autogen_ext.models.cache

class ChatCompletionCache(client: ChatCompletionClient, store: CacheStore[CreateResult | List[str | CreateResult]] | None = None)

Bases: ChatCompletionClient

A wrapper around a ChatCompletionClient that caches creation results from an underlying client. Cache hits do not contribute to token usage of the original client.

Typical Usage:

Lets use caching on disk with openai client as an example. First install autogen-ext with the required packages:

pip install -U "autogen-ext[openai, diskcache]"

And use it as:

import asyncio
import tempfile

from autogen_core.models import UserMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.cache import ChatCompletionCache, CHAT_CACHE_VALUE_TYPE
from autogen_ext.cache_store.diskcache import DiskCacheStore
from diskcache import Cache


async def main():
    with tempfile.TemporaryDirectory() as tmpdirname:
        # Initialize the original client
        openai_model_client = OpenAIChatCompletionClient(model="gpt-4o")

        # Then initialize the CacheStore, in this case with diskcache.Cache.
        # You can also use redis like:
        # from autogen_ext.cache_store.redis import RedisStore
        # import redis
        # redis_instance = redis.Redis()
        # cache_store = RedisCacheStore[CHAT_CACHE_VALUE_TYPE](redis_instance)
        cache_store = DiskCacheStore[CHAT_CACHE_VALUE_TYPE](Cache(tmpdirname))
        cache_client = ChatCompletionCache(openai_model_client, cache_store)

        response = await cache_client.create([UserMessage(content="Hello, how are you?", source="user")])
        print(response)  # Should print response from OpenAI
        response = await cache_client.create([UserMessage(content="Hello, how are you?", source="user")])
        print(response)  # Should print cached response


asyncio.run(main())

You can now use the cached_client as you would the original client, but with caching enabled.

Parameters:

• client (ChatCompletionClient) – The original ChatCompletionClient to wrap.

• store (CacheStore) – A store object that implements get and set methods. The user is responsible for managing the store’s lifecycle & clearing it (if needed). Defaults to using in-memory cache.

actual_usage() → RequestUsage

property capabilities: ModelCapabilities

count_tokens(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = []) → int

async create(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = [], json_output: bool | None = None, extra_create_args: Mapping[str, Any] = {}, cancellation_token: CancellationToken | None = None) → CreateResult

Cached version of ChatCompletionClient.create. If the result of a call to create has been cached, it will be returned immediately without invoking the underlying client.

NOTE: cancellation_token is ignored for cached results.

create_stream(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = [], json_output: bool | None = None, extra_create_args: Mapping[str, Any] = {}, cancellation_token: CancellationToken | None = None) → AsyncGenerator[str | CreateResult, None]

Cached version of ChatCompletionClient.create_stream. If the result of a call to create_stream has been cached, it will be returned without streaming from the underlying client.

NOTE: cancellation_token is ignored for cached results.

property model_info: ModelInfo

remaining_tokens(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = []) → int

total_usage() → RequestUsage