🚀 Implement Advanced Condition Extraction for Medical Query Processing

## 主要變更
- 新增 `src/medical_conditions.py`：集中管理醫學條件和關鍵詞配置
- 更新 `src/user_prompt.py`：實現多層 Fallback 的 Condition Extraction 機制

## 新增文件
- `src/medical_conditions.py`
- 集中醫學條件映射
- 提供條件關鍵詞查詢函數
- 支持條件驗證和詳細信息檢索

- `src/user_prompt.py`
- 實現四層 Condition Extraction 策略
- 支持預定義映射、Meditron 提取
- 添加語義搜索和通用醫學搜索 Fallback

## 參考文檔
- `docs/next/20250729Condition_Conversion_simplified.md`
- `docs/next/20250729Condition_Conversion_more_details.md`
- `docs/next/20250729Test_Retrieval.md`

## 實現特點
- 多層 Fallback 機制
- 靈活的條件提取
- 可擴展的醫學條件配置
- 用戶確認機制

## 性能目標
- 預定義映射：< 10ms
- Meditron 提取：< 2000ms
- 語義搜索：< 1s
- 總響應時間：< 7s

## 下一步
- 完善 Meditron 整合
- 添加更多醫學條件
- 優化語義搜索算法

Signed-off-by: OnCall.ai Team <[email protected]>

src/medical_conditions.py

@@ -0,0 +1,99 @@
+"""
+OnCall.ai Medical Conditions Configuration
+
+This module provides centralized configuration for:
+1. Predefined medical conditions
+2. Condition-to-keyword mappings
+3. Fallback condition keywords
+
+Author: OnCall.ai Team
+Date: 2025-07-29
+"""
+
+from typing import Dict, Optional
+
+# Comprehensive Condition-to-Keyword Mapping
+CONDITION_KEYWORD_MAPPING: Dict[str, Dict[str, str]] = {
+    "acute myocardial infarction": {
+        "emergency": "MI|chest pain|cardiac arrest",
+        "treatment": "aspirin|nitroglycerin|thrombolytic|PCI"
+    },
+    "acute stroke": {
+        "emergency": "stroke|neurological deficit|sudden weakness", 
+        "treatment": "tPA|thrombolysis|stroke unit care"
+    },
+    "pulmonary embolism": {
+        "emergency": "chest pain|shortness of breath|sudden dyspnea",
+        "treatment": "anticoagulation|heparin|embolectomy"
+    },
+    # 從 @20250729Test_Retrieval.md 擴展的條件
+    "acute_ischemic_stroke": {
+        "emergency": "ischemic stroke|neurological deficit",
+        "treatment": "tPA|stroke unit management"
+    },
+    "hemorrhagic_stroke": {
+        "emergency": "hemorrhagic stroke|intracranial bleeding",
+        "treatment": "blood pressure control|neurosurgery"
+    },
+    "transient_ischemic_attack": {
+        "emergency": "TIA|temporary stroke symptoms",
+        "treatment": "antiplatelet|lifestyle modification"
+    },
+    "acute_coronary_syndrome": {
+        "emergency": "ACS|chest pain|ECG changes",
+        "treatment": "antiplatelet|statins|cardiac monitoring"
+    }
+}
+
+# Fallback Condition Keywords
+FALLBACK_CONDITION_KEYWORDS: Dict[str, str] = {
+    "acute_ischemic_stroke": "acute ischemic stroke treatment",
+    "hemorrhagic_stroke": "hemorrhagic stroke management", 
+    "transient_ischemic_attack": "TIA treatment protocol",
+    "acute_coronary_syndrome": "ACS treatment guidelines",
+    "stable_angina": "stable angina management",
+    "non_cardiac_chest_pain": "non-cardiac chest pain evaluation",
+    "witnessed_cardiac_arrest": "witnessed cardiac arrest protocol",
+    "unwitnessed_cardiac_arrest": "unwitnessed cardiac arrest management",
+    "post_resuscitation_care": "post-resuscitation care guidelines"
+}
+
+def get_condition_keywords(specific_condition: str) -> Optional[str]:
+    """
+    Retrieve fallback keywords for a specific condition
+    
+    Args:
+        specific_condition: Medical condition name
+    
+    Returns:
+        Corresponding keywords or the original condition
+    """
+    return FALLBACK_CONDITION_KEYWORDS.get(specific_condition, specific_condition)
+
+def validate_condition(condition: str) -> bool:
+    """
+    Check if a condition exists in our predefined mapping
+    
+    Args:
+        condition: Medical condition to validate
+    
+    Returns:
+        Boolean indicating condition validity
+    """
+    return condition.lower() in {k.lower() for k in CONDITION_KEYWORD_MAPPING.keys()}
+
+def get_condition_details(condition: str) -> Optional[Dict[str, str]]:
+    """
+    Retrieve detailed information for a specific condition
+    
+    Args:
+        condition: Medical condition name
+    
+    Returns:
+        Dict with emergency and treatment keywords, or None
+    """
+    normalized_condition = condition.lower()
+    for key, value in CONDITION_KEYWORD_MAPPING.items():
+        if key.lower() == normalized_condition:
+            return value
+    return None 

src/user_prompt.py

@@ -0,0 +1,321 @@
+"""
+OnCall.ai User Prompt Processing Module
+
+This module handles:
+1. Condition extraction from user queries
+2. Keyword mapping
+3. User confirmation workflow
+4. Fallback mechanisms
+
+Author: OnCall.ai Team
+Date: 2025-07-29
+"""
+
+import logging
+from typing import Dict, Optional, Any, List
+from sentence_transformers import SentenceTransformer
+import numpy as np # Added missing import for numpy
+
+# Import our centralized medical conditions configuration
+from medical_conditions import (
+    CONDITION_KEYWORD_MAPPING, 
+    get_condition_keywords, 
+    validate_condition
+)
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class UserPromptProcessor:
+    def __init__(self, meditron_client=None, retrieval_system=None):
+        """
+        Initialize UserPromptProcessor with optional Meditron and retrieval system
+        
+        Args:
+            meditron_client: Optional Meditron client for advanced condition extraction
+            retrieval_system: Optional retrieval system for semantic search
+        """
+        self.meditron_client = meditron_client
+        self.retrieval_system = retrieval_system
+        self.embedding_model = SentenceTransformer("NeuML/pubmedbert-base-embeddings")
+        logger.info("UserPromptProcessor initialized")
+
+    def extract_condition_keywords(self, user_query: str) -> Dict[str, str]:
+        """
+        Extract condition keywords with multi-level fallback
+        
+        Args:
+            user_query: User's medical query
+        
+        Returns:
+            Dict with condition and keywords
+        """
+        # Level 1: Predefined Mapping (Fast Path)
+        predefined_result = self._predefined_mapping(user_query)
+        if predefined_result:
+            return predefined_result
+        
+        # Level 2: Meditron Extraction (if available)
+        if self.meditron_client:
+            meditron_result = self._extract_with_meditron(user_query)
+            if meditron_result:
+                return meditron_result
+        
+        # Level 3: Semantic Search Fallback
+        semantic_result = self._semantic_search_fallback(user_query)
+        if semantic_result:
+            return semantic_result
+        
+        # Level 4: Generic Medical Search
+        generic_result = self._generic_medical_search(user_query)
+        if generic_result:
+            return generic_result
+        
+        # No match found
+        return {
+            'condition': '',
+            'emergency_keywords': '',
+            'treatment_keywords': ''
+        }
+
+    def _predefined_mapping(self, user_query: str) -> Optional[Dict[str, str]]:
+        """
+        Fast predefined condition mapping
+        
+        Args:
+            user_query: User's medical query
+        
+        Returns:
+            Mapped condition keywords or None
+        """
+        query_lower = user_query.lower()
+        
+        for condition, mappings in CONDITION_KEYWORD_MAPPING.items():
+            if condition.lower() in query_lower:
+                logger.info(f"Matched predefined condition: {condition}")
+                return {
+                    'condition': condition,
+                    'emergency_keywords': mappings['emergency'],
+                    'treatment_keywords': mappings['treatment']
+                }
+        
+        return None
+
+    def _extract_with_meditron(self, user_query: str) -> Optional[Dict[str, str]]:
+        """
+        Use Meditron for advanced condition extraction
+        
+        Args:
+            user_query: User's medical query
+        
+        Returns:
+            Dict with condition and keywords, or None
+        """
+        if not self.meditron_client:
+            return None
+        
+        try:
+            meditron_response = self.meditron_client.analyze_medical_query(
+                query=user_query,
+                max_tokens=100,
+                timeout=2.0
+            )
+            
+            extracted_condition = meditron_response.get('extracted_condition', '')
+            
+            if extracted_condition and validate_condition(extracted_condition):
+                condition_details = get_condition_keywords(extracted_condition)
+                return {
+                    'condition': extracted_condition,
+                    'emergency_keywords': condition_details.get('emergency', ''),
+                    'treatment_keywords': condition_details.get('treatment', '')
+                }
+            
+            return None
+        
+        except Exception as e:
+            logger.error(f"Meditron condition extraction error: {e}")
+            return None
+
+    def _semantic_search_fallback(self, user_query: str) -> Optional[Dict[str, str]]:
+        """
+        Perform semantic search for condition extraction
+        
+        Args:
+            user_query: User's medical query
+        
+        Returns:
+            Dict with condition and keywords, or None
+        """
+        if not self.retrieval_system:
+            return None
+        
+        try:
+            # Perform semantic search on sliding window chunks
+            semantic_results = self.retrieval_system.search_sliding_window_chunks(user_query)
+            
+            if semantic_results:
+                # Extract condition from top semantic result
+                top_result = semantic_results[0]
+                condition = self._infer_condition_from_text(top_result['text'])
+                
+                if condition and validate_condition(condition):
+                    condition_details = get_condition_keywords(condition)
+                    return {
+                        'condition': condition,
+                        'emergency_keywords': condition_details.get('emergency', ''),
+                        'treatment_keywords': condition_details.get('treatment', ''),
+                        'semantic_confidence': top_result.get('distance', 0)
+                    }
+            
+            return None
+        
+        except Exception as e:
+            logger.error(f"Semantic search fallback error: {e}")
+            return None
+
+    def _generic_medical_search(self, user_query: str) -> Optional[Dict[str, str]]:
+        """
+        Perform generic medical search as final fallback
+        
+        Args:
+            user_query: User's medical query
+        
+        Returns:
+            Dict with generic medical keywords
+        """
+        generic_medical_terms = [
+            "medical", "treatment", "management", "protocol",
+            "guidelines", "emergency", "acute", "chronic"
+        ]
+        
+        generic_query = f"{user_query} medical treatment"
+        
+        try:
+            # Perform generic medical search
+            generic_results = self.retrieval_system.search_generic_medical_content(generic_query)
+            
+            if generic_results:
+                return {
+                    'condition': 'generic medical query',
+                    'emergency_keywords': 'medical|emergency',
+                    'treatment_keywords': 'treatment|management',
+                    'generic_confidence': 0.5
+                }
+            
+            return None
+        
+        except Exception as e:
+            logger.error(f"Generic medical search error: {e}")
+            return None
+
+    def _infer_condition_from_text(self, text: str) -> Optional[str]:
+        """
+        Infer medical condition from text using embedding similarity
+        
+        Args:
+            text: Input medical text
+        
+        Returns:
+            Inferred condition or None
+        """
+        # Implement a simple condition inference using embedding similarity
+        # This is a placeholder and would need more sophisticated implementation
+        conditions = list(CONDITION_KEYWORD_MAPPING.keys())
+        text_embedding = self.embedding_model.encode(text)
+        condition_embeddings = [self.embedding_model.encode(condition) for condition in conditions]
+        
+        similarities = [
+            np.dot(text_embedding, condition_emb) / 
+            (np.linalg.norm(text_embedding) * np.linalg.norm(condition_emb))
+            for condition_emb in condition_embeddings
+        ]
+        
+        max_similarity_index = np.argmax(similarities)
+        return conditions[max_similarity_index] if similarities[max_similarity_index] > 0.7 else None
+
+    def validate_keywords(self, keywords: Dict[str, str]) -> bool:
+        """
+        Validate if extracted keywords exist in our medical indices
+        
+        Args:
+            keywords: Dict of emergency and treatment keywords
+        
+        Returns:
+            Boolean indicating keyword validity
+        """
+        emergency_kws = keywords.get('emergency_keywords', '').split('|')
+        treatment_kws = keywords.get('treatment_keywords', '').split('|')
+        
+        # Basic validation: check if any keyword is non-empty
+        return any(kw.strip() for kw in emergency_kws + treatment_kws)
+
+    def handle_user_confirmation(self, extracted_info: Dict[str, str]) -> Dict[str, Any]:
+        """
+        Handle user confirmation for extracted condition and keywords
+        
+        Args:
+            extracted_info: Dict with condition and keyword information
+        
+        Returns:
+            Dict with confirmation status and options
+        """
+        # If no condition found, request user to rephrase
+        if not extracted_info.get('condition'):
+            return {
+                'type': 'rephrase_needed',
+                'message': "Could not identify a specific medical condition. Please rephrase your query.",
+                'suggestions': [
+                    "Try: 'how to treat chest pain'",
+                    "Try: 'acute stroke management'",
+                    "Try: 'pulmonary embolism treatment'"
+                ]
+            }
+        
+        # Prepare confirmation message
+        confirmation_message = f"""
+I understand you're asking about: "{extracted_info.get('condition', 'Unknown Condition')}"
+
+Extracted Keywords:
+- Emergency: {extracted_info.get('emergency_keywords', 'None')}
+- Treatment: {extracted_info.get('treatment_keywords', 'None')}
+
+Please confirm:
+1) Yes, proceed with search
+2) No, please rephrase my query
+3) Modify keywords
+        """
+        
+        return {
+            'type': 'confirmation_needed',
+            'message': confirmation_message,
+            'extracted_info': extracted_info
+        }
+
+def main():
+    """
+    Example usage and testing of UserPromptProcessor
+    """
+    processor = UserPromptProcessor()
+    
+    # Test cases
+    test_queries = [
+        "how to treat acute MI?",
+        "patient with stroke symptoms",
+        "chest pain and breathing difficulty"
+    ]
+    
+    for query in test_queries:
+        print(f"\nQuery: {query}")
+        result = processor.extract_condition_keywords(query)
+        print("Extracted Keywords:", result)
+        
+        confirmation = processor.handle_user_confirmation(result)
+        print("Confirmation:", confirmation['message'])
+
+if __name__ == "__main__":
+    main()