Could This Gemini Trick Finally Replace RAG?
AI Summary
Summary of Video on Context Caching for LLM API Cost Reduction
Goal: Slash LLM API costs by up to 90% using context caching.
Key Concepts:
- Context Caching: Reduces API calls and costs; serves as an alternative to retrieval augmented generation (RAG).
- API Providers: Major providers like OpenAI, Enthropic, and Google have implemented context caching.
- Google’s Implementation: Originally needed 32,000 tokens; now reduced to 4,000 tokens for caching.
Advantages:
- Speeds up interactions with large documents (e.g., PDFs, videos) while decreasing costs.
- Users can set cache expiration (default 1 hour).
- Can replace RAG for smaller documents, enhancing in-context learning.
Cost Impact:
- Example: For Gemini 2.5 Pro, caching prompts can lead to 75% cost reduction for tokens beyond 200,000.
- Importance of considering both caching and storage costs.
Practical Implementation:
- Install Google generative AI package and relevant libraries.
- Upload a scanned document (e.g., a 600-page plan).
- Create a cache for future interactions with the LLM.
- Monitor token usage and metadata from the request.
Additional Techniques:
- Use the delete function to manage cache after user sessions.
- Iterate through multiple caches and update cache duration dynamically.
Example Application:
- Caching GitHub repository contents using Git Ingest to facilitate LLM interactions for creating MCP servers at lower costs.
Final Thoughts:
- Context caching is a crucial technique for those using LLMs via APIs to significantly lower costs and improve performance. Google offers more control compared to other providers.