multimodal Data
+
+```python
+from pathlib import Path
+
+rows = lance_ds.take([0, 1], columns=["image", "caption"]).to_pylist()
+for idx, row in enumerate(rows):
+    Path("samples").mkdir(exist_ok=True)
+    with open(f"samples/{idx}.jpg", "wb") as f:
+        f.write(row["image"])
+```
+
+In Lance, images are stored inline as binary columns (regular Lance binary, not the special blob handle used in OpenVid). They behave like any other column—scan captions without touching `image`, then `take()` when you want the bytes.
+
+## Usage Examples
+
+### 1. Browse metadata
+
+```python
+scanner = ds.scanner(columns=["caption", "url", "similarity"], limit=5)
+for row in scanner.to_table().to_pylist():
+    print(row)
+```
+
+### 2. Export images
+
+```python
+rows = ds.take(range(3), columns=["image", "caption"]).to_pylist()
+for i, row in enumerate(rows):
+    with open(f"sample_{i}.jpg", "wb") as f:
+        f.write(row["image"])
+```
+
+### 3. Vector similarity search
+
+```python
+emb_field = ds.schema.field("img_emb")
+ref = ds.take([123], columns=["img_emb"]).to_pylist()[0]
+query = pa.array([ref["img_emb"]], type=emb_field.type)
+
+neighbors = ds.scanner(
+    nearest={
+        "column": emb_field.name,
+        "q": query[0],
+        "k": 6,
+        "nprobes": 16,
+        "refine_factor": 30,
+    },
+    columns=["caption", "url", "similarity"],
+).to_table().to_pylist()
+```
+
+## Dataset Evolution
+
+Lance supports flexible schema and data evolution ([docs](https://lance.org/guide/data_evolution/)). You can add/drop columns, backfill with SQL or Python, rename fields, or change data types without rewriting the whole dataset. In practice this lets you:
+- Introduce fresh metadata (moderation labels, embeddings, quality scores) as new signals become available.
+- Add new columns to existing datasets without re-exporting terabytes of video.
+- Adjust column names or shrink storage (e.g., cast embeddings to float16) while keeping previous snapshots queryable for reproducibility.
+
+```python
+import lance
+import pyarrow as pa
+import numpy as np
+
+# Assumes you ran the export to Lance example above to store a local subset of the data
+# ds = lance.dataset("./laion_1m_local")
+
+# 1. Add a schema-only column (data to be added later)
+dataset.add_columns(pa.field("moderation_label", pa.string()))
+
+# 2. Add a column with data backfill using a SQL expression
+dataset.add_columns(
+    {
+        "moderation_label": "case WHEN \"NSFW\" > 0.5 THEN 'review' ELSE 'ok' END"
+    }
+)
+
+# 3. Generate rich columns via Python batch UDFs
+@lance.batch_udf()
+def random_embedding(batch):
+    arr = np.random.rand(batch.num_rows, 128).astype("float32")
+    return pa.RecordBatch.from_arrays(
+        [pa.FixedSizeListArray.from_arrays(arr.ravel(), 128)],
+        names=["embedding"],
+    )
+
+dataset.add_columns(random_embedding)
+
+# 4. Bring in offline annotations with merge
+labels = pa.table({
+    "id": pa.array([1, 2, 3]),
+    "label": pa.array(["horse", "rabbit", "cat"]),
+})
+dataset.merge(labels, "id")
+
+# 5. Rename or cast columns as needs change
+dataset.alter_columns({"path": "quality_bucket", "name": "quality_tier"})
+dataset.alter_columns({"path": "embedding", "data_type": pa.list_(pa.float16(), 128)})
+```
+
+These operations are automatically versioned, so prior experiments can still point to earlier versions while the dataset keeps evolving.
+
+## LanceDB
+
+LanceDB users can follow the following examples to run search queries on the dataset.
+
+### LanceDB Vector Similarity Search
+
+```python
+import lancedb
+
+# In LanceDB, you open a database, then a table
+db = lancedb.connect("hf://datasets/lance-format/laion-1m/data")
+tbl = db.open_table("train")
+query_embedding = list(range(768))
+
+results = tbl.search(query_embedding, vector_column_name="img_emb") \
+    .limit(5) \
+    .to_list()
+```
+
+### LanceDB Full-Text Search
+
+```python
+import lancedb
+
+# In LanceDB, you open a database, then a table
+db = lancedb.connect("hf://datasets/lance-format/laion-1m/data")
+tbl = db.open_table("train")
+
+results = tbl.search("dog running") \
+    .select(["caption", "url", "similarity"]) \
+    .limit(10) \
+    .to_list()
+```
+
+## Citation
+
+```
+@article{schuhmann2022laion5b,
+  title={LAION-5B: An open large-scale dataset for training next generation image-text models},
+  author={Schuhmann, Christoph and others},
+  journal={NeurIPS Datasets and Benchmarks Track},
+  year={2022}
+}
+```
+
+## License
+
+Content inherits LAION’s original licensing and safety guidelines. Review [LAION policy](https://laion.ai/blog/laion-5b/) before downstream use.

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