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import json
import time
from datetime import timedelta
from typing import List, Dict, Any, Optional
from .redis_connection import RedisConnection
from src.llm.models.schemas import ConversationResponse
from src.llm.core.config import settings
class RedisHistory:
def __init__(self, session_ttl: int = settings.SESSION_TTL):
self.redis = RedisConnection().client
self.session_ttl = session_ttl
def add_conversation(self, session_id: str, chat_id: str, response: ConversationResponse) -> None:
"""
Store complete conversation response in history
"""
# Store in session-specific list
self.redis.rpush(
f"session:{session_id}:history",
json.dumps({
'chat_id': chat_id,
'response': response.dict(),
'timestamp': time.time()
})
)
# Set TTL for session history
self.redis.expire(f"session:{session_id}:history", self.session_ttl)
def get_conversation_history(self, session_id: str, limit: int = 10) -> List[Dict[str, Any]]:
"""
Retrieve conversation history with optional limit
"""
messages = self.redis.lrange(f"session:{session_id}:history", -limit, -1)
return [
{
'chat_id': json.loads(msg)['chat_id'],
'response': ConversationResponse(**json.loads(msg)['response']),
'timestamp': json.loads(msg)['timestamp']
}
for msg in messages
]
def get_full_context(self, session_id: str) -> str:
"""
Generate conversation context string for LLM prompts
"""
history = self.get_conversation_history(session_id)
context_lines = []
for entry in history:
response = entry['response']
context_lines.append(
f"User: {response.query}\n"
f"Therapist: {response.response}\n"
f"Emotions: {response.emotion_analysis.primary_emotion} "
f"(Intensity: {response.emotion_analysis.intensity})\n"
)
return "\n".join(context_lines)
def clear_history(self, session_id: str) -> None:
"""
Clear session history
"""
self.redis.delete(f"session:{session_id}:history") |