Update README with LanceDB examples

README.md

@@ -4,6 +4,7 @@ task_categories:
 - image-to-text
 - image-text-to-text
 - image-feature-extraction
+- lance
 language:
 - en
 tags:
@@ -18,127 +19,144 @@ size_categories:
 ---
 # Flickr30k (Lance Format)
 
-Lance-formatted version of [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/) (re-distributed via [`lmms-lab/flickr30k`](https://huggingface.co/datasets/lmms-lab/flickr30k)) — **31,783 images, each paired with 5 human-written captions**, with CLIP image **and** text embeddings stored inline and pre-built ANN indices on both.
+A Lance-formatted version of [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/), redistributed via [`lmms-lab/flickr30k`](https://huggingface.co/datasets/lmms-lab/flickr30k). Each row is one image with **5 human-written captions**, a cosine-normalized CLIP image embedding, and a cosine-normalized CLIP text embedding of the canonical caption — all stored inline and available directly from the Hub at `hf://datasets/lance-format/flickr30k-lance/data`.
 
 ## Key features
 
-- **Inline images** — full JPEG bytes per row.
-- **Pre-computed CLIP embeddings** for both image and caption text — `IVF_PQ` indices on both columns let you do cross-modal retrieval (image→caption or caption→image) without any model at query time.
-- **Full-text inverted index** on the canonical caption.
-- Self-contained: no sidecar files or external image downloads.
+- **Inline JPEG bytes** in the `image` column — no sidecar files, no image folders.
+- **Paired CLIP embeddings in the same row** — `image_emb` and `text_emb` (ViT-B/32, 512-dim, cosine-normalized) — so cross-modal retrieval is one indexed lookup.
+- **All 5 raw captions kept in `captions`** alongside a `caption` canonical string used for full-text search.
+- **Pre-built ANN, FTS, and scalar indices** covering both embedding columns, the canonical caption, and `image_id`.
+
+## Splits
+
+| Split | Rows | Notes |
+|-------|------|-------|
+| `train.lance` | 31,783 | All Flickr30k images; the `lmms-lab/flickr30k` redistribution merges the original train/val/test labels into a single split |
 
 ## Schema
 
 | Column | Type | Notes |
 |---|---|---|
-| `id` | `int64` | Row index |
+| `id` | `int64` | Row index within split (natural join key) |
 | `image` | `large_binary` | Inline JPEG bytes |
 | `image_id` | `string` | Original Flickr image id |
-| `filename` | `string` | Original filename (e.g. `1000092795.jpg`) |
+| `filename` | `string?` | Original filename (e.g. `1000092795.jpg`) |
 | `captions` | `list<string>` | All 5 captions for the image |
-| `caption` | `string` | First caption — used as canonical text for FTS / quick browsing |
+| `caption` | `string` | First caption — canonical text used for FTS |
 | `image_emb` | `fixed_size_list<float32, 512>` | CLIP image embedding (cosine-normalized) |
 | `text_emb` | `fixed_size_list<float32, 512>` | CLIP text embedding of the canonical caption |
 
 ## Pre-built indices
 
-- `IVF_PQ` on `image_emb` — `metric=cosine`
-- `IVF_PQ` on `text_emb` — `metric=cosine` (cross-modal retrieval works out of the box)
-- `INVERTED` on `caption`
-- `BTREE` on `image_id`
+- `IVF_PQ` on `image_emb` — image-side vector search (cosine)
+- `IVF_PQ` on `text_emb` — text-side vector search (cosine)
+- `INVERTED` (FTS) on `caption` — keyword and hybrid search
+- `BTREE` on `image_id` — fast lookup by Flickr image id
 
-## Splits
+## Why Lance?
 
-A single `train.lance` table containing all 31,783 rows (the `lmms-lab/flickr30k` redistribution exposes them as a single split). The original train/val/test labels are not preserved in the source parquet.
+1. **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.
+2. **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.
+3. **Native Index Support**: Lance comes with fast, on-disk, scalable vector and FTS indexes that sit right alongside the dataset on the Hub, so you can share not only your data but also your embeddings and indexes without your users needing to recompute them.
+4. **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.
+5. **Versatile Querying**: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
+6. **Data Versioning**: Every mutation commits a new version; previous versions remain intact on disk. Tags pin a snapshot by name, so retrieval systems and training runs can reproduce against an exact slice of history.
 
-## Load with Lance
+## Load with `datasets.load_dataset`
+
+You can load Lance datasets via the standard HuggingFace `datasets` interface, suitable when your pipeline already speaks `Dataset` / `IterableDataset` or you want a quick streaming sample.
 
 ```python
-import lance
+import datasets
 
-ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
-print(ds.count_rows(), ds.schema.names, ds.list_indices())
+hf_ds = datasets.load_dataset("lance-format/flickr30k-lance", split="train", streaming=True)
+for row in hf_ds.take(3):
+    print(row["caption"])
 ```
 
 ## Load with LanceDB
 
-These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.
+LanceDB is the embedded retrieval library built on top of the Lance format ([docs](https://lancedb.com/docs)), and is the interface most users interact with. It wraps the dataset as a queryable table with search and filter builders, and is the entry point used by the Search, Curate, Evolve, Train, Versioning, and Materialize-a-subset sections below.
 
 ```python
 import lancedb
 
 db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
 tbl = db.open_table("train")
-print(f"LanceDB table opened with {len(tbl)} image-caption pairs")
+print(len(tbl))
 ```
 
-## Cross-modal text→image search
+## Load with Lance
+
+`pylance` is the Python binding for the Lance format and works directly with the format's lower-level APIs. Reach for it when you want to inspect dataset internals — schema, scanner, fragments, and the list of pre-built indices.
 
 ```python
 import lance
-import pyarrow as pa
-import open_clip
-import torch
-
-# 1. Encode the query text once with the same CLIP model used at conversion.
-model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
-tokenizer = open_clip.get_tokenizer("ViT-B-32")
-model = model.eval().cuda().half()
-with torch.no_grad():
-    q = model.encode_text(tokenizer(["a man surfing at sunset"]).cuda())
-    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]
 
 ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
-emb_field = ds.schema.field("image_emb")
-query = pa.array([q.tolist()], type=emb_field.type)
-
-# 2. Nearest-neighbour search against the image embedding index.
-hits = ds.scanner(
-    nearest={"column": "image_emb", "q": query[0], "k": 10, "nprobes": 16, "refine_factor": 30},
-    columns=["image_id", "caption"],
-).to_table().to_pylist()
-for h in hits:
-    print(h)
+print(ds.count_rows(), ds.schema.names)
+print(ds.list_indices())
 ```
 
-### LanceDB cross-modal text→image search
+> **Tip — for production use, download locally first.** Streaming from the Hub works for exploration, but heavy random access and ANN search are far faster against a local copy:
+> ```bash
+> hf download lance-format/flickr30k-lance --repo-type dataset --local-dir ./flickr30k-lance
+> ```
+> Then point Lance or LanceDB at `./flickr30k-lance/data`.
 
-```python
-import lancedb, open_clip, torch
+## Search
+
+The bundled `IVF_PQ` index on `image_emb` makes cross-modal text→image retrieval a single call: encode a text query with the same CLIP model used at ingest (ViT-B/32, cosine-normalized), then pass the resulting 512-d vector to `tbl.search(...)` and target `image_emb`. The example below uses the `text_emb` already stored in row 42 as a runnable stand-in for "the CLIP encoding of a caption", so the snippet works without any model loaded.
 
-model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
-tokenizer = open_clip.get_tokenizer("ViT-B-32")
-model = model.eval().cuda().half()
-with torch.no_grad():
-    q = model.encode_text(tokenizer(["a man surfing at sunset"]).cuda())
-    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]
+```python
+import lancedb
 
 db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
 tbl = db.open_table("train")
 
-results = (
-    tbl.search(q.tolist(), vector_column_name="image_emb")
+seed = (
+    tbl.search()
+    .select(["text_emb", "caption"])
+    .limit(1)
+    .offset(42)
+    .to_list()[0]
+)
+
+hits = (
+    tbl.search(seed["text_emb"], vector_column_name="image_emb")
     .metric("cosine")
     .select(["image_id", "caption"])
     .limit(10)
     .to_list()
 )
+print("query caption:", seed["caption"])
+for r in hits:
+    print(f"  {r['image_id']:>12}  {r['caption'][:70]}")
 ```
 
-## Image→caption (image-to-text retrieval)
+Because OpenAI-style CLIP embeddings are normalized, cosine is the right metric and the first hit will typically be the source image itself — a useful sanity check. Swap `vector_column_name="image_emb"` for `text_emb` to do text→text retrieval against the canonical captions instead.
+
+Because the dataset also ships an `INVERTED` index on `caption`, the same query can be issued as a hybrid search that combines the dense vector with a keyword query. LanceDB merges the two result lists and reranks them in a single call, which is useful when a phrase like "dog playing in the snow" must literally appear in the caption but you still want CLIP to do the heavy lifting on visual similarity.
 
 ```python
-ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
-ref = ds.take([0], columns=["image_emb", "caption"]).to_pylist()[0]
-emb_field = ds.schema.field("text_emb")
-query = pa.array([ref["image_emb"]], type=emb_field.type)
-neighbors = ds.scanner(
-    nearest={"column": "text_emb", "q": query[0], "k": 10},
-    columns=["caption"],
-).to_table().to_pylist()
+hybrid_hits = (
+    tbl.search(query_type="hybrid", vector_column_name="image_emb")
+    .vector(seed["text_emb"])
+    .text("dog playing in the snow")
+    .select(["image_id", "caption"])
+    .limit(10)
+    .to_list()
+)
+for r in hybrid_hits:
+    print(f"  {r['image_id']:>12}  {r['caption'][:70]}")
 ```
 
-### LanceDB image→caption search
+Tune `metric`, `nprobes`, and `refine_factor` on the vector side to trade recall against latency.
+
+## Curate
+
+A typical curation pass for a captioning or contrastive-training workflow combines a content filter on the captions with a structural filter on the row. Stacking both inside a single filtered scan keeps the result small and explicit, and the bounded `.limit(500)` makes it cheap to inspect before committing the subset to anything downstream.
 
 ```python
 import lancedb
@@ -146,61 +164,127 @@ import lancedb
 db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
 tbl = db.open_table("train")
 
-ref = tbl.search().limit(1).select(["image_emb", "caption"]).to_list()[0]
-query_embedding = ref["image_emb"]
-
-results = (
-    tbl.search(query_embedding, vector_column_name="text_emb")
-    .metric("cosine")
-    .select(["caption"])
-    .limit(10)
+candidates = (
+    tbl.search("surfer OR surfboard OR wave")
+    .where("array_length(captions) = 5", prefilter=True)
+    .select(["image_id", "caption", "captions"])
+    .limit(500)
     .to_list()
 )
+print(f"{len(candidates)} candidates; first caption: {candidates[0]['caption'][:80]}")
 ```
 
-## Full-text search on captions
+The result is a plain list of dictionaries, ready to inspect, persist as a manifest of `image_id`s, or feed into the Evolve and Train workflows below. The `image` column is never read, so the network traffic for a 500-row candidate scan is dominated by caption text rather than JPEG bytes.
+
+## Evolve
+
+Lance stores each column independently, so a new column can be appended without rewriting the existing data. The lightest form is a SQL expression: derive the new column from columns that already exist, and Lance computes it once and persists it. The example below adds `num_captions` and a `long_caption` flag, either of which can then be used directly in `where` clauses without recomputing the predicate on every query.
+
+> **Note:** Mutations require a local copy of the dataset, since the Hub mount is read-only. See the Materialize-a-subset section at the end of this card for a streaming pattern that downloads only the rows and columns you need, or use `hf download` to pull the full split first.
 
 ```python
-import lance
-ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
-hits = ds.scanner(
-    full_text_query="dog playing in the snow",
-    columns=["image_id", "caption"],
-    limit=10,
-).to_table().to_pylist()
+import lancedb
+
+db = lancedb.connect("./flickr30k-lance/data")  # local copy required for writes
+tbl = db.open_table("train")
+
+tbl.add_columns({
+    "num_captions": "array_length(captions)",
+    "long_caption": "length(caption) >= 80",
+})
 ```
 
-### LanceDB full-text search
+If the values you want to attach already live in another table (offline labels, classifier predictions, an aesthetic or NSFW score, a second-pass caption from a different model), merge them in by joining on `image_id`:
+
+```python
+import pyarrow as pa
+
+labels = pa.table({
+    "image_id": pa.array(["1000092795", "10002456"]),
+    "scene_label": pa.array(["outdoor", "indoor"]),
+})
+tbl.merge(labels, on="image_id")
+```
+
+The original columns and indices are untouched, so existing code that does not reference the new columns continues to work unchanged. New columns become visible to every reader as soon as the operation commits. For column values that require a Python computation (e.g., running a second CLIP variant over the image bytes), Lance provides a batch-UDF API — see the [Lance data evolution docs](https://lance.org/guide/data_evolution/).
+
+## Train
+
+Projection lets a training loop read only the columns each step actually needs. LanceDB tables expose this through `Permutation.identity(tbl).select_columns([...])`, which plugs straight into the standard `torch.utils.data.DataLoader` so prefetching, shuffling, and batching behave as in any PyTorch pipeline. For a CLIP-style contrastive run, project the JPEG bytes and a sampled caption; for a reranker or probe on top of frozen features, project the precomputed embeddings instead.
 
 ```python
 import lancedb
+from lancedb.permutation import Permutation
+from torch.utils.data import DataLoader
 
 db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
 tbl = db.open_table("train")
 
-results = (
-    tbl.search("dog playing in the snow")
-    .select(["image_id", "caption"])
-    .limit(10)
-    .to_list()
-)
+train_ds = Permutation.identity(tbl).select_columns(["image", "caption"])
+loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)
+
+for batch in loader:
+    # batch carries only the projected columns; decode the JPEG bytes,
+    # tokenize the captions, encode, contrastive loss...
+    ...
 ```
 
-## Working with images
+Switching feature sets is a configuration change: passing `["image_emb", "text_emb"]` to `select_columns(...)` on the next run skips JPEG decoding entirely and reads only the cached 512-d vectors, which is the right shape for training a lightweight reranker or a linear probe.
+
+## Versioning
+
+Every mutation to a Lance dataset, whether it adds a column, merges labels, or builds an index, commits a new version. Previous versions remain intact on disk. You can list versions and inspect the history directly from the Hub copy; creating new tags requires a local copy since tags are writes.
 
 ```python
-from pathlib import Path
-import lance
-ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
-row = ds.take([0], columns=["image", "filename"]).to_pylist()[0]
-Path(row["filename"]).write_bytes(row["image"])
+import lancedb
+
+db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
+tbl = db.open_table("train")
+
+print("Current version:", tbl.version)
+print("History:", tbl.list_versions())
+print("Tags:", tbl.tags.list())
 ```
 
-## Why Lance?
+Once you have a local copy, tag a version for reproducibility:
+
+```python
+local_db = lancedb.connect("./flickr30k-lance/data")
+local_tbl = local_db.open_table("train")
+local_tbl.tags.create("clip-vitb32-v1", local_tbl.version)
+```
+
+A tagged version can be opened by name, or any version reopened by its number, against either the Hub copy or a local one:
+
+```python
+tbl_v1 = db.open_table("train", version="clip-vitb32-v1")
+tbl_v5 = db.open_table("train", version=5)
+```
+
+Pinning supports two workflows. A retrieval system locked to `clip-vitb32-v1` keeps returning stable results while the dataset evolves in parallel — newly added embeddings or labels do not change what the tag resolves to. A training experiment pinned to the same tag can be rerun later against the exact same images and captions, so changes in metrics reflect model changes rather than data drift. Neither workflow needs shadow copies or external manifest tracking.
+
+## Materialize a subset
+
+Reads from the Hub are lazy, so exploratory queries only transfer the columns and row groups they touch. Mutating operations (Evolve, tag creation) need a writable backing store, and a training loop benefits from a local copy with fast random access. Both can be served by a subset of the dataset rather than the full split. The pattern is to stream a filtered query through `.to_batches()` into a new local table; only the projected columns and matching row groups cross the wire, and the bytes never fully materialize in Python memory.
+
+```python
+import lancedb
+
+remote_db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
+remote_tbl = remote_db.open_table("train")
+
+batches = (
+    remote_tbl.search("surfer OR surfboard OR wave")
+    .where("array_length(captions) = 5")
+    .select(["image_id", "image", "caption", "captions", "image_emb", "text_emb"])
+    .to_batches()
+)
+
+local_db = lancedb.connect("./flickr30k-surf-subset")
+local_db.create_table("train", batches)
+```
 
-- One dataset carries images + image embeddings + text embeddings + indices — no sidecar files.
-- On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
-- Schema evolution: add columns (new captions, alternate embeddings, moderation labels) without rewriting the data.
+The resulting `./flickr30k-surf-subset` is a first-class LanceDB database. Every snippet in the Evolve, Train, and Versioning sections above works against it by swapping `hf://datasets/lance-format/flickr30k-lance/data` for `./flickr30k-surf-subset`.
 
 ## Source & license