532 lines
23 KiB
Python
532 lines
23 KiB
Python
#
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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 LLM adapters' get_llm_invocation_params() method.
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These tests focus specifically on the "messages" field generation for different adapters, ensuring:
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For OpenAI adapter:
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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. System instructions are preserved throughout messages at any position
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For Gemini adapter:
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1. LLMStandardMessage objects are converted to Gemini Content format
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2. LLMSpecificMessage objects with llm='google' are included unchanged
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3. LLMSpecificMessage objects with llm != 'google' are filtered out
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4. Complex message structures (image, audio, multi-text) are converted to appropriate Gemini format
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5. System messages are extracted as system_instruction (without duplication)
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6. Single system instruction is converted to user message when no other messages exist
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7. Multiple system instructions: first extracted, later ones converted to user messages
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"""
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import unittest
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from google.genai.types import Content, Part
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from openai.types.chat import ChatCompletionMessage
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from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
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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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def test_system_instructions_preserved_throughout_messages(self):
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"""Test that OpenAI adapter preserves system instructions sprinkled throughout messages."""
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# Create messages with system instructions at different positions
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello!"},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "system", "content": "Remember to be concise."},
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{"role": "user", "content": "Tell me about Python."},
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{"role": "system", "content": "Use simple language."},
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{"role": "assistant", "content": "Python is a programming language."},
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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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# OpenAI should preserve all messages unchanged, including multiple system messages
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self.assertEqual(len(params["messages"]), 7)
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# Verify system messages are preserved at their original positions
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self.assertEqual(params["messages"][0]["role"], "system")
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self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
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self.assertEqual(params["messages"][3]["role"], "system")
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self.assertEqual(params["messages"][3]["content"], "Remember to be concise.")
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self.assertEqual(params["messages"][5]["role"], "system")
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self.assertEqual(params["messages"][5]["content"], "Use simple language.")
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# Verify other messages remain unchanged
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self.assertEqual(params["messages"][1]["role"], "user")
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self.assertEqual(params["messages"][2]["role"], "assistant")
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self.assertEqual(params["messages"][4]["role"], "user")
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self.assertEqual(params["messages"][6]["role"], "assistant")
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class TestGeminiGetLLMInvocationParams(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 = GeminiLLMAdapter()
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def test_standard_messages_converted_to_gemini_format(self):
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"""Test that LLMStandardMessage objects are converted to Gemini Content format."""
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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 system instruction is extracted
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self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
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# Verify messages are converted to Gemini format (2 messages: user + model)
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self.assertEqual(len(params["messages"]), 2)
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# Check first message (user)
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user_msg = params["messages"][0]
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self.assertIsInstance(user_msg, Content)
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self.assertEqual(user_msg.role, "user")
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self.assertEqual(len(user_msg.parts), 1)
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self.assertEqual(user_msg.parts[0].text, "Hello, how are you?")
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# Check second message (assistant -> model)
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model_msg = params["messages"][1]
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self.assertIsInstance(model_msg, Content)
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self.assertEqual(model_msg.role, "model")
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self.assertEqual(len(model_msg.parts), 1)
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self.assertEqual(model_msg.parts[0].text, "I'm doing well, thank you for asking!")
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def test_gemini_specific_messages_included(self):
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"""Test that LLMSpecificMessage objects with llm='google' are included unchanged."""
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# Create a Gemini-specific message
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gemini_message = Content(role="user", parts=[Part(text="Gemini specific message")])
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# Create a mix of standard and Gemini-specific messages
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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LLMSpecificMessage(llm="google", message=gemini_message),
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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 system instruction
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self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
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# Verify messages (2 total: gemini-specific user + converted model)
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self.assertEqual(len(params["messages"]), 2)
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# First message should be the Gemini-specific one (unchanged)
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self.assertEqual(params["messages"][0], gemini_message)
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self.assertEqual(params["messages"][0].parts[0].text, "Gemini specific message")
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# Second message should be converted standard message
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self.assertEqual(params["messages"][1].role, "model")
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self.assertEqual(params["messages"][1].parts[0].text, "Standard response")
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def test_non_gemini_specific_messages_filtered_out(self):
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"""Test that LLMSpecificMessage objects with llm != 'google' 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="openai", message={"role": "user", "content": "OpenAI specific message"}
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),
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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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{"role": "user", "content": "Standard user message"},
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LLMSpecificMessage(
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llm="google",
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message=Content(role="model", parts=[Part(text="Gemini 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 Gemini-specific ones
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# (2 total: converted standard user + gemini model)
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self.assertEqual(len(params["messages"]), 2)
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# Verify system instruction
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self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
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# Verify the correct messages are included
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self.assertEqual(params["messages"][0].role, "user")
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self.assertEqual(params["messages"][0].parts[0].text, "Standard user message")
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self.assertEqual(params["messages"][1].role, "model")
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self.assertEqual(params["messages"][1].parts[0].text, "Gemini specific response")
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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 and converted.
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This test covers image, audio, and multi-text content conversion to Gemini format.
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"""
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# Create a message with complex content structure (text + image)
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# Using a minimal valid base64 image data
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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": {
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"url": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
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},
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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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# Create a message with audio input (text + audio)
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# Using a minimal valid base64 audio data (16 bytes of WAV header)
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audio_message = {
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"role": "user",
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"content": [
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{"type": "text", "text": "Can you transcribe this audio?"},
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{
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"type": "input_audio",
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"input_audio": {
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"data": "UklGRiQAAABXQVZFZm10IBAAAAABAAEARKwAAIhYAQACABAAZGF0YQAAAAA=",
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"format": "wav",
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},
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},
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],
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}
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant that can analyze images and audio.",
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},
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complex_image_message,
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multi_text_message,
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audio_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 system instruction
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self.assertEqual(
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params["system_instruction"],
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"You are a helpful assistant that can analyze images and audio.",
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)
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# Verify complex content is converted to Gemini format
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# Note: Gemini adapter may add system instruction back as user message in some cases
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self.assertGreaterEqual(len(params["messages"]), 3)
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# Find the different message types
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user_with_image = None
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model_with_text = None
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user_with_audio = None
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for msg in params["messages"]:
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if msg.role == "user" and len(msg.parts) == 2:
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# Check if it's image or audio based on the text content
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if hasattr(msg.parts[0], "text") and "image" in msg.parts[0].text:
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user_with_image = msg
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elif hasattr(msg.parts[0], "text") and "audio" in msg.parts[0].text:
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user_with_audio = msg
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elif msg.role == "model" and len(msg.parts) == 3:
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model_with_text = msg
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# Verify the image message structure is converted properly
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self.assertIsNotNone(user_with_image, "Should have user message with image")
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self.assertEqual(len(user_with_image.parts), 2)
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# First part should be text
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self.assertEqual(user_with_image.parts[0].text, "What's in this image?")
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# Second part should be image data (converted to Blob)
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self.assertIsNotNone(user_with_image.parts[1].inline_data)
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self.assertEqual(user_with_image.parts[1].inline_data.mime_type, "image/jpeg")
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# Verify the audio message structure is converted properly
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self.assertIsNotNone(user_with_audio, "Should have user message with audio")
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self.assertEqual(len(user_with_audio.parts), 2)
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# First part should be text
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self.assertEqual(user_with_audio.parts[0].text, "Can you transcribe this audio?")
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# Second part should be audio data (converted to Blob)
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self.assertIsNotNone(user_with_audio.parts[1].inline_data)
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self.assertEqual(user_with_audio.parts[1].inline_data.mime_type, "audio/wav")
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# Verify the multi-text message structure is converted properly
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self.assertIsNotNone(model_with_text, "Should have model message with multi-text")
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self.assertEqual(len(model_with_text.parts), 3)
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# All parts should be text
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expected_texts = [
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"Let me analyze this step by step:",
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"1. First, I'll examine the visual elements",
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"2. Then I'll provide my conclusions",
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]
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for i, expected_text in enumerate(expected_texts):
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self.assertEqual(model_with_text.parts[i].text, expected_text)
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def test_single_system_instruction_converted_to_user(self):
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"""Test that when there's only a system instruction, it gets converted to user message."""
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# Create context with only a system message
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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]
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context = LLMContext(messages=messages)
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params = self.adapter.get_llm_invocation_params(context)
|
||
|
||
# System instruction should be extracted
|
||
self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
|
||
|
||
# But since there are no other messages, it should also be added back as a user message
|
||
self.assertEqual(len(params["messages"]), 1)
|
||
self.assertEqual(params["messages"][0].role, "user")
|
||
self.assertEqual(params["messages"][0].parts[0].text, "You are a helpful assistant.")
|
||
|
||
def test_multiple_system_instructions_handling(self):
|
||
"""Test that first system instruction is extracted, later ones converted to user messages."""
|
||
# Create messages with multiple system instructions
|
||
messages = [
|
||
{"role": "system", "content": "You are a helpful assistant."},
|
||
{"role": "user", "content": "Hello!"},
|
||
{"role": "assistant", "content": "Hi there!"},
|
||
{"role": "system", "content": "Remember to be concise."},
|
||
{"role": "user", "content": "Tell me about Python."},
|
||
{"role": "system", "content": "Use simple language."},
|
||
{"role": "assistant", "content": "Python is a programming language."},
|
||
]
|
||
|
||
context = LLMContext(messages=messages)
|
||
params = self.adapter.get_llm_invocation_params(context)
|
||
|
||
# First system instruction should be extracted
|
||
self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
|
||
|
||
# Should have 6 messages (original 7 minus 1 system instruction that was extracted)
|
||
self.assertEqual(len(params["messages"]), 6)
|
||
|
||
# Find the converted system messages (should be user role now)
|
||
converted_system_messages = []
|
||
for msg in params["messages"]:
|
||
if msg.role == "user" and (
|
||
msg.parts[0].text == "Remember to be concise."
|
||
or msg.parts[0].text == "Use simple language."
|
||
):
|
||
converted_system_messages.append(msg.parts[0].text)
|
||
|
||
# Should have 2 converted system messages
|
||
self.assertEqual(len(converted_system_messages), 2)
|
||
self.assertIn("Remember to be concise.", converted_system_messages)
|
||
self.assertIn("Use simple language.", converted_system_messages)
|
||
|
||
# Verify that regular user and assistant messages are preserved
|
||
user_messages = [msg for msg in params["messages"] if msg.role == "user"]
|
||
model_messages = [msg for msg in params["messages"] if msg.role == "model"]
|
||
|
||
# Should have 4 user messages: 2 original + 2 converted from system
|
||
self.assertEqual(len(user_messages), 4)
|
||
# Should have 2 model messages (converted from assistant)
|
||
self.assertEqual(len(model_messages), 2)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
unittest.main()
|