Mark cache as a protocol and update type hints to reflect

autogen/agentchat/chat.py

@@ -150,7 +150,7 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
             - "recipient": the recipient agent.
             - "clear_history" (bool): whether to clear the chat history with the agent. Default is True.
             - "silent" (bool or None): (Experimental) whether to print the messages in this conversation. Default is False.
-            - "cache" (Cache or None): the cache client to use for this conversation. Default is None.
+            - "cache" (AbstractCache or None): the cache client to use for this conversation. Default is None.
             - "max_turns" (int or None): maximum number of turns for the chat. If None, the chat will continue until a termination condition is met. Default is None.
             - "summary_method" (str or callable): a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
             - "summary_args" (dict): a dictionary of arguments to be passed to the summary_method. Default is {}.

autogen/agentchat/contrib/capabilities/generate_images.py

@@ -5,7 +5,7 @@
 from PIL.Image import Image
 
 from autogen import Agent, ConversableAgent, code_utils
-from autogen.cache import Cache
+from autogen.cache import AbstractCache
 from autogen.agentchat.contrib import img_utils
 from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
@@ -142,7 +142,7 @@ class ImageGeneration(AgentCapability):
     def __init__(
         self,
         image_generator: ImageGenerator,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         text_analyzer_llm_config: Optional[Dict] = None,
         text_analyzer_instructions: str = PROMPT_INSTRUCTIONS,
         verbosity: int = 0,
@@ -151,7 +151,7 @@ def __init__(
         """
         Args:
             image_generator (ImageGenerator): The image generator you would like to use to generate images.
-            cache (None or Cache): The cache client to use to store and retrieve generated images. If None,
+            cache (None or AbstractCache): The cache client to use to store and retrieve generated images. If None,
                 no caching will be used.
             text_analyzer_llm_config (Dict or None): The LLM config for the text analyzer. If None, the LLM config will
                 be retrieved from the agent you're adding the ability to.

autogen/agentchat/conversable_agent.py

@@ -15,7 +15,7 @@
 from autogen.exception_utils import InvalidCarryOverType, SenderRequired
 
 from .._pydantic import model_dump
-from ..cache.cache import Cache
+from ..cache.cache import AbstractCache
 from ..code_utils import (
     UNKNOWN,
     check_can_use_docker_or_throw,
@@ -865,7 +865,7 @@ def initiate_chat(
         recipient: "ConversableAgent",
         clear_history: bool = True,
         silent: Optional[bool] = False,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         max_turns: Optional[int] = None,
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
@@ -882,7 +882,7 @@ def initiate_chat(
             recipient: the recipient agent.
             clear_history (bool): whether to clear the chat history with the agent. Default is True.
             silent (bool or None): (Experimental) whether to print the messages for this conversation. Default is False.
-            cache (Cache or None): the cache client to be used for this conversation. Default is None.
+            cache (AbstractCache or None): the cache client to be used for this conversation. Default is None.
             max_turns (int or None): the maximum number of turns for the chat between the two agents. One turn means one conversation round trip. Note that this is different from
                 [max_consecutive_auto_reply](#max_consecutive_auto_reply) which is the maximum number of consecutive auto replies; and it is also different from [max_rounds in GroupChat](./groupchat#groupchat-objects) which is the maximum number of rounds in a group chat session.
                 If max_turns is set to None, the chat will continue until a termination condition is met. Default is None.
@@ -1007,7 +1007,7 @@ async def a_initiate_chat(
         recipient: "ConversableAgent",
         clear_history: bool = True,
         silent: Optional[bool] = False,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         max_turns: Optional[int] = None,
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
@@ -1073,7 +1073,7 @@ def _summarize_chat(
         summary_method,
         summary_args,
         recipient: Optional[Agent] = None,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
     ) -> str:
         """Get a chat summary from an agent participating in a chat.
 
@@ -1141,15 +1141,15 @@ def _relfection_with_llm_as_summary(sender, recipient, summary_args):
         return summary
 
     def _reflection_with_llm(
-        self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[Cache] = None
+        self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[AbstractCache] = None
     ) -> str:
         """Get a chat summary using reflection with an llm client based on the conversation history.
 
         Args:
             prompt (str): The prompt (in this method it is used as system prompt) used to get the summary.
             messages (list): The messages generated as part of a chat conversation.
             llm_agent: the agent with an llm client.
-            cache (Cache or None): the cache client to be used for this conversation.
+            cache (AbstractCache or None): the cache client to be used for this conversation.
         """
         system_msg = [
             {

autogen/cache/__init__.py

@@ -1,3 +1,3 @@
-from .cache import Cache
+from .cache import Cache, AbstractCache
 
-__all__ = ["Cache"]
+__all__ = ["Cache", "AbstractCache"]

autogen/cache/abstract_cache_base.py

@@ -1,6 +1,5 @@
-from abc import ABC, abstractmethod
 from types import TracebackType
-from typing import Any, Optional, Type
+from typing import Any, Optional, Protocol, Type
 import sys
 
 if sys.version_info >= (3, 11):
@@ -9,77 +8,53 @@
     from typing_extensions import Self
 
 
-class AbstractCache(ABC):
+class AbstractCache(Protocol):
     """
-    Abstract base class for cache implementations.
-
-    This class defines the basic interface for cache operations.
+    This protocol defines the basic interface for cache operations.
     Implementing classes should provide concrete implementations for
     these methods to handle caching mechanisms.
     """
 
-    @abstractmethod
     def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]:
         """
         Retrieve an item from the cache.
 
-        Abstract method that must be implemented by subclasses to
-        retrieve an item from the cache.
-
         Args:
             key (str): The key identifying the item in the cache.
             default (optional): The default value to return if the key is not found.
                                 Defaults to None.
 
         Returns:
             The value associated with the key if found, else the default value.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def set(self, key: str, value: Any) -> None:
         """
         Set an item in the cache.
 
-        Abstract method that must be implemented by subclasses to
-        store an item in the cache.
-
         Args:
             key (str): The key under which the item is to be stored.
             value: The value to be stored in the cache.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def close(self) -> None:
         """
-        Close the cache.
-
-        Abstract method that should be implemented by subclasses to
-        perform any necessary cleanup, such as closing network connections or
+        Close the cache. Perform any necessary cleanup, such as closing network connections or
         releasing resources.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def __enter__(self) -> Self:
         """
         Enter the runtime context related to this object.
 
-        The with statement will bind this method’s return value to the target(s)
+        The with statement will bind this method's return value to the target(s)
         specified in the as clause of the statement, if any.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def __exit__(
         self,
         exc_type: Optional[Type[BaseException]],
@@ -89,15 +64,9 @@ def __exit__(
         """
         Exit the runtime context and close the cache.
 
-        Abstract method that should be implemented by subclasses to handle
-        the exit from a with statement. It is responsible for resource
-        release and cleanup.
-
         Args:
             exc_type: The exception type if an exception was raised in the context.
             exc_value: The exception value if an exception was raised in the context.
             traceback: The traceback if an exception was raised in the context.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...

autogen/cache/cache.py

@@ -14,7 +14,7 @@
     from typing_extensions import Self
 
 
-class Cache:
+class Cache(AbstractCache):
     """
     A wrapper class for managing cache configuration and instances.
 

autogen/oai/__init__.py

@@ -9,7 +9,6 @@
     config_list_from_dotenv,
     filter_config,
 )
-from autogen.cache.cache import Cache
 
 __all__ = [
     "OpenAIWrapper",
@@ -23,5 +22,4 @@
     "config_list_from_json",
     "config_list_from_dotenv",
     "filter_config",
-    "Cache",
 ]

autogen/oai/client.py

@@ -10,7 +10,7 @@
 from pydantic import BaseModel
 from typing import Protocol
 
-from autogen.cache.cache import Cache
+from autogen.cache import Cache
 from autogen.io.base import IOStream
 from autogen.oai.openai_utils import get_key, is_valid_api_key, OAI_PRICE1K
 from autogen.token_count_utils import count_token
@@ -517,7 +517,7 @@ def create(self, **config: Any) -> ModelClient.ModelClientResponseProtocol:
                 The actual prompt will be:
                 "Complete the following sentence: Today I feel".
                 More examples can be found at [templating](/docs/Use-Cases/enhanced_inference#templating).
-            - cache (Cache | None): A Cache object to use for response cache. Default to None.
+            - cache (AbstractCache | None): A Cache object to use for response cache. Default to None.
                 Note that the cache argument overrides the legacy cache_seed argument: if this argument is provided,
                 then the cache_seed argument is ignored. If this argument is not provided or None,
                 then the cache_seed argument is used.

test/agentchat/contrib/test_web_surfer.py

@@ -6,7 +6,6 @@
 import pytest
 from autogen import UserProxyAgent, config_list_from_json
 from autogen.oai.openai_utils import filter_config
-from autogen.cache import Cache
 
 sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
 from conftest import MOCK_OPEN_AI_API_KEY, skip_openai  # noqa: E402