caikit_nlp/modules/text_embedding/embedding.py

@@ -15,7 +15,7 @@
 # Standard
 from collections.abc import Sized
 from enum import Enum, auto
-from typing import Callable, Dict, List, NamedTuple, Optional, TypeVar, Union
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, TypeVar, Union
 import importlib
 import os
 import time
@@ -178,6 +178,20 @@ def load(cls, model_path: str, *args, **kwargs) -> "EmbeddingModule":
 
         return cls(model)
 
+    @property
+    def public_model_info(cls) -> Dict[str, Any]:  # pylint: disable=no-self-argument
+        """Helper property to return public metadata about a specific Model. This
+        function is separate from `metdata` as that contains the entire ModelConfig
+        which might not want to be shared/exposed.
+
+        Returns:
+            Dict[str, str]: A dictionary of this models's public metadata
+        """
+        return {
+            "max_seq_length": cls.model.max_seq_length,
+            "sentence_embedding_dimension": cls.model.get_sentence_embedding_dimension(),
+        }
+
     @classmethod
     def _get_ipex(cls, ipex_flag):
         """Get IPEX optimization library if enabled and available, else return False

pyproject.toml

@@ -14,7 +14,7 @@ classifiers=[
     "License :: OSI Approved :: Apache Software License"
 ]
 dependencies = [
-    "caikit[runtime-grpc,runtime-http]>=0.26.17,<0.27.0",
+    "caikit[runtime-grpc,runtime-http]>=0.26.27,<0.27.0",
     "caikit-tgis-backend>=0.1.27,<0.2.0",
     # TODO: loosen dependencies
     "grpcio>=1.62.2", # explicitly pin grpc dependencies to a recent version to avoid pip backtracking

tests/modules/text_embedding/test_embedding.py

@@ -197,6 +197,21 @@ def test_save_load_and_run():
     _assert_is_expected_embedding_result(result)
 
 
+def test_public_model_info():
+    """Check if we can get model info successfully"""
+    model_id = "model_id"
+    with tempfile.TemporaryDirectory(suffix="-1st") as model_dir:
+        model_path = os.path.join(model_dir, model_id)
+        BOOTSTRAPPED_MODEL.save(model_path)
+        new_model = EmbeddingModule.load(model_path)
+
+    result = new_model.public_model_info
+    assert "max_seq_length" in result
+    assert "sentence_embedding_dimension" in result
+    assert type(result["max_seq_length"]) is int
+    assert type(result["sentence_embedding_dimension"]) is int
+
+
 @pytest.mark.parametrize(
     "model_path", ["", " ", " " * 100], ids=["empty", "space", "spaces"]
 )