Large volume LLM applications suffer from high latency and scaling costs. A significant percentage of user queries are semantically similar. By implementing **Semantic Caching** using Redis and embeddings cosine similarity, we can bypass LLM generation entirely for repeat queries.

Semantic Matching Logic

When a query arrives, we compute its vector embedding and query Redis using Vector Similarity Search. If the distance is below our similarity threshold (e.g. cosine distance < 0.1), we return the cached response.

# Semantic cache verification logic
def get_cached_response(query_vector, redis_client):
    match = redis_client.ft("idx:cache").search(
        Query("*=>[VECTOR_RANGE 0.1 $vec] AS score").sort_by("score")
    )
    if match.docs:
        return match.docs[0].response
    return None