support llm_config in AgentOptimizer

autogen/agentchat/contrib/agent_optimizer.py

@@ -1,6 +1,6 @@
 import copy
 import json
-from typing import Dict, List, Optional
+from typing import Dict, List, Literal, Optional, Union
 
 import autogen
 from autogen.code_utils import execute_code
@@ -172,16 +172,16 @@ class AgentOptimizer:
     def __init__(
         self,
         max_actions_per_step: int,
-        config_file_or_env: Optional[str] = "OAI_CONFIG_LIST",
-        config_file_location: Optional[str] = "",
+        llm_config: dict,
         optimizer_model: Optional[str] = "gpt-4-1106-preview",
     ):
         """
         (These APIs are experimental and may change in the future.)
         Args:
             max_actions_per_step (int): the maximum number of actions that the optimizer can take in one step.
-            config_file_or_env: path or environment of the OpenAI api configs.
-            config_file_location: the location of the OpenAI config file.
+            llm_config (dict): llm inference configuration.
+                Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) for available options.
+                When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `llm_config` or in each config of 'config_list' in `llm_config`.
             optimizer_model: the model used for the optimizer.
         """
         self.max_actions_per_step = max_actions_per_step
@@ -199,14 +199,17 @@ def __init__(
         self._failure_functions_performance = []
         self._best_performance = -1
 
-        config_list = autogen.config_list_from_json(
-            config_file_or_env,
-            file_location=config_file_location,
-            filter_dict={"model": [self.optimizer_model]},
+        assert isinstance(llm_config, dict), "llm_config must be a dict"
+        llm_config = copy.deepcopy(llm_config)
+        self.llm_config = llm_config
+        if self.llm_config in [{}, {"config_list": []}, {"config_list": [{"model": ""}]}]:
+            raise ValueError(
+                "When using OpenAI or Azure OpenAI endpoints, specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'."
+            )
+        self.llm_config["config_list"] = autogen.filter_config(
+            llm_config["config_list"], {"model": [self.optimizer_model]}
         )
-        if len(config_list) == 0:
-            raise RuntimeError("No valid openai config found in the config file or environment variable.")
-        self._client = autogen.OpenAIWrapper(config_list=config_list)
+        self._client = autogen.OpenAIWrapper(**self.llm_config)
 
     def record_one_conversation(self, conversation_history: List[Dict], is_satisfied: bool = None):
         """
@@ -266,7 +269,7 @@ def step(self):
                 actions_num=action_index,
                 best_functions=best_functions,
                 incumbent_functions=incumbent_functions,
-                accumerated_experience=failure_experience_prompt,
+                accumulated_experience=failure_experience_prompt,
                 statistic_informations=statistic_prompt,
             )
             messages = [{"role": "user", "content": prompt}]

notebook/agentchat_agentoptimizer.ipynb

@@ -41,6 +41,7 @@
    "source": [
     "import copy\n",
     "import json\n",
+    "import os\n",
     "from typing import Any, Callable, Dict, List, Optional, Tuple, Union\n",
     "\n",
     "from openai import BadRequestError\n",
@@ -299,16 +300,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
+    "llm_config = {\n",
+    "    \"config_list\": [\n",
+    "        {\n",
+    "            \"model\": \"gpt-4-1106-preview\",\n",
+    "            \"api_type\": \"azure\",\n",
+    "            \"api_key\": os.environ[\"AZURE_OPENAI_API_KEY\"],\n",
+    "            \"base_url\": \"https://ENDPOINT.openai.azure.com/\",\n",
+    "            \"api_version\": \"2023-07-01-preview\",\n",
+    "        }\n",
+    "    ]\n",
+    "}\n",
     "\n",
     "assistant = autogen.AssistantAgent(\n",
     "    name=\"assistant\",\n",
     "    system_message=\"You are a helpful assistant.\",\n",
-    "    llm_config={\n",
-    "        \"timeout\": 600,\n",
-    "        \"seed\": 42,\n",
-    "        \"config_list\": config_list,\n",
-    "    },\n",
+    "    llm_config=llm_config,\n",
     ")\n",
     "user_proxy = MathUserProxyAgent(\n",
     "    name=\"mathproxyagent\",\n",
@@ -361,9 +368,7 @@
    "source": [
     "EPOCH = 10\n",
     "optimizer_model = \"gpt-4-1106-preview\"\n",
-    "optimizer = AgentOptimizer(\n",
-    "    max_actions_per_step=3, config_file_or_env=\"OAI_CONFIG_LIST\", optimizer_model=optimizer_model\n",
-    ")\n",
+    "optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config, optimizer_model=optimizer_model)\n",
     "for i in range(EPOCH):\n",
     "    for index, query in enumerate(train_data):\n",
     "        is_correct = user_proxy.initiate_chat(assistant, answer=query[\"answer\"], problem=query[\"question\"])\n",

test/agentchat/contrib/test_agent_optimizer.py

@@ -22,15 +22,13 @@ def test_record_conversation():
         OAI_CONFIG_LIST,
         file_location=KEY_LOC,
     )
-    assistant = AssistantAgent(
-        "assistant",
-        system_message="You are a helpful assistant.",
-        llm_config={
-            "timeout": 60,
-            "cache_seed": 42,
-            "config_list": config_list,
-        },
-    )
+    llm_config = {
+        "config_list": config_list,
+        "timeout": 60,
+        "cache_seed": 42,
+    }
+
+    assistant = AssistantAgent("assistant", system_message="You are a helpful assistant.", llm_config=llm_config)
     user_proxy = UserProxyAgent(
         name="user_proxy",
         human_input_mode="NEVER",
@@ -43,7 +41,7 @@ def test_record_conversation():
     )
 
     user_proxy.initiate_chat(assistant, message=problem)
-    optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
+    optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
     optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
 
     assert len(optimizer._trial_conversations_history) == 1
@@ -66,14 +64,15 @@ def test_step():
         OAI_CONFIG_LIST,
         file_location=KEY_LOC,
     )
+    llm_config = {
+        "config_list": config_list,
+        "timeout": 60,
+        "cache_seed": 42,
+    }
     assistant = AssistantAgent(
         "assistant",
         system_message="You are a helpful assistant.",
-        llm_config={
-            "timeout": 60,
-            "cache_seed": 42,
-            "config_list": config_list,
-        },
+        llm_config=llm_config,
     )
     user_proxy = UserProxyAgent(
         name="user_proxy",
@@ -86,7 +85,7 @@ def test_step():
         max_consecutive_auto_reply=3,
     )
 
-    optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
+    optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
     user_proxy.initiate_chat(assistant, message=problem)
     optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
 

website/blog/2023-12-23-AgentOptimizer/index.mdx

@@ -42,7 +42,7 @@ is_satisfied is a bool value that represents whether the user is satisfied with
 Example:
 
 ```python
-optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
+optimizer = AgentOptimizer(max_actions_per_step=3, llm_config = llm_config)
 # ------------ code to solve a problem ------------
 # ......
 # -------------------------------------------------
@@ -76,7 +76,7 @@ Moreover, it also includes mechanisms to check whether each update is feasible,
 The optimization process is as follows:
 
 ```python
-optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
+optimizer = AgentOptimizer(max_actions_per_step=3, llm_config = llm_config)
 for i in range(EPOCH):
     is_correct = user_proxy.initiate_chat(assistant, message = problem)
     history = assistant.chat_messages_for_summary(user_proxy)