Add back types that were removed, when they should only have been deprecated
This commit is contained in:
Paul Kompfner
@@ -10,7 +10,8 @@ This module provides specialized context aggregators and message handling for AW
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including conversation history management and role-specific message processing.
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.. deprecated:: 0.0.91
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AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
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AWS Nova Sonic no longer uses types from this module under the hood.
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It now uses `LLMContext` and `LLMContextAggregatorPair`.
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Using the new patterns should allow you to not need types from this module.
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BEFORE:
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@@ -53,8 +54,10 @@ import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("always")
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warnings.warn(
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"Types in pipecat.services.aws.nova_sonic.context are deprecated. \n"
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"AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
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"Types in pipecat.services.aws.nova_sonic.context (or "
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"pipecat.services.aws_nova_sonic.context) are deprecated. \n"
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"AWS Nova Sonic no longer uses types from this module under the hood. \n"
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"It now uses `LLMContext` and `LLMContextAggregatorPair`. \n"
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"Using the new patterns should allow you to not need types from this module.\n\n"
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"BEFORE:\n"
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"```\n"
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@@ -85,3 +88,359 @@ with warnings.catch_warnings():
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DeprecationWarning,
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stacklevel=2,
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)
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import copy
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from dataclasses import dataclass, field
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from enum import Enum
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from loguru import logger
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from pipecat.frames.frames import (
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BotStoppedSpeakingFrame,
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DataFrame,
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Frame,
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FunctionCallResultFrame,
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InterruptionFrame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMMessagesAppendFrame,
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LLMMessagesUpdateFrame,
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LLMSetToolChoiceFrame,
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LLMSetToolsFrame,
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TextFrame,
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UserImageRawFrame,
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)
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.aws.nova_sonic.frames import AWSNovaSonicFunctionCallResultFrame
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from pipecat.services.openai.llm import (
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OpenAIAssistantContextAggregator,
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OpenAIUserContextAggregator,
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)
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class Role(Enum):
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"""Roles supported in AWS Nova Sonic conversations.
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Parameters:
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SYSTEM: System-level messages (not used in conversation history).
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USER: Messages sent by the user.
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ASSISTANT: Messages sent by the assistant.
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TOOL: Messages sent by tools (not used in conversation history).
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"""
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SYSTEM = "SYSTEM"
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USER = "USER"
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ASSISTANT = "ASSISTANT"
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TOOL = "TOOL"
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@dataclass
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class AWSNovaSonicConversationHistoryMessage:
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"""A single message in AWS Nova Sonic conversation history.
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Parameters:
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role: The role of the message sender (USER or ASSISTANT only).
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text: The text content of the message.
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"""
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role: Role # only USER and ASSISTANT
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text: str
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@dataclass
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class AWSNovaSonicConversationHistory:
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"""Complete conversation history for AWS Nova Sonic initialization.
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Parameters:
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system_instruction: System-level instruction for the conversation.
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messages: List of conversation messages between user and assistant.
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"""
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system_instruction: str = None
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messages: list[AWSNovaSonicConversationHistoryMessage] = field(default_factory=list)
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class AWSNovaSonicLLMContext(OpenAILLMContext):
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"""Specialized LLM context for AWS Nova Sonic service.
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Extends OpenAI context with Nova Sonic-specific message handling,
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conversation history management, and text buffering capabilities.
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"""
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def __init__(self, messages=None, tools=None, **kwargs):
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"""Initialize AWS Nova Sonic LLM context.
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Args:
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messages: Initial messages for the context.
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tools: Available tools for the context.
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**kwargs: Additional arguments passed to parent class.
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"""
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super().__init__(messages=messages, tools=tools, **kwargs)
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self.__setup_local()
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def __setup_local(self, system_instruction: str = ""):
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self._assistant_text = ""
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self._user_text = ""
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self._system_instruction = system_instruction
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@staticmethod
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def upgrade_to_nova_sonic(
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obj: OpenAILLMContext, system_instruction: str
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) -> "AWSNovaSonicLLMContext":
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"""Upgrade an OpenAI context to AWS Nova Sonic context.
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Args:
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obj: The OpenAI context to upgrade.
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system_instruction: System instruction for the context.
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Returns:
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The upgraded AWS Nova Sonic context.
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"""
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if isinstance(obj, OpenAILLMContext) and not isinstance(obj, AWSNovaSonicLLMContext):
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obj.__class__ = AWSNovaSonicLLMContext
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obj.__setup_local(system_instruction)
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return obj
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# NOTE: this method has the side-effect of updating _system_instruction from messages
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def get_messages_for_initializing_history(self) -> AWSNovaSonicConversationHistory:
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"""Get conversation history for initializing AWS Nova Sonic session.
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Processes stored messages and extracts system instruction and conversation
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history in the format expected by AWS Nova Sonic.
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Returns:
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Formatted conversation history with system instruction and messages.
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"""
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history = AWSNovaSonicConversationHistory(system_instruction=self._system_instruction)
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# Bail if there are no messages
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if not self.messages:
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return history
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messages = copy.deepcopy(self.messages)
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# If we have a "system" message as our first message, let's pull that out into "instruction"
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if messages[0].get("role") == "system":
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system = messages.pop(0)
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content = system.get("content")
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if isinstance(content, str):
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history.system_instruction = content
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elif isinstance(content, list):
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history.system_instruction = content[0].get("text")
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if history.system_instruction:
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self._system_instruction = history.system_instruction
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# Process remaining messages to fill out conversation history.
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# Nova Sonic supports "user" and "assistant" messages in history.
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for message in messages:
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history_message = self.from_standard_message(message)
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if history_message:
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history.messages.append(history_message)
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return history
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def get_messages_for_persistent_storage(self):
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"""Get messages formatted for persistent storage.
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Returns:
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List of messages including system instruction if present.
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"""
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messages = super().get_messages_for_persistent_storage()
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# If we have a system instruction and messages doesn't already contain it, add it
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if self._system_instruction and not (messages and messages[0].get("role") == "system"):
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messages.insert(0, {"role": "system", "content": self._system_instruction})
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return messages
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def from_standard_message(self, message) -> AWSNovaSonicConversationHistoryMessage:
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"""Convert standard message format to Nova Sonic format.
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Args:
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message: Standard message dictionary to convert.
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Returns:
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Nova Sonic conversation history message, or None if not convertible.
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"""
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role = message.get("role")
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if message.get("role") == "user" or message.get("role") == "assistant":
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content = message.get("content")
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if isinstance(message.get("content"), list):
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content = ""
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for c in message.get("content"):
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if c.get("type") == "text":
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content += " " + c.get("text")
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else:
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logger.error(
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f"Unhandled content type in context message: {c.get('type')} - {message}"
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)
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# There won't be content if this is an assistant tool call entry.
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# We're ignoring those since they can't be loaded into AWS Nova Sonic conversation
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# history
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if content:
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return AWSNovaSonicConversationHistoryMessage(role=Role[role.upper()], text=content)
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# NOTE: we're ignoring messages with role "tool" since they can't be loaded into AWS Nova
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# Sonic conversation history
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def buffer_user_text(self, text):
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"""Buffer user text for later flushing to context.
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Args:
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text: User text to buffer.
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"""
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self._user_text += f" {text}" if self._user_text else text
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# logger.debug(f"User text buffered: {self._user_text}")
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def flush_aggregated_user_text(self) -> str:
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"""Flush buffered user text to context as a complete message.
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Returns:
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The flushed user text, or empty string if no text was buffered.
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"""
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if not self._user_text:
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return ""
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user_text = self._user_text
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message = {
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"role": "user",
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"content": [{"type": "text", "text": user_text}],
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}
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self._user_text = ""
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self.add_message(message)
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# logger.debug(f"Context updated (user): {self.get_messages_for_logging()}")
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return user_text
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def buffer_assistant_text(self, text):
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"""Buffer assistant text for later flushing to context.
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Args:
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text: Assistant text to buffer.
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"""
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self._assistant_text += text
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# logger.debug(f"Assistant text buffered: {self._assistant_text}")
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def flush_aggregated_assistant_text(self):
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"""Flush buffered assistant text to context as a complete message."""
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if not self._assistant_text:
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return
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message = {
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"role": "assistant",
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"content": [{"type": "text", "text": self._assistant_text}],
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}
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self._assistant_text = ""
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self.add_message(message)
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# logger.debug(f"Context updated (assistant): {self.get_messages_for_logging()}")
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@dataclass
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class AWSNovaSonicMessagesUpdateFrame(DataFrame):
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"""Frame containing updated AWS Nova Sonic context.
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Parameters:
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context: The updated AWS Nova Sonic LLM context.
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"""
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context: AWSNovaSonicLLMContext
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class AWSNovaSonicUserContextAggregator(OpenAIUserContextAggregator):
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"""Context aggregator for user messages in AWS Nova Sonic conversations.
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Extends the OpenAI user context aggregator to emit Nova Sonic-specific
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context update frames.
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"""
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async def process_frame(
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self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM
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):
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"""Process frames and emit Nova Sonic-specific context updates.
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Args:
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frame: The frame to process.
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direction: The direction the frame is traveling.
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"""
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await super().process_frame(frame, direction)
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# Parent does not push LLMMessagesUpdateFrame
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if isinstance(frame, LLMMessagesUpdateFrame):
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await self.push_frame(AWSNovaSonicMessagesUpdateFrame(context=self._context))
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class AWSNovaSonicAssistantContextAggregator(OpenAIAssistantContextAggregator):
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"""Context aggregator for assistant messages in AWS Nova Sonic conversations.
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Provides specialized handling for assistant responses and function calls
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in AWS Nova Sonic context, with custom frame processing logic.
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"""
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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"""Process frames with Nova Sonic-specific logic.
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Args:
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frame: The frame to process.
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direction: The direction the frame is traveling.
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"""
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# HACK: For now, disable the context aggregator by making it just pass through all frames
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# that the parent handles (except the function call stuff, which we still need).
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# For an explanation of this hack, see
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# AWSNovaSonicLLMService._report_assistant_response_text_added.
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if isinstance(
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frame,
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(
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InterruptionFrame,
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LLMFullResponseStartFrame,
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LLMFullResponseEndFrame,
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TextFrame,
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LLMMessagesAppendFrame,
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LLMMessagesUpdateFrame,
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LLMSetToolsFrame,
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LLMSetToolChoiceFrame,
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UserImageRawFrame,
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BotStoppedSpeakingFrame,
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),
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):
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await self.push_frame(frame, direction)
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else:
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await super().process_frame(frame, direction)
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async def handle_function_call_result(self, frame: FunctionCallResultFrame):
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"""Handle function call results for AWS Nova Sonic.
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Args:
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frame: The function call result frame to handle.
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"""
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await super().handle_function_call_result(frame)
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# The standard function callback code path pushes the FunctionCallResultFrame from the LLM
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# itself, so we didn't have a chance to add the result to the AWS Nova Sonic server-side
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# context. Let's push a special frame to do that.
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await self.push_frame(
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AWSNovaSonicFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
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)
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@dataclass
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class AWSNovaSonicContextAggregatorPair:
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"""Pair of user and assistant context aggregators for AWS Nova Sonic.
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Parameters:
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_user: The user context aggregator.
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_assistant: The assistant context aggregator.
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"""
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_user: AWSNovaSonicUserContextAggregator
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_assistant: AWSNovaSonicAssistantContextAggregator
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def user(self) -> AWSNovaSonicUserContextAggregator:
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"""Get the user context aggregator.
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Returns:
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The user context aggregator instance.
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"""
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return self._user
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def assistant(self) -> AWSNovaSonicAssistantContextAggregator:
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"""Get the assistant context aggregator.
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Returns:
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The assistant context aggregator instance.
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"""
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return self._assistant
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@@ -10,78 +10,12 @@ This module provides specialized context aggregators and message handling for AW
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including conversation history management and role-specific message processing.
|
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|
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.. deprecated:: 0.0.91
|
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AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
|
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AWS Nova Sonic no longer uses types from this module under the hood.
|
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It now uses `LLMContext` and `LLMContextAggregatorPair`.
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Using the new patterns should allow you to not need types from this module.
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|
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BEFORE:
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```
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# Setup
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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# Context frame type
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frame: OpenAILLMContextFrame
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# Context type
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context: AWSNovaSonicLLMContext
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# or
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context: OpenAILLMContext
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# Reading messages from context
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messages = context.messages
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```
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AFTER:
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```
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# Setup
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context = LLMContext(messages, tools)
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context_aggregator = LLMContextAggregatorPair(context)
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# Context frame type
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frame: LLMContextFrame
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# Context type
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context: LLMContext
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# Reading messages from context
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messages = context.get_messages()
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```
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See deprecation warning in pipecat.services.aws.nova_sonic.context for more
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details.
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"""
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import warnings
|
||||
|
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with warnings.catch_warnings():
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warnings.simplefilter("always")
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||||
warnings.warn(
|
||||
"Types in pipecat.services.aws_nova_sonic.context are deprecated. \n"
|
||||
"AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
|
||||
"Using the new patterns should allow you to not need types from this module.\n\n"
|
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"BEFORE:\n"
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"```\n"
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"# Setup\n"
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"context = OpenAILLMContext(messages, tools)\n"
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"context_aggregator = llm.create_context_aggregator(context)\n\n"
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"# Context frame type\n"
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"frame: OpenAILLMContextFrame\n\n"
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"# Context type\n"
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"context: AWSNovaSonicLLMContext\n"
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"# or\n"
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"context: OpenAILLMContext\n\n"
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"# Reading messages from context\n"
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"messages = context.messages\n"
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"```\n\n"
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"AFTER:\n"
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"```\n"
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"# Setup\n"
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"context = LLMContext(messages, tools)\n"
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"context_aggregator = LLMContextAggregatorPair(context)\n\n"
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"# Context frame type\n"
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"frame: LLMContextFrame\n\n"
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"# Context type\n"
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"context: LLMContext\n\n"
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"# Reading messages from context\n"
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"messages = context.messages\n"
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"```",
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DeprecationWarning,
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||||
stacklevel=2,
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)
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from pipecat.services.aws.nova_sonic.context import *
|
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|
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Reference in New Issue
Block a user