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96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import os
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import sys
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import time
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from unittest.mock import Mock, patch
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import json
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from litellm.main import completion
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import opentelemetry.exporter.otlp.proto.grpc.trace_exporter
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system-path
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from litellm.integrations._types.open_inference import SpanAttributes
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from litellm.integrations.arize.arize import ArizeConfig, ArizeLogger
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import litellm
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from litellm.types.utils import Choices
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def test_arize_callback():
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litellm.callbacks = ["arize"]
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os.environ["ARIZE_SPACE_KEY"] = "test_space_key"
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os.environ["ARIZE_API_KEY"] = "test_api_key"
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os.environ["ARIZE_ENDPOINT"] = "https://otlp.arize.com/v1"
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# Set the batch span processor to quickly flush after a span has been added
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# This is to ensure that the span is exported before the test ends
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os.environ["OTEL_BSP_MAX_QUEUE_SIZE"] = "1"
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os.environ["OTEL_BSP_MAX_EXPORT_BATCH_SIZE"] = "1"
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os.environ["OTEL_BSP_SCHEDULE_DELAY_MILLIS"] = "1"
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os.environ["OTEL_BSP_EXPORT_TIMEOUT_MILLIS"] = "5"
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with patch.object(
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opentelemetry.exporter.otlp.proto.grpc.trace_exporter.OTLPSpanExporter,
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"export",
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new=Mock(),
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) as patched_export:
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completion(
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model="openai/test-model",
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messages=[{"role": "user", "content": "arize test content"}],
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stream=False,
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mock_response="hello there!",
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)
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time.sleep(1) # Wait for the batch span processor to flush
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assert patched_export.called
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def test_arize_set_attributes():
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"""
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Test setting attributes for Arize
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"""
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from unittest.mock import MagicMock
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from litellm.types.utils import ModelResponse
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span = MagicMock()
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kwargs = {
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"role": "user",
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"content": "simple arize test",
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"model": "gpt-4o",
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"messages": [{"role": "user", "content": "basic arize test"}],
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"standard_logging_object": {
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"model_parameters": {"user": "test_user"},
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"metadata": {"key": "value", "key2": None},
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},
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}
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response_obj = ModelResponse(
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usage={"total_tokens": 100, "completion_tokens": 60, "prompt_tokens": 40},
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choices=[Choices(message={"role": "assistant", "content": "response content"})],
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)
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ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
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assert span.set_attribute.call_count == 14
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span.set_attribute.assert_any_call(
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SpanAttributes.METADATA, json.dumps({"key": "value", "key2": None})
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)
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span.set_attribute.assert_any_call(SpanAttributes.LLM_MODEL_NAME, "gpt-4o")
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span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
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span.set_attribute.assert_any_call(SpanAttributes.INPUT_VALUE, "basic arize test")
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span.set_attribute.assert_any_call("llm.input_messages.0.message.role", "user")
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span.set_attribute.assert_any_call(
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"llm.input_messages.0.message.content", "basic arize test"
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)
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span.set_attribute.assert_any_call(
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SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
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)
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span.set_attribute.assert_any_call(SpanAttributes.USER_ID, "test_user")
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span.set_attribute.assert_any_call(SpanAttributes.OUTPUT_VALUE, "response content")
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span.set_attribute.assert_any_call(
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"llm.output_messages.0.message.role", "assistant"
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)
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span.set_attribute.assert_any_call(
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"llm.output_messages.0.message.content", "response content"
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)
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span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100)
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span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60)
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span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40)
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