[ALGO-2] Fully Integrate Semantic Cache into MCTS Algorithm

[MVPandey]

🚀 SEMANTIC CACHE INTEGRATION

Priority: MEDIUM - Performance Optimization

Problem

Sophisticated semantic cache exists but isn't fully integrated into MCTS algorithm. Missing 60-80% potential performance improvement.

Current State: Cache check exists in algorithm.py:114-124 but not properly integrated into node expansion.

Solution

Complete semantic cache integration with multi-level caching and partial hit optimization.

Enhanced Cache Integration

# In algorithm.py _expand_and_simulate() 
async def _expand_and_simulate(self, node: MCTSNode, config: MCTSConfig):
    # Multi-level cache check
    cache_results = await self.semantic_cache.get_multilevel(
        exact_messages=extended_messages,
        similar_threshold=0.85,
        domain=config.domain.name if config.domain else "general"
    )
    
    if cache_results.exact_hit:
        # Use cached result directly
        return cache_results.result
    elif cache_results.similar_hits:
        # Use similar result as starting point
        return await self._refine_cached_result(cache_results.best_match)
    else:
        # Generate new result and cache it
        result = await self._generate_new_result(extended_messages, config)
        await self.semantic_cache.store(extended_messages, result, config.domain)
        return result

Cache Hit Rate Optimization

class SemanticCacheOptimizer:
    async def optimize_cache_strategy(self, conversation_patterns: List[Dict]):
        """Optimize cache parameters based on usage patterns"""
        # Analyze conversation similarity patterns
        # Adjust similarity thresholds dynamically
        # Implement cache warming for common patterns
        pass
    
    async def precompute_common_branches(self, domain: str):
        """Pre-compute responses for common conversation patterns"""
        common_patterns = await self._get_common_patterns(domain)
        for pattern in common_patterns:
            if not await self.cache.exists(pattern):
                result = await self._compute_response(pattern)
                await self.cache.store(pattern, result, domain)

Implementation Steps

• Complete cache integration in MCTS node expansion
• Implement multi-level caching (exact + similarity)
• Add cache warming for common patterns
• Optimize similarity thresholds per domain
• Add cache hit rate monitoring
• Implement cache invalidation strategies

Expected Impact

• 60-80% performance improvement for repeated conversation patterns
• Reduced LLM API calls for similar conversations
• Lower latency for cache hits
• Cost savings on repeated analysis

Acceptance Criteria

• Cache hit rate > 40% for similar conversations
• Performance improvement > 60% for cached responses
• Cache hit rate monitoring dashboard
• Domain-specific cache optimization
• Graceful fallback when cache unavailable

Effort: Medium (3-5 days)