LLM replies caching

[akurniawan]

Hi folks, in langchain and autogen there's a feature to cache to llm replies. I tried to find the same thing for haystack but could only find caching for documents. Am I missing something?

[bkrmm]

You're absolutely right! Haystack doesn't currently have a built-in feature to cache LLM responses directly. It primarily focuses on caching documents within its document store.

However, you can implement LLM response caching manually using a variety of approaches:

1. In-Memory Caching:

Use Python's built-in dict or a more sophisticated caching library like cachetools to store LLM responses.
Key the cache by the prompt or query to efficiently retrieve cached responses.
2. File-Based Caching:

Serialize LLM responses to disk (e.g., JSON, Pickle) and read them from the file system when needed.
Consider using a database like SQLite for more structured storage.
3. External Caching Services:

Utilize cloud-based caching services like Redis or Memcached for scalable and distributed caching.
Important Considerations:

Cache Expiration: Implement a strategy to expire old or irrelevant cache entries.
Cache Invalidation: Ensure that the cache is updated when underlying data or LLM models change.
Cache Consistency: Maintain consistency between the cache and the LLM, especially if the LLM's behavior evolves.
Cost Optimization: Balance the benefits of caching with the costs of storing and retrieving cached data.
While Haystack doesn't offer native LLM response caching, these approaches provide effective ways to implement caching in your applications, potentially improving performance and reducing costs.

Would you like to explore any of these approaches in more detail or discuss specific use cases?