Merge branch 'main' into feature/qianfan

metagpt/actions/action_node.py

@@ -237,12 +237,19 @@ class ActionNode:
         """基于pydantic v2的模型动态生成，用来检验结果类型正确性"""
 
         def check_fields(cls, values):
-            required_fields = set(mapping.keys())
+            all_fields = set(mapping.keys())
+            required_fields = set()
+            for k, v in mapping.items():
+                type_v, field_info = v
+                if ActionNode.is_optional_type(type_v):
+                    continue
+                required_fields.add(k)
+
             missing_fields = required_fields - set(values.keys())
             if missing_fields:
                 raise ValueError(f"Missing fields: {missing_fields}")
 
-            unrecognized_fields = set(values.keys()) - required_fields
+            unrecognized_fields = set(values.keys()) - all_fields
             if unrecognized_fields:
                 logger.warning(f"Unrecognized fields: {unrecognized_fields}")
             return values
@@ -717,3 +724,12 @@ class ActionNode:
             root_node.add_child(child_node)
 
         return root_node
+
+    @staticmethod
+    def is_optional_type(tp) -> bool:
+        """Return True if `tp` is `typing.Optional[...]`"""
+        if typing.get_origin(tp) is Union:
+            args = typing.get_args(tp)
+            non_none_types = [arg for arg in args if arg is not type(None)]
+            return len(non_none_types) == 1 and len(args) == 2
+        return False

metagpt/actions/project_management_an.py

@@ -12,7 +12,7 @@ from metagpt.actions.action_node import ActionNode
 REQUIRED_PACKAGES = ActionNode(
     key="Required packages",
     expected_type=Optional[List[str]],
-    instruction="Provide required packages in requirements.txt format.",
+    instruction="Provide required third-party packages in requirements.txt format.",
     example=["flask==1.1.2", "bcrypt==3.2.0"],
 )
 

metagpt/actions/write_code_an_draft.py

@@ -139,7 +139,7 @@ Language: Please use the same language as the user requirement, but the title an
     end", "Anything UNCLEAR": "目前项目要求明确，没有不清楚的地方。"}
 
 ## Tasks
-{"Required packages": ["无需Python包"], "Required Other language third-party packages": ["vue.js"], "Logic Analysis": [["index.html", "作为游戏的入口文件和主要的HTML结构"], ["styles.css", "包含所有的CSS样式，确保游戏界面美观"], ["main.js", "包含Main类，负责初始化游戏和绑定事件"], ["game.js", "包含Game类，负责游戏逻辑，如开始游戏、移动方块等"], ["storage.js", "包含Storage类，用于获取和设置玩家的最高分"]], "Task list": ["index.html", "styles.css", "storage.js", "game.js", "main.js"], "Full API spec": "", "Shared Knowledge": "\'game.js\' 包含游戏逻辑相关的函数，被 \'main.js\' 调用。", "Anything UNCLEAR": "目前项目要求明确，没有不清楚的地方。"}
+{"Required packages": ["无需第三方包"], "Required Other language third-party packages": ["vue.js"], "Logic Analysis": [["index.html", "作为游戏的入口文件和主要的HTML结构"], ["styles.css", "包含所有的CSS样式，确保游戏界面美观"], ["main.js", "包含Main类，负责初始化游戏和绑定事件"], ["game.js", "包含Game类，负责游戏逻辑，如开始游戏、移动方块等"], ["storage.js", "包含Storage类，用于获取和设置玩家的最高分"]], "Task list": ["index.html", "styles.css", "storage.js", "game.js", "main.js"], "Full API spec": "", "Shared Knowledge": "\'game.js\' 包含游戏逻辑相关的函数，被 \'main.js\' 调用。", "Anything UNCLEAR": "目前项目要求明确，没有不清楚的地方。"}
 
 ## Code Files
 ----- index.html

metagpt/configs/llm_config.py

@@ -33,7 +33,7 @@ class LLMType(Enum):
     YI = "yi"  # lingyiwanwu
     OPENROUTER = "openrouter"
     BEDROCK = "bedrock"
-    ARK = "ark"
+    ARK = "ark"  # https://www.volcengine.com/docs/82379/1263482#python-sdk
 
     def __missing__(self, key):
         return self.OPENAI

metagpt/provider/ark_api.py

@@ -1,12 +1,33 @@
-from openai import AsyncStream
-from openai.types import CompletionUsage
-from openai.types.chat import ChatCompletion, ChatCompletionChunk
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Provider for volcengine.
+See Also: https://console.volcengine.com/ark/region:ark+cn-beijing/model
+
+config2.yaml example:
+```yaml
+llm:
+  base_url: "https://ark.cn-beijing.volces.com/api/v3"
+  api_type: "ark"
+  endpoint: "ep-2024080514****-d****"
+  api_key: "d47****b-****-****-****-d6e****0fd77"
+  pricing_plan: "doubao-lite"
+```
+"""
+from typing import Optional, Union
+
+from pydantic import BaseModel
+from volcenginesdkarkruntime import AsyncArk
+from volcenginesdkarkruntime._base_client import AsyncHttpxClientWrapper
+from volcenginesdkarkruntime._streaming import AsyncStream
+from volcenginesdkarkruntime.types.chat import ChatCompletion, ChatCompletionChunk
 
 from metagpt.configs.llm_config import LLMType
 from metagpt.const import USE_CONFIG_TIMEOUT
 from metagpt.logs import log_llm_stream
 from metagpt.provider.llm_provider_registry import register_provider
 from metagpt.provider.openai_api import OpenAILLM
+from metagpt.utils.token_counter import DOUBAO_TOKEN_COSTS
 
 
 @register_provider(LLMType.ARK)
@@ -16,11 +37,45 @@ class ArkLLM(OpenAILLM):
     见：https://www.volcengine.com/docs/82379/1263482
     """
 
+    aclient: Optional[AsyncArk] = None
+
+    def _init_client(self):
+        """SDK: https://github.com/openai/openai-python#async-usage"""
+        self.model = (
+            self.config.endpoint or self.config.model
+        )  # endpoint name, See more: https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint
+        self.pricing_plan = self.config.pricing_plan or self.model
+        kwargs = self._make_client_kwargs()
+        self.aclient = AsyncArk(**kwargs)
+
+    def _make_client_kwargs(self) -> dict:
+        kvs = {
+            "ak": self.config.access_key,
+            "sk": self.config.secret_key,
+            "api_key": self.config.api_key,
+            "base_url": self.config.base_url,
+        }
+        kwargs = {k: v for k, v in kvs.items() if v}
+
+        # to use proxy, openai v1 needs http_client
+        if proxy_params := self._get_proxy_params():
+            kwargs["http_client"] = AsyncHttpxClientWrapper(**proxy_params)
+
+        return kwargs
+
+    def _update_costs(self, usage: Union[dict, BaseModel], model: str = None, local_calc_usage: bool = True):
+        if next(iter(DOUBAO_TOKEN_COSTS)) not in self.cost_manager.token_costs:
+            self.cost_manager.token_costs.update(DOUBAO_TOKEN_COSTS)
+        if model in self.cost_manager.token_costs:
+            self.pricing_plan = model
+        if self.pricing_plan in self.cost_manager.token_costs:
+            super()._update_costs(usage, self.pricing_plan, local_calc_usage)
+
     async def _achat_completion_stream(self, messages: list[dict], timeout=USE_CONFIG_TIMEOUT) -> str:
         response: AsyncStream[ChatCompletionChunk] = await self.aclient.chat.completions.create(
             **self._cons_kwargs(messages, timeout=self.get_timeout(timeout)),
             stream=True,
-            extra_body={"stream_options": {"include_usage": True}}  # 只有增加这个参数才会在流式时最后返回usage
+            extra_body={"stream_options": {"include_usage": True}},  # 只有增加这个参数才会在流式时最后返回usage
         )
         usage = None
         collected_messages = []
@@ -30,7 +85,7 @@ class ArkLLM(OpenAILLM):
             collected_messages.append(chunk_message)
             if chunk.usage:
                 # 火山方舟的流式调用会在最后一个chunk中返回usage,最后一个chunk的choices为[]
-                usage = CompletionUsage(**chunk.usage)
+                usage = chunk.usage
 
         log_llm_stream("\n")
         full_reply_content = "".join(collected_messages)

metagpt/roles/role.py

@@ -170,7 +170,8 @@ class Role(SerializationMixin, ContextMixin, BaseModel):
         self._check_actions()
         self.llm.system_prompt = self._get_prefix()
         self.llm.cost_manager = self.context.cost_manager
-        self._watch(kwargs.pop("watch", [UserRequirement]))
+        if not self.rc.watch:
+            self._watch(kwargs.pop("watch", [UserRequirement]))
 
         if self.latest_observed_msg:
             self.recovered = True

metagpt/utils/make_sk_kernel.py

@@ -21,12 +21,12 @@ def make_sk_kernel():
     if llm := config.get_azure_llm():
         kernel.add_chat_service(
             "chat_completion",
-            AzureChatCompletion(llm.model, llm.base_url, llm.api_key),
+            AzureChatCompletion(deployment_name=llm.model, base_url=llm.base_url, api_key=llm.api_key),
         )
     elif llm := config.get_openai_llm():
         kernel.add_chat_service(
             "chat_completion",
-            OpenAIChatCompletion(llm.model, llm.api_key),
+            OpenAIChatCompletion(ai_model_id=llm.model, api_key=llm.api_key),
         )
 
     return kernel

metagpt/utils/token_counter.py

@@ -40,6 +40,7 @@ TOKEN_COSTS = {
     "gpt-4-vision-preview": {"prompt": 0.01, "completion": 0.03},  # TODO add extra image price calculator
     "gpt-4-1106-vision-preview": {"prompt": 0.01, "completion": 0.03},
     "gpt-4o": {"prompt": 0.005, "completion": 0.015},
+    "gpt-4o-mini": {"prompt": 0.00015, "completion": 0.0006},
     "gpt-4o-2024-05-13": {"prompt": 0.005, "completion": 0.015},
     "text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
     "glm-3-turbo": {"prompt": 0.0007, "completion": 0.0007},  # 128k version, prompt + completion tokens=0.005￥/k-tokens
@@ -187,6 +188,14 @@ FIREWORKS_GRADE_TOKEN_COSTS = {
     "mixtral-8x7b": {"prompt": 0.4, "completion": 1.6},
 }
 
+# https://console.volcengine.com/ark/region:ark+cn-beijing/model
+DOUBAO_TOKEN_COSTS = {
+    "doubao-lite": {"prompt": 0.0003, "completion": 0.0006},
+    "doubao-lite-128k": {"prompt": 0.0008, "completion": 0.0010},
+    "doubao-pro": {"prompt": 0.0008, "completion": 0.0020},
+    "doubao-pro-128k": {"prompt": 0.0050, "completion": 0.0090},
+}
+
 # https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
 TOKEN_MAX = {
     "gpt-4o-2024-05-13": 128000,
@@ -202,6 +211,7 @@ TOKEN_MAX = {
     "gpt-4-0613": 8192,
     "gpt-4-32k": 32768,
     "gpt-4-32k-0613": 32768,
+    "gpt-4o-mini": 128000,
     "gpt-3.5-turbo-0125": 16385,
     "gpt-3.5-turbo": 16385,
     "gpt-3.5-turbo-1106": 16385,
@@ -347,8 +357,9 @@ def count_input_tokens(messages, model="gpt-3.5-turbo-0125"):
         "gpt-4-turbo",
         "gpt-4-vision-preview",
         "gpt-4-1106-vision-preview",
-        "gpt-4o-2024-05-13",
         "gpt-4o",
+        "gpt-4o-2024-05-13",
+        "gpt-4o-mini",
     }:
         tokens_per_message = 3  # # every reply is primed with <|start|>assistant<|message|>
         tokens_per_name = 1

requirements.txt

@@ -72,6 +72,9 @@ qianfan~=0.4.4
 dashscope~=1.19.3
 rank-bm25==0.2.2  # for tool recommendation
 jieba==0.42.1  # for tool recommendation
+volcengine-python-sdk[ark]~=1.0.94
+# llama-index-vector-stores-elasticsearch~=0.2.5 # Used by `metagpt/memory/longterm_memory.py`
+# llama-index-vector-stores-chroma~=0.1.10 # Used by `metagpt/memory/longterm_memory.py`
 gymnasium==0.29.1
 boto3~=1.34.69
 spark_ai_python~=0.3.30

tests/metagpt/actions/test_action_node.py

@@ -6,7 +6,7 @@
 @File    : test_action_node.py
 """
 from pathlib import Path
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import pytest
 from pydantic import BaseModel, Field, ValidationError
@@ -302,6 +302,19 @@ def test_action_node_from_pydantic_and_print_everything():
     assert "tasks" in code, "tasks should be in code"
 
 
+def test_optional():
+    mapping = {
+        "Logic Analysis": (Optional[List[Tuple[str, str]]], Field(default=None)),
+        "Task list": (Optional[List[str]], None),
+        "Plan": (Optional[str], ""),
+        "Anything UNCLEAR": (Optional[str], None),
+    }
+    m = {"Anything UNCLEAR": "a"}
+    t = ActionNode.create_model_class("test_class_1", mapping)
+
+    t1 = t(**m)
+    assert t1
+
+
 if __name__ == "__main__":
-    test_create_model_class()
-    test_create_model_class_with_mapping()
+    pytest.main([__file__, "-s"])

tests/metagpt/serialize_deserialize/test_environment.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # @Desc   :
-
+import pytest
 
 from metagpt.actions.action_node import ActionNode
 from metagpt.actions.add_requirement import UserRequirement
@@ -55,6 +55,7 @@ def test_environment_serdeser(context):
     assert isinstance(list(environment.roles.values())[0].actions[0], ActionOK)
     assert type(list(new_env.roles.values())[0].actions[0]) == ActionOK
     assert type(list(new_env.roles.values())[0].actions[1]) == ActionRaise
+    assert list(new_env.roles.values())[0].rc.watch == role_c.rc.watch
 
 
 def test_environment_serdeser_v2(context):
@@ -69,6 +70,7 @@ def test_environment_serdeser_v2(context):
     assert isinstance(role, ProjectManager)
     assert isinstance(role.actions[0], WriteTasks)
     assert isinstance(list(new_env.roles.values())[0].actions[0], WriteTasks)
+    assert list(new_env.roles.values())[0].rc.watch == pm.rc.watch
 
 
 def test_environment_serdeser_save(context):
@@ -85,3 +87,8 @@ def test_environment_serdeser_save(context):
     new_env: Environment = Environment(**env_dict, context=context)
     assert len(new_env.roles) == 1
     assert type(list(new_env.roles.values())[0].actions[0]) == ActionOK
+    assert list(new_env.roles.values())[0].rc.watch == role_c.rc.watch
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-s"])

tests/metagpt/serialize_deserialize/test_role.py

@@ -28,9 +28,9 @@ from tests.metagpt.serialize_deserialize.test_serdeser_base import (
 
 def test_roles(context):
     role_a = RoleA()
-    assert len(role_a.rc.watch) == 1
+    assert len(role_a.rc.watch) == 2
     role_b = RoleB()
-    assert len(role_a.rc.watch) == 1
+    assert len(role_a.rc.watch) == 2
     assert len(role_b.rc.watch) == 1
 
     role_d = RoleD(actions=[ActionOK()])

tests/metagpt/serialize_deserialize/test_serdeser_base.py

@@ -8,9 +8,9 @@ from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from metagpt.actions import Action, ActionOutput
+from metagpt.actions import Action, ActionOutput, UserRequirement
 from metagpt.actions.action_node import ActionNode
-from metagpt.actions.add_requirement import UserRequirement
+from metagpt.actions.fix_bug import FixBug
 from metagpt.roles.role import Role, RoleReactMode
 
 serdeser_path = Path(__file__).absolute().parent.joinpath("..", "..", "data", "serdeser_storage")
@@ -68,7 +68,7 @@ class RoleA(Role):
     def __init__(self, **kwargs):
         super(RoleA, self).__init__(**kwargs)
         self.set_actions([ActionPass])
-        self._watch([UserRequirement])
+        self._watch([FixBug, UserRequirement])
 
 
 class RoleB(Role):
@@ -93,7 +93,7 @@ class RoleC(Role):
     def __init__(self, **kwargs):
         super(RoleC, self).__init__(**kwargs)
         self.set_actions([ActionOK, ActionRaise])
-        self._watch([UserRequirement])
+        self._watch([FixBug, UserRequirement])
         self.rc.react_mode = RoleReactMode.BY_ORDER
         self.rc.memory.ignore_id = True
 

tests/metagpt/serialize_deserialize/test_sk_agent.py

@@ -15,3 +15,7 @@ async def test_sk_agent_serdeser():
     new_role = SkAgent(**ser_role_dict)
     assert new_role.name == "Sunshine"
     assert len(new_role.actions) == 1
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-s"])

tests/metagpt/serialize_deserialize/test_write_code_review.py

@@ -29,3 +29,7 @@ def div(a: int, b: int = 0):
 
     assert new_action.name == "WriteCodeReview"
     await new_action.run()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-s"])

tests/metagpt/test_config.py

@@ -14,8 +14,8 @@ from tests.metagpt.provider.mock_llm_config import mock_llm_config
 def test_config_1():
     cfg = Config.default()
     llm = cfg.get_openai_llm()
-    assert llm is not None
-    assert llm.api_type == LLMType.OPENAI
+    if cfg.llm.api_type == LLMType.OPENAI:
+        assert llm is not None
 
 
 def test_config_from_dict():

tests/metagpt/test_context.py

@@ -53,8 +53,8 @@ def test_context_1():
 def test_context_2():
     ctx = Context()
     llm = ctx.config.get_openai_llm()
-    assert llm is not None
-    assert llm.api_type == LLMType.OPENAI
+    if ctx.config.llm.api_type == LLMType.OPENAI:
+        assert llm is not None
 
     kwargs = ctx.kwargs
     assert kwargs is not None