Merge pull request #2653 from pipecat-ai/pk/llm-context-adapting-tests

`LLMContext`-adapting unit tests

src/pipecat/adapters/base_llm_adapter.py

@@ -16,7 +16,12 @@ from typing import Any, Dict, Generic, List, TypeVar
 from loguru import logger
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMContextMessage,
+    LLMSpecificMessage,
+    NotGiven,
+)
 
 # Should be a TypedDict
 TLLMInvocationParams = TypeVar("TLLMInvocationParams", bound=dict[str, Any])
@@ -38,6 +43,16 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     Subclasses must implement provider-specific conversion logic.
     """
 
+    @property
+    @abstractmethod
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for this LLM provider.
+
+        Returns:
+            The identifier string.
+        """
+        pass
+
     @abstractmethod
     def get_llm_invocation_params(self, context: LLMContext, **kwargs) -> TLLMInvocationParams:
         """Get provider-specific LLM invocation parameters from a universal LLM context.
@@ -76,6 +91,28 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
         """
         pass
 
+    def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
+        """Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
+
+        Args:
+            message: The message content.
+
+        Returns:
+            A LLMSpecificMessage instance.
+        """
+        return LLMSpecificMessage(llm=self.id_for_llm_specific_messages, message=message)
+
+    def get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
+        """Get messages from the LLM context, including standard and LLM-specific messages.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages including standard and LLM-specific messages.
+        """
+        return context.get_messages(self.id_for_llm_specific_messages)
+
     def from_standard_tools(self, tools: Any) -> List[Any] | NotGiven:
         """Convert tools from standard format to provider format.
 

src/pipecat/adapters/services/anthropic_adapter.py

@@ -42,6 +42,11 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
     to the specific format required by Anthropic's Claude models for function calling.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for Anthropic."""
+        return "anthropic"
+
     def get_llm_invocation_params(
         self, context: LLMContext, enable_prompt_caching: bool
     ) -> AnthropicLLMInvocationParams:
@@ -54,7 +59,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         Returns:
             Dictionary of parameters for invoking Anthropic's LLM API.
         """
-        messages = self._from_universal_context_messages(self._get_messages(context))
+        messages = self._from_universal_context_messages(self.get_messages(context))
         return {
             "system": messages.system,
             "messages": (
@@ -78,7 +83,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
             List of messages in a format ready for logging about Anthropic.
         """
         # Get messages in Anthropic's format
-        messages = self._from_universal_context_messages(self._get_messages(context)).messages
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
         messages_for_logging = []
@@ -92,9 +97,6 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
             messages_for_logging.append(msg)
         return messages_for_logging
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("anthropic")
-
     @dataclass
     class ConvertedMessages:
         """Container for Anthropic-formatted messages converted from universal context."""

src/pipecat/adapters/services/aws_nova_sonic_adapter.py

@@ -31,6 +31,11 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
     specific function-calling format, enabling tool use with Nova Sonic models.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for AWS Nova Sonic."""
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
+
     def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
         """Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.
 

src/pipecat/adapters/services/bedrock_adapter.py

@@ -42,6 +42,11 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
     into AWS Bedrock's expected tool format for function calling capabilities.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for AWS Bedrock."""
+        return "aws"
+
     def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
         """Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
 
@@ -51,7 +56,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
         Returns:
             Dictionary of parameters for invoking AWS Bedrock's LLM API.
         """
-        messages = self._from_universal_context_messages(self._get_messages(context))
+        messages = self._from_universal_context_messages(self.get_messages(context))
         return {
             "system": messages.system,
             "messages": messages.messages,
@@ -75,7 +80,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
             List of messages in a format ready for logging about AWS Bedrock.
         """
         # Get messages in Anthropic's format
-        messages = self._from_universal_context_messages(self._get_messages(context)).messages
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
         messages_for_logging = []
@@ -89,9 +94,6 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
             messages_for_logging.append(msg)
         return messages_for_logging
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("anthropic")
-
     @dataclass
     class ConvertedMessages:
         """Container for Anthropic-formatted messages converted from universal context."""

src/pipecat/adapters/services/gemini_adapter.py

@@ -54,6 +54,11 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
     - Extracting and sanitizing messages from the LLM context for logging with Gemini.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for Google."""
+        return "google"
+
     def get_llm_invocation_params(self, context: LLMContext) -> GeminiLLMInvocationParams:
         """Get Gemini-specific LLM invocation parameters from a universal LLM context.
 
@@ -63,7 +68,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         Returns:
             Dictionary of parameters for Gemini's API.
         """
-        messages = self._from_universal_context_messages(self._get_messages(context))
+        messages = self._from_universal_context_messages(self.get_messages(context))
         return {
             "system_instruction": messages.system_instruction,
             "messages": messages.messages,
@@ -103,7 +108,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             List of messages in a format ready for logging about Gemini.
         """
         # Get messages in Gemini's format
-        messages = self._from_universal_context_messages(self._get_messages(context)).messages
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
         messages_for_logging = []
@@ -119,9 +124,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             messages_for_logging.append(obj)
         return messages_for_logging
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("google")
-
     @dataclass
     class ConvertedMessages:
         """Container for Google-formatted messages converted from universal context."""

src/pipecat/adapters/services/open_ai_adapter.py

@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.llm_context import (
     LLMContext,
     LLMContextMessage,
     LLMContextToolChoice,
+    LLMSpecificMessage,
     NotGiven,
 )
 
@@ -47,6 +48,11 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
     - Extracting and sanitizing messages from the LLM context for logging about OpenAI.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for OpenAI."""
+        return "openai"
+
     def get_llm_invocation_params(self, context: LLMContext) -> OpenAILLMInvocationParams:
         """Get OpenAI-specific LLM invocation parameters from a universal LLM context.
 
@@ -57,7 +63,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             Dictionary of parameters for OpenAI's ChatCompletion API.
         """
         return {
-            "messages": self._from_universal_context_messages(self._get_messages(context)),
+            "messages": self._from_universal_context_messages(self.get_messages(context)),
             # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
             "tools": self.from_standard_tools(context.tools),
             "tool_choice": context.tool_choice,
@@ -91,7 +97,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             List of messages in a format ready for logging about OpenAI.
         """
         msgs = []
-        for message in self._get_messages(context):
+        for message in self.get_messages(context):
             msg = copy.deepcopy(message)
             if "content" in msg:
                 if isinstance(msg["content"], list):
@@ -104,14 +110,18 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             msgs.append(msg)
         return msgs
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("openai")
-
     def _from_universal_context_messages(
         self, messages: List[LLMContextMessage]
     ) -> List[ChatCompletionMessageParam]:
-        # Just a pass-through: messages are already the right type
+        result = []
-        return messages
+        for message in messages:
+            if isinstance(message, LLMSpecificMessage):
+                # Extract the actual message content from LLMSpecificMessage
+                result.append(message.message)
+            else:
+                # Standard message, pass through unchanged
+                result.append(message)
+        return result
 
     def _from_standard_tool_choice(
         self, tool_choice: LLMContextToolChoice | NotGiven

src/pipecat/adapters/services/open_ai_realtime_adapter.py

@@ -30,6 +30,11 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
     OpenAI's Realtime API for function calling capabilities.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for OpenAI Realtime."""
+        raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
+
     def get_llm_invocation_params(self, context: LLMContext) -> OpenAIRealtimeLLMInvocationParams:
         """Get OpenAI Realtime-specific LLM invocation parameters from a universal LLM context.
 

src/pipecat/services/llm_service.py

@@ -44,7 +44,7 @@ from pipecat.frames.frames import (
     StartFrame,
     UserImageRequestFrame,
 )
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
 from pipecat.processors.aggregators.llm_response import (
     LLMAssistantAggregatorParams,
     LLMUserAggregatorParams,
@@ -195,6 +195,17 @@ class LLMService(AIService):
         """
         return self._adapter
 
+    def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
+        """Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
+
+        Args:
+            message: The message content.
+
+        Returns:
+            A LLMSpecificMessage instance.
+        """
+        return self.get_llm_adapter().create_llm_specific_message(message)
+
     async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 

tests/test_get_llm_invocation_params.py

@@ -0,0 +1,998 @@
+#
+# Copyright (c) 2024–2025, 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 openai.types.chat import ChatCompletionMessage
+
+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.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMSpecificMessage,
+    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)
+
+
+if __name__ == "__main__":
+    unittest.main()