refactor

metagpt/provider/anthropic_api.py

@@ -13,7 +13,7 @@ from metagpt.configs.llm_config import LLMConfig
 
 
 class Claude2:
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.config = config
 
     def ask(self, prompt: str) -> str:

metagpt/provider/base_llm.py

@@ -29,7 +29,7 @@ class BaseLLM(ABC):
     cost_manager: Optional[CostManager] = None
 
     @abstractmethod
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         pass
 
     def _user_msg(self, msg: str) -> dict[str, str]:

metagpt/provider/fireworks_api.py

@@ -72,7 +72,7 @@ class FireworksCostManager(CostManager):
 
 @register_provider(LLMType.FIREWORKS)
 class FireworksLLM(OpenAILLM):
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         super().__init__(config=config)
         self.auto_max_tokens = False
         self.cost_manager = FireworksCostManager()

metagpt/provider/google_gemini_api.py

@@ -47,10 +47,11 @@ class GeminiLLM(BaseLLM):
     Refs to `https://ai.google.dev/tutorials/python_quickstart`
     """
 
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.use_system_prompt = False  # google gemini has no system prompt when use api
 
         self.__init_gemini(config)
+        self.config = config
         self.model = "gemini-pro"  # so far only one model
         self.llm = GeminiGenerativeModel(model_name=self.model)
 

metagpt/provider/human_provider.py

@@ -15,7 +15,7 @@ class HumanProvider(BaseLLM):
     This enables replacing LLM anywhere in the framework with a human, thus introducing human interaction
     """
 
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         pass
 
     def ask(self, msg: str, timeout=3) -> str:

metagpt/provider/ollama_api.py

@@ -29,16 +29,17 @@ class OllamaLLM(BaseLLM):
     Refs to `https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-chat-completion`
     """
 
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.__init_ollama(config)
-        self.client = GeneralAPIRequestor(base_url=config.api_base)
+        self.client = GeneralAPIRequestor(base_url=config.base_url)
+        self.config = config
         self.suffix_url = "/chat"
         self.http_method = "post"
         self.use_system_prompt = False
         self._cost_manager = TokenCostManager()
 
     def __init_ollama(self, config: LLMConfig):
-        assert config.api_base
+        assert config.base_url, "ollama base url is required!"
         self.model = config.model
 
     def _const_kwargs(self, messages: list[dict], stream: bool = False) -> dict:

metagpt/provider/openai_api.py

@@ -54,7 +54,7 @@ See FAQ 5.8
 class OpenAILLM(BaseLLM):
     """Check https://platform.openai.com/examples for examples"""
 
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.config = config
         self._init_model()
         self._init_client()

metagpt/provider/spark_api.py

@@ -24,7 +24,7 @@ from metagpt.provider.llm_provider_registry import register_provider
 
 @register_provider(LLMType.SPARK)
 class SparkLLM(BaseLLM):
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.config = config
         logger.warning("当前方法无法支持异步运行。当你使用acompletion时，并不能并行访问。")
 

metagpt/provider/zhipuai_api.py

@@ -38,7 +38,7 @@ class ZhiPuAILLM(BaseLLM):
     From now, there is only one model named `chatglm_turbo`
     """
 
-    def __init__(self, config: LLMConfig = None):
+    def __init__(self, config: LLMConfig):
         self.__init_zhipuai(config)
         self.llm = ZhiPuModelAPI
         self.model = "chatglm_turbo"  # so far only one model, just use it

tests/metagpt/provider/mock_llm_config.py

@@ -11,4 +11,13 @@ from metagpt.configs.llm_config import LLMConfig
 mock_llm_config = LLMConfig(
     llm_type="mock",
     api_key="mock_api_key",
+    base_url="mock_base_url",
+)
+
+
+mock_llm_config_proxy = LLMConfig(
+    llm_type="mock",
+    api_key="mock_api_key",
+    base_url="mock_base_url",
+    proxy="http://localhost:8080",
 )

tests/metagpt/provider/test_google_gemini_api.py

@@ -9,10 +9,8 @@ import pytest
 from google.ai import generativelanguage as glm
 from google.generativeai.types import content_types
 
-from metagpt.config import CONFIG
 from metagpt.provider.google_gemini_api import GeminiLLM
-
-CONFIG.gemini_api_key = "xx"
+from tests.metagpt.provider.mock_llm_config import mock_llm_config
 
 
 @dataclass
@@ -62,7 +60,7 @@ async def test_gemini_acompletion(mocker):
         mock_gemini_generate_content_async,
     )
 
-    gemini_gpt = GeminiLLM()
+    gemini_gpt = GeminiLLM(mock_llm_config)
 
     assert gemini_gpt._user_msg(prompt_msg) == {"role": "user", "parts": [prompt_msg]}
     assert gemini_gpt._assistant_msg(prompt_msg) == {"role": "model", "parts": [prompt_msg]}

tests/metagpt/provider/test_metagpt_api.py

@@ -1,14 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/12/28
-@Author  : mashenquan
-@File    : test_metagpt_api.py
-"""
-from metagpt.configs.llm_config import LLMType
-from metagpt.llm import LLM
-
-
-def test_llm():
-    llm = LLM(provider=LLMType.METAGPT)
-    assert llm

tests/metagpt/provider/test_metagpt_llm.py

@@ -3,13 +3,14 @@
 """
 @Time    : 2023/8/30
 @Author  : mashenquan
-@File    : test_metagpt_llm_api.py
+@File    : test_metagpt_llm.py
 """
 from metagpt.provider.metagpt_api import MetaGPTLLM
+from tests.metagpt.provider.mock_llm_config import mock_llm_config
 
 
 def test_metagpt():
-    llm = MetaGPTLLM()
+    llm = MetaGPTLLM(mock_llm_config)
     assert llm
 
 

tests/metagpt/provider/test_ollama_api.py

@@ -9,6 +9,7 @@ import pytest
 
 from metagpt.config import CONFIG
 from metagpt.provider.ollama_api import OllamaLLM
+from tests.metagpt.provider.mock_llm_config import mock_llm_config
 
 prompt_msg = "who are you"
 messages = [{"role": "user", "content": prompt_msg}]
@@ -44,7 +45,7 @@ async def mock_ollama_arequest(self, stream: bool = False, **kwargs) -> Tuple[An
 async def test_gemini_acompletion(mocker):
     mocker.patch("metagpt.provider.general_api_requestor.GeneralAPIRequestor.arequest", mock_ollama_arequest)
 
-    ollama_gpt = OllamaLLM()
+    ollama_gpt = OllamaLLM(mock_llm_config)
 
     resp = await ollama_gpt.acompletion(messages)
     assert resp["message"]["content"] == default_resp["message"]["content"]

tests/metagpt/provider/test_open_llm_api.py

@@ -16,6 +16,7 @@ from openai.types.completion_usage import CompletionUsage
 from metagpt.config import CONFIG
 from metagpt.provider.open_llm_api import OpenLLM
 from metagpt.utils.cost_manager import Costs
+from tests.metagpt.provider.mock_llm_config import mock_llm_config
 
 CONFIG.max_budget = 10
 CONFIG.calc_usage = True
@@ -71,7 +72,7 @@ async def mock_openai_acompletions_create(self, stream: bool = False, **kwargs)
 async def test_openllm_acompletion(mocker):
     mocker.patch("openai.resources.chat.completions.AsyncCompletions.create", mock_openai_acompletions_create)
 
-    openllm_gpt = OpenLLM()
+    openllm_gpt = OpenLLM(mock_llm_config)
     openllm_gpt.model = "llama-v2-13b-chat"
 
     openllm_gpt._update_costs(usage=CompletionUsage(prompt_tokens=100, completion_tokens=100, total_tokens=200))

tests/metagpt/provider/test_openai.py

@@ -1,10 +1,13 @@
-from unittest.mock import Mock
-
 import pytest
 
 from metagpt.llm import LLM
 from metagpt.logs import logger
+from metagpt.provider import OpenAILLM
 from metagpt.schema import UserMessage
+from tests.metagpt.provider.mock_llm_config import (
+    mock_llm_config,
+    mock_llm_config_proxy,
+)
 
 
 @pytest.mark.asyncio
@@ -40,74 +43,13 @@ async def test_aask_code_message():
 
 
 class TestOpenAI:
-    @pytest.fixture
-    def config(self):
-        return Mock(
-            openai_api_key="test_key",
-            OPENAI_API_KEY="test_key",
-            openai_base_url="test_url",
-            OPENAI_BASE_URL="test_url",
-            openai_proxy=None,
-            openai_api_type="other",
-        )
-
-    @pytest.fixture
-    def config_azure(self):
-        return Mock(
-            openai_api_key="test_key",
-            OPENAI_API_KEY="test_key",
-            openai_api_version="test_version",
-            openai_base_url="test_url",
-            OPENAI_BASE_URL="test_url",
-            openai_proxy=None,
-            openai_api_type="azure",
-        )
-
-    @pytest.fixture
-    def config_proxy(self):
-        return Mock(
-            openai_api_key="test_key",
-            OPENAI_API_KEY="test_key",
-            openai_base_url="test_url",
-            OPENAI_BASE_URL="test_url",
-            openai_proxy="http://proxy.com",
-            openai_api_type="other",
-        )
-
-    @pytest.fixture
-    def config_azure_proxy(self):
-        return Mock(
-            openai_api_key="test_key",
-            OPENAI_API_KEY="test_key",
-            openai_api_version="test_version",
-            openai_base_url="test_url",
-            OPENAI_BASE_URL="test_url",
-            openai_proxy="http://proxy.com",
-            openai_api_type="azure",
-        )
-
-    def test_make_client_kwargs_without_proxy(self, config):
-        instance = OpenAILLM()
-        instance.config = config
+    def test_make_client_kwargs_without_proxy(self):
+        instance = OpenAILLM(mock_llm_config)
         kwargs = instance._make_client_kwargs()
-        assert kwargs == {"api_key": "test_key", "base_url": "test_url"}
+        assert kwargs == {"api_key": "mock_api_key", "base_url": "mock_base_url"}
         assert "http_client" not in kwargs
 
-    def test_make_client_kwargs_without_proxy_azure(self, config_azure):
-        instance = OpenAILLM()
-        instance.config = config_azure
-        kwargs = instance._make_client_kwargs()
-        assert kwargs == {"api_key": "test_key", "base_url": "test_url"}
-        assert "http_client" not in kwargs
-
-    def test_make_client_kwargs_with_proxy(self, config_proxy):
-        instance = OpenAILLM()
-        instance.config = config_proxy
-        kwargs = instance._make_client_kwargs()
-        assert "http_client" in kwargs
-
-    def test_make_client_kwargs_with_proxy_azure(self, config_azure_proxy):
-        instance = OpenAILLM()
-        instance.config = config_azure_proxy
+    def test_make_client_kwargs_with_proxy(self):
+        instance = OpenAILLM(mock_llm_config_proxy)
         kwargs = instance._make_client_kwargs()
         assert "http_client" in kwargs