feat(python): add support new openai embedding functions (#912)

@PrashantDixit0

---------

Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>

python/lancedb/embeddings/openai.py

@@ -12,7 +12,7 @@
 #  limitations under the License.
 import os
 from functools import cached_property
-from typing import List, Union
+from typing import List, Optional, Union
 
 import numpy as np
 
@@ -30,10 +30,21 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
     """
 
     name: str = "text-embedding-ada-002"
+    dim: Optional[int] = None
 
     def ndims(self):
-        # TODO don't hardcode this
-        return 1536
+        return self._ndims
+
+    @cached_property
+    def _ndims(self):
+        if self.name == "text-embedding-ada-002":
+            return 1536
+        elif self.name == "text-embedding-3-large":
+            return self.dim or 3072
+        elif self.name == "text-embedding-3-small":
+            return self.dim or 1536
+        else:
+            raise ValueError(f"Unknown model name {self.name}")
 
     def generate_embeddings(
         self, texts: Union[List[str], np.ndarray]
@@ -47,7 +58,12 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
             The texts to embed
         """
         # TODO retry, rate limit, token limit
-        rs = self._openai_client.embeddings.create(input=texts, model=self.name)
+        if self.name == "text-embedding-ada-002":
+            rs = self._openai_client.embeddings.create(input=texts, model=self.name)
+        else:
+            rs = self._openai_client.embeddings.create(
+                input=texts, model=self.name, dimensions=self.ndims()
+            )
         return [v.embedding for v in rs.data]
 
     @cached_property

python/tests/test_embeddings_slow.py

@@ -23,11 +23,6 @@ import lancedb
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector
 
-try:
-    if importlib.util.find_spec("mlx.core") is not None:
-        _mlx = True
-except ImportError:
-    _mlx = None
 # These are integration tests for embedding functions.
 # They are slow because they require downloading models
 # or connection to external api
@@ -210,6 +205,13 @@ def test_gemini_embedding(tmp_path):
     assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
 
 
+try:
+    if importlib.util.find_spec("mlx.core") is not None:
+        _mlx = True
+except ImportError:
+    _mlx = None
+
+
 @pytest.mark.skipif(
     _mlx is None,
     reason="mlx tests only required for apple users.",
@@ -266,3 +268,49 @@ def test_bedrock_embedding(tmp_path):
 
         tbl.add(df)
         assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
+)
+def test_openai_embedding(tmp_path):
+    def _get_table(model):
+        class TextModel(LanceModel):
+            text: str = model.SourceField()
+            vector: Vector(model.ndims()) = model.VectorField()
+
+        db = lancedb.connect(tmp_path)
+        tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+        return tbl
+
+    model = get_registry().get("openai").create(max_retries=0)
+    tbl = _get_table(model)
+    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+
+    tbl.add(df)
+    assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
+    assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
+
+    model = (
+        get_registry()
+        .get("openai")
+        .create(max_retries=0, name="text-embedding-3-large")
+    )
+    tbl = _get_table(model)
+
+    tbl.add(df)
+    assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
+    assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
+
+    model = (
+        get_registry()
+        .get("openai")
+        .create(max_retries=0, name="text-embedding-3-large", dim=1024)
+    )
+    tbl = _get_table(model)
+
+    tbl.add(df)
+    assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
+    assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"