Semantic Graph Caching: Scale LLMs, Slash Costs

Large Language Models (LLMs) are transforming applications, but their widespread adoption faces a significant challenge: the cost and latency associated with frequent API calls. A common issue observed in LLM-powered applications is the processing of countless semantically identical questions, phrased in diverse ways. For instance, queries like “How do I reset my password?”, “What’s the password recovery process?”, and “I forgot my password, help me recover it?” all seek the same information, yet each typically triggers a separate, costly LLM API request.

Traditional caching mechanisms, reliant on exact string matching, are ill-equipped to handle this semantic variability. Any slight difference in phrasing results in a cache miss, leading to redundant LLM calls and escalating operational expenses. The core problem lies in the inability of these systems to understand that different wordings can convey the same underlying meaning.

A Novel Approach: Graph Theory Meets Semantic Similarity

To overcome this limitation, an innovative solution proposes combining graph theory with semantic similarity search. Instead of treating cached responses as isolated entries, this method envisions them as nodes within a connected graph. Semantically similar queries are linked by edges, with the strength of these connections represented by a similarity score. This approach allows the cache to recognize and serve responses for queries that mean the same thing, even if phrased differently.

The theoretical benefits are substantial: reduced API costs, lower response latency, and more efficient scaling compared to conventional methods.

Why String Matching Fails

Consider a simple string-based cache. If a user asks “How do I reset my password?”, the response is cached under that exact string. Subsequent queries like “What’s the password recovery process?” or “I forgot my password, help me recover it?” will not match the cached entry, forcing a new LLM call every time. This directly impacts both performance and cost. The fundamental flaw is that string matching ignores the crucial element of meaning.

The Breakthrough: Queries as a Connected Graph

The key innovation lies in treating each query and its corresponding response as a node in a graph. Edges connect nodes representing semantically similar queries, with the edge weight indicating their similarity score. This structure allows the system to navigate through related queries efficiently. If a new query is semantically similar to an existing node, its “neighbors” (other semantically similar queries) are also likely to be relevant.

Leveraging Redis for Graph Operations

Remarkably, standard Redis data structures prove highly suitable for implementing this graph-based cache without requiring a specialized graph database. Queries and their associated data (response, embedding, timestamp) are stored as Redis hashes, acting as graph nodes. The connections between semantically similar queries—the edges—are managed using Redis sorted sets, where the similarity score serves as the set’s score, allowing quick retrieval of the most similar neighbors.

Strategic Graph Building

To prevent the computationally expensive scenario of connecting every new query to every existing one (an O(n²) problem), the graph is built strategically. When a new query and its response are added, the system doesn’t compare it against the entire cache. Instead, it prioritizes connections to recent nodes (which are often relevant) and a small, diverse sample of random nodes. Similarity calculations are only performed for this selected subset, and bidirectional edges are created only if the similarity exceeds a defined threshold. This ensures efficient graph growth while maintaining high accuracy.

Intelligent Cache Retrieval

When a new query arrives, the search for a cached response becomes an intelligent graph traversal. The system calculates the new query’s semantic embedding and then starts by checking a few promising candidates, such as the most recently added nodes. From these starting points, it follows the strongest edges—those leading to the most semantically similar neighbors—prioritizing exploration based on pre-computed similarity scores. This guided traversal allows the system to bypass entire regions of semantically unrelated cached queries, significantly reducing the number of items that need to be checked. Instead of a linear scan of hundreds of cached entries, the algorithm can find a match by checking only a handful of strategically chosen nodes.

Redis’s sorted sets facilitate instant access to top-k neighbors, while its hash storage keeps node data cohesive, allowing the entire system to scale horizontally.

Compelling Performance Results

Testing this graph-based semantic caching approach with a diverse set of queries yielded significant improvements:

• Search Efficiency: The graph algorithm required checking an average of just 12.1 nodes per query, a remarkable 71.3% reduction compared to the 42 nodes needed for a traditional linear search. This translates to a 3.5x speedup in cache lookup operations.

• Cost Impact: With a 44.8% cache hit rate on semantic matches, LLM API costs plummeted. The system required only 116 expensive API calls instead of 210, resulting in a 44.8% saving in operational costs.

• Scalability: Crucially, the efficiency of this method improves as the cache grows. Unlike linear search, which slows down with more cached items, graph traversal maintains consistent performance by intelligently skipping irrelevant data, ensuring robust scalability.

Production Considerations

Graph-based semantic caching is particularly well-suited for applications with high query volumes and significant semantic variations, such as customer support systems, documentation portals, and FAQ sections, where LLM API costs are a major concern. It’s also beneficial in scenarios where a slight increase in cache lookup latency (e.g., 50-100ms) is acceptable in exchange for substantial cost savings.

Key configuration tips for optimal performance include:

• A similarity threshold of 0.7 generally works well for most use cases.

• Connecting each new node to 10-15 neighbors typically provides optimal graph connectivity.

• Using embeddings with 512 dimensions strikes a good balance between accuracy and storage efficiency.

Conclusion

By transforming semantic caching from a brute-force problem into an intelligent search challenge, graph theory offers a powerful solution for scaling LLM applications. Treating similar queries as interconnected neighbors rather than isolated strings dramatically reduces both operational costs and response latency while maintaining high accuracy. This paradigm shift in data structuring opens new avenues for efficient semantic search at scale, demonstrating that sometimes, the most impactful improvements come not from better algorithms alone, but from fundamentally rethinking how data is organized and related.