fix(python): scope Instructor compatibility shim

python/python/lancedb/embeddings/instructor.py

@@ -3,6 +3,7 @@
 
 
 from typing import List
+from urllib.parse import unquote, urlparse
 
 import numpy as np
 
@@ -125,9 +126,20 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
 
     @weak_lru(maxsize=1)
     def get_model(self):
-        instructor_embedding = attempt_import_or_raise(
+        huggingface_hub = attempt_import_or_raise("huggingface_hub", "huggingface-hub")
-            "InstructorEmbedding", "InstructorEmbedding"
+        missing = object()
-        )
+        original_cached_download = getattr(huggingface_hub, "cached_download", missing)
+        if original_cached_download is missing:
+            huggingface_hub.cached_download = _cached_download(huggingface_hub)
+
+        try:
+            instructor_embedding = attempt_import_or_raise(
+                "InstructorEmbedding", "InstructorEmbedding"
+            )
+        finally:
+            if original_cached_download is missing:
+                del huggingface_hub.cached_download
+
         torch = attempt_import_or_raise("torch", "torch")
 
         model = instructor_embedding.INSTRUCTOR(self.name)
@@ -140,3 +152,44 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
                 model, {torch.nn.Linear}, dtype=torch.qint8
             )
         return model
+
+
+def _cached_download(huggingface_hub):
+    """Provide the legacy download API used by sentence-transformers 2.2.x."""
+
+    def cached_download(
+        *,
+        url,
+        cache_dir=None,
+        force_filename=None,
+        library_name=None,
+        library_version=None,
+        user_agent=None,
+        use_auth_token=None,
+        **_,
+    ):
+        path = urlparse(url).path.lstrip("/")
+        try:
+            repo_id, resolved_path = path.split("/resolve/", maxsplit=1)
+            revision, filename = resolved_path.split("/", maxsplit=1)
+        except ValueError as err:
+            raise ValueError(f"Unsupported Hugging Face Hub URL: {url}") from err
+
+        repo_id = unquote(repo_id)
+        revision = unquote(revision)
+        filename = unquote(filename)
+        # sentence-transformers derives force_filename from this Hub path with
+        # os.path.join. Using the URL path beneath local_dir produces the same
+        # local destination without sending Windows separators to the Hub.
+        return huggingface_hub.hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            revision=revision,
+            local_dir=cache_dir,
+            library_name=library_name,
+            library_version=library_version,
+            user_agent=user_agent,
+            token=use_auth_token,
+        )
+
+    return cached_download

python/python/tests/test_embeddings.py

@@ -1,8 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+import ntpath
 import os
 import pickle
+import sys
+from types import ModuleType
 from typing import List, Optional, Union
 from unittest.mock import MagicMock, patch
 
@@ -522,6 +525,59 @@ def test_embedding_function_safe_model_dump(embedding_type):
             )
 
 
+def test_instructor_embedding_supports_huggingface_hub_without_cached_download(
+    tmp_path, monkeypatch
+):
+    from lancedb.embeddings.instructor import InstructorEmbeddingFunction
+
+    hub_download = MagicMock(return_value="/cache/1_Pooling/config.json")
+    huggingface_hub = ModuleType("huggingface_hub")
+    huggingface_hub.hf_hub_download = hub_download
+    torch = ModuleType("torch")
+    monkeypatch.setitem(sys.modules, "huggingface_hub", huggingface_hub)
+    monkeypatch.setitem(sys.modules, "torch", torch)
+    monkeypatch.delitem(sys.modules, "InstructorEmbedding", raising=False)
+    monkeypatch.syspath_prepend(str(tmp_path))
+
+    (tmp_path / "InstructorEmbedding.py").write_text(
+        "from huggingface_hub import cached_download\n\n"
+        "class INSTRUCTOR:\n"
+        "    def __init__(self, name):\n"
+        "        self.name = name\n"
+    )
+
+    embedding = InstructorEmbeddingFunction.create(show_progress_bar=False)
+    instructor_model = embedding.get_model()
+
+    assert instructor_model.name == "hkunlp/instructor-base"
+    assert not hasattr(huggingface_hub, "cached_download")
+
+    instructor_embedding = sys.modules["InstructorEmbedding"]
+    path = instructor_embedding.cached_download(
+        url=(
+            "https://huggingface.co/hkunlp/instructor-base/resolve/abc123/"
+            "1_Pooling/config.json"
+        ),
+        cache_dir="/cache",
+        force_filename=ntpath.join("1_Pooling", "config.json"),
+        library_name="sentence-transformers",
+        library_version="2.2.2",
+        use_auth_token="token",
+    )
+
+    assert path == "/cache/1_Pooling/config.json"
+    hub_download.assert_called_once_with(
+        repo_id="hkunlp/instructor-base",
+        filename="1_Pooling/config.json",
+        revision="abc123",
+        local_dir="/cache",
+        library_name="sentence-transformers",
+        library_version="2.2.2",
+        user_agent=None,
+        token="token",
+    )
+
+
 @patch("time.sleep")
 def test_retry(mock_sleep):
     test_function = MagicMock(side_effect=[Exception] * 9 + ["result"])