Merge #21 into develop from feature/emotion-engine
feature/情绪引擎 * feature/emotion-engine: (7 commits squashed) - [feature]Emotion Engine Development - [feature]Emotion Engine Development - Merge branch 'feature/emotion-engine' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/emotion-engine - [fix]1.Fix the front-end files;2.Cache Management Deletion;3.Delete "check_code.py" - [fix]1.Fix the front-end files;2.Cache Management Deletion;3.Delete "check_code.py" - Merge branch 'feature/emotion-engine' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/emotion-engine - [fix]fix vite.config.ts Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com> Commented-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> Commented-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com> Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/21
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乐力齐
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api/app/services/emotion_extraction_service.py
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api/app/services/emotion_extraction_service.py
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"""Emotion extraction service for analyzing emotions from statements.
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This service extracts emotion information from user statements using LLM,
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including emotion type, intensity, keywords, subject classification, and target.
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Classes:
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EmotionExtractionService: Service for extracting emotions from statements
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"""
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import logging
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from typing import Optional
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from app.core.memory.models.emotion_models import EmotionExtraction
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from app.models.data_config_model import DataConfig
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from app.core.memory.utils.llm.llm_utils import get_llm_client
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from app.core.memory.llm_tools.llm_client import LLMClientException
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logger = logging.getLogger(__name__)
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class EmotionExtractionService:
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"""Service for extracting emotion information from statements.
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This service uses LLM to analyze statements and extract structured emotion
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information including type, intensity, keywords, subject, and target.
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It respects configuration settings for enabling/disabling extraction and
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filtering by intensity threshold.
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Attributes:
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llm_client: LLM client for making structured output calls
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"""
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def __init__(self, llm_id: Optional[str] = None):
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"""Initialize the emotion extraction service.
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Args:
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llm_id: Optional LLM model ID. If None, uses default from config.
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"""
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self.llm_client = None
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self.llm_id = llm_id
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logger.info(f"Initialized EmotionExtractionService with llm_id={llm_id}")
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def _get_llm_client(self, model_id: Optional[str] = None):
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"""Get or create LLM client instance.
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Args:
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model_id: Optional model ID to use. If None, uses instance llm_id.
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Returns:
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LLM client instance
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"""
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if self.llm_client is None or model_id:
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effective_model_id = model_id or self.llm_id
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self.llm_client = get_llm_client(effective_model_id)
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return self.llm_client
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async def extract_emotion(
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self,
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statement: str,
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config: DataConfig
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) -> Optional[EmotionExtraction]:
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"""Extract emotion information from a statement.
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This method checks if emotion extraction is enabled in the config,
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builds an appropriate prompt, calls the LLM for structured output,
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and applies intensity threshold filtering.
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Args:
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statement: The statement text to analyze
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config: Data configuration object containing emotion settings
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Returns:
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EmotionExtraction object if extraction succeeds and passes threshold,
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None if extraction is disabled, fails, or doesn't meet threshold
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Raises:
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No exceptions are raised - failures are logged and return None
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"""
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# Check if emotion extraction is enabled
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if not config.emotion_enabled:
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logger.debug("Emotion extraction is disabled in config")
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return None
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# Validate statement
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if not statement or not statement.strip():
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logger.warning("Empty statement provided for emotion extraction")
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return None
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try:
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# Build the emotion extraction prompt
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prompt = await self._build_emotion_prompt(
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statement=statement,
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extract_keywords=config.emotion_extract_keywords,
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enable_subject=config.emotion_enable_subject
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)
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# Call LLM for structured output
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emotion = await self._call_llm_structured(
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prompt=prompt,
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model_id=config.emotion_model_id
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)
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# Apply intensity threshold filtering
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if emotion.emotion_intensity < config.emotion_min_intensity:
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logger.debug(
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f"Emotion intensity {emotion.emotion_intensity} below threshold "
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f"{config.emotion_min_intensity}, skipping storage"
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)
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return None
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logger.info(
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f"Successfully extracted emotion: type={emotion.emotion_type}, "
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f"intensity={emotion.emotion_intensity}, subject={emotion.emotion_subject}"
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)
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return emotion
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except Exception as e:
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logger.error(
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f"Emotion extraction failed for statement: {statement[:50]}..., "
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f"error: {str(e)}",
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exc_info=True
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)
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return None
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async def _build_emotion_prompt(
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self,
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statement: str,
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extract_keywords: bool,
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enable_subject: bool
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) -> str:
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"""Build the emotion extraction prompt based on configuration.
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This method constructs a detailed prompt for the LLM that includes
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instructions for emotion type classification, intensity assessment,
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and optionally keyword extraction and subject classification.
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Args:
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statement: The statement to analyze
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extract_keywords: Whether to extract emotion keywords
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enable_subject: Whether to enable subject classification
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Returns:
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Formatted prompt string for LLM
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"""
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from app.core.memory.utils.prompt.prompt_utils import render_emotion_extraction_prompt
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prompt = await render_emotion_extraction_prompt(
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statement=statement,
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extract_keywords=extract_keywords,
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enable_subject=enable_subject
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)
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return prompt
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async def _call_llm_structured(
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self,
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prompt: str,
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model_id: Optional[str] = None
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) -> EmotionExtraction:
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"""Call LLM for structured emotion extraction output.
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This method uses the LLM client's response_structured method to get
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a validated EmotionExtraction object from the LLM.
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Args:
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prompt: The formatted prompt for emotion extraction
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model_id: Optional model ID to use for this call
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Returns:
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EmotionExtraction object with validated emotion data
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Raises:
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LLMClientException: If LLM call fails or times out
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ValidationError: If LLM response doesn't match expected schema
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"""
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try:
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# Get LLM client
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llm_client = self._get_llm_client(model_id)
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# Prepare messages
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messages = [
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{"role": "user", "content": prompt}
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]
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# Call LLM with structured output
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emotion = await llm_client.response_structured(
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messages=messages,
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response_model=EmotionExtraction,
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temperature=0.3,
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max_tokens=500
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)
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return emotion
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except LLMClientException as e:
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logger.error(f"LLM call failed: {str(e)}")
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raise
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except Exception as e:
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logger.error(f"Unexpected error in LLM structured call: {str(e)}")
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raise LLMClientException(f"Emotion extraction LLM call failed: {str(e)}")
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