Semantic Caching for LLMs: TTLs, Confidence, and Cache Safety

Based on the provided project structure, here's a breakdown of each component in the app/ directory for an MLOps project focused on semantic caching with FastAPI and Redis:

Directory Structure Overview

• app/api: Contains API-related logic.

  • ask.py: Handles incoming requests and orchestrates the flow (e.g., embedding, cache lookup, LLM call).

• app/cache: Manages caching logic.

  • semantic_cache.py: Implements the semantic cache using Redis.
  • ttl.py: Manages time-to-live (TTL) for cache entries.
  • poisoning.py: Prevents malicious data from poisoning the cache.
  • schemas.py: Defines Pydantic models used in caching operations.

• app/config: Contains configuration settings.

  • settings.py: Holds environment-specific configurations like Redis connection details, LLM API keys, etc.

• app/embeddings: Manages embedding logic for text inputs.

  • embedder.py: Converts text into numerical embeddings (e.g., using Sentence Transformers).

• app/llm: Handles interactions with the language model.

  • ollama_client.py: Interface to interact with an LLM

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