seed -> cache_seed (#600)

autogen/agentchat/groupchat.py

@@ -120,7 +120,6 @@ def __init__(
         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 @@ def __init__(
         # 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,

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},
 ]
 
 

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 @@ def create(self, **config):
                 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 @@ def yes_or_no_filter(context, response):
             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 @@ def yes_or_no_filter(context, response):
                     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

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 @@ def set_cache(cls, seed: Optional[int] = 41, cache_path_root: Optional[str] = ".
             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 @@ def clear_cache(cls, seed: Optional[int] = None, cache_path_root: Optional[str]
             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 @@ def yes_or_no_filter(context, config, response):
                 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 @@ def yes_or_no_filter(context, config, response):
             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

autogen/version.py

@@ -1 +1 @@
-__version__ = "0.2.0b3"
+__version__ = "0.2.0b4"

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",

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": {

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",
     ")"
    ]
   },

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",

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",

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",

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",

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",

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",

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",

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",

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",