add nvidia embedding implementation for new signature, task_type, output_dimention, text_truncation

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -142,18 +142,38 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         #
         # we can ignore str and always pass List[str] to OpenAI
         #
-        flat_contents = [
+        flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents]
-            item.text if isinstance(item, TextContentItem) else item
-            for content in contents
-            for item in (content if isinstance(content, list) else [content])
-        ]
         input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents]
         model = self.get_provider_model_id(model_id)
 
+        extra_body = {}
+
+        if text_truncation is not None:
+            text_truncation_options = {
+                TextTruncation.none: "NONE",
+                TextTruncation.end: "END",
+                TextTruncation.start: "START",
+            }
+            if text_truncation not in text_truncation_options:
+                raise ValueError(f"Invalid text_truncation: {text_truncation}")
+            extra_body["truncate"] = text_truncation_options[text_truncation]
+
+        if output_dimension is not None:
+            extra_body["dimensions"] = output_dimension
+
+        if task_type is not None:
+            task_type_options = {
+                EmbeddingTaskType.document: "DOCUMENT",
+                EmbeddingTaskType.query: "QUERY",
+            }
+            if task_type not in task_type_options:
+                raise ValueError(f"Invalid task_type: {task_type}")
+            extra_body["input_type"] = task_type_options[task_type]
+
         response = await self._client.embeddings.create(
             model=model,
             input=input,
-            # extra_body={"input_type": "passage"|"query"},  # TODO(mf): how to tell caller's intent?
+            extra_body=extra_body,
         )
 
         #

tests/client-sdk/inference/test_embedding.py

@@ -14,6 +14,23 @@
 #   - array of a text (TextContentItem)
 #  Types of output:
 #   - list of list of floats
+#  Params:
+#   - text_truncation
+#     - absent w/ long text -> error
+#     - none w/ long text -> error
+#     - absent w/ short text -> ok
+#     - none w/ short text -> ok
+#     - end w/ long text -> ok
+#     - end w/ short text -> ok
+#     - start w/ long text -> ok
+#     - start w/ short text -> ok
+#   - output_dimension
+#     - response dimension matches
+#   - task_type, only for asymmetric models
+#     - query embedding != passage embedding
+#  Negative:
+#   - long string
+#   - long text
 #
 # Todo:
 #  - negative tests
@@ -23,8 +40,6 @@
 #      - empty text
 #      - empty image
 #    - long
-#      - long string
-#      - long text
 #      - large image
 #      - appropriate combinations
 #    - batch size
@@ -48,10 +63,14 @@ from llama_stack_client.types.shared.interleaved_content import (
     TextContentItem,
 )
 
+from llama_stack.apis.inference import EmbeddingTaskType, TextTruncation
+
 DUMMY_STRING = "hello"
 DUMMY_STRING2 = "world"
+DUMMY_LONG_STRING = "NVDA " * 10240
 DUMMY_TEXT = TextContentItem(text=DUMMY_STRING, type="text")
 DUMMY_TEXT2 = TextContentItem(text=DUMMY_STRING2, type="text")
+DUMMY_LONG_TEXT = TextContentItem(text=DUMMY_LONG_STRING, type="text")
 # TODO(mf): add a real image URL and base64 string
 DUMMY_IMAGE_URL = ImageContentItem(
     image=ImageContentItemImage(url=ImageContentItemImageURL(uri="https://example.com/image.jpg")), type="image"
@@ -96,3 +115,80 @@ def test_embedding_image(llama_stack_client, embedding_model_id, contents):
     assert len(response.embeddings) == sum(len(content) if isinstance(content, list) else 1 for content in contents)
     assert isinstance(response.embeddings[0], list)
     assert isinstance(response.embeddings[0][0], float)
+
+
+@pytest.mark.parametrize(
+    "text_truncation",
+    [
+        TextTruncation.end,
+        TextTruncation.start,
+    ],
+)
+@pytest.mark.parametrize(
+    "contents",
+    [
+        [DUMMY_LONG_TEXT],
+        [DUMMY_STRING],
+    ],
+    ids=[
+        "long",
+        "short",
+    ],
+)
+def test_embedding_truncation(llama_stack_client, embedding_model_id, text_truncation, contents):
+    response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=contents, text_truncation=text_truncation
+    )
+    assert isinstance(response, EmbeddingsResponse)
+    assert len(response.embeddings) == 1
+    assert isinstance(response.embeddings[0], list)
+    assert isinstance(response.embeddings[0][0], float)
+
+
+@pytest.mark.parametrize(
+    "text_truncation",
+    [
+        None,
+        TextTruncation.none,
+    ],
+)
+@pytest.mark.parametrize(
+    "contents",
+    [
+        [DUMMY_LONG_TEXT],
+        [DUMMY_LONG_STRING],
+    ],
+    ids=[
+        "long-text",
+        "long-str",
+    ],
+)
+def test_embedding_truncation_error(llama_stack_client, embedding_model_id, text_truncation, contents):
+    response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=[DUMMY_LONG_TEXT], text_truncation=text_truncation
+    )
+    assert isinstance(response, EmbeddingsResponse)
+    assert len(response.embeddings) == 1
+    assert isinstance(response.embeddings[0], list)
+    assert isinstance(response.embeddings[0][0], float)
+
+
+@pytest.mark.xfail(reason="Only valid for model supporting dimension reduction")
+def test_embedding_output_dimension(llama_stack_client, embedding_model_id):
+    base_response = llama_stack_client.inference.embeddings(model_id=embedding_model_id, contents=[DUMMY_STRING])
+    test_response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=[DUMMY_STRING], output_dimension=32
+    )
+    assert len(base_response.embeddings[0]) != len(test_response.embeddings[0])
+    assert len(test_response.embeddings[0]) == 32
+
+
+@pytest.mark.xfail(reason="Only valid for model supporting task type")
+def test_embedding_task_type(llama_stack_client, embedding_model_id):
+    query_embedding = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=[DUMMY_STRING], task_type=EmbeddingTaskType.query
+    )
+    document_embedding = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=[DUMMY_STRING], task_type=EmbeddingTaskType.document
+    )
+    assert query_embedding.embeddings != document_embedding.embeddings