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Semantic Caching

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Semantic caching is an intelligent caching pattern that stores and retrieves LLM responses based on semantic similarity rather than exact string matches, significantly reducing costs and improving response times for agent systems.

Why semantic caching is important for agents:

  • Cost Reduction: Avoid redundant LLM API calls for semantically similar queries
  • Performance: Sub-second responses for cached semantic matches
  • Consistency: Consistent agent behavior for similar user inputs
  • Scalability: Handle higher agent workloads without proportional cost increases
  • Offline Capabilities: Serve responses when external LLM APIs are unavailable

How semantic caching works:

  1. Query Embedding: Convert incoming prompts to vector embeddings
  2. Similarity Search: Find semantically similar cached responses using vector search
  3. Threshold Matching: Return cached response if similarity exceeds threshold
  4. Cache Updates: Store new responses with their embeddings for future use
  5. Cache Eviction: Remove old or low-quality responses based on usage patterns

Implementation approaches:

  • Vector Databases: Use Pinecone, Weaviate, or pgvector for similarity search
  • Embedding Models: OpenAI embeddings or open-source alternatives like Sentence-BERT
  • Hybrid Caching: Combine exact string matching with semantic similarity
  • Multi-Level Caching: Redis for fast access, vector DB for semantic search
  • Cache Invalidation: Time-based or manual invalidation for dynamic content

Agent-specific considerations:

  • Context Awareness: Include conversation history in cache key calculation
  • Agent Identity: Separate cache namespaces for different agent types
  • Response Quality: Monitor cached response relevance and user satisfaction
  • Privacy: Ensure cached responses don't leak between customers or contexts
  • Cache Warming: Pre-populate cache with common agent interactions

Integration with our platform:

  • Redis: Fast in-memory caching layer for recent and frequent responses
  • Vector Storage: Persistent semantic search using our existing vector infrastructure
  • Monitoring: Track cache hit rates, cost savings, and response quality
  • A/B Testing: Compare cached vs. fresh responses for quality assessment

Cost impact estimates:

  • Development Environment: 60-80% reduction in LLM API costs
  • Production Environment: 30-50% reduction with proper cache management
  • Agent Training: Significant savings during development and testing phases

Best practices:

  • Set appropriate similarity thresholds (0.85-0.95 for most use cases)
  • Include relevant context in embedding calculation
  • Monitor cache performance and adjust strategies based on usage patterns
  • Implement cache analytics to identify optimization opportunities
  • Use cache versioning for agent behavior updates