Add unit tests for OpenAILLMAdapter.get_llm_invocation_params(), focusing on messages specifically. Also, fix a bug in OpenAILLMAdapter (found thanks to the unit tests) where it wasn't "unwrapping" LLMSpecificMessages.
This commit is contained in:
Paul Kompfner
@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.llm_context import (
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LLMContext,
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LLMContextMessage,
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LLMContextToolChoice,
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LLMSpecificMessage,
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NotGiven,
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)
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@@ -110,8 +111,15 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
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def _from_universal_context_messages(
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self, messages: List[LLMContextMessage]
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) -> List[ChatCompletionMessageParam]:
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# Just a pass-through: messages are already the right type
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return messages
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result = []
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for message in messages:
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if isinstance(message, LLMSpecificMessage):
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# Extract the actual message content from LLMSpecificMessage
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result.append(message.message)
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else:
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# Standard message, pass through unchanged
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result.append(message)
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return result
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def _from_standard_tool_choice(
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self, tool_choice: LLMContextToolChoice | NotGiven
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184
tests/test_get_llm_invocation_params.py
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184
tests/test_get_llm_invocation_params.py
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@@ -0,0 +1,184 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""
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Unit tests for OpenAI adapter's get_llm_invocation_params() method.
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These tests focus specifically on the "messages" field generation, ensuring:
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1. LLMStandardMessage objects are passed through unchanged
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2. LLMSpecificMessage objects with llm='openai' are included and their content extracted
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3. LLMSpecificMessage objects with llm != 'openai' are filtered out
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4. Complex message structures (like multi-part content) are preserved
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5. Edge cases like empty message lists are handled correctly
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"""
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import unittest
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from openai.types.chat import ChatCompletionMessage
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from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
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from pipecat.processors.aggregators.llm_context import (
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LLMContext,
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LLMSpecificMessage,
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LLMStandardMessage,
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)
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class TestOpenAIGetLLMInvocationParams(unittest.TestCase):
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def setUp(self) -> None:
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"""Sets up a common adapter instance for all tests."""
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self.adapter = OpenAILLMAdapter()
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def test_standard_messages_passed_through_unchanged(self):
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"""Test that LLMStandardMessage objects are passed through unchanged to OpenAI params."""
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# Create standard messages (OpenAI format)
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standard_messages: list[LLMStandardMessage] = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello, how are you?"},
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{"role": "assistant", "content": "I'm doing well, thank you for asking!"},
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]
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# Create context with these messages
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context = LLMContext(messages=standard_messages)
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# Get invocation params
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params = self.adapter.get_llm_invocation_params(context)
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# Verify messages are passed through unchanged
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self.assertEqual(params["messages"], standard_messages)
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self.assertEqual(len(params["messages"]), 3)
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# Verify content matches exactly
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self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
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self.assertEqual(params["messages"][1]["content"], "Hello, how are you?")
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self.assertEqual(params["messages"][2]["content"], "I'm doing well, thank you for asking!")
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def test_openai_specific_messages_included(self):
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"""Test that LLMSpecificMessage objects with llm='openai' are included."""
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# Create a mix of standard and OpenAI-specific messages
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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LLMSpecificMessage(
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llm="openai", message={"role": "user", "content": "OpenAI specific message"}
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),
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{"role": "assistant", "content": "Standard response"},
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]
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# Create context with these messages
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context = LLMContext(messages=messages)
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# Get invocation params
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params = self.adapter.get_llm_invocation_params(context)
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# Verify all messages are included (OpenAI-specific should be included)
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self.assertEqual(len(params["messages"]), 3)
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# First message should be standard
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self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
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# Second message should be the OpenAI-specific one
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self.assertEqual(
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params["messages"][1], {"role": "user", "content": "OpenAI specific message"}
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)
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# Third message should be standard
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self.assertEqual(params["messages"][2]["content"], "Standard response")
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def test_non_openai_specific_messages_filtered_out(self):
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"""Test that LLMSpecificMessage objects with llm != 'openai' are filtered out."""
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# Create messages with different LLM-specific ones
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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LLMSpecificMessage(
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llm="anthropic", message={"role": "user", "content": "Anthropic specific message"}
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),
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LLMSpecificMessage(
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llm="gemini", message={"role": "user", "content": "Gemini specific message"}
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),
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{"role": "user", "content": "Standard user message"},
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LLMSpecificMessage(
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llm="openai", message={"role": "assistant", "content": "OpenAI specific response"}
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),
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]
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# Create context with these messages
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context = LLMContext(messages=messages)
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# Get invocation params
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params = self.adapter.get_llm_invocation_params(context)
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# Should only include standard messages and OpenAI-specific ones
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# (3 total: system, standard user, openai assistant)
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self.assertEqual(len(params["messages"]), 3)
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# Verify the correct messages are included
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self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
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self.assertEqual(params["messages"][1]["content"], "Standard user message")
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self.assertEqual(
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params["messages"][2], {"role": "assistant", "content": "OpenAI specific response"}
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)
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def test_complex_message_content_preserved(self):
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"""Test that complex message content (like multi-part messages) is preserved."""
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# Create a message with complex content structure (text + image)
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complex_image_message = {
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD..."},
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},
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],
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}
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# Create a message with multiple text blocks
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multi_text_message = {
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"role": "assistant",
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"content": [
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{"type": "text", "text": "Let me analyze this step by step:"},
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{"type": "text", "text": "1. First, I'll examine the visual elements"},
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{"type": "text", "text": "2. Then I'll provide my conclusions"},
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],
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}
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messages = [
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{"role": "system", "content": "You are a helpful assistant that can analyze images."},
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complex_image_message,
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multi_text_message,
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]
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# Create context with these messages
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context = LLMContext(messages=messages)
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# Get invocation params
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params = self.adapter.get_llm_invocation_params(context)
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# Verify complex content is preserved
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self.assertEqual(len(params["messages"]), 3)
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self.assertEqual(params["messages"][1], complex_image_message)
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self.assertEqual(params["messages"][2], multi_text_message)
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# Verify the image message structure is maintained
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image_content = params["messages"][1]["content"]
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self.assertIsInstance(image_content, list)
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self.assertEqual(len(image_content), 2)
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self.assertEqual(image_content[0]["type"], "text")
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self.assertEqual(image_content[1]["type"], "image_url")
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# Verify the multi-text message structure is maintained
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text_content = params["messages"][2]["content"]
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self.assertIsInstance(text_content, list)
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self.assertEqual(len(text_content), 3)
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for i, text_block in enumerate(text_content):
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self.assertEqual(text_block["type"], "text")
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self.assertEqual(text_content[0]["text"], "Let me analyze this step by step:")
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self.assertEqual(text_content[1]["text"], "1. First, I'll examine the visual elements")
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self.assertEqual(text_content[2]["text"], "2. Then I'll provide my conclusions")
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if __name__ == "__main__":
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unittest.main()
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