[NLP] Add prefix_string config option to the import model hub script

eland/cli/eland_import_hub_model.py

@@ -128,6 +128,19 @@ def get_arg_parser():
         "--ca-certs", required=False, default=DEFAULT, help="Path to CA bundle"
     )
 
+    parser.add_argument(
+        "--ingest-prefix",
+        required=False,
+        default=None,
+        help="String to prepend to model input at ingest",
+    )
+    parser.add_argument(
+        "--search-prefix",
+        required=False,
+        default=None,
+        help="String to prepend to model input at search",
+    )
+
     return parser
 
 
@@ -244,6 +257,8 @@ def main():
                 task_type=args.task_type,
                 es_version=cluster_version,
                 quantize=args.quantize,
+                ingest_prefix=args.ingest_prefix,
+                search_prefix=args.search_prefix,
             )
             model_path, config, vocab_path = tm.save(tmp_dir)
         except TaskTypeError as err:

eland/ml/pytorch/nlp_ml_model.py

@@ -308,6 +308,23 @@ def to_dict(self) -> t.Dict[str, t.Any]:
         return self.__dict__
 
 
+class PrefixStrings:
+    def __init__(
+        self, *, ingest_prefix: t.Optional[str], search_prefix: t.Optional[str]
+    ):
+        self.ingest_prefix = ingest_prefix
+        self.search_prefix = search_prefix
+
+    def to_dict(self) -> t.Dict[str, t.Any]:
+        config = {}
+        if self.ingest_prefix is not None:
+            config["ingest"] = self.ingest_prefix
+        if self.search_prefix is not None:
+            config["search"] = self.search_prefix
+
+        return config
+
+
 class NlpTrainedModelConfig:
     def __init__(
         self,
@@ -318,13 +335,15 @@ def __init__(
         metadata: t.Optional[dict] = None,
         model_type: t.Union["t.Literal['pytorch']", str] = "pytorch",
         tags: t.Optional[t.Union[t.List[str], t.Tuple[str, ...]]] = None,
+        prefix_strings: t.Optional[PrefixStrings],
     ):
         self.tags = tags
         self.description = description
         self.inference_config = inference_config
         self.input = input
         self.metadata = metadata
         self.model_type = model_type
+        self.prefix_strings = prefix_strings
 
     def to_dict(self) -> t.Dict[str, t.Any]:
         return {

eland/ml/pytorch/transformers.py

@@ -53,6 +53,7 @@
     NlpTrainedModelConfig,
     NlpXLMRobertaTokenizationConfig,
     PassThroughInferenceOptions,
+    PrefixStrings,
     QuestionAnsweringInferenceOptions,
     TextClassificationInferenceOptions,
     TextEmbeddingInferenceOptions,
@@ -596,6 +597,8 @@ def __init__(
         es_version: Optional[Tuple[int, int, int]] = None,
         quantize: bool = False,
         access_token: Optional[str] = None,
+        ingest_prefix: Optional[str] = None,
+        search_prefix: Optional[str] = None,
     ):
         """
         Loads a model from the Hugging Face repository or local file and creates
@@ -618,11 +621,22 @@ def __init__(
 
         quantize: bool, default False
             Quantize the model.
+
+        access_token: Optional[str]
+            For the HuggingFace Hub private model access
+
+        ingest_prefix: Optional[str]
+            Prefix string to prepend to input at ingest
+
+        search_prefix: Optional[str]
+            Prefix string to prepend to input at search
         """
 
         self._model_id = model_id
         self._access_token = access_token
         self._task_type = task_type.replace("-", "_")
+        self._ingest_prefix = ingest_prefix
+        self._search_prefix = search_prefix
 
         # load Hugging Face model and tokenizer
         # use padding in the tokenizer to ensure max length sequences are used for tracing (at call time)
@@ -783,6 +797,19 @@ def _create_config(
             "per_allocation_memory_bytes": per_allocation_memory_bytes,
         }
 
+        prefix_strings = (
+            PrefixStrings(
+                ingest_prefix=self._ingest_prefix, search_prefix=self._search_prefix
+            )
+            if self._ingest_prefix or self._search_prefix
+            else None
+        )
+        prefix_strings_supported = es_version is None or es_version >= (8, 12, 0)
+        if not prefix_strings_supported and prefix_strings:
+            raise Exception(
+                f"The Elasticsearch cluster version {es_version} does not support prefix strings. Support was added in version 8.12.0"
+            )
+
         return NlpTrainedModelConfig(
             description=f"Model {self._model_id} for task type '{self._task_type}'",
             model_type="pytorch",
@@ -791,6 +818,7 @@ def _create_config(
                 field_names=["text_field"],
             ),
             metadata=metadata,
+            prefix_strings=prefix_strings,
         )
 
     def _get_per_deployment_memory(self) -> float:

tests/ml/pytorch/test_pytorch_model_config_pytest.py

@@ -154,13 +154,13 @@
     MODEL_CONFIGURATIONS = []
 
 
-@pytest.mark.skip(reason="https://github.com/elastic/eland/issues/633")
 class TestModelConfguration:
+    @pytest.mark.skip(reason="https://github.com/elastic/eland/issues/633")
     @pytest.mark.parametrize(
         "model_id,task_type,config_type,tokenizer_type,max_sequence_len,embedding_size",
         MODEL_CONFIGURATIONS,
     )
-    def test_text_prediction(
+    def test_model_config(
         self,
         model_id,
         task_type,
@@ -170,7 +170,6 @@ def test_text_prediction(
         embedding_size,
     ):
         with tempfile.TemporaryDirectory() as tmp_dir:
-            print("loading model " + model_id)
             tm = TransformerModel(
                 model_id=model_id,
                 task_type=task_type,
@@ -183,6 +182,7 @@ def test_text_prediction(
             assert isinstance(config.inference_config, config_type)
             tokenization = config.inference_config.tokenization
             assert isinstance(config.metadata, dict)
+            assert config.prefix_strings is None
             assert (
                 "per_deployment_memory_bytes" in config.metadata
                 and config.metadata["per_deployment_memory_bytes"] > 0
@@ -210,3 +210,28 @@ def test_text_prediction(
                 assert len(config.inference_config.classification_labels) > 0
 
             del tm
+
+    def test_model_config_with_prefix_string(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tm = TransformerModel(
+                model_id="sentence-transformers/all-distilroberta-v1",
+                task_type="text_embedding",
+                es_version=(8, 12, 0),
+                quantize=False,
+                ingest_prefix="INGEST:",
+                search_prefix="SEARCH:",
+            )
+            _, config, _ = tm.save(tmp_dir)
+            assert config.prefix_strings.to_dict()["ingest"] == "INGEST:"
+            assert config.prefix_strings.to_dict()["search"] == "SEARCH:"
+
+    def test_model_config_with_prefix_string_not_supported(self):
+        with pytest.raises(Exception):
+            TransformerModel(
+                model_id="sentence-transformers/all-distilroberta-v1",
+                task_type="text_embedding",
+                es_version=(8, 11, 0),
+                quantize=False,
+                ingest_prefix="INGEST:",
+                search_prefix="SEARCH:",
+            )