Summary
The video introduces context caching as a cost-effective method to reduce LLM API expenses by up to 90% by storing previously processed tokens. It discusses how prominent providers like OpenAI, Enthropic, and Google have integrated context caching into their systems. The demo showcases the benefits of context caching for speed and cost efficiency by demonstrating how to set cache duration and manage cache options with Gemini. Additionally, it explores creating caches for lengthy documents and illustrates the significant cost reduction by utilizing cached tokens compared to processing input tokens from scratch.
Introduction to Context Caching
Introduces the concept of context caching as a way to reduce LLM API costs by up to 90% and save money while avoiding the overhead of vector stores.
Implementation by Providers
Discusses how providers like OpenAI, Enthropic, and Google have implemented context caching, initially requiring 32,000 tokens to cache but now making it more accessible.
How Context Caching Works
Explains the process of context caching, including how to choose caching duration, benefits for speed and cost, and using it as a replacement for retrieving embeddings.
Practical Example with Gemini
Demonstrates how to use context caching with Gemini by creating a cache for a lengthy document and interacting with the cached content through the Gemini API.
Setting Cache Duration
Explains how to set the cache duration, default expiration, and options for managing cache, showcasing an example of setting cache duration to 300 seconds.
Example with GitHub Repo and LLM
Showcases an example using a GitHub repo to create LLM context, converting the repo into an LLM version, and creating MCP servers based on the repo contents.
Comparison of Cached and Non-Cached Tokens
Compares the number of tokens processed by the cache and input tokens, illustrating the cost reduction by utilizing cached tokens.
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