Making Long Context LLMs Usable with Context Caching

Google's Gemini API now supports context caching, aimed at addressing limitations of long context LLMs by reducing processing time and costs. This video explains how to use the caching feature, its impact on performance, and implementation details with examples.
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TIMESTAMPS
00:00 Introduction to Google's Context Caching
00:48 How Context Caching Works
01:00 Setting Up Your Cache
03:07 Cost and Storage Considerations
04:46 Example Implementation
08:57 Creating and Using the Cache
11:06 Managing Cache Metadata
12:53 Conclusion and Future Prospects
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[@unclecode]

Thanks! I found the ability to update the TTL very interesting. Imagine building an assistant application for answering questions or customer service. On the server side, we could update the TTL another let's say 5 minutes. When a new user sends a question, we can update it again. When there's no new user, it will be gone. Five minutes is just an example, but it's a great way to keep your cache ready and clear it when you don't need it.
I think the minimum token requirement is likely about profit. They need a minimum number to offer the service economically, saving expenses. Below that threshold, it wouldn't be cost-effective for them. That's my guess.

[@engineerprompt]

Dynamically controlling TTL can be really helpful and I agree the token limit is probably related to cost. I hope they implement the latency reduction soon, since that will make more sense.

[@paraconscious790]

this is very helpful buddy, very time saving and quickly updating my own biological cache without searching for it explicitly. Thanks!

[@boooosh2007]

Seems similar but more expensive to vector storage. What am I missing?

[@engineerprompt]

A couple of thing that differentiate it from vector storage. When you use retrieve info with vector based search, you only get some "chunks" where the LLM doesn't have the whole context of the document, an approach like this will provide complete context to the LLM. Caching can also be really useful with RAG as well. I agree it is going to be more expensive than vectorstores but will potentially save on the infra. Will be interesting to see how it evolves.

[@boooosh2007]

@@engineerprompt yeah chunking would have to be perfect to match the context. But if vector representation and chunking are accurate it should match in context quality. Time will tell ehh?

[@DearGeorge3]

Great news! Thanks!!

[@engineerprompt]

thank you.