forked from rohit/spurrin-backend
email crid, automation test 1
This commit is contained in:
Ubuntu
parent
2debd6f73a
commit
9137286651
792
chat.py
792
chat.py
@ -130,7 +130,7 @@ def setup_logging():
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return root_logger, access_logger, perf_logger
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load_dotenv()
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# Initialize loggers
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logger, access_logger, perf_logger = setup_logging()
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@ -149,20 +149,6 @@ logger, access_logger, perf_logger = setup_logging()
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# }
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# Redis Configuration
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# REDIS_CONFIG = {
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# "host": "localhost",
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# "port": 6379,
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# "db": 0,
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# "decode_responses": True, # For string operations
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# }
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# DB_CONFIG = {
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# "host": os.getenv("DB_HOST", "localhost"),
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# "user": os.getenv("DB_USER", "spurrinuser"),
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# "password": os.getenv("DB_PASSWORD", "Admin@123"),
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# "database": os.getenv("DB_NAME", "spurrin-live"),
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# }
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REDIS_CONFIG = {
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"host": "localhost",
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"port": 6379,
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@ -171,10 +157,10 @@ REDIS_CONFIG = {
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}
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DB_CONFIG = {
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"host": os.getenv("DB_HOST", "localhost"),
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"user": os.getenv("DB_USER", "testuser"),
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"password": os.getenv("DB_PASSWORD", "Admin@123"),
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"database": os.getenv("DB_NAME", "spurrintest"),
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"host": os.getenv("DB_HOST"),
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"user": os.getenv("DB_USER"),
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"password": os.getenv("DB_PASSWORD"),
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"database": os.getenv("DB_NAME"),
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}
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# Redis connection pool
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@ -229,7 +215,6 @@ if not os.path.exists(uploads_dir):
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nlp = spacy.load("en_core_web_sm")
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load_dotenv()
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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client = OpenAI(api_key=OPENAI_API_KEY)
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@ -284,8 +269,8 @@ async def get_hospital_id(hospital_code):
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await pool.wait_closed()
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CHUNK_SIZE = 1000
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CHUNK_OVERLAP = 50
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CHUNK_SIZE = 4000
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CHUNK_OVERLAP = 150
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BATCH_SIZE = 1000
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text_splitter = RecursiveCharacterTextSplitter(
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@ -669,6 +654,108 @@ async def add_document_to_index(doc_id, hospital_id):
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return False
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def is_general_knowledge_question(
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query: str, context: str, conversation_context=None
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) -> bool:
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"""
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Determine if a question is likely a general knowledge question not covered in the documents.
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Takes conversation history into account to reduce repeated confirmations.
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"""
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query_lower = query.lower()
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context_lower = context.lower()
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if conversation_context:
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for interaction in conversation_context:
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prev_question = interaction.get("question", "").lower()
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if (
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prev_question
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and query_lower in prev_question
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or prev_question in query_lower
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):
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logging.info(
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f"Question is similar to previous conversation, skipping confirmation"
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)
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return False
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stop_words = {
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"search",
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"query:",
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"can",
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"you",
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"some",
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"at",
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"the",
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"a",
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"an",
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"in",
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"on",
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"at",
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"to",
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"for",
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"with",
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"by",
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"about",
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"give",
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"full",
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"is",
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"are",
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"was",
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"were",
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"define",
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"what",
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"how",
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"why",
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"when",
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"where",
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"year",
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"list",
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"form",
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"table",
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"who",
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"which",
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"me",
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"tell",
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"explain",
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"describe",
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"of",
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"and",
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"or",
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"there",
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"their",
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"please",
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"could",
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"would",
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"various",
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"different",
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"type",
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"types",
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"kind",
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"kinds",
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"has",
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"have",
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"had",
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"many",
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"say",
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}
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key_words = [
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word for word in query_lower.split() if word not in stop_words and len(word) > 2
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]
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logging.info(f"Key words: {key_words}")
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if not key_words:
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logging.info("No significant keywords found, directing to general knowledge")
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return True
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matches = sum(1 for word in key_words if word in context_lower)
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logging.info(f"Matches: {matches} out of {len(key_words)} keywords")
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match_ratio = matches / len(key_words)
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logging.info(f"Match ratio: {match_ratio}")
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return match_ratio < 0.6
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def is_table_request(query: str) -> bool:
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"""
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Determine if the user is requesting a response in tabular format.
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@ -768,15 +855,19 @@ def ensure_html_response(text: str) -> str:
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return text
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class RAGConversationManager:
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class HybridConversationManager:
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"""
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Conversation manager that uses Redis for RAG-based conversations only.
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Hybrid conversation manager that uses Redis for RAG-based conversations
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and in-memory storage for general knowledge conversations.
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"""
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def __init__(self, redis_client, ttl=3600, max_history_items=5):
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self.redis_client = redis_client
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self.ttl = ttl
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self.max_history_items = max_history_items
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# For general knowledge questions (in-memory)
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self.general_knowledge_histories = {}
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self.lock = Lock()
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def _get_redis_key(self, user_id, hospital_id, session_id=None):
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@ -785,6 +876,12 @@ class RAGConversationManager:
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return f"conv_history:{user_id}:{hospital_id}:{session_id}"
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return f"conv_history:{user_id}:{hospital_id}"
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def _get_memory_key(self, user_id, hospital_id, session_id=None):
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"""Create memory key for general knowledge conversations."""
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if session_id:
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return f"{user_id}:{hospital_id}:{session_id}"
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return f"{user_id}:{hospital_id}"
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async def add_rag_interaction(
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self, user_id, hospital_id, question, answer, session_id=None
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):
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@ -814,6 +911,35 @@ class RAGConversationManager:
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except Exception as e:
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logging.error(f"Failed to store RAG interaction in Redis: {e}")
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def add_general_knowledge_interaction(
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self, user_id, hospital_id, question, answer, session_id=None
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):
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"""Add general knowledge interaction to in-memory store."""
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key = self._get_memory_key(user_id, hospital_id, session_id)
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with self.lock:
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if key not in self.general_knowledge_histories:
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self.general_knowledge_histories[key] = []
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self.general_knowledge_histories[key].append(
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{
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"question": question,
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"answer": answer,
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"timestamp": time.time(),
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"type": "general", # Mark as general knowledge interaction
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}
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)
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# Keep only the most recent interactions
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if len(self.general_knowledge_histories[key]) > self.max_history_items:
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self.general_knowledge_histories[key] = (
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self.general_knowledge_histories[key][-self.max_history_items :]
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)
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logging.info(
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f"Stored general knowledge interaction in memory for {user_id}:{hospital_id}:{session_id}"
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)
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def get_rag_history(self, user_id, hospital_id, session_id=None):
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"""Get document-based (RAG) conversation history from Redis."""
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key = self._get_redis_key(user_id, hospital_id, session_id)
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@ -824,21 +950,51 @@ class RAGConversationManager:
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logging.error(f"Failed to retrieve RAG history from Redis: {e}")
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return []
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def get_general_knowledge_history(self, user_id, hospital_id, session_id=None):
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"""Get general knowledge conversation history from memory."""
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key = self._get_memory_key(user_id, hospital_id, session_id)
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with self.lock:
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return self.general_knowledge_histories.get(key, []).copy()
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def get_combined_history(self, user_id, hospital_id, session_id=None):
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"""Get combined conversation history from both sources, sorted by timestamp."""
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rag_history = self.get_rag_history(user_id, hospital_id, session_id)
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general_history = self.get_general_knowledge_history(
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user_id, hospital_id, session_id
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)
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# Combine histories
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combined_history = rag_history + general_history
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# Sort by timestamp (newest first)
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combined_history.sort(key=lambda x: x.get("timestamp", 0), reverse=True)
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# Return most recent N items
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return combined_history[: self.max_history_items]
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def get_context_window(self, user_id, hospital_id, session_id=None, window_size=2):
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"""Get the most recent interactions for context."""
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history = self.get_rag_history(user_id, hospital_id, session_id)
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"""Get the most recent interactions for context from combined history."""
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combined_history = self.get_combined_history(user_id, hospital_id, session_id)
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# Sort by timestamp (oldest first) for context window
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sorted_history = sorted(history, key=lambda x: x.get("timestamp", 0))
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sorted_history = sorted(combined_history, key=lambda x: x.get("timestamp", 0))
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return sorted_history[-window_size:] if sorted_history else []
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def clear_history(self, user_id, hospital_id):
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"""Clear conversation history."""
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"""Clear conversation history from both stores."""
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# Clear Redis history
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redis_key = self._get_redis_key(user_id, hospital_id)
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try:
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self.redis_client.delete(redis_key)
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except Exception as e:
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logging.error(f"Failed to clear Redis history: {e}")
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# Clear memory history
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memory_key = self._get_memory_key(user_id, hospital_id)
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with self.lock:
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if memory_key in self.general_knowledge_histories:
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del self.general_knowledge_histories[memory_key]
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class ContextMapper:
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"""Enhanced context mapping using shared model manager"""
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@ -1013,7 +1169,6 @@ def is_follow_up(current_question: str, conversation_history: list) -> bool:
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)
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)
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async def get_relevant_context(question, hospital_id, doc_id=None):
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try:
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cache_key = f"context:hospital_{hospital_id}"
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@ -1022,15 +1177,10 @@ async def get_relevant_context(question, hospital_id, doc_id=None):
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cache_key += f":{question.lower().strip()}"
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redis_client = get_redis_client()
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cached_context = redis_client.get(cache_key)
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if cached_context:
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logging.info(f"Cache hit for key: {cache_key}")
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return (
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cached_context.decode("utf-8")
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if isinstance(cached_context, bytes)
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else cached_context
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)
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return cached_context.decode("utf-8") if isinstance(cached_context, bytes) else cached_context
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vector_store = await initialize_or_load_vector_store(hospital_id)
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if not vector_store:
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@ -1039,17 +1189,33 @@ async def get_relevant_context(question, hospital_id, doc_id=None):
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retriever = vector_store.as_retriever(
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search_type="mmr",
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search_kwargs={
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"k": 10,
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"fetch_k": 20,
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"lambda_mult": 0.6,
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# "filter": {"doc_id": str(doc_id)} if doc_id else {"hospital_id": str(hospital_id)}
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"k": 5, # Reduced number of documents for precision
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"fetch_k": 10, # Reduced fetch size
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"lambda_mult": 0.8, # Increase diversity in MMR
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"score_threshold": 0.7, # Add minimum similarity score
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"filter": {"doc_id": str(doc_id)} if doc_id else {"hospital_id": str(hospital_id)}
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},
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)
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docs = await asyncio.to_thread(retriever.get_relevant_documents, question)
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if not docs:
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logging.info(f"No relevant documents found for question: {question}")
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return ""
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# # Filter documents by relevance using spaCy similarity
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# question_doc = nlp(question)
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# relevant_docs = []
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# for doc in docs:
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# doc_content = nlp(doc.page_content)
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# similarity = question_doc.similarity(doc_content)
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# if similarity >= 0.7: # Strict similarity threshold
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# relevant_docs.append(doc)
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# if not relevant_docs:
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# logging.info({relevant_docs})
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# logging.info(f"No sufficiently relevant documents after similarity filtering for: {question}")
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# return ""
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sorted_docs = sorted(
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docs,
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key=lambda x: (
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@ -1076,7 +1242,6 @@ async def get_relevant_context(question, hospital_id, doc_id=None):
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logging.error(f"Error getting relevant context: {e}")
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return ""
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def format_conversation_context(conv_history):
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"""Format conversation history into a string"""
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if not conv_history:
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@ -1122,6 +1287,348 @@ def get_fuzzy_icd_context(question, hospital_id, top_n=5, threshold=70):
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return "\n".join(matched_context)
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# async def generate_answer_with_rag(
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# question,
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# hospital_id,
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# client,
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# doc_id=None,
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# user_id="default",
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# conversation_manager=None,
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# session_id=None,
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# ):
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# """Generate an answer using RAG with improved conversation flow"""
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# try:
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# # Continue with regular RAG processing if not an ICD code or if no ICD match found
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# html_instruction = """
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# IMPORTANT: Format your ENTIRE response as HTML. Use appropriate HTML tags for all content:
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# - Use <p> tags for paragraphs
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# - Use <h2>, <h3> tags for headings and subheadings
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# - Use <ul>, <li> tags for bullet points
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# - Use <ol>, <li> tags for numbered lists
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# - Use <blockquote> for quoted text
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# - Use <strong> for bold text and <em> for emphasis
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# """
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# table_instruction = """
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# - For tables, use proper HTML table structure:
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# <table border="1">
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# <thead>
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# <tr>
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# <th colspan="{total_columns}">{table_title}</th>
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# </tr>
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# <tr>
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# {table_headers}
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# </tr>
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# </thead>
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# <tbody>
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# {table_rows}
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# </tbody>
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# </table>
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# """
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# # Get conversation history first
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# conv_history = (
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# conversation_manager.get_context_window(user_id, hospital_id, session_id)
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# if conversation_manager
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# else []
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# )
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# # Get contextual query and relevant context first
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# contextual_query = await generate_contextual_query(
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# question, user_id, hospital_id, conversation_manager
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# )
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# # Track ICD context across conversation
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# icd_context = {}
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# if conv_history:
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# # Extract ICD code from previous interaction
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# last_answer = conv_history[-1].get("answer", "")
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# icd_codes = re.findall(r"\b([A-Z][0-9A-Z]{2,6}[A-Z]?)\b", last_answer)
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# if icd_codes:
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# icd_context["last_code"] = icd_codes[0]
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# # Check if current question is about a previously discussed ICD code
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# is_icd_followup = False
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# if icd_context.get("last_code"):
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# followup_indicators = [
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# "what causes",
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# "what is causing",
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# "why",
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# "how",
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# "symptoms",
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# "treatment",
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# "diagnosis",
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# "causes",
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# "effects",
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# "complications",
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# "risk factors",
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# "prevention",
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# "prognosis",
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# "this",
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# "disease",
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# "that",
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# "it",
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# ]
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# is_icd_followup = any(
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# indicator in question.lower() for indicator in followup_indicators
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# )
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# if is_icd_followup:
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# # Add the previous ICD code context to the current question
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# icd_exact_context = get_icd_context_from_question(
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# icd_context["last_code"], hospital_id
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# )
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# icd_fuzzy_context = get_fuzzy_icd_context(
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# f"{icd_context['last_code']} {question}", hospital_id
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# )
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# else:
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# icd_exact_context = get_icd_context_from_question(question, hospital_id)
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# icd_fuzzy_context = get_fuzzy_icd_context(question, hospital_id)
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# else:
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# icd_exact_context = get_icd_context_from_question(question, hospital_id)
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# icd_fuzzy_context = get_fuzzy_icd_context(question, hospital_id)
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# # Get contextual query and relevant context
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# contextual_query = await generate_contextual_query(
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# question, user_id, hospital_id, conversation_manager
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# )
|
||||
# doc_context = await get_relevant_context(contextual_query, hospital_id, doc_id)
|
||||
|
||||
# # Combine context with priority for ICD information
|
||||
# context_parts = []
|
||||
# if is_icd_followup:
|
||||
# context_parts.append(
|
||||
# f"## Previous ICD Code Context\nContinuing discussion about: {icd_context['last_code']}"
|
||||
# )
|
||||
# if icd_exact_context:
|
||||
# context_parts.append("## ICD Code Match\n" + icd_exact_context)
|
||||
# if icd_fuzzy_context:
|
||||
# context_parts.append("## Related ICD Suggestions\n" + icd_fuzzy_context)
|
||||
# if doc_context:
|
||||
# context_parts.append("## Document Context\n" + doc_context)
|
||||
|
||||
# context = "\n\n".join(context_parts)
|
||||
|
||||
# # Initialize follow-up detection
|
||||
# is_follow_up = False
|
||||
|
||||
# # Check if this is a follow-up question
|
||||
# if conv_history:
|
||||
# last_interaction = conv_history[-1]
|
||||
# last_question = last_interaction["question"].lower()
|
||||
# last_answer = last_interaction.get("answer", "").lower()
|
||||
# current_question = question.lower()
|
||||
|
||||
# # Define meaningful keywords that indicate entity-related follow-ups
|
||||
# entity_related_keywords = {
|
||||
# "achievements",
|
||||
# "awards",
|
||||
# "accomplishments",
|
||||
# "work",
|
||||
# "contributions",
|
||||
# "career",
|
||||
# "company",
|
||||
# "products",
|
||||
# "life",
|
||||
# "background",
|
||||
# "education",
|
||||
# "role",
|
||||
# "experience",
|
||||
# "history",
|
||||
# "details",
|
||||
# "places",
|
||||
# "place",
|
||||
# "information",
|
||||
# "facts",
|
||||
# "about",
|
||||
# "birth",
|
||||
# "death",
|
||||
# "family",
|
||||
# "books",
|
||||
# "projects",
|
||||
# "population",
|
||||
# }
|
||||
|
||||
# # Check if question is asking about attributes/achievements of previously discussed entity
|
||||
# has_entity_attribute = any(
|
||||
# word in current_question.split() for word in entity_related_keywords
|
||||
# )
|
||||
|
||||
# # Extract entities from last answer to maintain context
|
||||
# def extract_entities(text):
|
||||
# # Split into words and get potential entities (capitalized words)
|
||||
# words = text.split()
|
||||
# entities = set()
|
||||
# current_entity = []
|
||||
|
||||
# for word in words:
|
||||
# if word[0].isupper():
|
||||
# current_entity.append(word)
|
||||
# elif current_entity:
|
||||
# if len(current_entity) > 0:
|
||||
# entities.add(" ".join(current_entity))
|
||||
# current_entity = []
|
||||
|
||||
# if current_entity:
|
||||
# entities.add(" ".join(current_entity))
|
||||
# return entities
|
||||
|
||||
# last_entities = extract_entities(last_answer)
|
||||
|
||||
# # Check for referential words
|
||||
# referential_words = {
|
||||
# "it",
|
||||
# "this",
|
||||
# "that",
|
||||
# "these",
|
||||
# "those",
|
||||
# "they",
|
||||
# "their",
|
||||
# "he",
|
||||
# "she",
|
||||
# "him",
|
||||
# "her",
|
||||
# "his",
|
||||
# "hers",
|
||||
# "them",
|
||||
# "there",
|
||||
# "such",
|
||||
# "its",
|
||||
# }
|
||||
# has_referential = any(
|
||||
# word in referential_words for word in current_question.split()
|
||||
# )
|
||||
|
||||
# # Calculate term overlap with both question and answer context
|
||||
# def get_significant_terms(text):
|
||||
# stop_words = {
|
||||
# "what",
|
||||
# "when",
|
||||
# "where",
|
||||
# "who",
|
||||
# "why",
|
||||
# "how",
|
||||
# "is",
|
||||
# "are",
|
||||
# "was",
|
||||
# "were",
|
||||
# "be",
|
||||
# "been",
|
||||
# "the",
|
||||
# "a",
|
||||
# "an",
|
||||
# "in",
|
||||
# "on",
|
||||
# "at",
|
||||
# "to",
|
||||
# "for",
|
||||
# "of",
|
||||
# "with",
|
||||
# "by",
|
||||
# "about",
|
||||
# "as",
|
||||
# "tell",
|
||||
# "me",
|
||||
# "please",
|
||||
# }
|
||||
# return set(
|
||||
# word
|
||||
# for word in text.split()
|
||||
# if len(word) > 2 and word.lower() not in stop_words
|
||||
# )
|
||||
|
||||
# current_terms = get_significant_terms(current_question)
|
||||
# last_terms = get_significant_terms(last_question)
|
||||
# answer_terms = get_significant_terms(last_answer)
|
||||
|
||||
# # Include terms from both question and answer in context
|
||||
# all_prev_terms = last_terms | answer_terms
|
||||
# term_overlap = len(current_terms & all_prev_terms)
|
||||
# total_terms = len(current_terms | all_prev_terms)
|
||||
# term_similarity = term_overlap / total_terms if total_terms > 0 else 0
|
||||
|
||||
# # Enhanced follow-up detection combining multiple signals
|
||||
# is_follow_up = (
|
||||
# has_referential
|
||||
# or term_similarity
|
||||
# >= 0.2 # Lower threshold when including answer context
|
||||
# or (
|
||||
# has_entity_attribute and bool(last_entities)
|
||||
# ) # Check if asking about attributes of known entity
|
||||
# or (
|
||||
# last_interaction.get("type") == "general"
|
||||
# and term_similarity >= 0.15
|
||||
# )
|
||||
# )
|
||||
|
||||
# logging.info(f"Follow-up analysis enhanced:")
|
||||
# logging.info(f"- Referential words: {has_referential}")
|
||||
# logging.info(f"- Term similarity: {term_similarity:.2f}")
|
||||
# logging.info(f"- Entity attribute question: {has_entity_attribute}")
|
||||
# logging.info(f"- Last entities found: {last_entities}")
|
||||
# logging.info(f"- Is follow-up: {is_follow_up}")
|
||||
|
||||
# # For entirely new topics (not follow-ups), use is_general_knowledge_question
|
||||
# if not is_follow_up:
|
||||
# if not context or is_general_knowledge_question(question, context, conv_history):
|
||||
# logging.info("No relevant context or general knowledge question detected")
|
||||
# answer = "<p>No relevant information found in the hospital documents for this query.</p>"
|
||||
# if conversation_manager:
|
||||
# await conversation_manager.add_rag_interaction(
|
||||
# user_id, hospital_id, question, answer, session_id
|
||||
# )
|
||||
# return {"answer": answer}, 200
|
||||
|
||||
# # Generate RAG answer with enhanced context
|
||||
# prompt_template = f"""Based on the following context and conversation history, provide a detailed answer to the question.
|
||||
# Previous conversation:
|
||||
# {format_conversation_context(conv_history)}
|
||||
|
||||
# Context from documents:
|
||||
# {context}
|
||||
|
||||
# Current question: {question}
|
||||
|
||||
# Instructions:
|
||||
# 1. When providing medical codes (ICD, CPT, etc.):
|
||||
# - Always use the ICD codes listed in the sections titled "ICD Code Match" and "Related ICD Suggestions" from the context.
|
||||
# - Do not use or invent ICD codes from your own training knowledge unless they appear in the provided context.
|
||||
# - If multiple codes are relevant, return the one that best matches the user’s question. If unsure, return multiple options in HTML list format.
|
||||
# - Remove all decimal points (e.g., use 'A150' instead of 'A15.0')
|
||||
# - Format the response as: '<p>The medical code for [condition] is [code]</p>'
|
||||
# 2. Address the current question while maintaining conversation continuity
|
||||
# 3. Resolve any ambiguous references using conversation history
|
||||
# 4. Format the response in clear HTML
|
||||
|
||||
# {html_instruction}
|
||||
# {table_instruction if is_table_request(question) else ""}
|
||||
# """
|
||||
|
||||
# response = await asyncio.to_thread(
|
||||
# lambda: client.chat.completions.create(
|
||||
# model="gpt-3.5-turbo-16k",
|
||||
# messages=[
|
||||
# {"role": "system", "content": prompt_template},
|
||||
# {"role": "user", "content": question},
|
||||
# ],
|
||||
# temperature=0.3,
|
||||
# max_tokens=1000,
|
||||
# )
|
||||
# )
|
||||
|
||||
# answer = ensure_html_response(response.choices[0].message.content)
|
||||
# logging.info(f"Generated RAG answer for question: {question}")
|
||||
|
||||
# # Store interaction in history
|
||||
# if conversation_manager:
|
||||
# await conversation_manager.add_rag_interaction(
|
||||
# user_id, hospital_id, question, answer, session_id
|
||||
# )
|
||||
|
||||
# return {"answer": answer}, 200
|
||||
|
||||
# except Exception as e:
|
||||
# logging.error(f"Error in generate_answer_with_rag: {e}")
|
||||
# return {"answer": f"<p>Error: {str(e)}</p>"}, 500
|
||||
|
||||
async def generate_answer_with_rag(
|
||||
question,
|
||||
hospital_id,
|
||||
@ -1131,7 +1638,7 @@ async def generate_answer_with_rag(
|
||||
conversation_manager=None,
|
||||
session_id=None,
|
||||
):
|
||||
"""Generate an answer using RAG with improved conversation flow"""
|
||||
"""Generate an answer using RAG, strictly using provided document context and ICD data."""
|
||||
try:
|
||||
html_instruction = """
|
||||
IMPORTANT: Format your ENTIRE response as HTML. Use appropriate HTML tags for all content:
|
||||
@ -1141,6 +1648,8 @@ async def generate_answer_with_rag(
|
||||
- Use <ol>, <li> tags for numbered lists
|
||||
- Use <blockquote> for quoted text
|
||||
- Use <strong> for bold text and <em> for emphasis
|
||||
- If no relevant information is found in the provided context, respond ONLY with:
|
||||
<p>No relevant information found in the hospital documents for this query.</p>
|
||||
"""
|
||||
|
||||
table_instruction = """
|
||||
@ -1159,51 +1668,179 @@ async def generate_answer_with_rag(
|
||||
</tbody>
|
||||
</table>
|
||||
"""
|
||||
# Get conversation history first
|
||||
|
||||
# Get conversation history
|
||||
conv_history = (
|
||||
conversation_manager.get_context_window(user_id, hospital_id, session_id)
|
||||
if conversation_manager
|
||||
else []
|
||||
)
|
||||
|
||||
# Get contextual query and relevant context first
|
||||
# Generate contextual query
|
||||
contextual_query = await generate_contextual_query(
|
||||
question, user_id, hospital_id, conversation_manager
|
||||
)
|
||||
|
||||
# Get document context
|
||||
# Retrieve document context with strict relevance
|
||||
doc_context = await get_relevant_context(contextual_query, hospital_id, doc_id)
|
||||
|
||||
if not doc_context:
|
||||
return {
|
||||
"answer": """
|
||||
<p>I apologize, but I couldn't find any relevant information in the hospital documents to answer your question.</p>
|
||||
<p>Please try rephrasing your question or asking about a different topic that might be covered in the documents.</p>
|
||||
"""
|
||||
}, 200
|
||||
# Handle ICD context
|
||||
icd_context = {}
|
||||
if conv_history:
|
||||
last_answer = conv_history[-1].get("answer", "")
|
||||
icd_codes = re.findall(r"\b([A-Z][0-9A-Z]{2,6}[A-Z]?)\b", last_answer)
|
||||
if icd_codes:
|
||||
icd_context["last_code"] = icd_codes[0]
|
||||
|
||||
is_icd_followup = False
|
||||
if icd_context.get("last_code"):
|
||||
followup_indicators = [
|
||||
"what causes", "what is causing", "why", "how", "symptoms",
|
||||
"treatment", "diagnosis", "causes", "effects", "complications",
|
||||
"risk factors", "prevention", "prognosis", "this", "disease",
|
||||
"that", "it",
|
||||
]
|
||||
is_icd_followup = any(indicator in question.lower() for indicator in followup_indicators)
|
||||
if is_icd_followup:
|
||||
icd_exact_context = get_icd_context_from_question(icd_context["last_code"], hospital_id)
|
||||
icd_fuzzy_context = get_fuzzy_icd_context(f"{icd_context['last_code']} {question}", hospital_id)
|
||||
else:
|
||||
icd_exact_context = get_icd_context_from_question(question, hospital_id)
|
||||
icd_fuzzy_context = get_fuzzy_icd_context(question, hospital_id)
|
||||
else:
|
||||
icd_exact_context = get_icd_context_from_question(question, hospital_id)
|
||||
icd_fuzzy_context = get_fuzzy_icd_context(question, hospital_id)
|
||||
|
||||
# Combine context with priority for ICD information
|
||||
context_parts = []
|
||||
if is_icd_followup:
|
||||
context_parts.append(f"## Previous ICD Code Context\nContinuing discussion about: {icd_context['last_code']}")
|
||||
if icd_exact_context:
|
||||
context_parts.append("## ICD Code Match\n" + icd_exact_context)
|
||||
if icd_fuzzy_context:
|
||||
context_parts.append("## Related ICD Suggestions\n" + icd_fuzzy_context)
|
||||
if doc_context:
|
||||
context_parts.append("## Document Context\n" + doc_context)
|
||||
|
||||
context = "\n\n".join(context_parts)
|
||||
logging.info(f"Total context length: {len(context.split())} words")
|
||||
logging.info({context})
|
||||
|
||||
# Check context length
|
||||
if len(doc_context.split()) == 0:
|
||||
logging.info("A")
|
||||
logging.info(f"Context too short ({len(context.split())} words), returning no information found")
|
||||
answer = "<p>No relevant information found in the hospital documents for this query.</p>"
|
||||
if conversation_manager:
|
||||
await conversation_manager.add_rag_interaction(
|
||||
user_id, hospital_id, question, answer, session_id
|
||||
)
|
||||
return {"answer": answer}, 200
|
||||
|
||||
# Check if question lacks relevant context
|
||||
if not context or is_general_knowledge_question(question, context, conv_history):
|
||||
logging.info("B")
|
||||
logging.info("No relevant context or general knowledge question detected")
|
||||
answer = "<p>No relevant information found in the hospital documents for this query.</p>"
|
||||
if conversation_manager:
|
||||
await conversation_manager.add_rag_interaction(
|
||||
user_id, hospital_id, question, answer, session_id
|
||||
)
|
||||
return {"answer": answer}, 200
|
||||
|
||||
# Check follow-up status with stricter criteria
|
||||
is_follow_up = False
|
||||
if conv_history:
|
||||
last_interaction = conv_history[-1]
|
||||
last_question = last_interaction["question"].lower()
|
||||
last_answer = last_interaction.get("answer", "").lower()
|
||||
current_question = question.lower()
|
||||
|
||||
# Define entity-related keywords
|
||||
entity_related_keywords = {
|
||||
"achievements", "awards", "accomplishments", "work", "contributions",
|
||||
"career", "company", "products", "life", "background", "education",
|
||||
"role", "experience", "history", "details", "places", "place",
|
||||
"information", "facts", "about", "birth", "death", "family",
|
||||
"books", "projects", "population",
|
||||
}
|
||||
|
||||
has_entity_attribute = any(word in current_question.split() for word in entity_related_keywords)
|
||||
|
||||
# Extract entities using spaCy for better precision
|
||||
doc_last = nlp(f"{last_question} {last_answer}")
|
||||
doc_current = nlp(current_question)
|
||||
last_entities = {ent.text.lower() for ent in doc_last.ents}
|
||||
current_entities = {ent.text.lower() for ent in doc_current.ents}
|
||||
|
||||
# Check for referential words
|
||||
referential_words = {
|
||||
"it", "this", "that", "these", "those", "they", "their",
|
||||
"he", "she", "him", "her", "his", "hers", "them", "there",
|
||||
"such", "its",
|
||||
}
|
||||
has_referential = any(word in referential_words for word in current_question.split())
|
||||
|
||||
# Calculate term overlap with stricter criteria
|
||||
def get_significant_terms(text):
|
||||
stop_words = {
|
||||
"what", "when", "where", "who", "why", "how", "is", "are",
|
||||
"was", "were", "be", "been", "the", "a", "an", "in", "on",
|
||||
"at", "to", "for", "of", "with", "by", "about", "as",
|
||||
"tell", "me", "please",
|
||||
}
|
||||
return set(word for word in text.split() if len(word) > 2 and word.lower() not in stop_words)
|
||||
|
||||
current_terms = get_significant_terms(current_question)
|
||||
last_terms = get_significant_terms(last_question)
|
||||
answer_terms = get_significant_terms(last_answer)
|
||||
all_prev_terms = last_terms | answer_terms
|
||||
term_overlap = len(current_terms & all_prev_terms)
|
||||
total_terms = len(current_terms | all_prev_terms)
|
||||
term_similarity = term_overlap / total_terms if total_terms > 0 else 0
|
||||
|
||||
# Use spaCy similarity for follow-up detection
|
||||
similarity = doc_current.similarity(doc_last)
|
||||
|
||||
is_follow_up = (
|
||||
has_referential
|
||||
or term_similarity >= 0.4 # Stricter threshold
|
||||
or (has_entity_attribute and bool(last_entities & current_entities))
|
||||
or (last_interaction.get("type") == "general" and term_similarity >= 0.3)
|
||||
)
|
||||
|
||||
logging.info(f"Follow-up analysis:")
|
||||
logging.info(f"- Referential words: {has_referential}")
|
||||
logging.info(f"- Term similarity: {term_similarity:.2f}")
|
||||
logging.info(f"- Entity overlap: {bool(last_entities & current_entities)}")
|
||||
logging.info(f"- SpaCy similarity: {similarity:.2f}")
|
||||
logging.info(f"- Is follow-up: {is_follow_up}")
|
||||
|
||||
# Generate answer with strict document-based instruction
|
||||
prompt_template = f"""You are a document-based question-answering system. You must ONLY use the provided context and conversation history to answer the question. Do NOT use any external knowledge, assumptions, or definitions beyond the given context, even if the query seems familiar. If the context does not contain sufficient information to directly answer the question, respond ONLY with:
|
||||
<p>No relevant information found in the hospital documents for this query.</p>
|
||||
|
||||
# Generate RAG answer with enhanced context
|
||||
prompt_template = f"""Based on the following context and conversation history, provide a detailed answer to the question.
|
||||
Previous conversation:
|
||||
{format_conversation_context(conv_history)}
|
||||
|
||||
Context from documents:
|
||||
{doc_context}
|
||||
{context}
|
||||
|
||||
Current question: {question}
|
||||
|
||||
Instructions:
|
||||
1. ONLY use information from the provided document context to answer the question
|
||||
2. If the answer cannot be fully derived from the context, state that clearly
|
||||
3. Do not use any external knowledge or make assumptions
|
||||
4. When providing medical codes (ICD, CPT, etc.):
|
||||
- Only use codes that appear in the provided context
|
||||
- Do not invent or use codes from external knowledge
|
||||
- If multiple codes are relevant, list them all
|
||||
- Remove all decimal points (e.g., use 'A150' instead of 'A15.0')
|
||||
5. Format the response in clear HTML with appropriate tags
|
||||
6. If the context doesn't contain enough information to answer the question completely,
|
||||
acknowledge this limitation and only provide the information that is available
|
||||
1. When providing medical codes (ICD, CPT, etc.):
|
||||
- ONLY use the ICD codes listed in the sections titled "ICD Code Match" and "Related ICD Suggestions" from the context.
|
||||
- Do not use or invent ICD codes from your own knowledge.
|
||||
- If multiple codes are relevant, return the one that best matches the user’s question. If unsure, return multiple options in HTML list format.
|
||||
- Remove all decimal points (e.g., use 'A150' instead of 'A15.0').
|
||||
- Format the response as: '<p>The medical code for [condition] is [code]</p>'.
|
||||
2. Address the current question while maintaining conversation continuity.
|
||||
3. Resolve any ambiguous references using conversation history.
|
||||
4. Format the response in clear HTML.
|
||||
5. Strictly adhere and provide a detailed answer only from the {context}.No extra knowledge or assumptions.
|
||||
6. Every answer must be detailed and only from the provided context.
|
||||
7. Answer should be 400-500 words long.
|
||||
|
||||
{html_instruction}
|
||||
{table_instruction if is_table_request(question) else ""}
|
||||
@ -1211,12 +1848,12 @@ async def generate_answer_with_rag(
|
||||
|
||||
response = await asyncio.to_thread(
|
||||
lambda: client.chat.completions.create(
|
||||
model="gpt-3.5-turbo-16k",
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{"role": "system", "content": prompt_template},
|
||||
{"role": "user", "content": question},
|
||||
],
|
||||
temperature=0.3,
|
||||
temperature=0.1, # Lower temperature for strict adherence
|
||||
max_tokens=1000,
|
||||
)
|
||||
)
|
||||
@ -1236,7 +1873,6 @@ async def generate_answer_with_rag(
|
||||
logging.error(f"Error in generate_answer_with_rag: {e}")
|
||||
return {"answer": f"<p>Error: {str(e)}</p>"}, 500
|
||||
|
||||
|
||||
async def load_existing_vector_stores():
|
||||
"""Load existing Chroma vector stores for each hospital"""
|
||||
pool = await get_db_pool()
|
||||
@ -1405,9 +2041,9 @@ def process_pdf():
|
||||
return jsonify({"error": str(e)}), 500
|
||||
|
||||
|
||||
# Initialize the RAG conversation manager
|
||||
# Initialize the hybrid conversation manager
|
||||
redis_client = get_redis_client()
|
||||
conversation_manager = RAGConversationManager(redis_client)
|
||||
conversation_manager = HybridConversationManager(redis_client)
|
||||
|
||||
|
||||
@app.route("/flask-api/generate-answer", methods=["POST"])
|
||||
@ -1426,6 +2062,9 @@ def rag_answer_api():
|
||||
logging.info(f"Received hospital code: {hospital_code}")
|
||||
logging.info(f"Received session_id: {session_id}")
|
||||
|
||||
# is_confirmation_response = data.get("is_confirmation_response", False)
|
||||
original_query = data.get("original_query", "")
|
||||
|
||||
def process_rag_answer():
|
||||
try:
|
||||
hospital_id = async_to_sync(get_hospital_id(hospital_code))
|
||||
@ -1436,6 +2075,15 @@ def rag_answer_api():
|
||||
"error": "Invalid or missing 'hospital_code' in request"
|
||||
}, 400
|
||||
|
||||
if original_query:
|
||||
response_message = """
|
||||
<p>I can only answer questions based on information found in the hospital documents.</p>
|
||||
<p>The question you asked doesn't seem to be covered in the available documents.</p>
|
||||
<p>You can try rephrasing your question or asking about a different topic.</p>
|
||||
"""
|
||||
return {"answer": response_message}, 200
|
||||
|
||||
else:
|
||||
# Regular RAG answer
|
||||
return async_to_sync(
|
||||
generate_answer_with_rag(
|
||||
@ -1444,12 +2092,12 @@ def rag_answer_api():
|
||||
client=client,
|
||||
doc_id=doc_id,
|
||||
user_id=user_id,
|
||||
conversation_manager=conversation_manager,
|
||||
conversation_manager=conversation_manager, # Pass the hybrid manager
|
||||
session_id=session_id,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error in process_rag_answer: {e}")
|
||||
logging.error(f"Thread processing error: {str(e)}")
|
||||
return {"error": str(e)}, 500
|
||||
|
||||
if not question:
|
||||
|
||||
@ -63,3 +63,89 @@
|
||||
2025-06-09 09:46:52,876 - INFO - "DELETE /flask-api/delete-document-vectors" 200 - Duration: 0.009s - IP: 127.0.0.1
|
||||
2025-06-09 09:46:52,899 - INFO - "DELETE /flask-api/delete-document-vectors" 400 - Duration: 0.000s - IP: 127.0.0.1
|
||||
2025-06-09 11:46:24,367 - INFO - "GET /flask-api/" 404 - Duration: 0.001s - IP: 127.0.0.1
|
||||
2025-06-09 13:03:46,712 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 13:03:48,998 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 2.286s - IP: 127.0.0.1
|
||||
2025-06-09 13:04:09,164 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 13:04:10,537 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.373s - IP: 127.0.0.1
|
||||
2025-06-09 13:05:37,649 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 13:05:40,884 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 3.235s - IP: 127.0.0.1
|
||||
2025-06-09 13:13:27,932 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:13:30,230 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.298s - IP: 127.0.0.1
|
||||
2025-06-09 13:13:54,677 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:13:59,714 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 5.037s - IP: 127.0.0.1
|
||||
2025-06-09 13:14:17,423 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:14:21,231 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 3.809s - IP: 127.0.0.1
|
||||
2025-06-09 13:15:00,789 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:15:02,264 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.475s - IP: 127.0.0.1
|
||||
2025-06-09 13:15:22,589 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:15:27,812 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 5.224s - IP: 127.0.0.1
|
||||
2025-06-09 13:15:46,770 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:15:48,958 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.188s - IP: 127.0.0.1
|
||||
2025-06-09 13:15:57,542 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:16:00,169 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.627s - IP: 127.0.0.1
|
||||
2025-06-09 13:16:28,464 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:16:30,519 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.056s - IP: 127.0.0.1
|
||||
2025-06-09 13:16:44,514 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:16:47,898 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 3.384s - IP: 127.0.0.1
|
||||
2025-06-09 13:16:58,457 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:17:04,739 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 6.282s - IP: 127.0.0.1
|
||||
2025-06-09 13:17:18,870 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:17:23,466 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 4.596s - IP: 127.0.0.1
|
||||
2025-06-09 13:20:14,649 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 13:20:15,718 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.069s - IP: 127.0.0.1
|
||||
2025-06-09 13:20:32,522 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:20:40,394 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 7.873s - IP: 127.0.0.1
|
||||
2025-06-09 13:21:34,927 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:21:35,482 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 0.555s - IP: 127.0.0.1
|
||||
2025-06-09 13:21:57,067 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:22:00,013 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.945s - IP: 127.0.0.1
|
||||
2025-06-09 13:22:31,965 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:22:46,900 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 14.935s - IP: 127.0.0.1
|
||||
2025-06-09 13:24:29,026 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:24:31,314 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.289s - IP: 127.0.0.1
|
||||
2025-06-09 13:26:40,340 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:26:42,389 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 2.049s - IP: 127.0.0.1
|
||||
2025-06-09 13:26:51,824 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:26:53,120 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.296s - IP: 127.0.0.1
|
||||
2025-06-09 13:30:06,806 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:30:08,790 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.985s - IP: 127.0.0.1
|
||||
2025-06-09 13:30:23,416 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:30:24,623 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.207s - IP: 127.0.0.1
|
||||
2025-06-09 13:30:34,372 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:30:35,613 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.241s - IP: 127.0.0.1
|
||||
2025-06-09 13:30:47,901 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 13:30:49,237 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 1.336s - IP: 127.0.0.1
|
||||
2025-06-09 14:22:55,613 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:22:56,751 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.138s - IP: 127.0.0.1
|
||||
2025-06-09 14:23:38,767 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:24:10,108 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 31.340s - IP: 127.0.0.1
|
||||
2025-06-09 14:28:06,695 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:28:07,741 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.046s - IP: 127.0.0.1
|
||||
2025-06-09 14:32:26,845 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:32:28,707 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.862s - IP: 127.0.0.1
|
||||
2025-06-09 14:32:53,549 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:32:54,767 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.219s - IP: 127.0.0.1
|
||||
2025-06-09 14:35:10,483 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:35:11,708 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.225s - IP: 127.0.0.1
|
||||
2025-06-09 14:36:59,949 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:37:00,881 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 0.933s - IP: 127.0.0.1
|
||||
2025-06-09 14:38:11,319 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:38:12,359 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 1.039s - IP: 127.0.0.1
|
||||
2025-06-09 14:39:57,788 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:39:58,523 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 0.734s - IP: 127.0.0.1
|
||||
2025-06-09 14:41:12,960 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:41:15,346 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 2.387s - IP: 127.0.0.1
|
||||
2025-06-09 14:43:29,539 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:43:29,954 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 0.415s - IP: 127.0.0.1
|
||||
2025-06-09 14:43:48,119 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:43:50,697 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 2.577s - IP: 127.0.0.1
|
||||
2025-06-09 14:50:29,262 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 14:50:31,330 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 2.068s - IP: 127.0.0.1
|
||||
2025-06-09 15:47:22,968 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 15:47:28,583 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 5.616s - IP: 127.0.0.1
|
||||
2025-06-09 16:02:30,977 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 16:02:31,615 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 0.639s - IP: 127.0.0.1
|
||||
2025-06-09 16:11:44,675 - INFO - PDF processing request received from 127.0.0.1
|
||||
2025-06-09 16:11:45,079 - INFO - "POST /flask-api/process-pdf" 200 - Duration: 0.404s - IP: 127.0.0.1
|
||||
2025-06-09 18:14:32,690 - INFO - Generate answer request received from 127.0.0.1
|
||||
2025-06-09 18:14:50,087 - INFO - "POST /flask-api/generate-answer" 200 - Duration: 17.397s - IP: 127.0.0.1
|
||||
|
||||
2331
logs/app.log
2331
logs/app.log
File diff suppressed because it is too large
Load Diff
11221
logs/combined.log
11221
logs/combined.log
File diff suppressed because one or more lines are too long
@ -315,3 +315,23 @@ TypeError: cannot unpack non-iterable coroutine object
|
||||
2025-06-09 09:45:53,436 - root - ERROR - [chat.py:1299] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 09:46:12,551 - root - ERROR - [chat.py:1299] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 09:46:14,686 - root - ERROR - [chat.py:1299] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 13:03:46,719 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 13:04:09,169 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 13:05:37,658 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 13:20:14,654 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:22:55,622 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:23:38,899 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:28:06,701 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:32:26,852 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:32:53,553 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:35:10,487 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:36:59,953 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:38:11,324 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:39:57,795 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:41:12,969 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:43:29,632 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:43:48,129 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 14:50:29,272 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 15:47:23,058 - root - ERROR - [chat.py:1934] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 16:02:31,062 - root - ERROR - [chat.py:1935] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
2025-06-09 16:11:44,795 - root - ERROR - [chat.py:1935] - Database update error: (1265, "Data truncated for column 'processed_status' at row 1")
|
||||
|
||||
@ -22,8 +22,8 @@ const transporter = nodemailer.createTransport({
|
||||
port: 465,
|
||||
secure: true,
|
||||
auth: {
|
||||
user: "no-reply@spurrin.com", // Your Zoho email address
|
||||
pass: "8TFvKswgH69Y", // Your Zoho App Password (not your account password)
|
||||
user: "kavya.j@tech4biz.io", // Your Zoho email address
|
||||
pass: "8pQfkBw8gbrz", // Your Zoho App Password (not your account password)
|
||||
},
|
||||
// tls: {
|
||||
// rejectUnauthorized: false, // Allow self-signed certificates
|
||||
@ -1026,7 +1026,7 @@ exports.changePinByOtp = async (req, res) => {
|
||||
|
||||
async function sendMail(email, hospital_name, username, otp) {
|
||||
const mailOptions = {
|
||||
from: "no-reply@spurrin.com", // Sender's email
|
||||
from: "kavya.j@tech4biz.io", // Sender's email
|
||||
to: email, // Recipient's email
|
||||
subject: "Spurrinai Login Credentials", // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
@ -1555,7 +1555,7 @@ exports.forgotPin = async (req, res) => {
|
||||
|
||||
// Send pin via email
|
||||
const mailOptions = {
|
||||
from: "no-reply@spurrin.com",
|
||||
from: "kavya.j@tech4biz.io",
|
||||
to: email,
|
||||
subject: "Your Spurrinai PIN",
|
||||
html: `<!DOCTYPE html>
|
||||
|
||||
@ -14,8 +14,8 @@ const transporter = nodemailer.createTransport({
|
||||
port: 465,
|
||||
secure: true,
|
||||
auth: {
|
||||
user: "no-reply@spurrin.com", // Your Zoho email address
|
||||
pass: "8TFvKswgH69Y", // Your Zoho App Password (not your account password)
|
||||
user: "kavya.j@tech4biz.io", // Your Zoho email address
|
||||
pass: "8pQfkBw8gbrz", // Your Zoho App Password (not your account password)
|
||||
},
|
||||
// tls: {
|
||||
// rejectUnauthorized: false, // Allow self-signed certificates
|
||||
@ -138,7 +138,7 @@ async function sendEmails(users, hospitalResult, back_url) {
|
||||
|
||||
for (const user of users) {
|
||||
const mailOptions = {
|
||||
from: "no-reply@spurrin.com", // Sender's email
|
||||
from: "kavya.j@tech4biz.io", // Sender's email
|
||||
to: user.email, // Unique recipient email
|
||||
subject: 'Spurrinai Login Credentials', // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
|
||||
@ -14,8 +14,8 @@ const transporter = nodemailer.createTransport({
|
||||
port: 465, // Use 465 for SSL or 587 for TLS
|
||||
secure: true, // Set to true for port 465, false for port 587
|
||||
auth: {
|
||||
user: "no-reply@spurrin.com", // Your Zoho email address
|
||||
pass: "8TFvKswgH69Y", // Your Zoho App Password (not your account password)
|
||||
user: "kavya.j@tech4biz.io", // Your Zoho email address
|
||||
pass: "8pQfkBw8gbrz", // Your Zoho App Password (not your account password)
|
||||
}
|
||||
// tls: {
|
||||
// minVersion: "TLSv1.2",
|
||||
@ -116,7 +116,7 @@ exports.createHospital = async (req, res) => {
|
||||
|
||||
// ✅ Step 6: Send an email to notify
|
||||
const mailOptions = {
|
||||
from: 'no-reply@spurrin.com', // Sender's email
|
||||
from: 'kavya.j@tech4biz.io', // Sender's email
|
||||
to: primary_admin_email, // Recipient's email
|
||||
subject: "Spurrinai Login Credentials", // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
@ -1117,7 +1117,7 @@ exports.changeTempPassword = async (req, res) => {
|
||||
|
||||
async function sendMail(email, hospital_name, adminName, randomPassword) {
|
||||
const mailOptions = {
|
||||
from: "no-reply@spurrin.com", // Sender's email
|
||||
from: "kavya.j@tech4biz.io", // Sender's email
|
||||
to: email, // Recipient's email
|
||||
subject: "Spurrinai temporary password", // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
@ -1291,9 +1291,11 @@ async function sendMail(email, hospital_name, adminName, randomPassword) {
|
||||
};
|
||||
|
||||
try {
|
||||
await transporter.sendMail(mailOptions);
|
||||
const info = await transporter.sendMail(mailOptions);
|
||||
return info; // Return the info object for further processing if needed
|
||||
} catch (error) {
|
||||
console.error(`Error sending email to ${email}:`, error);
|
||||
return error
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -10,8 +10,8 @@ const transporter = nodemailer.createTransport({
|
||||
port: 465, // Use 465 for SSL or 587 for TLS
|
||||
secure: true, // Set to true for port 465, false for port 587
|
||||
auth: {
|
||||
user: "no-reply@spurrin.com", // Your Zoho email address
|
||||
pass: "8TFvKswgH69Y", // Your Zoho App Password (not your account password)
|
||||
user: "kavya.j@tech4biz.io", // Your Zoho email address
|
||||
pass: "8pQfkBw8gbrz", // Your Zoho App Password (not your account password)
|
||||
},
|
||||
// tls: {
|
||||
// minVersion: "TLSv1.2",
|
||||
@ -318,7 +318,7 @@ exports.changeTempPassword = async (req, res) => {
|
||||
|
||||
async function sendMail(email, hospital_name, adminName, randomPassword) {
|
||||
const mailOptions = {
|
||||
from: 'no-reply@spurrin.com', // Sender's email
|
||||
from: 'kavya.j@tech4biz.io', // Sender's email
|
||||
to: email, // Recipient's email
|
||||
subject: "Spurrinai temporary password", // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
|
||||
@ -22,8 +22,8 @@ const transporter = nodemailer.createTransport({
|
||||
port: 465,
|
||||
secure: true,
|
||||
auth: {
|
||||
user: "no-reply@spurrin.com", // Your Zoho email address
|
||||
pass: "8TFvKswgH69Y", // Your Zoho App Password (not your account password)
|
||||
user: "kavya.j@tech4biz.io", // Your Zoho email address
|
||||
pass: "8pQfkBw8gbrz", // Your Zoho App Password (not your account password)
|
||||
},
|
||||
// tls: {
|
||||
// rejectUnauthorized: false, // Allow self-signed certificates
|
||||
@ -112,7 +112,7 @@ WHERE hospital_id = ?
|
||||
const passwordHash = await bcrypt.hash(req.body.password, 10);
|
||||
|
||||
const mailOptions = {
|
||||
from: "no-reply@spurrin.com", // Sender's email
|
||||
from: "kavya.j@tech4biz.io", // Sender's email
|
||||
to: rest.email, // Unique recipient email
|
||||
subject: 'Spurrinai Login Credentials', // Email subject
|
||||
html: `<!DOCTYPE html>
|
||||
|
||||
Binary file not shown.
Binary file not shown.
BIN
uploads/documents/file-1749454251950-519404861-catholic.pdf
Normal file
BIN
uploads/documents/file-1749454251950-519404861-catholic.pdf
Normal file
Binary file not shown.
@ -0,0 +1,747 @@
|
||||
%PDF-1.3
|
||||
%ºß¬à
|
||||
3 0 obj
|
||||
<</Type /Page
|
||||
/Parent 1 0 R
|
||||
/Resources 2 0 R
|
||||
/MediaBox [0 0 595.2799999999999727 841.8899999999999864]
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||||
/Contents 4 0 R
|
||||
>>
|
||||
endobj
|
||||
4 0 obj
|
||||
<<
|
||||
/Length 8084
|
||||
>>
|
||||
stream
|
||||
0.5670000000000001 w
|
||||
0 G
|
||||
q
|
||||
141.7322834645669332 0 0 56.6929133858267775 28.3464566929133888 756.8506299212598378 cm
|
||||
/I0 Do
|
||||
Q
|
||||
BT
|
||||
/F1 16 Tf
|
||||
18.3999999999999986 TL
|
||||
0 g
|
||||
198.4251968503937178 785.1970866141732586 Td
|
||||
(Hospital Name: test) Tj
|
||||
ET
|
||||
BT
|
||||
/F1 12 Tf
|
||||
13.7999999999999989 TL
|
||||
0 g
|
||||
42.5196850393700814 728.5041732283464171 Td
|
||||
(Mobile: 916498798798) Tj
|
||||
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|
||||
BT
|
||||
/F1 12 Tf
|
||||
13.7999999999999989 TL
|
||||
0 g
|
||||
396.8503937007874356 728.5041732283464171 Td
|
||||
(Email: testone@gmail.com) Tj
|
||||
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|
||||
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|
||||
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||||
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0.78 G
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||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
45. 686.65771653543311 Td
|
||||
(S/N) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
66.5999999999999943 700.1577165354329964 59.9434536368753896 -21.4999999999999964 re
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||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
71.5999999999999943 686.65771653543311 Td
|
||||
(Name) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
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||||
0.78 G
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0. w
|
||||
0.1 0.74 0.61 rg
|
||||
126.5434536368753839 700.1577165354329964 103.4000000000000199 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
131.5434536368753697 686.65771653543311 Td
|
||||
(Email) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
229.9434536368754038 700.1577165354329964 92.4000000000000057 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
234.9434536368754038 686.65771653543311 Td
|
||||
(Hospital Code) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
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||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
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||||
322.3434536368753811 700.1577165354329964 86.9999999999999858 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
327.3434536368753811 686.65771653543311 Td
|
||||
(Location) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
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||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
409.3434536368753811 700.1577165354329964 40.3000000000000043 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
414.3434536368753811 686.65771653543311 Td
|
||||
(Status) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
449.6434536368753356 700.1577165354329964 105.6365463631438786 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
454.6434536368753356 686.65771653543311 Td
|
||||
(Department) Tj
|
||||
ET
|
||||
0. G
|
||||
0.5670000000000001 w
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 678.65771653543311 26.6000000000000014 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 665.15771653543311 Td
|
||||
(61) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 678.65771653543311 59.9434536368753896 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 665.15771653543311 Td
|
||||
(test) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 678.65771653543311 103.4000000000000199 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 665.15771653543311 Td
|
||||
(testone@gmail.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 678.65771653543311 92.4000000000000057 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 665.15771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 678.65771653543311 86.9999999999999858 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 665.15771653543311 Td
|
||||
(##############) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 678.65771653543311 40.3000000000000043 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 665.15771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 678.65771653543311 105.6365463631438786 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 665.15771653543311 Td
|
||||
(Emergency) Tj
|
||||
T* (Department) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 645.6577165354329964 26.6000000000000014 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 632.15771653543311 Td
|
||||
(64) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 645.6577165354329964 59.9434536368753896 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 632.15771653543311 Td
|
||||
(John Doee) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 645.6577165354329964 103.4000000000000199 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 632.15771653543311 Td
|
||||
(johnn@example.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 645.6577165354329964 92.4000000000000057 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 632.15771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 645.6577165354329964 86.9999999999999858 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 632.15771653543311 Td
|
||||
(New York) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 645.6577165354329964 40.3000000000000043 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 632.15771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 645.6577165354329964 105.6365463631438786 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 632.15771653543311 Td
|
||||
(Cardiology) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 624.1577165354329964 26.6000000000000014 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 610.65771653543311 Td
|
||||
(65) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 624.1577165354329964 59.9434536368753896 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 610.65771653543311 Td
|
||||
(Jane) Tj
|
||||
T* (Smithh) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 624.1577165354329964 103.4000000000000199 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 610.65771653543311 Td
|
||||
(janne@example.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 624.1577165354329964 92.4000000000000057 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 610.65771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
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(##############) Tj
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1. g
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0.78 G
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0.2834645669291339 w
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1. g
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BT
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0.2834645669291339 w
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1. g
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BT
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414.3434536368753811 632.15771653543311 Td
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(Active) Tj
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0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
66.5999999999999943 700.1577165354329964 59.9434536368753896 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
71.5999999999999943 686.65771653543311 Td
|
||||
(Name) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
126.5434536368753839 700.1577165354329964 103.4000000000000199 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
131.5434536368753697 686.65771653543311 Td
|
||||
(Email) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
229.9434536368754038 700.1577165354329964 92.4000000000000057 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
234.9434536368754038 686.65771653543311 Td
|
||||
(Hospital Code) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
322.3434536368753811 700.1577165354329964 86.9999999999999858 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
327.3434536368753811 686.65771653543311 Td
|
||||
(Location) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
409.3434536368753811 700.1577165354329964 40.3000000000000043 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
414.3434536368753811 686.65771653543311 Td
|
||||
(Status) Tj
|
||||
ET
|
||||
0.1 0.74 0.61 rg
|
||||
0.78 G
|
||||
0. w
|
||||
0.1 0.74 0.61 rg
|
||||
449.6434536368753356 700.1577165354329964 105.6365463631438786 -21.4999999999999964 re
|
||||
f
|
||||
BT
|
||||
/F2 10 Tf
|
||||
11.5 TL
|
||||
1. g
|
||||
454.6434536368753356 686.65771653543311 Td
|
||||
(Department) Tj
|
||||
ET
|
||||
0. G
|
||||
0.5670000000000001 w
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 678.65771653543311 26.6000000000000014 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 665.15771653543311 Td
|
||||
(61) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 678.65771653543311 59.9434536368753896 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 665.15771653543311 Td
|
||||
(test) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 678.65771653543311 103.4000000000000199 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 665.15771653543311 Td
|
||||
(testone@gmail.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 678.65771653543311 92.4000000000000057 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 665.15771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 678.65771653543311 86.9999999999999858 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 665.15771653543311 Td
|
||||
(##############) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 678.65771653543311 40.3000000000000043 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 665.15771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 678.65771653543311 105.6365463631438786 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 665.15771653543311 Td
|
||||
(Emergency) Tj
|
||||
T* (Department) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 645.6577165354329964 26.6000000000000014 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 632.15771653543311 Td
|
||||
(64) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 645.6577165354329964 59.9434536368753896 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 632.15771653543311 Td
|
||||
(John Doee) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 645.6577165354329964 103.4000000000000199 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 632.15771653543311 Td
|
||||
(johnn@example.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 645.6577165354329964 92.4000000000000057 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 632.15771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 645.6577165354329964 86.9999999999999858 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 632.15771653543311 Td
|
||||
(New York) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 645.6577165354329964 40.3000000000000043 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 632.15771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 645.6577165354329964 105.6365463631438786 -21.4999999999999964 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 632.15771653543311 Td
|
||||
(Cardiology) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 624.1577165354329964 26.6000000000000014 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 610.65771653543311 Td
|
||||
(65) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 624.1577165354329964 59.9434536368753896 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 610.65771653543311 Td
|
||||
(Jane) Tj
|
||||
T* (Smithh) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 624.1577165354329964 103.4000000000000199 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 610.65771653543311 Td
|
||||
(janne@example.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 624.1577165354329964 92.4000000000000057 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 610.65771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 624.1577165354329964 86.9999999999999858 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 610.65771653543311 Td
|
||||
(Boston) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 624.1577165354329964 40.3000000000000043 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 610.65771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 624.1577165354329964 105.6365463631438786 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 610.65771653543311 Td
|
||||
(Neurology) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
40. 591.1577165354329964 26.6000000000000014 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
45. 577.65771653543311 Td
|
||||
(66) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
66.5999999999999943 591.1577165354329964 59.9434536368753896 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
71.5999999999999943 577.65771653543311 Td
|
||||
(adminone) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
126.5434536368753839 591.1577165354329964 103.4000000000000199 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
131.5434536368753697 577.65771653543311 Td
|
||||
(admin@gmail.com) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
229.9434536368754038 591.1577165354329964 92.4000000000000057 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
234.9434536368754038 577.65771653543311 Td
|
||||
(1ZOLW8USOW6Z) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
322.3434536368753811 591.1577165354329964 86.9999999999999858 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
327.3434536368753811 577.65771653543311 Td
|
||||
(Andhra Pradesh) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
409.3434536368753811 591.1577165354329964 40.3000000000000043 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
414.3434536368753811 577.65771653543311 Td
|
||||
(Active) Tj
|
||||
ET
|
||||
1. g
|
||||
0.78 G
|
||||
0.2834645669291339 w
|
||||
1. g
|
||||
449.6434536368753356 591.1577165354329964 105.6365463631438786 -32.9999999999999929 re
|
||||
B
|
||||
BT
|
||||
/F1 10 Tf
|
||||
11.5 TL
|
||||
0.314 g
|
||||
454.6434536368753356 577.65771653543311 Td
|
||||
(Emergency) Tj
|
||||
T* (Department) Tj
|
||||
ET
|
||||
0. G
|
||||
0.5670000000000001 w
|
||||
0.78 G
|
||||
0. w
|
||||
0. G
|
||||
0.5670000000000001 w
|
||||
0. G
|
||||
0.5670000000000001 w
|
||||
endstream
|
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endobj
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1 0 obj
|
||||
<</Type /Pages
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||||
/Kids [3 0 R ]
|
||||
/Count 1
|
||||
>>
|
||||
endobj
|
||||
5 0 obj
|
||||
<<
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||||
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>>
|
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endobj
|
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6 0 obj
|
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<<
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/Type /Font
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/Subtype /Type1
|
||||
/Encoding /WinAnsiEncoding
|
||||
/FirstChar 32
|
||||
/LastChar 255
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||||
>>
|
||||
endobj
|
||||
7 0 obj
|
||||
<<
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||||
/Type /Font
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/BaseFont /Helvetica-Oblique
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/Subtype /Type1
|
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/Encoding /WinAnsiEncoding
|
||||
/FirstChar 32
|
||||
/LastChar 255
|
||||
>>
|
||||
endobj
|
||||
8 0 obj
|
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<<
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||||
/Type /Font
|
||||
/BaseFont /Helvetica-BoldOblique
|
||||
/Subtype /Type1
|
||||
/Encoding /WinAnsiEncoding
|
||||
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|
||||
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|
||||
>>
|
||||
endobj
|
||||
9 0 obj
|
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<<
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||||
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|
||||
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||||
/Subtype /Type1
|
||||
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|
||||
/FirstChar 32
|
||||
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|
||||
>>
|
||||
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Loading…
Reference in New Issue
Block a user