chore: import upstream snapshot with attribution

examples/python/lancedb/build_table.py

@@ -0,0 +1,98 @@
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+
+import lancedb
+import pyarrow as pa
+from lancedb.embeddings import EmbeddingFunction, get_registry
+from lancedb.pydantic import LanceModel, Vector
+
+FASHION_VOCAB = [
+    "men", "women", "tshirt", "shirt", "jeans", "shoes", "sneakers", "heels",
+    "jacket", "dress", "blue", "red", "black", "white", "green", "running",
+    "casual", "formal", "sports", "summer", "winter"
+]
+_INDEX = {token: i for i, token in enumerate(FASHION_VOCAB)}
+
+_PRODUCTS = [
+    ("Men", "Tshirts", "Blue", "Roadster Men Blue Casual Tshirt"),
+    ("Men", "Tshirts", "Black", "HRX Men Black Sports Tshirt"),
+    ("Men", "Shoes", "White", "Nike Men White Running Sneakers"),
+    ("Men", "Shoes", "Black", "Puma Men Black Formal Shoes"),
+    ("Men", "Jeans", "Blue", "Levis Men Blue Casual Jeans"),
+    ("Women", "Tshirts", "Red", "Roadster Women Red Casual Tshirt"),
+    ("Women", "Shoes", "Red", "Steve Madden Women Red Heels"),
+    ("Women", "Shoes", "White", "Adidas Women White Running Sneakers"),
+    ("Women", "Dress", "Black", "Zara Women Black Formal Dress"),
+    ("Women", "Jeans", "Blue", "H&M Women Blue Summer Jeans"),
+]
+
+
+def _tokens(text: str) -> list[str]:
+    return [word.strip(".,").lower() for word in text.split()]
+
+
+def _embed(text: str) -> list[float]:
+    vector = [0.0] * len(FASHION_VOCAB)
+    for token in _tokens(text):
+        idx = _INDEX.get(token)
+        if idx is not None:
+            vector[idx] = 1.0
+    norm = sum(value * value for value in vector)**0.5 or 1.0
+    return [value / norm for value in vector]
+
+
+@get_registry().register("fashion-keyword")
+class KeywordEmbedding(EmbeddingFunction):
+
+    def ndims(self) -> int:
+        return len(FASHION_VOCAB)
+
+    def compute_query_embeddings(self, query, *args, **kwargs):
+        if isinstance(query, str):
+            return [_embed(query)]
+        return [_embed(str(item)) for item in query]
+
+    def compute_source_embeddings(self, texts, *args, **kwargs):
+        items = texts.to_pylist() if isinstance(texts,
+                                                pa.Array) else list(texts)
+        return [_embed(str(item)) for item in items]
+
+
+def build_table(uri: str, table_name: str = "fashion") -> None:
+    func = get_registry().get("fashion-keyword").create()
+
+    class Product(LanceModel):
+        id: int
+        gender: str
+        articleType: str
+        baseColour: str
+        productDisplayName: str = func.SourceField()
+        image_bytes: bytes
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    db = lancedb.connect(uri)
+    if table_name in db.table_names():
+        db.drop_table(table_name)
+    table = db.create_table(table_name, schema=Product)
+    rows = []
+    for idx, (gender, article, colour, name) in enumerate(_PRODUCTS, start=1):
+        rows.append({
+            "id": idx,
+            "gender": gender,
+            "articleType": article,
+            "baseColour": colour,
+            "productDisplayName": name,
+            "image_bytes": b"\xff\xd8\xff" + name.encode(),
+        })
+    table.add(rows)

examples/python/lancedb/lancedb_resource.py

@@ -0,0 +1,72 @@
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+
+import asyncio
+import tempfile
+
+from build_table import build_table
+
+from mirage import MountMode, Workspace
+from mirage.resource.lancedb import LanceDBConfig, LanceDBResource
+
+
+async def show(ws: Workspace, cmd: str) -> None:
+    print(f"\n=== {cmd} ===")
+    result = await ws.execute(cmd)
+    print((await result.stdout_str()).rstrip())
+
+
+async def main() -> None:
+    uri = tempfile.mkdtemp(prefix="mirage-fashion-")
+    build_table(uri, "fashion")
+
+    config = LanceDBConfig(
+        uri=uri,
+        table="fashion",
+        group_by=["gender", "articleType", "baseColour"],
+        id_column="id",
+        title_column="productDisplayName",
+        blob_column="image_bytes",
+        blob_ext="jpg",
+        text_column="productDisplayName",
+        vector_column="vector",
+        search_limit=4,
+    )
+    ws = Workspace({"/fashion/": LanceDBResource(config)}, mode=MountMode.READ)
+
+    print(f"=== mounted LanceDB table 'fashion' ({uri}) at /fashion/ ===")
+
+    await show(ws, "ls /fashion/")
+    await show(ws, "tree -L 2 /fashion/")
+    await show(ws, "ls /fashion/Men/Shoes")
+    await show(ws, "cat /fashion/Men/Shoes/White/3.md")
+    await show(ws, "head -n 3 /fashion/Men/Shoes/White/3.md")
+    await show(ws, "tail -n 2 /fashion/Men/Shoes/White/3.md")
+
+    print("\n=== stat /fashion/Men/Shoes/White/3.jpg (raw image bytes) ===")
+    r = await ws.execute("stat -c '%s' /fashion/Men/Shoes/White/3.jpg")
+    print(f"  image size: {(await r.stdout_str()).strip()} bytes")
+
+    await show(ws, 'search "white running sneakers" /fashion')
+
+    await show(ws, "grep -ril blue /fashion/Women")
+    await show(ws, "rg -li running /fashion/Men")
+
+    print("\n=== find /fashion -name '*.md' | wc -l ===")
+    r = await ws.execute("find /fashion -name '*.md' | wc -l")
+    print(f"  product cards: {(await r.stdout_str()).strip()}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())