Optimization
fluiq.optimize() enables semantic caching on Fluiq-managed Redis. Calls with semantically similar prompts are served from cache: instant response, zero API cost.
One call to optimize() enables semantic caching for all traced OpenAI calls: chat, streaming, and embeddings.
Python
import openai
from fluiq import instrument, optimize
instrument(api_key="fl_...")
optimize() # semantic caching (Team+ plan)
client = openai.OpenAI()
# First call: hits OpenAI, response cached in Redis
r1 = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is machine learning?"}],
)
# Semantically similar call: served instantly from cache
r2 = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Explain machine learning to me"}],
)
# r2: same quality, zero latency, zero API cost