Semantic Caching: Reusing Intelligence

In traditional web development, we use a cache (like Redis) to store the result of a database query. In AI development, we use Semantic Caching to store the result of an LLM query.

1. Traditional vs. Semantic

• Traditional Cache: If the user asks "Hi" and then asks "Hi ", those are different keys. The cache misses.
• Semantic Cache: If the user asks "What's the weather?" and then "Tell me the weather," the cache realizes the meaning is identical and returns the saved result instantly.

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2. Why it matters for Agents

1. Cost: Identical queries cost $0.00.
2. Speed: Cache returns in 10ms. The LLM takes 2,000ms.
3. Consistency: You can ensure that common questions always receive the "Approved" company answer.

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3. Visualizing the Cache Gate

graph LR
    User[Human Query] --> Emb[Embedding Model]
    Emb --> Search[Semantic Cache Search]
    Search -- Hit --> Result[Return Saved Answer]
    Search -- Miss --> Brain[LLM Brain]
    Brain --> Save[Save to Cache]
    Save --> Result

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4. Setting the "Similarity Threshold"

How "Similar" should two questions be before you use the cache?

• 0.99: Must be almost exactly the same words. (Very Safe).
• 0.85: Can be different words but same intent. (Risk of serving the wrong answer).

For customer support, 0.90 - 0.95 is usually the sweet spot.

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5. Tools for the Job

• GPTCache: A popular Python library that integrates with LangChain to handle the database lookups and similarity math for you.
• Redis (Vector Search): Redis now supports vector search, making it the fastest option for high-traffic agent systems.

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Key Takeaways

• Semantic Caching identifies identical intents, not just identical words.
• It is the primary tool for reducing LLM costs in production.
• It significantly improves user experience by providing instant answers.
• Careful threshold management is required to prevent "Wrong Answer" hits.