seed -> cache_seed (#600)

2026-04-20 03:02:16 -04:00 · 2023-11-08 15:39:02 -08:00
parent 2a96e4d9d2
commit dfcbea9777
29 changed files with 87 additions and 79 deletions
--- a/autogen/agentchat/groupchat.py
+++ b/autogen/agentchat/groupchat.py
@@ -120,7 +120,6 @@ class GroupChatManager(ConversableAgent):
        max_consecutive_auto_reply: Optional[int] = sys.maxsize,
        human_input_mode: Optional[str] = "NEVER",
        system_message: Optional[str] = "Group chat manager.",
-        # seed: Optional[int] = 4,
        **kwargs,
    ):
        super().__init__(
@@ -136,8 +135,6 @@ class GroupChatManager(ConversableAgent):
        # Allow async chat if initiated using a_initiate_chat
        self.register_reply(Agent, GroupChatManager.a_run_chat, config=groupchat, reset_config=GroupChat.reset)

-        # self._random = random.Random(seed)
-
    def run_chat(
        self,
        messages: Optional[List[Dict]] = None,
--- a/autogen/code_utils.py
+++ b/autogen/code_utils.py
@@ -509,11 +509,11 @@ def eval_function_completions(
 _FUNC_COMPLETION_PROMPT = "# Python 3{definition}"
 _FUNC_COMPLETION_STOP = ["\nclass", "\ndef", "\nif", "\nprint"]
 _IMPLEMENT_CONFIGS = [
-    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "seed": 0},
-    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 7, "seed": 0},
-    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "seed": 1},
-    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 2, "seed": 2},
-    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 1, "seed": 2},
+    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "cache_seed": 0},
+    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 7, "cache_seed": 0},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "cache_seed": 1},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 2, "cache_seed": 2},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 1, "cache_seed": 2},
 ]


--- a/autogen/oai/client.py
+++ b/autogen/oai/client.py
@@ -31,7 +31,7 @@ class OpenAIWrapper:
    """A wrapper class for openai client."""

    cache_path_root: str = ".cache"
-    extra_kwargs = {"seed", "filter_func", "allow_format_str_template", "context", "api_version"}
+    extra_kwargs = {"cache_seed", "filter_func", "allow_format_str_template", "context", "api_version"}
    openai_kwargs = set(inspect.getfullargspec(OpenAI.__init__).kwonlyargs)

    def __init__(self, *, config_list: List[Dict] = None, **base_config):
@@ -191,8 +191,8 @@ class OpenAIWrapper:
                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).
-            - `seed` (int | None) for the cache. Default to 41.
-                An integer seed is useful when implementing "controlled randomness" for the completion.
+            - `cache_seed` (int | None) for the cache. Default to 41.
+                An integer cache_seed is useful when implementing "controlled randomness" for the completion.
                None for no caching.
            - filter_func (Callable | None): A function that takes in the context and the response
                and returns a boolean to indicate whether the response is valid. E.g.,
@@ -219,12 +219,12 @@ class OpenAIWrapper:
            self._process_for_azure(create_config, extra_kwargs, "extra")
            # construct the create params
            params = self._construct_create_params(create_config, extra_kwargs)
-            # get the seed, filter_func and context
-            seed = extra_kwargs.get("seed", 41)
+            # get the cache_seed, filter_func and context
+            cache_seed = extra_kwargs.get("cache_seed", 41)
            filter_func = extra_kwargs.get("filter_func")
            context = extra_kwargs.get("context")
-            with diskcache.Cache(f"{self.cache_path_root}/{seed}") as cache:
-                if seed is not None:
+            with diskcache.Cache(f"{self.cache_path_root}/{cache_seed}") as cache:
+                if cache_seed is not None:
                    # Try to get the response from cache
                    key = get_key(params)
                    response = cache.get(key, None)
@@ -245,7 +245,7 @@ class OpenAIWrapper:
                    if i == last:
                        raise
                else:
-                    if seed is not None:
+                    if cache_seed is not None:
                        # Cache the response
                        cache.set(key, response)
                    return response
--- a/autogen/oai/completion.py
+++ b/autogen/oai/completion.py
@@ -109,8 +109,8 @@ class Completion(openai_Completion):
        "prompt": "{prompt}",
    }

-    seed = 41
-    cache_path = f".cache/{seed}"
+    cache_seed = 41
+    cache_path = f".cache/{cache_seed}"
    # retry after this many seconds
    retry_wait_time = 10
    # fail a request after hitting RateLimitError for this many seconds
@@ -134,7 +134,7 @@ class Completion(openai_Completion):
            cache_path (str, Optional): The root path for the cache.
                The complete cache path will be {cache_path}/{seed}.
        """
-        cls.seed = seed
+        cls.cache_seed = seed
        cls.cache_path = f"{cache_path_root}/{seed}"

    @classmethod
@@ -145,7 +145,7 @@ class Completion(openai_Completion):
            seed (int, Optional): The integer identifier for the pseudo seed.
                If omitted, all caches under cache_path_root will be cleared.
            cache_path (str, Optional): The root path for the cache.
-                The complete cache path will be {cache_path}/{seed}.
+                The complete cache path will be {cache_path}/{cache_seed}.
        """
        if seed is None:
            shutil.rmtree(cache_path_root, ignore_errors=True)
@@ -773,7 +773,7 @@ class Completion(openai_Completion):
                Besides the parameters for the openai API call, it can also contain:
                - `max_retry_period` (int): the total time (in seconds) allowed for retrying failed requests.
                - `retry_wait_time` (int): the time interval to wait (in seconds) before retrying a failed request.
-                - `seed` (int) for the cache. This is useful when implementing "controlled randomness" for the completion.
+                - `cache_seed` (int) for the cache. This is useful when implementing "controlled randomness" for the completion.

        Returns:
            Responses from OpenAI API, with additional fields.
@@ -831,11 +831,11 @@ class Completion(openai_Completion):
            return cls._get_response(
                params, raise_on_ratelimit_or_timeout=raise_on_ratelimit_or_timeout, use_cache=False
            )
-        seed = cls.seed
-        if "seed" in params:
-            cls.set_cache(params.pop("seed"))
+        cache_seed = cls.cache_seed
+        if "cache_seed" in params:
+            cls.set_cache(params.pop("cache_seed"))
        with diskcache.Cache(cls.cache_path) as cls._cache:
-            cls.set_cache(seed)
+            cls.set_cache(cache_seed)
            return cls._get_response(params, raise_on_ratelimit_or_timeout=raise_on_ratelimit_or_timeout)

    @classmethod
--- a/autogen/version.py
+++ b/autogen/version.py
@@ -1 +1 @@
-__version__ = "0.2.0b3"
+__version__ = "0.2.0b4"
--- a/notebook/agentchat_RetrieveChat.ipynb
+++ b/notebook/agentchat_RetrieveChat.ipynb
@@ -185,7 +185,7 @@
    "    system_message=\"You are a helpful assistant.\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": config_list,\n",
    "    },\n",
    ")\n",
--- a/notebook/agentchat_auto_feedback_from_code_execution.ipynb
+++ b/notebook/agentchat_auto_feedback_from_code_execution.ipynb
@@ -45,7 +45,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -330,7 +330,7 @@
    "assistant = autogen.AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    llm_config={\n",
-    "        \"seed\": 42,  # seed for caching and reproducibility\n",
+    "        \"cache_seed\": 42,  # seed for caching and reproducibility\n",
    "        \"config_list\": config_list,  # a list of OpenAI API configurations\n",
    "        \"temperature\": 0,  # temperature for sampling\n",
    "    },  # configuration for autogen's enhanced inference API which is compatible with OpenAI API\n",
@@ -806,7 +806,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
  },
  "vscode": {
   "interpreter": {
--- a/notebook/agentchat_chess.ipynb
+++ b/notebook/agentchat_chess.ipynb
@@ -35,7 +35,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "# %pip install \"pyautogen~=0.2.0b2\"\n",
+    "# %pip install \"pyautogen~=0.2.0b4\"\n",
    "%pip install chess -U"
   ]
  },
@@ -292,13 +292,13 @@
    "    color=\"black\",\n",
    "    board_agent=board_agent,\n",
    "    max_turns=max_turn,\n",
-    "    llm_config={\"temperature\": 0.5, \"seed\": 1, \"config_list\": config_list_gpt4},\n",
+    "    llm_config={\"temperature\": 0.5, \"cache_seed\": 1, \"config_list\": config_list_gpt4},\n",
    ")\n",
    "player_white = ChessPlayerAgent(\n",
    "    color=\"white\",\n",
    "    board_agent=board_agent,\n",
    "    max_turns=max_turn,\n",
-    "    llm_config={\"temperature\": 0.5, \"seed\": 2, \"config_list\": config_list_gpt4},\n",
+    "    llm_config={\"temperature\": 0.5, \"cache_seed\": 2, \"config_list\": config_list_gpt4},\n",
    ")"
   ]
  },
--- a/notebook/agentchat_groupchat.ipynb
+++ b/notebook/agentchat_groupchat.ipynb
@@ -35,7 +35,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -124,7 +124,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n",
+    "llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "   name=\"User_proxy\",\n",
    "   system_message=\"A human admin.\",\n",
--- a/notebook/agentchat_groupchat_research.ipynb
+++ b/notebook/agentchat_groupchat_research.ipynb
@@ -33,7 +33,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -111,7 +111,7 @@
   "outputs": [],
   "source": [
    "gpt4_config = {\n",
-    "    \"seed\": 42,  # change the seed for different trials\n",
+    "    \"cache_seed\": 42,  # change the cache_seed for different trials\n",
    "    \"temperature\": 0,\n",
    "    \"config_list\": config_list_gpt4,\n",
    "    \"timeout\": 120,\n",
--- a/notebook/agentchat_groupchat_vis.ipynb
+++ b/notebook/agentchat_groupchat_vis.ipynb
@@ -33,7 +33,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -132,7 +132,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n",
+    "llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "   name=\"User_proxy\",\n",
    "   system_message=\"A human admin.\",\n",
--- a/notebook/agentchat_hierarchy_flow_using_select_speaker.ipynb
+++ b/notebook/agentchat_hierarchy_flow_using_select_speaker.ipynb
@@ -50,7 +50,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "# %pip install pyautogen~=0.1.0"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -163,7 +163,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}"
+    "llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}"
   ]
  },
  {
@@ -359,7 +359,7 @@
    "\n",
    "\n",
    "# Create the manager\n",
-    "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42, \"use_cache\":False}  # use_cache is False because we want to observe if there is any communication pattern difference if we reran the group chat.\n",
+    "llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": None}  # cache_seed is None because we want to observe if there is any communication pattern difference if we reran the group chat.\n",
    "manager = autogen.GroupChatManager(groupchat=group_chat, llm_config=llm_config)\n",
    "\n",
    "\n",
--- a/notebook/agentchat_human_feedback.ipynb
+++ b/notebook/agentchat_human_feedback.ipynb
@@ -45,7 +45,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -123,7 +123,7 @@
    "assistant = autogen.AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    llm_config={\n",
-    "        \"seed\": 41,\n",
+    "        \"cache_seed\": 41,\n",
    "        \"config_list\": config_list,\n",
    "    }\n",
    ")\n",
--- a/notebook/agentchat_lmm_gpt-4v.ipynb
+++ b/notebook/agentchat_lmm_gpt-4v.ipynb
@@ -15,7 +15,7 @@
   "source": [
    "### Before everything starts, install AutoGen with the `lmm` option\n",
    "```bash\n",
-    "pip install pyautogen[lmm]\n",
+    "pip install \"pyautogen[lmm]~=0.2.0b4\"\n",
    "```"
   ]
  },
@@ -85,7 +85,7 @@
    "    },\n",
    ")\n",
    "\n",
-    "gpt4_llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}"
+    "gpt4_llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}"
   ]
  },
  {
@@ -699,7 +699,7 @@
    "#     },\n",
    "# )\n",
    "\n",
-    "# gpt35_llm_config = {\"config_list\": config_list_gpt35, \"seed\": 42}\n",
+    "# gpt35_llm_config = {\"config_list\": config_list_gpt35, \"cache_seed\": 42}\n",
    "\n",
    "\n",
    "creator = FigureCreator(\n",
--- a/notebook/agentchat_lmm_llava.ipynb
+++ b/notebook/agentchat_lmm_llava.ipynb
@@ -5,9 +5,9 @@
   "id": "2c75da30",
   "metadata": {},
   "source": [
-    "# Agent Chat with Multimodal Models\n",
+    "# Agent Chat with Multimodal Models: LLaVA\n",
    "\n",
-    "We use **LLaVA** as an example for the multimodal feature.  More information about LLaVA can be found in their [GitHub page](https://github.com/haotian-liu/LLaVA)\n",
+    "This notebook uses **LLaVA** as an example for the multimodal feature. More information about LLaVA can be found in their [GitHub page](https://github.com/haotian-liu/LLaVA)\n",
    "\n",
    "\n",
    "This notebook contains the following information and examples:\n",
@@ -26,7 +26,7 @@
   "source": [
    "### Before everything starts, install AutoGen with the `lmm` option\n",
    "```bash\n",
-    "pip install pyautogen[lmm]\n",
+    "pip install \"pyautogen[lmm]~=0.2.0b4\"\n",
    "```"
   ]
  },
@@ -848,7 +848,7 @@
    "    },\n",
    ")\n",
    "\n",
-    "gpt4_llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n",
+    "gpt4_llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}\n",
    "\n",
    "# config_list_gpt35 = autogen.config_list_from_json(\n",
    "#     \"OAI_CONFIG_LIST\",\n",
@@ -857,7 +857,7 @@
    "#     },\n",
    "# )\n",
    "\n",
-    "# gpt35_llm_config = {\"config_list\": config_list_gpt35, \"seed\": 42}\n",
+    "# gpt35_llm_config = {\"config_list\": config_list_gpt35, \"cache_seed\": 42}\n",
    "\n",
    "\n",
    "creator = FigureCreator(\n",
--- a/notebook/agentchat_planning.ipynb
+++ b/notebook/agentchat_planning.ipynb
@@ -45,7 +45,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2 docker"
+    "# %pip install pyautogen~=0.2.0b4 docker"
   ]
  },
  {
@@ -156,7 +156,7 @@
    "    llm_config={\n",
    "        \"temperature\": 0,\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": config_list,\n",
    "        \"functions\": [\n",
    "            {\n",
--- a/notebook/agentchat_stream.ipynb
+++ b/notebook/agentchat_stream.ipynb
@@ -45,7 +45,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -209,7 +209,7 @@
    "    name=\"assistant\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 41,\n",
+    "        \"cache_seed\": 41,\n",
    "        \"config_list\": config_list,\n",
    "        \"temperature\": 0,\n",
    "    },\n",
--- a/notebook/agentchat_two_users.ipynb
+++ b/notebook/agentchat_two_users.ipynb
@@ -44,7 +44,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2"
+    "# %pip install pyautogen~=0.2.0b4"
   ]
  },
  {
@@ -161,7 +161,7 @@
    "    system_message=\"You are a helpful assistant. Reply TERMINATE when the task is done.\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": config_list,\n",
    "        \"temperature\": 0,\n",
    "        \"functions\": [\n",
--- a/notebook/agentchat_web_info.ipynb
+++ b/notebook/agentchat_web_info.ipynb
@@ -49,7 +49,7 @@
   },
   "outputs": [],
   "source": [
-    "# %pip install pyautogen~=0.2.0b2 docker"
+    "# %pip install pyautogen~=0.2.0b4 docker"
   ]
  },
  {
@@ -79,7 +79,7 @@
    "\n",
    "llm_config={\n",
    "    \"timeout\": 600,\n",
-    "    \"seed\": 42,\n",
+    "    \"cache_seed\": 42,\n",
    "    \"config_list\": config_list,\n",
    "    \"temperature\": 0,\n",
    "}"
--- a/notebook/oai_openai_utils.ipynb
+++ b/notebook/oai_openai_utils.ipynb
@@ -39,7 +39,7 @@
    "    name=\"assistant\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": config_list,\n",
    "        \"temperature\": 0,\n",
    "    },\n",
@@ -60,7 +60,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "# ! pip install pyautogen"
+    "# %pip install \"pyautogen~=0.2.0b4\""
   ]
  },
  {
@@ -227,7 +227,7 @@
    "    name=\"3.5-assistant\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": cheap_config_list,\n",
    "        \"temperature\": 0,\n",
    "    },\n",
@@ -238,7 +238,7 @@
    "    name=\"4-assistant\",\n",
    "    llm_config={\n",
    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
+    "        \"cache_seed\": 42,\n",
    "        \"config_list\": costly_config_list,\n",
    "        \"temperature\": 0,\n",
    "    },\n",
--- a/test/agentchat/test_assistant_agent.py
+++ b/test/agentchat/test_assistant_agent.py
@@ -31,7 +31,7 @@ def test_ai_user_proxy_agent():
        system_message="You are a helpful assistant.",
        llm_config={
            "timeout": 600,
-            "seed": 42,
+            "cache_seed": 42,
            "config_list": config_list,
        },
    )
@@ -78,7 +78,7 @@ def test_gpt35(human_input_mode="NEVER", max_consecutive_auto_reply=5):
        },
    )
    llm_config = {
-        "seed": 42,
+        "cache_seed": 42,
        "config_list": config_list,
        "max_tokens": 1024,
    }
@@ -125,7 +125,7 @@ def test_create_execute_script(human_input_mode="NEVER", max_consecutive_auto_re
    # autogen.ChatCompletion.start_logging(conversations)
    llm_config = {
        "timeout": 600,
-        "seed": 42,
+        "cache_seed": 42,
        "config_list": config_list,
    }
    assistant = AssistantAgent(
--- a/test/agentchat/test_async.py
+++ b/test/agentchat/test_async.py
@@ -71,7 +71,7 @@ async def test_stream():
        name="assistant",
        llm_config={
            "timeout": 600,
-            "seed": 41,
+            "cache_seed": 41,
            "config_list": config_list,
            "temperature": 0,
        },
--- a/test/agentchat/test_async_get_human_input.py
+++ b/test/agentchat/test_async_get_human_input.py
@@ -16,7 +16,7 @@ async def test_async_get_human_input():
    assistant = autogen.AssistantAgent(
        name="assistant",
        max_consecutive_auto_reply=2,
-        llm_config={"timeout": 600, "seed": 41, "config_list": config_list, "temperature": 0},
+        llm_config={"timeout": 600, "cache_seed": 41, "config_list": config_list, "temperature": 0},
    )

    user_proxy = autogen.UserProxyAgent(name="user", human_input_mode="ALWAYS", code_execution_config=False)
--- a/test/agentchat/test_function_call_groupchat.py
+++ b/test/agentchat/test_function_call_groupchat.py
@@ -30,7 +30,7 @@ def test_function_call_groupchat():
    )
    llm_config = {
        "config_list": config_list_gpt4,
-        "seed": 42,
+        "cache_seed": 42,
        "functions": [
            {
                "name": "get_random_number",
--- a/test/agentchat/test_math_user_proxy_agent.py
+++ b/test/agentchat/test_math_user_proxy_agent.py
@@ -38,7 +38,7 @@ def test_math_user_proxy_agent():
        system_message="You are a helpful assistant.",
        llm_config={
            "timeout": 600,
-            "seed": 42,
+            "cache_seed": 42,
            "config_list": config_list,
        },
    )
--- a/test/agentchat/test_teachable_agent.py
+++ b/test/agentchat/test_teachable_agent.py
@@ -25,7 +25,7 @@ skill_verbosity = 3  # 0 for basic info, 1 to add memory operations, 2 for analy

 assert_on_error = False  # GPT-4 nearly always succeeds on these unit tests, but GPT-3.5 is a bit less reliable.
 recall_threshold = 1.5  # Higher numbers allow more (but less relevant) memos to be recalled.
-seed = None
+cache_seed = None
 # If int, cached LLM calls will be skipped and responses pulled from cache. None exposes LLM non-determinism.

 # Specify the model to use by uncommenting one of the following lines.
@@ -43,7 +43,7 @@ def create_teachable_agent(reset_db=False, verbosity=0):
    config_list = config_list_from_json(env_or_file=OAI_CONFIG_LIST, filter_dict=filter_dict, file_location=KEY_LOC)
    teachable_agent = TeachableAgent(
        name="teachableagent",
-        llm_config={"config_list": config_list, "timeout": 120, "seed": seed},
+        llm_config={"config_list": config_list, "timeout": 120, "cache_seed": cache_seed},
        teach_config={
            "verbosity": verbosity,
            "reset_db": reset_db,
--- a/test/oai/test_client.py
+++ b/test/oai/test_client.py
@@ -18,7 +18,11 @@ def test_aoai_chat_completion():
        filter_dict={"api_type": ["azure"], "model": ["gpt-3.5-turbo"]},
    )
    client = OpenAIWrapper(config_list=config_list)
-    response = client.create(messages=[{"role": "user", "content": "2+2="}])
+    # for config in config_list:
+    #     print(config)
+    #     client = OpenAIWrapper(**config)
+    #     response = client.create(messages=[{"role": "user", "content": "2+2="}], cache_seed=None)
+    response = client.create(messages=[{"role": "user", "content": "2+2="}], cache_seed=None)
    print(response)
    print(client.extract_text_or_function_call(response))

--- a/website/docs/Installation.md
+++ b/website/docs/Installation.md
@@ -65,7 +65,10 @@ print(client.extract_text_or_function_call(response))
 ```
 - Inference parameter tuning and inference logging features are currently unavailable in `OpenAIWrapper`. Logging will be added in a future release.
 Inference parameter tuning can be done via [`flaml.tune`](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
- `use_cache` is removed as a kwarg in `OpenAIWrapper.create()` for being automatically decided by `seed`: int | None.
+- `seed` in autogen is renamed into `cache_seed` to accommodate the newly added `seed` param in openai chat completion api. `use_cache` is removed as a kwarg in `OpenAIWrapper.create()` for being automatically decided by `cache_seed`: int | None. The difference between autogen's `cache_seed` and openai's `seed` is that:
+    * autogen uses local disk cache to guarantee the exactly same output is produced for the same input and when cache is hit, no openai api call will be made.
+    * openai's `seed` is a best-effort deterministic sampling with no guarantee of determinism. When using openai's `seed` with `cache_seed` set to None, even for the same input, an openai api call will be made and there is no guarantee for getting exactly the same output.
+

 ### Optional Dependencies
 - #### docker
--- a/website/docs/Use-Cases/enhanced_inference.md
+++ b/website/docs/Use-Cases/enhanced_inference.md
@@ -137,19 +137,23 @@ For local LLMs, one can spin up an endpoint using a package like [FastChat](http

 ## Caching

-API call results are cached locally and reused when the same request is issued. This is useful when repeating or continuing experiments for reproducibility and cost saving. It still allows controlled randomness by setting the "seed" specified in `OpenAIWrapper.create()` or the constructor of `OpenAIWrapper`.
+API call results are cached locally and reused when the same request is issued. This is useful when repeating or continuing experiments for reproducibility and cost saving. It still allows controlled randomness by setting the "cache_seed" specified in `OpenAIWrapper.create()` or the constructor of `OpenAIWrapper`.

 ```python
-client = OpenAIWrapper(seed=...)
+client = OpenAIWrapper(cache_seed=...)
 client.create(...)
 ```

 ```python
 client = OpenAIWrapper()
-client.create(seed=..., ...)
+client.create(cache_seed=..., ...)
 ```

-Caching is enabled by default with seed 41. To disable it please set `seed` to None.
+Caching is enabled by default with cache_seed 41. To disable it please set `cache_seed` to None.
+
+_NOTE_. openai v1.1 introduces a new param `seed`. The difference between autogen's `cache_seed` and openai's `seed` is that:
+* autogen uses local disk cache to guarantee the exactly same output is produced for the same input and when cache is hit, no openai api call will be made.
+* openai's `seed` is a best-effort deterministic sampling with no guarantee of determinism. When using openai's `seed` with `cache_seed` set to None, even for the same input, an openai api call will be made and there is no guarantee for getting exactly the same output.

 ## Error handling