333 lines
14 KiB
Python
333 lines
14 KiB
Python
"""
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Persistent Outline Cache Service
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Provides database-backed caching for outline generation results to survive server restarts
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and provide better cache management across multiple instances.
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"""
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import hashlib
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import json
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import sqlite3
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from typing import Dict, Any, Optional, List
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from datetime import datetime, timedelta
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from pathlib import Path
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from loguru import logger
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class PersistentOutlineCache:
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"""Database-backed cache for outline generation results with exact parameter matching."""
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def __init__(self, db_path: str = "outline_cache.db", max_cache_size: int = 500, cache_ttl_hours: int = 48):
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"""
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Initialize the persistent outline cache.
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Args:
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db_path: Path to SQLite database file
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max_cache_size: Maximum number of cached entries
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cache_ttl_hours: Time-to-live for cache entries in hours (longer than research cache)
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"""
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self.db_path = db_path
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self.max_cache_size = max_cache_size
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self.cache_ttl = timedelta(hours=cache_ttl_hours)
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# Ensure database directory exists
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Path(db_path).parent.mkdir(parents=True, exist_ok=True)
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# Initialize database
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self._init_database()
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def _init_database(self):
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"""Initialize the SQLite database with required tables."""
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS outline_cache (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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cache_key TEXT UNIQUE NOT NULL,
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keywords TEXT NOT NULL,
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industry TEXT NOT NULL,
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target_audience TEXT NOT NULL,
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word_count INTEGER NOT NULL,
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custom_instructions TEXT,
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persona_data TEXT,
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result_data TEXT NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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expires_at TIMESTAMP NOT NULL,
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access_count INTEGER DEFAULT 0,
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last_accessed TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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# Create indexes for better performance
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conn.execute("CREATE INDEX IF NOT EXISTS idx_outline_cache_key ON outline_cache(cache_key)")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_outline_expires_at ON outline_cache(expires_at)")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_outline_created_at ON outline_cache(created_at)")
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conn.execute("CREATE INDEX IF NOT EXISTS idx_outline_keywords ON outline_cache(keywords)")
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conn.commit()
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def _generate_cache_key(self, keywords: List[str], industry: str, target_audience: str,
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word_count: int, custom_instructions: str = None, persona_data: Dict = None) -> str:
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"""
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Generate a cache key based on exact parameter match.
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Args:
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keywords: List of research keywords
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industry: Industry context
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target_audience: Target audience context
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word_count: Target word count for outline
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custom_instructions: Custom instructions for outline generation
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persona_data: Persona information
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Returns:
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MD5 hash of the normalized parameters
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"""
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# Normalize and sort keywords for consistent hashing
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normalized_keywords = sorted([kw.lower().strip() for kw in keywords])
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normalized_industry = industry.lower().strip() if industry else "general"
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normalized_audience = target_audience.lower().strip() if target_audience else "general"
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normalized_instructions = custom_instructions.lower().strip() if custom_instructions else ""
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# Normalize persona data
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normalized_persona = ""
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if persona_data:
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# Sort persona keys and values for consistent hashing
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persona_str = json.dumps(persona_data, sort_keys=True, default=str)
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normalized_persona = persona_str.lower()
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# Create a consistent string representation
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cache_string = f"{normalized_keywords}|{normalized_industry}|{normalized_audience}|{word_count}|{normalized_instructions}|{normalized_persona}"
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# Generate MD5 hash
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return hashlib.md5(cache_string.encode('utf-8')).hexdigest()
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def _cleanup_expired_entries(self):
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"""Remove expired cache entries from database."""
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.execute(
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"DELETE FROM outline_cache WHERE expires_at < ?",
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(datetime.now().isoformat(),)
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)
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deleted_count = cursor.rowcount
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if deleted_count > 0:
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logger.debug(f"Removed {deleted_count} expired outline cache entries")
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conn.commit()
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def _evict_oldest_entries(self, num_to_evict: int):
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"""Evict the oldest cache entries when cache is full."""
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with sqlite3.connect(self.db_path) as conn:
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# Get oldest entries by creation time
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cursor = conn.execute("""
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SELECT id FROM outline_cache
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ORDER BY created_at ASC
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LIMIT ?
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""", (num_to_evict,))
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old_ids = [row[0] for row in cursor.fetchall()]
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if old_ids:
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placeholders = ','.join(['?' for _ in old_ids])
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conn.execute(f"DELETE FROM outline_cache WHERE id IN ({placeholders})", old_ids)
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logger.debug(f"Evicted {len(old_ids)} oldest outline cache entries")
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conn.commit()
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def get_cached_outline(self, keywords: List[str], industry: str, target_audience: str,
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word_count: int, custom_instructions: str = None, persona_data: Dict = None) -> Optional[Dict[str, Any]]:
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"""
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Get cached outline result for exact parameter match.
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Args:
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keywords: List of research keywords
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industry: Industry context
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target_audience: Target audience context
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word_count: Target word count for outline
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custom_instructions: Custom instructions for outline generation
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persona_data: Persona information
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Returns:
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Cached outline result if found and valid, None otherwise
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"""
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cache_key = self._generate_cache_key(keywords, industry, target_audience, word_count, custom_instructions, persona_data)
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.execute("""
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SELECT result_data, expires_at FROM outline_cache
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WHERE cache_key = ? AND expires_at > ?
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""", (cache_key, datetime.now().isoformat()))
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row = cursor.fetchone()
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if row is None:
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logger.debug(f"Outline cache miss for keywords: {keywords}, word_count: {word_count}")
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return None
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# Update access statistics
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conn.execute("""
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UPDATE outline_cache
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SET access_count = access_count + 1, last_accessed = CURRENT_TIMESTAMP
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WHERE cache_key = ?
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""", (cache_key,))
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conn.commit()
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try:
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result_data = json.loads(row[0])
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logger.info(f"Outline cache hit for keywords: {keywords}, word_count: {word_count} (saved expensive generation)")
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return result_data
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except json.JSONDecodeError:
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logger.error(f"Invalid JSON in outline cache for keywords: {keywords}")
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# Remove invalid entry
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conn.execute("DELETE FROM outline_cache WHERE cache_key = ?", (cache_key,))
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conn.commit()
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return None
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def cache_outline(self, keywords: List[str], industry: str, target_audience: str,
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word_count: int, custom_instructions: str, persona_data: Dict, result: Dict[str, Any]):
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"""
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Cache an outline generation result.
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Args:
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keywords: List of research keywords
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industry: Industry context
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target_audience: Target audience context
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word_count: Target word count for outline
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custom_instructions: Custom instructions for outline generation
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persona_data: Persona information
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result: Outline result to cache
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"""
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cache_key = self._generate_cache_key(keywords, industry, target_audience, word_count, custom_instructions, persona_data)
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# Cleanup expired entries first
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self._cleanup_expired_entries()
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# Check if cache is full and evict if necessary
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.execute("SELECT COUNT(*) FROM outline_cache")
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current_count = cursor.fetchone()[0]
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if current_count >= self.max_cache_size:
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num_to_evict = current_count - self.max_cache_size + 1
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self._evict_oldest_entries(num_to_evict)
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# Store the result
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expires_at = datetime.now() + self.cache_ttl
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("""
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INSERT OR REPLACE INTO outline_cache
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(cache_key, keywords, industry, target_audience, word_count, custom_instructions, persona_data, result_data, expires_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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cache_key,
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json.dumps(keywords),
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industry,
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target_audience,
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word_count,
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custom_instructions or "",
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json.dumps(persona_data) if persona_data else "",
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json.dumps(result),
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expires_at.isoformat()
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))
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conn.commit()
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logger.info(f"Cached outline result for keywords: {keywords}, word_count: {word_count}")
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def get_cache_stats(self) -> Dict[str, Any]:
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"""Get cache statistics."""
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self._cleanup_expired_entries()
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with sqlite3.connect(self.db_path) as conn:
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# Get basic stats
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cursor = conn.execute("SELECT COUNT(*) FROM outline_cache")
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total_entries = cursor.fetchone()[0]
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cursor = conn.execute("SELECT COUNT(*) FROM outline_cache WHERE expires_at > ?", (datetime.now().isoformat(),))
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valid_entries = cursor.fetchone()[0]
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# Get most accessed entries
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cursor = conn.execute("""
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SELECT keywords, industry, target_audience, word_count, access_count, created_at
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FROM outline_cache
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ORDER BY access_count DESC
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LIMIT 10
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""")
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top_entries = [
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{
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'keywords': json.loads(row[0]),
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'industry': row[1],
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'target_audience': row[2],
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'word_count': row[3],
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'access_count': row[4],
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'created_at': row[5]
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}
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for row in cursor.fetchall()
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]
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# Get database size
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cursor = conn.execute("SELECT page_count * page_size as size FROM pragma_page_count(), pragma_page_size()")
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db_size_bytes = cursor.fetchone()[0]
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db_size_mb = db_size_bytes / (1024 * 1024)
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return {
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'total_entries': total_entries,
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'valid_entries': valid_entries,
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'expired_entries': total_entries - valid_entries,
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'max_size': self.max_cache_size,
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'ttl_hours': self.cache_ttl.total_seconds() / 3600,
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'database_size_mb': round(db_size_mb, 2),
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'top_accessed_entries': top_entries
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}
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def clear_cache(self):
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"""Clear all cached entries."""
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("DELETE FROM outline_cache")
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conn.commit()
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logger.info("Outline cache cleared")
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def get_cache_entries(self, limit: int = 50) -> List[Dict[str, Any]]:
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"""Get recent cache entries for debugging."""
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.execute("""
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SELECT keywords, industry, target_audience, word_count, custom_instructions, created_at, expires_at, access_count
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FROM outline_cache
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ORDER BY created_at DESC
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LIMIT ?
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""", (limit,))
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return [
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{
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'keywords': json.loads(row[0]),
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'industry': row[1],
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'target_audience': row[2],
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'word_count': row[3],
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'custom_instructions': row[4],
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'created_at': row[5],
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'expires_at': row[6],
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'access_count': row[7]
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}
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for row in cursor.fetchall()
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]
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def invalidate_cache_for_keywords(self, keywords: List[str]):
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"""
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Invalidate all cache entries for specific keywords.
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Useful when research data is updated.
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Args:
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keywords: Keywords to invalidate cache for
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"""
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normalized_keywords = sorted([kw.lower().strip() for kw in keywords])
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keywords_json = json.dumps(normalized_keywords)
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.execute("DELETE FROM outline_cache WHERE keywords = ?", (keywords_json,))
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deleted_count = cursor.rowcount
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conn.commit()
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if deleted_count > 0:
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logger.info(f"Invalidated {deleted_count} outline cache entries for keywords: {keywords}")
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# Global persistent cache instance
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persistent_outline_cache = PersistentOutlineCache()
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