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language and endconversation detection tool (#210)
* added language and env_conversation detection tool * lang detection -> parallel pipeline instead of service switcher * little prompt fix for tool
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Lines changed: 534 additions & 276 deletions

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‎wavefront/server/apps/call_processing/call_processing/services/conversation_completion_tool.py‎

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"""
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Conversation Completion Tool for Voice Agents
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Provides LLM-callable conversation ending capabilities
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"""
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from typing import Dict, Any, Callable
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.frames.frames import TTSSpeakFrame, EndTaskFrame
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from pipecat.processors.frame_processor import FrameDirection
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from call_processing.log.logger import logger
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class ConversationCompletionToolFactory:
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"""Factory for creating conversation completion tool with runtime context"""
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@staticmethod
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def create_conversation_completion_tool(
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task_container: Dict[str, Any],
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) -> Callable:
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"""
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Create conversation completion tool function with captured context
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Args:
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task_container: Dictionary containing PipelineTask (populated after task creation)
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Format: {'task': PipelineTask | None}
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Returns:
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Async function compatible with Pipecat's function calling
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"""
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async def end_conversation(params: FunctionCallParams):
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"""
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LLM-callable function to end the conversation gracefully
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This function is called by the LLM when it determines the user
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wants to end the conversation. It sends a farewell message and
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terminates the pipeline.
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Parameters (from LLM):
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farewell_message: str - Optional custom farewell message
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(defaults to standard goodbye)
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"""
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try:
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# Get task from container
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task = task_container.get('task')
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if not task:
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error_msg = (
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'Pipeline task not initialized in conversation completion tool'
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)
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logger.error(error_msg)
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await params.result_callback({'success': False, 'error': error_msg})
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return
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# Extract parameters
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arguments = params.arguments
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farewell_message = arguments.get(
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'farewell_message', 'Thank you for using our service! Goodbye!'
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)
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logger.info(
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f'Conversation completion tool called - Farewell: "{farewell_message}"'
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)
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# Send farewell message via TTS
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await params.llm.push_frame(TTSSpeakFrame(farewell_message))
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# End the conversation
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await params.llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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logger.info('Conversation ended by LLM decision')
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# Return success result
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await params.result_callback(
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{
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'success': True,
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'status': 'complete',
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'farewell_sent': True,
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'farewell_message': farewell_message,
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}
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)
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except Exception as e:
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error_msg = f'Error ending conversation: {str(e)}'
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logger.error(error_msg, exc_info=True)
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await params.result_callback({'success': False, 'error': error_msg})
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return end_conversation

‎wavefront/server/apps/call_processing/call_processing/services/language_detection_tool.py‎

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"""
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Language Detection Tool for Multi-Language Voice Agents
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Provides LLM-callable language detection and switching capabilities
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"""
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from typing import Dict, Any, List, Callable
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.frames.frames import LLMMessagesUpdateFrame
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from call_processing.log.logger import logger
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from call_processing.constants.language_config import LANGUAGE_INSTRUCTIONS
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class LanguageDetectionToolFactory:
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"""Factory for creating language detection tool with runtime context"""
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@staticmethod
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def create_language_detection_tool(
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task_container: Dict[str, Any],
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language_switcher: Any,
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stt_language_switcher: Any,
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context_container: Dict[str, Any],
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supported_languages: List[str],
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default_language: str,
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language_state: Dict[str, Any],
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) -> Callable:
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"""
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Create language detection tool function with captured context
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Args:
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task_container: Dictionary containing PipelineTask (populated after task creation)
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Format: {'task': PipelineTask | None}
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language_switcher: LanguageSwitcher instance that manages TTS routing
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stt_language_switcher: STTLanguageSwitcher instance that manages STT routing
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context_container: Dictionary containing LLMContext (populated after context creation)
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Format: {'context': LLMContext | None}
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supported_languages: List of supported language codes
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default_language: Default language code
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language_state: Dictionary to track current language and switch count
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Format: {'current_language': str, 'switch_count': int, 'original_system_prompt': str}
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Returns:
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Async function compatible with Pipecat's function calling
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"""
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async def detect_and_switch_language(params: FunctionCallParams):
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"""
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LLM-callable function to detect and switch conversation language
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This function is called by the LLM when it determines the user
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wants to switch to a different language. It validates the request,
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performs the service switch, and updates the system prompt.
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Parameters (from LLM):
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target_language: str - Language code to switch to (e.g., 'es', 'hi', 'en')
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user_intent: str - User's stated language preference (for logging)
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"""
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try:
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# Get task and context from containers
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task = task_container.get('task')
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if not task:
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error_msg = (
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'Pipeline task not initialized in language detection tool'
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)
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logger.error(error_msg)
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await params.result_callback({'success': False, 'error': error_msg})
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return
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context = context_container.get('context')
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if not context:
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error_msg = 'LLM context not initialized in language detection tool'
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logger.error(error_msg)
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await params.result_callback({'success': False, 'error': error_msg})
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return
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# Extract parameters
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arguments = params.arguments
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target_language = arguments.get('target_language', '').lower()
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user_intent = arguments.get('user_intent', 'Unknown')
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current_language = language_state.get(
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'current_language', default_language
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)
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switch_count = language_state.get('switch_count', 0)
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logger.info(
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f'Language detection tool called - Target: {target_language}, '
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f'Current: {current_language}, User intent: {user_intent}'
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)
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# Validation 1: Check if target language is supported
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if target_language not in supported_languages:
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error_msg = (
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f"Language '{target_language}' is not supported. "
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f"Supported languages: {', '.join(supported_languages)}"
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)
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logger.warning(error_msg)
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await params.result_callback(
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{
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'success': False,
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'error': error_msg,
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'current_language': current_language,
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'supported_languages': supported_languages,
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}
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)
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return
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# Validation 2: Check if already in target language
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if target_language == current_language:
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logger.info(f'Already using language: {target_language}')
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await params.result_callback(
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{
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'success': True,
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'message': f'Already using {target_language}',
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'current_language': current_language,
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'switch_performed': False,
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}
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)
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return
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# Perform language switch
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try:
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# Update TTS language switcher state
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language_switcher.set_language(target_language)
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# Update STT language switcher state
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stt_language_switcher.set_language(target_language)
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logger.info(
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f'Switched TTS and STT language from {current_language} to {target_language}'
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)
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# Update system prompt with language instruction
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language_instruction = LANGUAGE_INSTRUCTIONS.get(
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target_language,
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LANGUAGE_INSTRUCTIONS.get('en', 'Respond in English.'),
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)
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# Get base prompt without language instruction (must exist for multi-language)
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base_prompt = language_state.get('original_system_prompt')
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if not base_prompt:
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error_msg = 'Original system prompt not found in language state'
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logger.error(error_msg)
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await params.result_callback(
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{'success': False, 'error': error_msg}
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)
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return
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# Append new language instruction to clean base prompt
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updated_content = f'{base_prompt}\n\n{language_instruction}'
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updated_system_message = {
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'role': 'system',
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'content': updated_content,
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}
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# Update context
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current_messages = context.get_messages()
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new_messages = [updated_system_message] + current_messages[1:]
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await task.queue_frame(
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LLMMessagesUpdateFrame(new_messages, run_llm=False)
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)
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logger.info(
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f'Updated system prompt with {target_language} instruction'
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)
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# Update state
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language_state['current_language'] = target_language
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language_state['switch_count'] = switch_count + 1
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# Return success result
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await params.result_callback(
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{
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'success': True,
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'message': f'Language switched to {target_language}',
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'previous_language': current_language,
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'current_language': target_language,
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'switch_performed': True,
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'switch_count': language_state['switch_count'],
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}
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)
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except Exception as e:
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error_msg = f'Error switching services: {str(e)}'
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logger.error(error_msg, exc_info=True)
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await params.result_callback(
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{
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'success': False,
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'error': error_msg,
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'current_language': current_language,
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}
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)
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except Exception as e:
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error_msg = f'Error in language detection tool: {str(e)}'
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logger.error(error_msg, exc_info=True)
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await params.result_callback({'success': False, 'error': error_msg})
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return detect_and_switch_language

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