Google introduced a new feature called context caching, available through the Gemini APIs in the Gemini 1.5 Pro and Flash models. This guide shows you how to use context caching with the Gemini 1.5 Flash model in a straightforward way.

Use Case: Analyzing a Year of ML Papers

This guide will illustrate how to use context caching to analyze summaries of machine learning (ML) papers we've collected over the past year. We save these summaries in a text file, which can now be easily processed by the Gemini 1.5 Flash model for efficient querying.

The Process: Uploading, Caching, and Querying

1. Prepare Your Data: Start by converting the readme file (that contains the summaries) into a plain text file.
  
2. Use the Gemini API: Upload the text file using the Google `generativeai` library.

3. Set Up Context Caching:
   - Create a cache using the `caching.CachedContent.create()` function, which involves:
     - Selecting the Gemini 1.5 Flash model.
     - Naming your cache.
     - Providing instructions for the model, like, "You are an expert AI researcher..."
     - Setting a time-to-live (TTL) for the cache (for example, 15 minutes).

4. Create the Model: Set up a generative model instance using the cached content.

5. Start Querying: You can ask the model natural language questions, such as:
   - "What are the latest AI papers this week?"
   - "Can you list papers mentioning Mamba, along with their titles and summaries?"
   - "What innovations exist around long-context LLMs? Please list the titles and summaries."

The results were impressive! The model accurately retrieved and summarized information from the text file. Context caching made the process very efficient, as it eliminated the need to resend the entire text file with each query.

Benefits for Researchers

This workflow can be an invaluable tool for researchers, enabling them to:

- Quickly analyze and query large volumes of research data.
- Retrieve specific findings without sifting through documents manually.
- Conduct interactive research sessions without wasting prompt tokens.

We look forward to exploring more uses for context caching, particularly in more complex scenarios like agentic workflows.