OpenAICacheAdapter

Wraps openai.OpenAI and openai.AsyncOpenAI chat.completions.create calls with response caching. Returns cached results for identical (model, messages, params) combinations without hitting the API.

Installation

pip install 'omnicache-ai[openai]'

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Usage

Sync

import openai
from omnicache_ai import CacheManager, InMemoryBackend, CacheKeyBuilder
from omnicache_ai.adapters.openai_adapter import OpenAICacheAdapter

client = openai.OpenAI()
manager = CacheManager(
    backend=InMemoryBackend(),
    key_builder=CacheKeyBuilder(namespace="myapp"),
)
adapter = OpenAICacheAdapter(client, manager)

# First call — hits the API
response = adapter.chat_create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is semantic caching?"}],
)

# Second call with same args — returns from cache instantly
response = adapter.chat_create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is semantic caching?"}],
)

Async

client = openai.AsyncOpenAI()
adapter = OpenAICacheAdapter(client, manager)

response = await adapter.achat_create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is semantic caching?"}],
)

With Redis backend

from omnicache_ai.backends.redis_backend import RedisBackend

manager = CacheManager(
    backend=RedisBackend(url="redis://localhost:6379/0"),
    key_builder=CacheKeyBuilder(namespace="prod"),
)
adapter = OpenAICacheAdapter(client, manager)

Invalidate by model

adapter.invalidate_model("gpt-4o")  # remove all cached gpt-4o responses

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How It Works

Cache key = hash(model + messages + non-stream params). The full ChatCompletion response object is serialised and replayed on hit. The stream parameter is excluded from the key so streaming vs non-streaming calls share the same cache entry.

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API Reference

Method	Description
chat_create(**kwargs)	Cached client.chat.completions.create() (sync)
achat_create(**kwargs)	Cached client.chat.completions.create() (async)
invalidate_model(model)	Invalidate all cached responses for a model