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pipecat/tests/test_get_llm_invocation_params.py
Paul Kompfner 0373f85b85 Add PerplexityLLMAdapter to enforce Perplexity's message ordering constraints 
Perplexity's API is stricter than OpenAI about conversation history:
- Requires strict alternation between user/tool and assistant messages
- Disallows system messages except as the initial message
- Requires the last message to be user or tool

The new adapter transforms messages before sending to satisfy all three
constraints: merging consecutive initial system messages, converting
non-initial system to user, merging consecutive same-role messages, and
removing trailing assistant messages.

Also adds dual-system-instruction warnings to Cerebras, Fireworks,
Mistral, Perplexity, and SambaNova services (matching the existing
BaseOpenAILLMService pattern), and updates the warning text in
BaseOpenAILLMService to be more descriptive.
2026-03-12 14:56:30 -04:00

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#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""
Unit tests for LLM adapters' get_llm_invocation_params() method.
These tests focus specifically on the "messages" field generation for different adapters, ensuring:
For OpenAI adapter:
1. LLMStandardMessage objects are passed through unchanged
2. LLMSpecificMessage objects with llm='openai' are included and others are filtered out
3. Complex message structures (like multi-part content) are preserved
4. System instructions are preserved throughout messages at any position
For Gemini adapter:
1. LLMStandardMessage objects are converted to Gemini Content format
2. LLMSpecificMessage objects with llm='google' are included and others are filtered out
3. Complex message structures (image, audio, multi-text) are converted to appropriate Gemini format
4. System messages are extracted as system_instruction (without duplication)
5. Single system instruction is converted to user message when no other messages exist
6. Multiple system instructions: first extracted, later ones converted to user messages
For Anthropic adapter:
1. LLMStandardMessage objects are converted to Anthropic MessageParam format
2. LLMSpecificMessage objects with llm='anthropic' are included and others are filtered out
3. Complex message structures (image, multi-text) are converted to appropriate Anthropic format
4. System messages: first extracted as system parameter, later ones converted to user messages
5. Consecutive messages with same role are merged into multi-content-block messages
6. Empty text content is converted to "(empty)"
For AWS Bedrock adapter:
1. LLMStandardMessage objects are converted to AWS Bedrock format
2. LLMSpecificMessage objects with llm='aws' are included and others are filtered out
3. Complex message structures (image, multi-text) are converted to appropriate AWS Bedrock format
4. System messages: first extracted as system parameter, later ones converted to user messages
5. Consecutive messages with same role are merged into multi-content-block messages
6. Empty text content is converted to "(empty)"
"""
import unittest
from google.genai.types import Content, Part
from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
from pipecat.adapters.services.perplexity_adapter import PerplexityLLMAdapter
from pipecat.processors.aggregators.llm_context import (
LLMContext,
LLMStandardMessage,
)
class TestOpenAIGetLLMInvocationParams(unittest.TestCase):
def setUp(self) -> None:
"""Sets up a common adapter instance for all tests."""
self.adapter = OpenAILLMAdapter()
def test_standard_messages_passed_through_unchanged(self):
"""Test that LLMStandardMessage objects are passed through unchanged to OpenAI params."""
# Create standard messages (OpenAI format)
standard_messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing well, thank you for asking!"},
]
# Create context with these messages
context = LLMContext(messages=standard_messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify messages are passed through unchanged
self.assertEqual(params["messages"], standard_messages)
self.assertEqual(len(params["messages"]), 3)
# Verify content matches exactly
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
self.assertEqual(params["messages"][1]["content"], "Hello, how are you?")
self.assertEqual(params["messages"][2]["content"], "I'm doing well, thank you for asking!")
def test_llm_specific_message_filtering(self):
"""Test that OpenAI-specific messages are included and others are filtered out."""
# Create messages with different LLM-specific ones
messages = [
{"role": "system", "content": "You are a helpful assistant."},
AnthropicLLMAdapter().create_llm_specific_message(
{"role": "user", "content": "Anthropic specific message"}
),
GeminiLLMAdapter().create_llm_specific_message(
{"role": "user", "content": "Gemini specific message"}
),
{"role": "user", "content": "Standard user message"},
self.adapter.create_llm_specific_message(
{"role": "assistant", "content": "OpenAI specific response"}
),
]
# Create context with these messages
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Should only include standard messages and OpenAI-specific ones
# (3 total: system, standard user, openai assistant)
self.assertEqual(len(params["messages"]), 3)
# Verify the correct messages are included
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
self.assertEqual(params["messages"][1]["content"], "Standard user message")
self.assertEqual(
params["messages"][2], {"role": "assistant", "content": "OpenAI specific response"}
)
def test_complex_message_content_preserved(self):
"""Test that complex message content (like multi-part messages) is preserved."""
# Create a message with complex content structure (text + image)
complex_image_message = {
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD..."},
},
],
}
# Create a message with multiple text blocks
multi_text_message = {
"role": "assistant",
"content": [
{"type": "text", "text": "Let me analyze this step by step:"},
{"type": "text", "text": "1. First, I'll examine the visual elements"},
{"type": "text", "text": "2. Then I'll provide my conclusions"},
],
}
messages = [
{"role": "system", "content": "You are a helpful assistant that can analyze images."},
complex_image_message,
multi_text_message,
]
# Create context with these messages
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify complex content is preserved
self.assertEqual(len(params["messages"]), 3)
self.assertEqual(params["messages"][1], complex_image_message)
self.assertEqual(params["messages"][2], multi_text_message)
# Verify the image message structure is maintained
image_content = params["messages"][1]["content"]
self.assertIsInstance(image_content, list)
self.assertEqual(len(image_content), 2)
self.assertEqual(image_content[0]["type"], "text")
self.assertEqual(image_content[1]["type"], "image_url")
# Verify the multi-text message structure is maintained
text_content = params["messages"][2]["content"]
self.assertIsInstance(text_content, list)
self.assertEqual(len(text_content), 3)
for i, text_block in enumerate(text_content):
self.assertEqual(text_block["type"], "text")
self.assertEqual(text_content[0]["text"], "Let me analyze this step by step:")
self.assertEqual(text_content[1]["text"], "1. First, I'll examine the visual elements")
self.assertEqual(text_content[2]["text"], "2. Then I'll provide my conclusions")
def test_system_instructions_preserved_throughout_messages(self):
"""Test that OpenAI adapter preserves system instructions sprinkled throughout messages."""
# Create messages with system instructions at different positions
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."},
]
# Create context with these messages
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# OpenAI should preserve all messages unchanged, including multiple system messages
self.assertEqual(len(params["messages"]), 7)
# Verify system messages are preserved at their original positions
self.assertEqual(params["messages"][0]["role"], "system")
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
self.assertEqual(params["messages"][3]["role"], "system")
self.assertEqual(params["messages"][3]["content"], "Remember to be concise.")
self.assertEqual(params["messages"][5]["role"], "system")
self.assertEqual(params["messages"][5]["content"], "Use simple language.")
# Verify other messages remain unchanged
self.assertEqual(params["messages"][1]["role"], "user")
self.assertEqual(params["messages"][2]["role"], "assistant")
self.assertEqual(params["messages"][4]["role"], "user")
self.assertEqual(params["messages"][6]["role"], "assistant")
class TestGeminiGetLLMInvocationParams(unittest.TestCase):
def setUp(self) -> None:
"""Sets up a common adapter instance for all tests."""
self.adapter = GeminiLLMAdapter()
def test_standard_messages_converted_to_gemini_format(self):
"""Test that LLMStandardMessage objects are converted to Gemini Content format."""
# Create standard messages (OpenAI format)
standard_messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing well, thank you for asking!"},
]
# Create context with these messages
context = LLMContext(messages=standard_messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify system instruction is extracted
self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
# Verify messages are converted to Gemini format (2 messages: user + model)
self.assertEqual(len(params["messages"]), 2)
# Check first message (user)
user_msg = params["messages"][0]
self.assertIsInstance(user_msg, Content)
self.assertEqual(user_msg.role, "user")
self.assertEqual(len(user_msg.parts), 1)
self.assertEqual(user_msg.parts[0].text, "Hello, how are you?")
# Check second message (assistant -> model)
model_msg = params["messages"][1]
self.assertIsInstance(model_msg, Content)
self.assertEqual(model_msg.role, "model")
self.assertEqual(len(model_msg.parts), 1)
self.assertEqual(model_msg.parts[0].text, "I'm doing well, thank you for asking!")
def test_llm_specific_message_filtering(self):
"""Test that Gemini-specific messages are included and others are filtered out."""
# Create messages with different LLM-specific ones
messages = [
{"role": "system", "content": "You are a helpful assistant."},
OpenAILLMAdapter().create_llm_specific_message(
{"role": "user", "content": "OpenAI specific message"}
),
AnthropicLLMAdapter().create_llm_specific_message(
{"role": "user", "content": "Anthropic specific message"}
),
{"role": "user", "content": "Standard user message"},
self.adapter.create_llm_specific_message(
Content(role="model", parts=[Part(text="Gemini specific response")]),
),
]
# Create context with these messages
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Should only include standard messages and Gemini-specific ones
# (2 total: converted standard user + gemini model)
self.assertEqual(len(params["messages"]), 2)
# Verify system instruction
self.assertEqual(params["system_instruction"], "You are a helpful assistant.")
# Verify the correct messages are included
self.assertEqual(params["messages"][0].role, "user")
self.assertEqual(params["messages"][0].parts[0].text, "Standard user message")
self.assertEqual(params["messages"][1].role, "model")
self.assertEqual(params["messages"][1].parts[0].text, "Gemini specific response")
def test_complex_message_content_preserved(self):
"""Test that complex message content (like multi-part messages) is preserved and converted.
This test covers image, audio, and multi-text content conversion to Gemini format.
"""
# Create a message with complex content structure (text + image)
# Using a minimal valid base64 image data
complex_image_message = {
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
},
},
],
}
# Create a message with multiple text blocks
multi_text_message = {
"role": "assistant",
"content": [
{"type": "text", "text": "Let me analyze this step by step:"},
{"type": "text", "text": "1. First, I'll examine the visual elements"},
{"type": "text", "text": "2. Then I'll provide my conclusions"},
],
}
# Create a message with audio input (text + audio)
# Using a minimal valid base64 audio data (16 bytes of WAV header)
audio_message = {
"role": "user",
"content": [
{"type": "text", "text": "Can you transcribe this audio?"},
{
"type": "input_audio",
"input_audio": {
"data": "UklGRiQAAABXQVZFZm10IBAAAAABAAEARKwAAIhYAQACABAAZGF0YQAAAAA=",
"format": "wav",
},
},
],
}
messages = [
{
"role": "system",
"content": "You are a helpful assistant that can analyze images and audio.",
},
complex_image_message,
multi_text_message,
audio_message,
]
# Create context with these messages
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify system instruction
self.assertEqual(
params["system_instruction"],
"You are a helpful assistant that can analyze images and audio.",
)
# Verify complex content is converted to Gemini format
# Note: Gemini adapter may add system instruction back as user message in some cases
self.assertGreaterEqual(len(params["messages"]), 3)
# Find the different message types
user_with_image = None
model_with_text = None
user_with_audio = None
for msg in params["messages"]:
if msg.role == "user" and len(msg.parts) == 2:
# Check if it's image or audio based on the text content
if hasattr(msg.parts[0], "text") and "image" in msg.parts[0].text:
user_with_image = msg
elif hasattr(msg.parts[0], "text") and "audio" in msg.parts[0].text:
user_with_audio = msg
elif msg.role == "model" and len(msg.parts) == 3:
model_with_text = msg
# Verify the image message structure is converted properly
self.assertIsNotNone(user_with_image, "Should have user message with image")
self.assertEqual(len(user_with_image.parts), 2)
# First part should be text
self.assertEqual(user_with_image.parts[0].text, "What's in this image?")
# Second part should be image data (converted to Blob)
self.assertIsNotNone(user_with_image.parts[1].inline_data)
self.assertEqual(user_with_image.parts[1].inline_data.mime_type, "image/jpeg")
# Verify the audio message structure is converted properly
self.assertIsNotNone(user_with_audio, "Should have user message with audio")
self.assertEqual(len(user_with_audio.parts), 2)
# First part should be text
self.assertEqual(user_with_audio.parts[0].text, "Can you transcribe this audio?")
# Second part should be audio data (converted to Blob)
self.assertIsNotNone(user_with_audio.parts[1].inline_data)
self.assertEqual(user_with_audio.parts[1].inline_data.mime_type, "audio/wav")
# Verify the multi-text message structure is converted properly
self.assertIsNotNone(model_with_text, "Should have model message with multi-text")
self.assertEqual(len(model_with_text.parts), 3)
# All parts should be text
expected_texts = [
"Let me analyze this step by step:",
"1. First, I'll examine the visual elements",
"2. Then I'll provide my conclusions",
]
for i, expected_text in enumerate(expected_texts):
self.assertEqual(model_with_text.parts[i].text, expected_text)
def test_single_system_instruction_converted_to_user(self):
"""Test that when there's only a system instruction, it gets converted to user message."""
# Create context with only a system message
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
context = LLMContext(messages=messages)
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)
class TestAnthropicGetLLMInvocationParams(unittest.TestCase):
def setUp(self) -> None:
"""Sets up a common adapter instance for all tests."""
self.adapter = AnthropicLLMAdapter()
def test_standard_messages_converted_to_anthropic_format(self):
"""Test that LLMStandardMessage objects are converted to Anthropic MessageParam format."""
# Create standard messages
standard_messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing well, thank you!"},
]
# Create context
context = LLMContext(messages=standard_messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# Verify system instruction is extracted
self.assertEqual(params["system"], "You are a helpful assistant.")
# Verify messages are in the params (2 messages after system extraction)
self.assertIn("messages", params)
self.assertEqual(len(params["messages"]), 2)
# Check first message (user)
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertEqual(user_msg["content"], "Hello, how are you?")
# Check second message (assistant)
assistant_msg = params["messages"][1]
self.assertEqual(assistant_msg["role"], "assistant")
self.assertEqual(assistant_msg["content"], "I'm doing well, thank you!")
def test_llm_specific_message_filtering(self):
"""Test that Anthropic-specific messages are included and others are filtered out."""
# Create anthropic-specific message content
anthropic_message_content = {
"role": "user",
"content": [
{"type": "text", "text": "Hello"},
{
"type": "image",
"source": {"type": "base64", "media_type": "image/jpeg", "data": "fake_data"},
},
],
}
messages = [
{"role": "user", "content": "Standard message"},
OpenAILLMAdapter().create_llm_specific_message(
{"role": "user", "content": "OpenAI specific"}
),
GeminiLLMAdapter().create_llm_specific_message(
{"role": "user", "content": "Google specific"}
),
self.adapter.create_llm_specific_message(anthropic_message_content),
{"role": "assistant", "content": "Response"},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# Should only have 2 messages after merging consecutive user messages: merged user + standard response
# (openai and google specific filtered out, standard + anthropic-specific merged)
self.assertEqual(len(params["messages"]), 2)
# First message: merged user message (standard + anthropic-specific)
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
# Should have 3 content blocks: standard text + anthropic text + anthropic image
self.assertEqual(len(user_msg["content"]), 3)
self.assertEqual(user_msg["content"][0]["type"], "text")
self.assertEqual(user_msg["content"][0]["text"], "Standard message")
self.assertEqual(user_msg["content"][1]["type"], "text")
self.assertEqual(user_msg["content"][1]["text"], "Hello")
self.assertEqual(user_msg["content"][2]["type"], "image")
# Second message: standard response
self.assertEqual(params["messages"][1]["content"], "Response")
def test_consecutive_same_role_messages_merged(self):
"""Test that consecutive messages with the same role are merged into multi-content blocks."""
messages = [
{"role": "user", "content": "First user message"},
{"role": "user", "content": "Second user message"},
{"role": "user", "content": "Third user message"},
{"role": "assistant", "content": "First assistant message"},
{"role": "assistant", "content": "Second assistant message"},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# Should have 2 messages after merging (1 user, 1 assistant)
self.assertEqual(len(params["messages"]), 2)
# Check merged user message
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(len(user_msg["content"]), 3)
self.assertEqual(user_msg["content"][0]["type"], "text")
self.assertEqual(user_msg["content"][0]["text"], "First user message")
self.assertEqual(user_msg["content"][1]["type"], "text")
self.assertEqual(user_msg["content"][1]["text"], "Second user message")
self.assertEqual(user_msg["content"][2]["type"], "text")
self.assertEqual(user_msg["content"][2]["text"], "Third user message")
# Check merged assistant message
assistant_msg = params["messages"][1]
self.assertEqual(assistant_msg["role"], "assistant")
self.assertIsInstance(assistant_msg["content"], list)
self.assertEqual(len(assistant_msg["content"]), 2)
self.assertEqual(assistant_msg["content"][0]["type"], "text")
self.assertEqual(assistant_msg["content"][0]["text"], "First assistant message")
self.assertEqual(assistant_msg["content"][1]["type"], "text")
self.assertEqual(assistant_msg["content"][1]["text"], "Second assistant message")
def test_empty_text_converted_to_empty_placeholder(self):
"""Test that empty text content is converted to "(empty)" string."""
messages = [
{"role": "user", "content": ""}, # Empty string
{
"role": "assistant",
"content": [
{"type": "text", "text": ""}, # Empty text in list content
{"type": "text", "text": "Valid text"},
],
},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# Check that empty string content was converted
user_msg = params["messages"][0]
self.assertEqual(user_msg["content"], "(empty)")
# Check that empty text in list content was converted
assistant_msg = params["messages"][1]
self.assertIsInstance(assistant_msg["content"], list)
self.assertEqual(assistant_msg["content"][0]["text"], "(empty)")
self.assertEqual(assistant_msg["content"][1]["text"], "Valid text")
def test_complex_message_content_preserved(self):
"""Test that complex message structures (text + image) are properly converted to Anthropic format."""
# Create a complex message with both text and image content
complex_message = {
"role": "user",
"content": [
{"type": "text", "text": "What do you see in this image?"},
{
"type": "image_url",
"image_url": {"url": "data:image/jpeg;base64,fake_image_data"},
},
{"type": "text", "text": "Please describe it in detail."},
],
}
messages = [
complex_message,
{"role": "assistant", "content": "I can see the image clearly."},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# Verify complex message structure is preserved and converted
self.assertEqual(len(params["messages"]), 2)
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(len(user_msg["content"]), 3)
# Note: Anthropic adapter reorders single images to come before text, as per Anthropic docs
# Check image part (should be moved to first position and converted from image_url to image)
self.assertEqual(user_msg["content"][0]["type"], "image")
self.assertIn("source", user_msg["content"][0])
self.assertEqual(user_msg["content"][0]["source"]["type"], "base64")
self.assertEqual(user_msg["content"][0]["source"]["media_type"], "image/jpeg")
self.assertEqual(user_msg["content"][0]["source"]["data"], "fake_image_data")
# Check first text part (moved to second position)
self.assertEqual(user_msg["content"][1]["type"], "text")
self.assertEqual(user_msg["content"][1]["text"], "What do you see in this image?")
# Check second text part (moved to third position)
self.assertEqual(user_msg["content"][2]["type"], "text")
self.assertEqual(user_msg["content"][2]["text"], "Please describe it in detail.")
def test_multiple_system_instructions_handling(self):
"""Test that first system instruction is extracted, later ones converted to user messages."""
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."}, # Later system message
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# System instruction should be extracted from first message
self.assertEqual(params["system"], "You are a helpful assistant.")
# Should have 3 messages remaining (system message was removed, later system converted to user)
self.assertEqual(len(params["messages"]), 3)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "Hello")
self.assertEqual(params["messages"][1]["role"], "assistant")
self.assertEqual(params["messages"][1]["content"], "Hi there!")
# Later system message should be converted to user role
self.assertEqual(params["messages"][2]["role"], "user")
self.assertEqual(params["messages"][2]["content"], "Remember to be concise.")
def test_single_system_message_converted_to_user(self):
"""Test that a single system message is converted to user role when no other messages exist."""
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)
# System should be NOT_GIVEN since we only have one message
from anthropic import NOT_GIVEN
self.assertEqual(params["system"], NOT_GIVEN)
# Single system message should be converted to user role
self.assertEqual(len(params["messages"]), 1)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
class TestAWSBedrockGetLLMInvocationParams(unittest.TestCase):
def setUp(self) -> None:
"""Sets up a common adapter instance for all tests."""
self.adapter = AWSBedrockLLMAdapter()
def test_standard_messages_converted_to_aws_bedrock_format(self):
"""Test that LLMStandardMessage objects are converted to AWS Bedrock format."""
# Create standard messages
standard_messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing well, thank you!"},
]
# Create context
context = LLMContext(messages=standard_messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify system instruction is extracted (in AWS Bedrock format)
self.assertIsInstance(params["system"], list)
self.assertEqual(len(params["system"]), 1)
self.assertEqual(params["system"][0]["text"], "You are a helpful assistant.")
# Verify messages are in the params (2 messages after system extraction)
self.assertIn("messages", params)
self.assertEqual(len(params["messages"]), 2)
# Check first message (user) - should be converted to AWS Bedrock format
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(len(user_msg["content"]), 1)
self.assertEqual(user_msg["content"][0]["text"], "Hello, how are you?")
# Check second message (assistant) - should be converted to AWS Bedrock format
assistant_msg = params["messages"][1]
self.assertEqual(assistant_msg["role"], "assistant")
self.assertIsInstance(assistant_msg["content"], list)
self.assertEqual(len(assistant_msg["content"]), 1)
self.assertEqual(assistant_msg["content"][0]["text"], "I'm doing well, thank you!")
def test_llm_specific_message_filtering(self):
"""Test that AWS-specific messages are included and others are filtered out."""
# Create aws-specific message content (which is what AWS Bedrock uses)
aws_message_content = {
"role": "user",
"content": [
{"text": "Hello"},
{"image": {"format": "jpeg", "source": {"bytes": b"fake_image_data"}}},
],
}
messages = [
{"role": "user", "content": "Standard message"},
OpenAILLMAdapter().create_llm_specific_message(
{"role": "user", "content": "OpenAI specific"}
),
GeminiLLMAdapter().create_llm_specific_message(
{"role": "user", "content": "Google specific"}
),
self.adapter.create_llm_specific_message(message=aws_message_content),
{"role": "assistant", "content": "Response"},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Should only have 2 messages after merging consecutive user messages: merged user + standard response
# (openai and google specific filtered out, standard + aws-specific merged)
self.assertEqual(len(params["messages"]), 2)
# First message: merged user message (standard + aws-specific)
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
# Should have 3 content blocks: standard text + aws text + aws image
self.assertEqual(len(user_msg["content"]), 3)
self.assertEqual(user_msg["content"][0]["text"], "Standard message")
self.assertEqual(user_msg["content"][1]["text"], "Hello")
self.assertIn("image", user_msg["content"][2])
# Second message: standard response
self.assertEqual(params["messages"][1]["content"][0]["text"], "Response")
def test_consecutive_same_role_messages_merged(self):
"""Test that consecutive messages with the same role are merged into multi-content blocks."""
messages = [
{"role": "user", "content": "First user message"},
{"role": "user", "content": "Second user message"},
{"role": "user", "content": "Third user message"},
{"role": "assistant", "content": "First assistant message"},
{"role": "assistant", "content": "Second assistant message"},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Should have 2 messages after merging (1 user, 1 assistant)
self.assertEqual(len(params["messages"]), 2)
# Check merged user message
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(len(user_msg["content"]), 3)
self.assertEqual(user_msg["content"][0]["text"], "First user message")
self.assertEqual(user_msg["content"][1]["text"], "Second user message")
self.assertEqual(user_msg["content"][2]["text"], "Third user message")
# Check merged assistant message
assistant_msg = params["messages"][1]
self.assertEqual(assistant_msg["role"], "assistant")
self.assertIsInstance(assistant_msg["content"], list)
self.assertEqual(len(assistant_msg["content"]), 2)
self.assertEqual(assistant_msg["content"][0]["text"], "First assistant message")
self.assertEqual(assistant_msg["content"][1]["text"], "Second assistant message")
def test_empty_text_converted_to_empty_placeholder(self):
"""Test that empty text content is converted to "(empty)" string."""
messages = [
{"role": "user", "content": ""}, # Empty string
{
"role": "assistant",
"content": [
{"type": "text", "text": ""}, # Empty text in list content
{"type": "text", "text": "Valid text"},
],
},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Check that empty string content was converted
user_msg = params["messages"][0]
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(user_msg["content"][0]["text"], "(empty)")
# Check that empty text in list content was converted
assistant_msg = params["messages"][1]
self.assertIsInstance(assistant_msg["content"], list)
self.assertEqual(assistant_msg["content"][0]["text"], "(empty)")
self.assertEqual(assistant_msg["content"][1]["text"], "Valid text")
def test_complex_message_content_preserved(self):
"""Test that complex message structures (text + image) are properly converted to AWS Bedrock format."""
# Create a complex message with both text and image content
# Use a valid base64 string for the image
complex_message = {
"role": "user",
"content": [
{"type": "text", "text": "What do you see in this image?"},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
},
},
{"type": "text", "text": "Please describe it in detail."},
],
}
messages = [
complex_message,
{"role": "assistant", "content": "I can see the image clearly."},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# Verify complex message structure is preserved and converted
self.assertEqual(len(params["messages"]), 2)
user_msg = params["messages"][0]
self.assertEqual(user_msg["role"], "user")
self.assertIsInstance(user_msg["content"], list)
self.assertEqual(len(user_msg["content"]), 3)
# Note: AWS Bedrock adapter reorders single images to come before text, like Anthropic
# Check image part (should be moved to first position and converted from image_url to image)
self.assertIn("image", user_msg["content"][0])
self.assertEqual(user_msg["content"][0]["image"]["format"], "jpeg")
self.assertIn("source", user_msg["content"][0]["image"])
self.assertIn("bytes", user_msg["content"][0]["image"]["source"])
# Check first text part (moved to second position)
self.assertEqual(user_msg["content"][1]["text"], "What do you see in this image?")
# Check second text part (moved to third position)
self.assertEqual(user_msg["content"][2]["text"], "Please describe it in detail.")
def test_multiple_system_instructions_handling(self):
"""Test that first system instruction is extracted, later ones converted to user messages."""
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."}, # Later system message
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# System instruction should be extracted from first message (in AWS Bedrock format)
self.assertIsInstance(params["system"], list)
self.assertEqual(len(params["system"]), 1)
self.assertEqual(params["system"][0]["text"], "You are a helpful assistant.")
# Should have 3 messages remaining (system message was removed, later system converted to user)
self.assertEqual(len(params["messages"]), 3)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"][0]["text"], "Hello")
self.assertEqual(params["messages"][1]["role"], "assistant")
self.assertEqual(params["messages"][1]["content"][0]["text"], "Hi there!")
# Later system message should be converted to user role
self.assertEqual(params["messages"][2]["role"], "user")
self.assertEqual(params["messages"][2]["content"][0]["text"], "Remember to be concise.")
def test_single_system_message_handling(self):
"""Test that a single system message is extracted as system parameter and no messages remain."""
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
# Create context
context = LLMContext(messages=messages)
# Get invocation params
params = self.adapter.get_llm_invocation_params(context)
# System should be extracted (in AWS Bedrock format)
self.assertIsInstance(params["system"], list)
self.assertEqual(len(params["system"]), 1)
self.assertEqual(params["system"][0]["text"], "You are a helpful assistant.")
# No messages should remain after system extraction
self.assertEqual(len(params["messages"]), 0)
class TestPerplexityGetLLMInvocationParams(unittest.TestCase):
def setUp(self) -> None:
"""Sets up a common adapter instance for all tests."""
self.adapter = PerplexityLLMAdapter()
def test_standard_messages_pass_through(self):
"""Test that a valid [user, assistant, user] sequence passes through unchanged."""
messages: list[LLMStandardMessage] = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
{"role": "user", "content": "How are you?"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 3)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "Hello")
self.assertEqual(params["messages"][1]["role"], "assistant")
self.assertEqual(params["messages"][1]["content"], "Hi there!")
self.assertEqual(params["messages"][2]["role"], "user")
self.assertEqual(params["messages"][2]["content"], "How are you?")
def test_initial_system_message_preserved(self):
"""Test that a valid [system, user, assistant, user] sequence passes through unchanged."""
messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi!"},
{"role": "user", "content": "Bye"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 4)
self.assertEqual(params["messages"][0]["role"], "system")
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
self.assertEqual(params["messages"][1]["role"], "user")
self.assertEqual(params["messages"][2]["role"], "assistant")
self.assertEqual(params["messages"][3]["role"], "user")
def test_consecutive_same_role_messages_merged(self):
"""Test that consecutive user messages are merged into list-of-dicts content."""
messages: list[LLMStandardMessage] = [
{"role": "user", "content": "First message"},
{"role": "user", "content": "Second message"},
{"role": "assistant", "content": "Response"},
{"role": "user", "content": "Third message"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 3)
# First message should be merged users
merged = params["messages"][0]
self.assertEqual(merged["role"], "user")
self.assertIsInstance(merged["content"], list)
self.assertEqual(len(merged["content"]), 2)
self.assertEqual(merged["content"][0]["type"], "text")
self.assertEqual(merged["content"][0]["text"], "First message")
self.assertEqual(merged["content"][1]["type"], "text")
self.assertEqual(merged["content"][1]["text"], "Second message")
self.assertEqual(params["messages"][1]["role"], "assistant")
self.assertEqual(params["messages"][2]["role"], "user")
def test_non_initial_system_converted_to_user(self):
"""Test that non-initial system messages are converted to user and merged with adjacent user."""
messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are helpful."},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi!"},
{"role": "system", "content": "Be concise."},
{"role": "user", "content": "Tell me about Python."},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
# system(initial), user, assistant, merged(system→user + user)
self.assertEqual(len(params["messages"]), 4)
self.assertEqual(params["messages"][0]["role"], "system")
self.assertEqual(params["messages"][1]["role"], "user")
self.assertEqual(params["messages"][2]["role"], "assistant")
# The converted system→user and the following user should be merged
merged = params["messages"][3]
self.assertEqual(merged["role"], "user")
self.assertIsInstance(merged["content"], list)
self.assertEqual(len(merged["content"]), 2)
self.assertEqual(merged["content"][0]["text"], "Be concise.")
self.assertEqual(merged["content"][1]["text"], "Tell me about Python.")
def test_multiple_system_messages_at_start_merged(self):
"""Test that multiple consecutive system messages at start are merged into one."""
messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "system", "content": "Always be polite."},
{"role": "user", "content": "Hello"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 2)
# First message should be merged system
system_msg = params["messages"][0]
self.assertEqual(system_msg["role"], "system")
self.assertIsInstance(system_msg["content"], list)
self.assertEqual(len(system_msg["content"]), 2)
self.assertEqual(system_msg["content"][0]["text"], "You are a helpful assistant.")
self.assertEqual(system_msg["content"][1]["text"], "Always be polite.")
self.assertEqual(params["messages"][1]["role"], "user")
self.assertEqual(params["messages"][1]["content"], "Hello")
def test_trailing_assistant_removed(self):
"""Test that a trailing assistant message is removed."""
messages: list[LLMStandardMessage] = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 1)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "Hello")
def test_only_system_message_converted_to_user(self):
"""Test that a single system message is converted to user role."""
messages: list[LLMStandardMessage] = [
{"role": "system", "content": "You are a helpful assistant."},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 1)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
def test_consecutive_assistants_merged_then_trailing_removed(self):
"""Test that consecutive assistant messages are merged, then trailing assistant is removed."""
messages: list[LLMStandardMessage] = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "First response"},
{"role": "assistant", "content": "Second response"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
# After merging assistants we get [user, assistant(merged)], then trailing
# assistant is removed, leaving just [user]
self.assertEqual(len(params["messages"]), 1)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][0]["content"], "Hello")
def test_tool_messages_preserved(self):
"""Test that tool messages pass through without modification."""
messages: list[LLMStandardMessage] = [
{"role": "user", "content": "What's the weather?"},
{
"role": "assistant",
"content": "Let me check.",
"tool_calls": [{"id": "1", "function": {"name": "get_weather", "arguments": "{}"}}],
},
{"role": "tool", "content": "Sunny, 72F", "tool_call_id": "1"},
{"role": "user", "content": "Thanks!"},
]
context = LLMContext(messages=messages)
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(len(params["messages"]), 4)
self.assertEqual(params["messages"][0]["role"], "user")
self.assertEqual(params["messages"][1]["role"], "assistant")
self.assertEqual(params["messages"][2]["role"], "tool")
self.assertEqual(params["messages"][2]["content"], "Sunny, 72F")
self.assertEqual(params["messages"][3]["role"], "user")
def test_empty_messages(self):
"""Test that empty messages list returns empty."""
context = LLMContext(messages=[])
params = self.adapter.get_llm_invocation_params(context)
self.assertEqual(params["messages"], [])
if __name__ == "__main__":
unittest.main()
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