Add Litellm Instrument Unit Test

Author: whoIam0987

instrumentation-loongsuite/loongsuite-instrumentation-litellm/src/opentelemetry/instrumentation/litellm/_embedding_wrapper.py

@@ -94,6 +94,31 @@ def __call__(self, *args, **kwargs):
                     response.usage, "total_tokens", None
                 )
 
+            # Extract embedding dimension count
+            if (
+                hasattr(response, "data")
+                and response.data
+                and len(response.data) > 0
+            ):
+                try:
+                    first_embedding = response.data[0]
+                    # Handle dict response
+                    if (
+                        isinstance(first_embedding, dict)
+                        and "embedding" in first_embedding
+                    ):
+                        embedding_vector = first_embedding["embedding"]
+                        if isinstance(embedding_vector, list):
+                            invocation.dimension_count = len(embedding_vector)
+                    # Handle object response
+                    elif hasattr(first_embedding, "embedding"):
+                        embedding_vector = first_embedding.embedding
+                        if isinstance(embedding_vector, list):
+                            invocation.dimension_count = len(embedding_vector)
+                except (IndexError, AttributeError, KeyError, TypeError):
+                    # If we can't extract dimension, just skip it
+                    pass
+
             # End Embedding invocation successfully
             self._handler.stop_embedding(invocation)
 
@@ -170,6 +195,31 @@ async def __call__(self, *args, **kwargs):
                     response.usage, "total_tokens", None
                 )
 
+            # Extract embedding dimension count
+            if (
+                hasattr(response, "data")
+                and response.data
+                and len(response.data) > 0
+            ):
+                try:
+                    first_embedding = response.data[0]
+                    # Handle dict response
+                    if (
+                        isinstance(first_embedding, dict)
+                        and "embedding" in first_embedding
+                    ):
+                        embedding_vector = first_embedding["embedding"]
+                        if isinstance(embedding_vector, list):
+                            invocation.dimension_count = len(embedding_vector)
+                    # Handle object response
+                    elif hasattr(first_embedding, "embedding"):
+                        embedding_vector = first_embedding.embedding
+                        if isinstance(embedding_vector, list):
+                            invocation.dimension_count = len(embedding_vector)
+                except (IndexError, AttributeError, KeyError, TypeError):
+                    # If we can't extract dimension, just skip it
+                    pass
+
             # End Embedding invocation successfully
             self._handler.stop_embedding(invocation)
 

instrumentation-loongsuite/loongsuite-instrumentation-litellm/tests/__init__.py

@@ -0,0 +1,14 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+

instrumentation-loongsuite/loongsuite-instrumentation-litellm/tests/test_embedding.py

@@ -0,0 +1,224 @@
+import asyncio
+import os
+from unittest.mock import patch
+
+import litellm
+
+from opentelemetry.instrumentation.litellm import LiteLLMInstrumentor
+from opentelemetry.test.test_base import TestBase
+from opentelemetry.util.genai.types import ContentCapturingMode
+
+
+class TestEmbedding(TestBase):
+    """
+    Test embedding calls with LiteLLM.
+    """
+
+    def setUp(self):
+        super().setUp()
+        # Set up environment variables for testing
+        os.environ["OPENAI_API_KEY"] = os.environ.get(
+            "OPENAI_API_KEY", "sk-..."
+        )
+        os.environ["DASHSCOPE_API_KEY"] = os.environ.get(
+            "DASHSCOPE_API_KEY", "sk-..."
+        )
+        os.environ["OPENAI_API_BASE"] = (
+            "https://dashscope.aliyuncs.com/compatible-mode/v1"
+        )
+        os.environ["DASHSCOPE_API_BASE"] = (
+            "https://dashscope.aliyuncs.com/compatible-mode/v1"
+        )
+
+        # Force experiment mode for content capture
+        self.patch_experimental = patch(
+            "opentelemetry.util.genai.span_utils.is_experimental_mode",
+            return_value=True,
+        )
+        self.patch_content_mode = patch(
+            "opentelemetry.util.genai.span_utils.get_content_capturing_mode",
+            return_value=ContentCapturingMode.SPAN_ONLY,
+        )
+
+        self.patch_experimental.start()
+        self.patch_content_mode.start()
+
+        # Instrument LiteLLM
+        LiteLLMInstrumentor().instrument(
+            tracer_provider=self.tracer_provider,
+        )
+
+    def tearDown(self):
+        super().tearDown()
+        # Uninstrument to avoid affecting other tests
+        LiteLLMInstrumentor().uninstrument()
+        self.patch_experimental.stop()
+        self.patch_content_mode.stop()
+
+    def test_sync_embedding_single_text(self):
+        """
+        Test synchronous embedding with single text input.
+        """
+
+        # Business demo: Single text embedding
+        response = litellm.embedding(
+            model="openai/text-embedding-v1",
+            api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            input="The quick brown fox jumps over the lazy dog",
+            encoding_format="float",
+        )
+
+        # Verify the response
+        self.assertIsNotNone(response)
+        self.assertTrue(hasattr(response, "data"))
+        self.assertGreater(len(response.data), 0)
+
+        # Verify embedding is a list of numbers
+        embedding = response.data[0].get("embedding")
+        self.assertIsInstance(embedding, list)
+        self.assertGreater(len(embedding), 0)
+
+        # Get spans
+        spans = self.get_finished_spans()
+        self.assertEqual(
+            len(spans), 1, "Expected exactly one span for embedding call"
+        )
+        span = spans[0]
+
+        # Verify span kind
+        self.assertEqual(
+            span.attributes.get("gen_ai.span.kind"),
+            "EMBEDDING",
+            "Span kind should be EMBEDDING",
+        )
+
+        # Verify model
+        self.assertIn("gen_ai.request.model", span.attributes)
+        self.assertEqual(
+            span.attributes.get("gen_ai.request.model"), "text-embedding-v1"
+        )
+
+        # Verify token usage (required for embedding)
+        self.assertIn("gen_ai.usage.input_tokens", span.attributes)
+        self.assertGreater(span.attributes.get("gen_ai.usage.input_tokens"), 0)
+
+        # Verify embedding dimension count
+        self.assertIn("gen_ai.embeddings.dimension.count", span.attributes)
+        dimension = span.attributes.get("gen_ai.embeddings.dimension.count")
+        self.assertEqual(dimension, len(embedding))
+        self.assertGreater(dimension, 0)
+
+    def test_sync_embedding_multiple_texts(self):
+        """
+        Test synchronous embedding with multiple text inputs.
+        """
+
+        # Business demo: Batch embedding
+        texts = [
+            "Hello, world!",
+            "Artificial intelligence is fascinating.",
+            "LiteLLM makes LLM integration easy.",
+        ]
+
+        response = litellm.embedding(
+            model="openai/text-embedding-v1",
+            api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            input=texts,
+            encoding_format="float",
+        )
+
+        # Verify the response
+        self.assertIsNotNone(response)
+        self.assertTrue(hasattr(response, "data"))
+        self.assertEqual(
+            len(response.data),
+            len(texts),
+            "Should have embedding for each text",
+        )
+
+        # Verify each embedding
+        self.assertIsInstance(response.data[0].get("embedding"), list)
+        self.assertGreater(len(response.data[0].get("embedding")), 0)
+
+        spans = self.get_finished_spans()
+        self.assertEqual(len(spans), 1)
+        span = spans[0]
+
+        self.assertEqual(span.attributes.get("gen_ai.span.kind"), "EMBEDDING")
+        self.assertGreater(span.attributes.get("gen_ai.usage.input_tokens"), 0)
+
+    def test_async_embedding(self):
+        """
+        Test asynchronous embedding call.
+        """
+
+        async def run_async_embedding():
+            response = await litellm.aembedding(
+                model="openai/text-embedding-v1",
+                input="Async test",
+                api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+                encoding_format="float",
+            )
+            return response
+
+        response = asyncio.run(run_async_embedding())
+
+        # Verify response
+        self.assertIsNotNone(response)
+        self.assertTrue(hasattr(response, "data"))
+        self.assertGreater(len(response.data), 0)
+
+        spans = self.get_finished_spans()
+        self.assertEqual(len(spans), 1)
+        span = spans[0]
+
+        self.assertEqual(span.attributes.get("gen_ai.span.kind"), "EMBEDDING")
+        self.assertIn("gen_ai.request.model", span.attributes)
+        self.assertIn("gen_ai.usage.input_tokens", span.attributes)
+        self.assertIn("gen_ai.embeddings.dimension.count", span.attributes)
+
+    def test_embedding_with_different_models(self):
+        """
+        Test embedding with different model providers.
+        """
+
+        response = litellm.embedding(
+            model="openai/text-embedding-v1",
+            api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            input="Testing different embedding models",
+            encoding_format="float",
+        )
+
+        self.assertIsNotNone(response)
+        spans = self.get_finished_spans()
+        self.assertEqual(len(spans), 1)
+
+        span = spans[0]
+        self.assertIn("gen_ai.request.model", span.attributes)
+        self.assertEqual(
+            span.attributes.get("gen_ai.request.model"), "text-embedding-v1"
+        )
+
+    def test_embedding_empty_input(self):
+        """
+        Test embedding with edge case inputs.
+        """
+
+        response = litellm.embedding(
+            model="openai/text-embedding-v1",
+            api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            input="Hi",
+            encoding_format="float",
+        )
+
+        # Verify response
+        self.assertIsNotNone(response)
+        self.assertTrue(hasattr(response, "data"))
+        self.assertGreater(len(response.data), 0)
+
+        spans = self.get_finished_spans()
+        self.assertEqual(len(spans), 1)
+
+        span = spans[0]
+        self.assertEqual(span.attributes.get("gen_ai.span.kind"), "EMBEDDING")
+        self.assertIn("gen_ai.usage.input_tokens", span.attributes)