import hashlib
import json
import warnings
from typing import Any, AsyncGenerator, List, Mapping, Optional, Sequence, Union, cast
from autogen_core import CacheStore, CancellationToken, Component, ComponentModel, InMemoryStore
from autogen_core.models import (
ChatCompletionClient,
CreateResult,
LLMMessage,
ModelCapabilities, # type: ignore
ModelInfo,
RequestUsage,
)
from autogen_core.tools import Tool, ToolSchema
from pydantic import BaseModel
from typing_extensions import Self
CHAT_CACHE_VALUE_TYPE = Union[CreateResult, List[Union[str, CreateResult]]]
class ChatCompletionCacheConfig(BaseModel):
""" """
client: ComponentModel
store: Optional[ComponentModel] = None
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class ChatCompletionCache(ChatCompletionClient, Component[ChatCompletionCacheConfig]):
"""
对 :class:`~autogen_ext.models.cache.ChatCompletionClient` 的包装器,用于缓存底层客户端的创建结果。
缓存命中不会计入原始客户端的令牌使用量。
典型用法:
以使用 `openai` 客户端进行磁盘缓存为例。
首先安装带有必要包的 `autogen-ext`:
.. code-block:: bash
pip install -U "autogen-ext[openai, diskcache]"
并按如下方式使用:
.. 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:
# 初始化原始客户端
openai_model_client = OpenAIChatCompletionClient(model="gpt-4o")
# 然后初始化 CacheStore,这里使用 diskcache.Cache。
# 也可以使用 redis 例如:
# 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) # 应打印来自 OpenAI 的响应
response = await cache_client.create([UserMessage(content="Hello, how are you?", source="user")])
print(response) # 应打印缓存的响应
asyncio.run(main())
现在可以像使用原始客户端一样使用 `cached_client`,但启用了缓存功能。
Args:
client (ChatCompletionClient): 要包装的原始 ChatCompletionClient。
store (CacheStore): 实现 get 和 set 方法的存储对象。
用户需负责管理存储的生命周期及清理(如果需要)。
默认使用内存缓存。
"""
component_type = "chat_completion_cache"
component_provider_override = "autogen_ext.models.cache.ChatCompletionCache"
component_config_schema = ChatCompletionCacheConfig
def __init__(
self,
client: ChatCompletionClient,
store: Optional[CacheStore[CHAT_CACHE_VALUE_TYPE]] = None,
):
self.client = client
self.store = store or InMemoryStore[CHAT_CACHE_VALUE_TYPE]()
def _check_cache(
self,
messages: Sequence[LLMMessage],
tools: Sequence[Tool | ToolSchema],
json_output: Optional[bool | type[BaseModel]],
extra_create_args: Mapping[str, Any],
) -> tuple[Optional[Union[CreateResult, List[Union[str, CreateResult]]]], str]:
"""
用于检查缓存结果的辅助函数。
返回一个元组 (cached_result, cache_key)。
"""
json_output_data: str | bool | None = None
if isinstance(json_output, type) and issubclass(json_output, BaseModel):
json_output_data = json.dumps(json_output.model_json_schema())
elif isinstance(json_output, bool):
json_output_data = json_output
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_data,
"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
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async def create(
self,
messages: Sequence[LLMMessage],
*,
tools: Sequence[Tool | ToolSchema] = [],
json_output: Optional[bool | type[BaseModel]] = None,
extra_create_args: Mapping[str, Any] = {},
cancellation_token: Optional[CancellationToken] = None,
) -> CreateResult:
"""
ChatCompletionClient.create 的缓存版本。
如果 create 调用的结果已被缓存,将立即返回缓存结果
而不会调用底层客户端。
注意:对于缓存结果,cancellation_token 将被忽略。
"""
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
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def create_stream(
self,
messages: Sequence[LLMMessage],
*,
tools: Sequence[Tool | ToolSchema] = [],
json_output: Optional[bool | type[BaseModel]] = None,
extra_create_args: Mapping[str, Any] = {},
cancellation_token: Optional[CancellationToken] = None,
) -> AsyncGenerator[Union[str, CreateResult], None]:
"""
ChatCompletionClient.create_stream 的缓存版本。
如果调用 create_stream 的结果已被缓存,将直接返回缓存结果
而不会从底层客户端进行流式传输。
注意:对于缓存结果,cancellation_token 将被忽略。
"""
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()
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async def close(self) -> None:
await self.client.close()
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def actual_usage(self) -> RequestUsage:
return self.client.actual_usage()
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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
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def remaining_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[Tool | ToolSchema] = []) -> int:
return self.client.remaining_tokens(messages, tools=tools)
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def total_usage(self) -> RequestUsage:
return self.client.total_usage()
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def _to_config(self) -> ChatCompletionCacheConfig:
return ChatCompletionCacheConfig(
client=self.client.dump_component(),
store=self.store.dump_component() if not isinstance(self.store, InMemoryStore) else None,
)
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@classmethod
def _from_config(cls, config: ChatCompletionCacheConfig) -> Self:
client = ChatCompletionClient.load_component(config.client)
store: Optional[CacheStore[CHAT_CACHE_VALUE_TYPE]] = (
CacheStore.load_component(config.store) if config.store else InMemoryStore()
)
return cls(client=client, store=store)