Fix classes that subclass BaseLLMAdapter by adding placeholder stuff until support for universal LLMContext machinery comes to all LLM services

src/pipecat/adapters/base_llm_adapter.py

@@ -22,7 +22,6 @@ from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
 TLLMInvocationParams = TypeVar("TLLMInvocationParams", bound=dict[str, Any])
 
 
-# TODO: fix everywhere we subclass BaseLLMAdapter...
 class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     """Abstract base class for LLM provider adapters.
 

src/pipecat/adapters/services/anthropic_adapter.py

@@ -6,20 +6,58 @@
 
 """Anthropic LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, TypedDict
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.processors.aggregators.llm_context import LLMContext
 
 
-class AnthropicLLMAdapter(BaseLLMAdapter):
+class AnthropicLLMInvocationParams(TypedDict):
+    """Context-based parameters for invoking Anthropic's LLM API.
+
+    This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
+    """
+
+    pass
+
+
+class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
     """Adapter for converting tool schemas to Anthropic's function-calling format.
 
     This adapter handles the conversion of Pipecat's standard function schemas
     to the specific format required by Anthropic's Claude models for function calling.
     """
 
+    def get_llm_invocation_params(self, context: LLMContext) -> AnthropicLLMInvocationParams:
+        """Get Anthropic-specific LLM invocation parameters from a universal LLM context.
+
+        This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
+
+        Args:
+            context: The LLM context containing messages, tools, etc.
+
+        Returns:
+            Dictionary of parameters for invoking Anthropic's LLM API.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+
+    def get_messages_for_logging(self, context) -> List[dict[str, Any]]:
+        """Get messages from a universal LLM context in a format ready for logging about Anthropic.
+
+        Removes or truncates sensitive data like image content for safe logging.
+
+        This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages in a format ready for logging about Anthropic.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+
     @staticmethod
     def _to_anthropic_function_format(function: FunctionSchema) -> Dict[str, Any]:
         """Convert a single function schema to Anthropic's format.

src/pipecat/adapters/services/aws_nova_sonic_adapter.py

@@ -7,20 +7,58 @@
 """AWS Nova Sonic LLM adapter for Pipecat."""
 
 import json
-from typing import Any, Dict, List
+from typing import Any, Dict, List, TypedDict
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.processors.aggregators.llm_context import LLMContext
 
 
-class AWSNovaSonicLLMAdapter(BaseLLMAdapter):
+class AWSNovaSonicLLMInvocationParams(TypedDict):
+    """Context-based parameters for invoking AWS Nova Sonic LLM API.
+
+    This is a placeholder until support for universal LLMContext machinery is added for AWS Nova Sonic.
+    """
+
+    pass
+
+
+class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
     """Adapter for AWS Nova Sonic language models.
 
     Converts Pipecat's standard function schemas into AWS Nova Sonic's
     specific function-calling format, enabling tool use with Nova Sonic models.
     """
 
+    def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
+        """Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.
+
+        This is a placeholder until support for universal LLMContext machinery is added for AWS Nova Sonic.
+
+        Args:
+            context: The LLM context containing messages, tools, etc.
+
+        Returns:
+            Dictionary of parameters for invoking AWS Nova Sonic's LLM API.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
+
+    def get_messages_for_logging(self, context) -> List[dict[str, Any]]:
+        """Get messages from a universal LLM context in a format ready for logging about AWS Nova Sonic.
+
+        Removes or truncates sensitive data like image content for safe logging.
+
+        This is a placeholder until support for universal LLMContext machinery is added for AWS Nova Sonic.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages in a format ready for logging about AWS Nova Sonic.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
+
     @staticmethod
     def _to_aws_nova_sonic_function_format(function: FunctionSchema) -> Dict[str, Any]:
         """Convert a function schema to AWS Nova Sonic format.

src/pipecat/adapters/services/bedrock_adapter.py

@@ -6,20 +6,58 @@
 
 """AWS Bedrock LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, TypedDict
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.processors.aggregators.llm_context import LLMContext
 
 
-class AWSBedrockLLMAdapter(BaseLLMAdapter):
+class AWSBedrockLLMInvocationParams(TypedDict):
+    """Context-based parameters for invoking AWS Bedrock's LLM API.
+
+    This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
+    """
+
+    pass
+
+
+class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
     """Adapter for AWS Bedrock LLM integration with Pipecat.
 
     Provides conversion utilities for transforming Pipecat function schemas
     into AWS Bedrock's expected tool format for function calling capabilities.
     """
 
+    def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
+        """Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
+
+        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
+
+        Args:
+            context: The LLM context containing messages, tools, etc.
+
+        Returns:
+            Dictionary of parameters for invoking AWS Bedrock's LLM API.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+
+    def get_messages_for_logging(self, context) -> List[dict[str, Any]]:
+        """Get messages from a universal LLM context in a format ready for logging about AWS Bedrock.
+
+        Removes or truncates sensitive data like image content for safe logging.
+
+        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages in a format ready for logging about AWS Bedrock.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+
     @staticmethod
     def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:
         """Convert a function schema to Bedrock's tool format.

src/pipecat/adapters/services/open_ai_adapter.py

@@ -36,7 +36,7 @@ class OpenAILLMInvocationParams(TypedDict):
     tool_choice: ChatCompletionToolChoiceOptionParam | OpenAINotGiven
 
 
-class OpenAILLMAdapter(BaseLLMAdapter):
+class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
     """OpenAI-specific adapter for Pipecat.
 
     Handles:

src/pipecat/adapters/services/open_ai_realtime_adapter.py

@@ -6,11 +6,21 @@
 
 """OpenAI Realtime LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, TypedDict, Union
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.processors.aggregators.llm_context import LLMContext
+
+
+class OpenAIRealtimeLLMInvocationParams(TypedDict):
+    """Context-based parameters for invoking OpenAI Realtime API.
+
+    This is a placeholder until support for universal LLMContext machinery is added for OpenAI Realtime.
+    """
+
+    pass
 
 
 class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
@@ -20,6 +30,34 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
     OpenAI's Realtime API for function calling capabilities.
     """
 
+    def get_llm_invocation_params(self, context: LLMContext) -> OpenAIRealtimeLLMInvocationParams:
+        """Get OpenAI Realtime-specific LLM invocation parameters from a universal LLM context.
+
+        This is a placeholder until support for universal LLMContext machinery is added for OpenAI Realtime.
+
+        Args:
+            context: The LLM context containing messages, tools, etc.
+
+        Returns:
+            Dictionary of parameters for invoking OpenAI Realtime's API.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
+
+    def get_messages_for_logging(self, context) -> List[dict[str, Any]]:
+        """Get messages from a universal LLM context in a format ready for logging about OpenAI Realtime.
+
+        Removes or truncates sensitive data like image content for safe logging.
+
+        This is a placeholder until support for universal LLMContext machinery is added for OpenAI Realtime.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages in a format ready for logging about OpenAI Realtime.
+        """
+        raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
+
     @staticmethod
     def _to_openai_realtime_function_format(function: FunctionSchema) -> Dict[str, Any]:
         """Convert a function schema to OpenAI Realtime format.