import hashlib
import json
import warnings
from typing import Any, AsyncGenerator, List, Mapping, Optional, Sequence, Union, cast
from autogen_core import CacheStore, CancellationToken
from autogen_core.models import (
ChatCompletionClient,
CreateResult,
LLMMessage,
ModelCapabilities, # type: ignore
ModelInfo,
RequestUsage,
)
from autogen_core.tools import Tool, ToolSchema
CHAT_CACHE_VALUE_TYPE = Union[CreateResult, List[Union[str, CreateResult]]]
[docs]
class ChatCompletionCache(ChatCompletionClient):
"""
A wrapper around a :class:`~autogen_ext.models.cache.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:
.. code-block:: bash
pip install -U "autogen-ext[openai, diskcache]"
And use it as:
.. code-block:: python
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.
Args:
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).
"""
def __init__(self, client: ChatCompletionClient, store: CacheStore[CHAT_CACHE_VALUE_TYPE]):
self.client = client
self.store = store
def _check_cache(
self,
messages: Sequence[LLMMessage],
tools: Sequence[Tool | ToolSchema],
json_output: Optional[bool],
extra_create_args: Mapping[str, Any],
) -> tuple[Optional[Union[CreateResult, List[Union[str, CreateResult]]]], str]:
"""
Helper function to check the cache for a result.
Returns a tuple of (cached_result, cache_key).
"""
data = {
"messages": [message.model_dump() for message in messages],
"tools": [(tool.schema if isinstance(tool, Tool) else tool) for tool in tools],
"json_output": json_output,
"extra_create_args": extra_create_args,
}
serialized_data = json.dumps(data, sort_keys=True)
cache_key = hashlib.sha256(serialized_data.encode()).hexdigest()
cached_result = cast(Optional[CreateResult], self.store.get(cache_key))
if cached_result is not None:
return cached_result, cache_key
return None, cache_key
[docs]
async def create(
self,
messages: Sequence[LLMMessage],
*,
tools: Sequence[Tool | ToolSchema] = [],
json_output: Optional[bool] = None,
extra_create_args: Mapping[str, Any] = {},
cancellation_token: Optional[CancellationToken] = 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.
"""
cached_result, cache_key = self._check_cache(messages, tools, json_output, extra_create_args)
if cached_result:
assert isinstance(cached_result, CreateResult)
cached_result.cached = True
return cached_result
result = await self.client.create(
messages,
tools=tools,
json_output=json_output,
extra_create_args=extra_create_args,
cancellation_token=cancellation_token,
)
self.store.set(cache_key, result)
return result
[docs]
def create_stream(
self,
messages: Sequence[LLMMessage],
*,
tools: Sequence[Tool | ToolSchema] = [],
json_output: Optional[bool] = None,
extra_create_args: Mapping[str, Any] = {},
cancellation_token: Optional[CancellationToken] = None,
) -> AsyncGenerator[Union[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.
"""
async def _generator() -> AsyncGenerator[Union[str, CreateResult], None]:
cached_result, cache_key = self._check_cache(
messages,
tools,
json_output,
extra_create_args,
)
if cached_result:
assert isinstance(cached_result, list)
for result in cached_result:
if isinstance(result, CreateResult):
result.cached = True
yield result
return
result_stream = self.client.create_stream(
messages,
tools=tools,
json_output=json_output,
extra_create_args=extra_create_args,
cancellation_token=cancellation_token,
)
output_results: List[Union[str, CreateResult]] = []
self.store.set(cache_key, output_results)
async for result in result_stream:
output_results.append(result)
yield result
return _generator()
[docs]
def actual_usage(self) -> RequestUsage:
return self.client.actual_usage()
[docs]
def count_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[Tool | ToolSchema] = []) -> int:
return self.client.count_tokens(messages, tools=tools)
@property
def capabilities(self) -> ModelCapabilities: # type: ignore
warnings.warn("capabilities is deprecated, use model_info instead", DeprecationWarning, stacklevel=2)
return self.client.capabilities
@property
def model_info(self) -> ModelInfo:
return self.client.model_info
[docs]
def remaining_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[Tool | ToolSchema] = []) -> int:
return self.client.remaining_tokens(messages, tools=tools)
[docs]
def total_usage(self) -> RequestUsage:
return self.client.total_usage()
