feat(python): Aws Bedrock embeddings integration (#822)

Supports amazon titan, cohere english & cohere multi-lingual base
models.

python/tests/test_embeddings_slow.py

@@ -202,3 +202,38 @@ def test_gemini_embedding(tmp_path):
     tbl.add(df)
     assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
     assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
+
+
+def aws_setup():
+    try:
+        import boto3
+
+        sts = boto3.client("sts")
+        sts.get_caller_identity()
+        return True
+    except Exception:
+        return False
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    not aws_setup(), reason="AWS credentials not set or libraries not installed"
+)
+def test_bedrock_embedding(tmp_path):
+    for name in [
+        "amazon.titan-embed-text-v1",
+        "cohere.embed-english-v3",
+        "cohere.embed-multilingual-v3",
+    ]:
+        model = get_registry().get("bedrock-text").create(max_retries=0, name=name)
+
+        class TextModel(LanceModel):
+            text: str = model.SourceField()
+            vector: Vector(model.ndims()) = model.VectorField()
+
+        df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+        db = lancedb.connect(tmp_path)
+        tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+        tbl.add(df)
+        assert len(tbl.to_pandas()["vector"][0]) == model.ndims()