mirror of https://github.com/microsoft/autogen.git
support llm_config in AgentOptimizer (#2299)
* support llm_config in agentoptimizer * fix doc * restore seed timeout --------- Co-authored-by: “skzhang1” <“shaokunzhang529@gmail.com”> Co-authored-by: Chi Wang <wang.chi@microsoft.com>
This commit is contained in:
Shaokun Zhang
GitHub
parent
97b5433cdb
commit
9069eb926a
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@ -1,6 +1,6 @@
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import copy
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import json
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from typing import Dict, List, Optional
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from typing import Dict, List, Literal, Optional, Union
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import autogen
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from autogen.code_utils import execute_code
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@ -172,16 +172,16 @@ class AgentOptimizer:
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def __init__(
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self,
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max_actions_per_step: int,
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config_file_or_env: Optional[str] = "OAI_CONFIG_LIST",
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config_file_location: Optional[str] = "",
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llm_config: dict,
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optimizer_model: Optional[str] = "gpt-4-1106-preview",
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):
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"""
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(These APIs are experimental and may change in the future.)
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Args:
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max_actions_per_step (int): the maximum number of actions that the optimizer can take in one step.
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config_file_or_env: path or environment of the OpenAI api configs.
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config_file_location: the location of the OpenAI config file.
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llm_config (dict): llm inference configuration.
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Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) for available options.
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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`.
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optimizer_model: the model used for the optimizer.
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"""
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self.max_actions_per_step = max_actions_per_step
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@ -199,14 +199,17 @@ class AgentOptimizer:
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self._failure_functions_performance = []
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self._best_performance = -1
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config_list = autogen.config_list_from_json(
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config_file_or_env,
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file_location=config_file_location,
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filter_dict={"model": [self.optimizer_model]},
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assert isinstance(llm_config, dict), "llm_config must be a dict"
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llm_config = copy.deepcopy(llm_config)
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self.llm_config = llm_config
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if self.llm_config in [{}, {"config_list": []}, {"config_list": [{"model": ""}]}]:
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raise ValueError(
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"When using OpenAI or Azure OpenAI endpoints, specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'."
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)
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self.llm_config["config_list"] = autogen.filter_config(
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llm_config["config_list"], {"model": [self.optimizer_model]}
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)
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if len(config_list) == 0:
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raise RuntimeError("No valid openai config found in the config file or environment variable.")
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self._client = autogen.OpenAIWrapper(config_list=config_list)
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self._client = autogen.OpenAIWrapper(**self.llm_config)
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def record_one_conversation(self, conversation_history: List[Dict], is_satisfied: bool = None):
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"""
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@ -266,7 +269,7 @@ class AgentOptimizer:
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actions_num=action_index,
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best_functions=best_functions,
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incumbent_functions=incumbent_functions,
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accumerated_experience=failure_experience_prompt,
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accumulated_experience=failure_experience_prompt,
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statistic_informations=statistic_prompt,
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)
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messages = [{"role": "user", "content": prompt}]
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@ -41,6 +41,7 @@
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"source": [
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"import copy\n",
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"import json\n",
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"import os\n",
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"from typing import Any, Callable, Dict, List, Optional, Tuple, Union\n",
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"\n",
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"from openai import BadRequestError\n",
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@ -299,16 +300,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"config_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
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"llm_config = {\n",
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" \"config_list\": [\n",
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" {\n",
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" \"model\": \"gpt-4-1106-preview\",\n",
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" \"api_type\": \"azure\",\n",
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" \"api_key\": os.environ[\"AZURE_OPENAI_API_KEY\"],\n",
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" \"base_url\": \"https://ENDPOINT.openai.azure.com/\",\n",
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" \"api_version\": \"2023-07-01-preview\",\n",
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" }\n",
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" ]\n",
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"}\n",
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"\n",
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"assistant = autogen.AssistantAgent(\n",
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" name=\"assistant\",\n",
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" system_message=\"You are a helpful assistant.\",\n",
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" llm_config={\n",
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" \"timeout\": 600,\n",
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" \"seed\": 42,\n",
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" \"config_list\": config_list,\n",
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" },\n",
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" llm_config=llm_config,\n",
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")\n",
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"user_proxy = MathUserProxyAgent(\n",
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" name=\"mathproxyagent\",\n",
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@ -361,9 +368,7 @@
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"source": [
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"EPOCH = 10\n",
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"optimizer_model = \"gpt-4-1106-preview\"\n",
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"optimizer = AgentOptimizer(\n",
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" max_actions_per_step=3, config_file_or_env=\"OAI_CONFIG_LIST\", optimizer_model=optimizer_model\n",
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")\n",
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"optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config, optimizer_model=optimizer_model)\n",
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"for i in range(EPOCH):\n",
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" for index, query in enumerate(train_data):\n",
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" is_correct = user_proxy.initiate_chat(assistant, answer=query[\"answer\"], problem=query[\"question\"])\n",
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@ -22,15 +22,13 @@ def test_record_conversation():
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OAI_CONFIG_LIST,
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file_location=KEY_LOC,
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)
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assistant = AssistantAgent(
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"assistant",
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system_message="You are a helpful assistant.",
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llm_config={
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"timeout": 60,
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"cache_seed": 42,
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"config_list": config_list,
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},
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)
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llm_config = {
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"config_list": config_list,
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"timeout": 60,
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"cache_seed": 42,
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}
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assistant = AssistantAgent("assistant", system_message="You are a helpful assistant.", llm_config=llm_config)
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user_proxy = UserProxyAgent(
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name="user_proxy",
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human_input_mode="NEVER",
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@ -43,7 +41,7 @@ def test_record_conversation():
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)
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user_proxy.initiate_chat(assistant, message=problem)
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optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
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optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
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assert len(optimizer._trial_conversations_history) == 1
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@ -66,14 +64,15 @@ def test_step():
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OAI_CONFIG_LIST,
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file_location=KEY_LOC,
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)
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llm_config = {
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"config_list": config_list,
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"timeout": 60,
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"cache_seed": 42,
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}
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assistant = AssistantAgent(
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"assistant",
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system_message="You are a helpful assistant.",
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llm_config={
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"timeout": 60,
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"cache_seed": 42,
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"config_list": config_list,
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},
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llm_config=llm_config,
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)
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user_proxy = UserProxyAgent(
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name="user_proxy",
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@ -86,7 +85,7 @@ def test_step():
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max_consecutive_auto_reply=3,
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)
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optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
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user_proxy.initiate_chat(assistant, message=problem)
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optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
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@ -42,7 +42,7 @@ is_satisfied is a bool value that represents whether the user is satisfied with
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Example:
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```python
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optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config = llm_config)
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# ------------ code to solve a problem ------------
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# ......
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# -------------------------------------------------
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@ -76,7 +76,7 @@ Moreover, it also includes mechanisms to check whether each update is feasible,
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The optimization process is as follows:
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```python
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optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config = llm_config)
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for i in range(EPOCH):
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is_correct = user_proxy.initiate_chat(assistant, message = problem)
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history = assistant.chat_messages_for_summary(user_proxy)
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