mirror of
https://github.com/FoundationAgents/MetaGPT.git
synced 2026-06-08 15:05:17 +02:00
simplify provider ut code
This commit is contained in:
betterwang
parent
ad88c5000d
commit
1583c0f718
17 changed files with 134 additions and 314 deletions
1
.github/workflows/fulltest.yaml
vendored
1
.github/workflows/fulltest.yaml
vendored
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@ -54,7 +54,6 @@ jobs:
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export ALLOW_OPENAI_API_CALL=0
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echo "${{ secrets.METAGPT_KEY_YAML }}" | base64 -d > config/key.yaml
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mkdir -p ~/.metagpt && echo "${{ secrets.METAGPT_CONFIG2_YAML }}" | base64 -d > ~/.metagpt/config2.yaml
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echo "${{ secrets.SPARK_YAML }}" | base64 -d > ~/.metagpt/spark.yaml
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pytest tests/ --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
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- name: Show coverage report
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run: |
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2
.github/workflows/unittest.yaml
vendored
2
.github/workflows/unittest.yaml
vendored
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@ -31,7 +31,7 @@ jobs:
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- name: Test with pytest
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run: |
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export ALLOW_OPENAI_API_CALL=0
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mkdir -p ~/.metagpt && cp tests/config2.yaml ~/.metagpt/config2.yaml && cp tests/spark.yaml ~/.metagpt/spark.yaml
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mkdir -p ~/.metagpt && cp tests/config2.yaml ~/.metagpt/config2.yaml
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pytest tests/ --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
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- name: Show coverage report
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run: |
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@ -19,7 +19,7 @@ from metagpt.configs.llm_config import LLMConfig, LLMType
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from metagpt.logs import log_llm_stream, logger
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from metagpt.provider.llm_provider_registry import register_provider
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from metagpt.provider.openai_api import OpenAILLM, log_and_reraise
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from metagpt.utils.cost_manager import CostManager, Costs
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from metagpt.utils.cost_manager import CostManager
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MODEL_GRADE_TOKEN_COSTS = {
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"-1": {"prompt": 0.0, "completion": 0.0}, # abnormal condition
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@ -81,17 +81,6 @@ class FireworksLLM(OpenAILLM):
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kwargs = dict(api_key=self.config.api_key, base_url=self.config.base_url)
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return kwargs
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def _update_costs(self, usage: CompletionUsage):
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if self.config.calc_usage and usage:
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try:
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# use FireworksCostManager not context.cost_manager
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self.cost_manager.update_cost(usage.prompt_tokens, usage.completion_tokens, self.model)
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except Exception as e:
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logger.error(f"updating costs failed!, exp: {e}")
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def get_costs(self) -> Costs:
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return self.cost_manager.get_costs()
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async def _achat_completion_stream(self, messages: list[dict], timeout=3) -> str:
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response: AsyncStream[ChatCompletionChunk] = await self.aclient.chat.completions.create(
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**self._cons_kwargs(messages), stream=True
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@ -72,16 +72,6 @@ class GeminiLLM(BaseLLM):
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kwargs = {"contents": messages, "generation_config": GenerationConfig(temperature=0.3), "stream": stream}
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return kwargs
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def _update_costs(self, usage: dict):
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"""update each request's token cost"""
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if self.config.calc_usage:
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try:
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prompt_tokens = int(usage.get("prompt_tokens", 0))
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completion_tokens = int(usage.get("completion_tokens", 0))
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self.cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
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except Exception as e:
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logger.error(f"google gemini updats costs failed! exp: {e}")
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def get_choice_text(self, resp: GenerateContentResponse) -> str:
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return resp.text
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@ -46,16 +46,6 @@ class OllamaLLM(BaseLLM):
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kwargs = {"model": self.model, "messages": messages, "options": {"temperature": 0.3}, "stream": stream}
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return kwargs
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def _update_costs(self, usage: dict):
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"""update each request's token cost"""
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if self.config.calc_usage:
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try:
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prompt_tokens = int(usage.get("prompt_tokens", 0))
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completion_tokens = int(usage.get("completion_tokens", 0))
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self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
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except Exception as e:
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logger.error(f"ollama updats costs failed! exp: {e}")
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def get_choice_text(self, resp: dict) -> str:
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"""get the resp content from llm response"""
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assist_msg = resp.get("message", {})
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@ -8,7 +8,7 @@ from metagpt.configs.llm_config import LLMConfig, LLMType
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from metagpt.logs import logger
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from metagpt.provider.llm_provider_registry import register_provider
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from metagpt.provider.openai_api import OpenAILLM
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from metagpt.utils.cost_manager import Costs, TokenCostManager
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from metagpt.utils.cost_manager import TokenCostManager
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from metagpt.utils.token_counter import count_message_tokens, count_string_tokens
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@ -34,14 +34,3 @@ class OpenLLM(OpenAILLM):
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logger.error(f"usage calculation failed!: {e}")
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return usage
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def _update_costs(self, usage: CompletionUsage):
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if self.config.calc_usage and usage:
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try:
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# use OpenLLMCostManager not CONFIG.cost_manager
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self._cost_manager.update_cost(usage.prompt_tokens, usage.completion_tokens, self.model)
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except Exception as e:
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logger.error(f"updating costs failed!, exp: {e}")
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def get_costs(self) -> Costs:
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return self._cost_manager.get_costs()
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@ -30,7 +30,7 @@ from metagpt.provider.constant import GENERAL_FUNCTION_SCHEMA
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from metagpt.provider.llm_provider_registry import register_provider
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from metagpt.schema import Message
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from metagpt.utils.common import CodeParser, decode_image
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from metagpt.utils.cost_manager import CostManager, Costs
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from metagpt.utils.cost_manager import CostManager
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from metagpt.utils.exceptions import handle_exception
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from metagpt.utils.token_counter import (
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count_message_tokens,
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@ -56,16 +56,13 @@ class OpenAILLM(BaseLLM):
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def __init__(self, config: LLMConfig):
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self.config = config
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self._init_model()
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self._init_client()
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self.auto_max_tokens = False
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self.cost_manager: Optional[CostManager] = None
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def _init_model(self):
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self.model = self.config.model # Used in _calc_usage & _cons_kwargs
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def _init_client(self):
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"""https://github.com/openai/openai-python#async-usage"""
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self.model = self.config.model # Used in _calc_usage & _cons_kwargs
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kwargs = self._make_client_kwargs()
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self.aclient = AsyncOpenAI(**kwargs)
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@ -272,16 +269,6 @@ class OpenAILLM(BaseLLM):
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return usage
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@handle_exception
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def _update_costs(self, usage: CompletionUsage):
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if self.config.calc_usage and usage and self.cost_manager:
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self.cost_manager.update_cost(usage.prompt_tokens, usage.completion_tokens, self.model)
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def get_costs(self) -> Costs:
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if not self.cost_manager:
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return Costs(0, 0, 0, 0)
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return self.cost_manager.get_costs()
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def _get_max_tokens(self, messages: list[dict]):
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if not self.auto_max_tokens:
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return self.config.max_token
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@ -53,16 +53,6 @@ class ZhiPuAILLM(BaseLLM):
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kwargs = {"model": self.model, "messages": messages, "stream": stream, "temperature": 0.3}
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return kwargs
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def _update_costs(self, usage: dict):
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"""update each request's token cost"""
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if self.config.calc_usage:
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try:
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prompt_tokens = int(usage.get("prompt_tokens", 0))
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completion_tokens = int(usage.get("completion_tokens", 0))
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self.cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
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except Exception as e:
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logger.error(f"zhipuai updats costs failed! exp: {e}")
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def completion(self, messages: list[dict], timeout=3) -> dict:
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resp: Completion = self.llm.chat.completions.create(**self._const_kwargs(messages))
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usage = resp.usage.model_dump()
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@ -8,25 +8,25 @@ from anthropic.resources.completions import Completion
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from metagpt.provider.anthropic_api import Claude2
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from tests.metagpt.provider.mock_llm_config import mock_llm_config
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from tests.metagpt.provider.req_resp_const import prompt, resp_cont_tmpl
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prompt = "who are you"
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resp = "I'am Claude2"
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resp_cont = resp_cont_tmpl.format(name="Claude")
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def mock_anthropic_completions_create(self, model: str, prompt: str, max_tokens_to_sample: int) -> Completion:
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return Completion(id="xx", completion=resp, model="claude-2", stop_reason="stop_sequence", type="completion")
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return Completion(id="xx", completion=resp_cont, model="claude-2", stop_reason="stop_sequence", type="completion")
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async def mock_anthropic_acompletions_create(self, model: str, prompt: str, max_tokens_to_sample: int) -> Completion:
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return Completion(id="xx", completion=resp, model="claude-2", stop_reason="stop_sequence", type="completion")
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return Completion(id="xx", completion=resp_cont, model="claude-2", stop_reason="stop_sequence", type="completion")
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def test_claude2_ask(mocker):
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mocker.patch("anthropic.resources.completions.Completions.create", mock_anthropic_completions_create)
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assert resp == Claude2(mock_llm_config).ask(prompt)
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assert resp_cont == Claude2(mock_llm_config).ask(prompt)
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@pytest.mark.asyncio
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async def test_claude2_aask(mocker):
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mocker.patch("anthropic.resources.completions.AsyncCompletions.create", mock_anthropic_acompletions_create)
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assert resp == await Claude2(mock_llm_config).aask(prompt)
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assert resp_cont == await Claude2(mock_llm_config).aask(prompt)
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@ -11,21 +11,13 @@ import pytest
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from metagpt.configs.llm_config import LLMConfig
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from metagpt.provider.base_llm import BaseLLM
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from metagpt.schema import Message
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from tests.metagpt.provider.req_resp_const import (
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default_resp_cont,
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get_part_chat_completion,
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prompt,
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)
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default_chat_resp = {
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "I'am GPT",
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},
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"finish_reason": "stop",
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}
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]
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}
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prompt_msg = "who are you"
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resp_content = default_chat_resp["choices"][0]["message"]["content"]
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name = "GPT"
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class MockBaseLLM(BaseLLM):
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@ -33,16 +25,13 @@ class MockBaseLLM(BaseLLM):
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pass
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def completion(self, messages: list[dict], timeout=3):
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return default_chat_resp
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return get_part_chat_completion(name)
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async def acompletion(self, messages: list[dict], timeout=3):
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return default_chat_resp
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return get_part_chat_completion(name)
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async def acompletion_text(self, messages: list[dict], stream=False, timeout=3) -> str:
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return resp_content
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async def close(self):
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return default_chat_resp
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return default_resp_cont
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def test_base_llm():
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@ -86,25 +75,25 @@ def test_base_llm():
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choice_text = base_llm.get_choice_text(openai_funccall_resp)
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assert choice_text == openai_funccall_resp["choices"][0]["message"]["content"]
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# resp = base_llm.ask(prompt_msg)
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# assert resp == resp_content
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# resp = base_llm.ask(prompt)
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# assert resp == default_resp_cont
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# resp = base_llm.ask_batch([prompt_msg])
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# assert resp == resp_content
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# resp = base_llm.ask_batch([prompt])
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# assert resp == default_resp_cont
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# resp = base_llm.ask_code([prompt_msg])
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# assert resp == resp_content
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# resp = base_llm.ask_code([prompt])
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# assert resp == default_resp_cont
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@pytest.mark.asyncio
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async def test_async_base_llm():
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base_llm = MockBaseLLM()
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resp = await base_llm.aask(prompt_msg)
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assert resp == resp_content
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resp = await base_llm.aask(prompt)
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assert resp == default_resp_cont
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resp = await base_llm.aask_batch([prompt_msg])
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assert resp == resp_content
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resp = await base_llm.aask_batch([prompt])
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assert resp == default_resp_cont
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# resp = await base_llm.aask_code([prompt_msg])
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# assert resp == resp_content
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# resp = await base_llm.aask_code([prompt])
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# assert resp == default_resp_cont
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@ -3,14 +3,7 @@
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# @Desc : the unittest of fireworks api
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import pytest
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from openai.types.chat.chat_completion import (
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ChatCompletion,
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ChatCompletionMessage,
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Choice,
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)
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from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
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from openai.types.chat.chat_completion_chunk import Choice as AChoice
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from openai.types.chat.chat_completion_chunk import ChoiceDelta
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from openai.types.completion_usage import CompletionUsage
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from metagpt.provider.fireworks_api import (
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@ -20,42 +13,19 @@ from metagpt.provider.fireworks_api import (
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)
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from metagpt.utils.cost_manager import Costs
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from tests.metagpt.provider.mock_llm_config import mock_llm_config
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resp_content = "I'm fireworks"
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default_resp = ChatCompletion(
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id="cmpl-a6652c1bb181caae8dd19ad8",
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model="accounts/fireworks/models/llama-v2-13b-chat",
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object="chat.completion",
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created=1703300855,
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choices=[
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Choice(
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finish_reason="stop",
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index=0,
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message=ChatCompletionMessage(role="assistant", content=resp_content),
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logprobs=None,
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)
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],
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usage=CompletionUsage(completion_tokens=110, prompt_tokens=92, total_tokens=202),
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from tests.metagpt.provider.req_resp_const import (
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get_openai_chat_completion,
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get_openai_chat_completion_chunk,
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llm_general_chat_funcs_test,
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messages,
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prompt,
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resp_cont_tmpl,
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)
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default_resp_chunk = ChatCompletionChunk(
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id=default_resp.id,
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model=default_resp.model,
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object="chat.completion.chunk",
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created=default_resp.created,
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choices=[
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AChoice(
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delta=ChoiceDelta(content=resp_content, role="assistant"),
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finish_reason="stop",
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index=0,
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logprobs=None,
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)
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],
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usage=dict(default_resp.usage),
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)
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prompt_msg = "who are you"
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messages = [{"role": "user", "content": prompt_msg}]
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name = "fireworks"
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resp_cont = resp_cont_tmpl.format(name=name)
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default_resp = get_openai_chat_completion(name)
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default_resp_chunk = get_openai_chat_completion_chunk(name, usage_as_dict=True)
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def test_fireworks_costmanager():
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@ -88,27 +58,17 @@ async def mock_openai_acompletions_create(self, stream: bool = False, **kwargs)
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async def test_fireworks_acompletion(mocker):
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mocker.patch("openai.resources.chat.completions.AsyncCompletions.create", mock_openai_acompletions_create)
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fireworks_gpt = FireworksLLM(mock_llm_config)
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fireworks_gpt.model = "llama-v2-13b-chat"
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fireworks_llm = FireworksLLM(mock_llm_config)
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fireworks_llm.model = "llama-v2-13b-chat"
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fireworks_gpt._update_costs(
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fireworks_llm._update_costs(
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usage=CompletionUsage(prompt_tokens=500000, completion_tokens=500000, total_tokens=1000000)
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)
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assert fireworks_gpt.get_costs() == Costs(
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assert fireworks_llm.get_costs() == Costs(
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total_prompt_tokens=500000, total_completion_tokens=500000, total_cost=0.5, total_budget=0
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)
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resp = await fireworks_gpt.acompletion(messages)
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assert resp.choices[0].message.content in resp_content
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resp = await fireworks_llm.acompletion(messages)
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assert resp.choices[0].message.content in resp_cont
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resp = await fireworks_gpt.aask(prompt_msg, stream=False)
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assert resp == resp_content
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resp = await fireworks_gpt.acompletion_text(messages, stream=False)
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assert resp == resp_content
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resp = await fireworks_gpt.acompletion_text(messages, stream=True)
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assert resp == resp_content
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resp = await fireworks_gpt.aask(prompt_msg)
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assert resp == resp_content
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await llm_general_chat_funcs_test(fireworks_llm, prompt, messages, resp_cont)
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|
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@ -11,6 +11,12 @@ from google.generativeai.types import content_types
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from metagpt.provider.google_gemini_api import GeminiLLM
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from tests.metagpt.provider.mock_llm_config import mock_llm_config
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from tests.metagpt.provider.req_resp_const import (
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gemini_messages,
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llm_general_chat_funcs_test,
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prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -18,10 +24,8 @@ class MockGeminiResponse(ABC):
|
|||
text: str
|
||||
|
||||
|
||||
prompt_msg = "who are you"
|
||||
messages = [{"role": "user", "parts": prompt_msg}]
|
||||
resp_content = "I'm gemini from google"
|
||||
default_resp = MockGeminiResponse(text=resp_content)
|
||||
resp_cont = resp_cont_tmpl.format(name="gemini")
|
||||
default_resp = MockGeminiResponse(text=resp_cont)
|
||||
|
||||
|
||||
def mock_gemini_count_tokens(self, contents: content_types.ContentsType) -> glm.CountTokensResponse:
|
||||
|
|
@ -60,28 +64,18 @@ async def test_gemini_acompletion(mocker):
|
|||
mock_gemini_generate_content_async,
|
||||
)
|
||||
|
||||
gemini_gpt = GeminiLLM(mock_llm_config)
|
||||
gemini_llm = 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]}
|
||||
assert gemini_llm._user_msg(prompt) == {"role": "user", "parts": [prompt]}
|
||||
assert gemini_llm._assistant_msg(prompt) == {"role": "model", "parts": [prompt]}
|
||||
|
||||
usage = gemini_gpt.get_usage(messages, resp_content)
|
||||
usage = gemini_llm.get_usage(gemini_messages, resp_cont)
|
||||
assert usage == {"prompt_tokens": 20, "completion_tokens": 20}
|
||||
|
||||
resp = gemini_gpt.completion(messages)
|
||||
resp = gemini_llm.completion(gemini_messages)
|
||||
assert resp == default_resp
|
||||
|
||||
resp = await gemini_gpt.acompletion(messages)
|
||||
resp = await gemini_llm.acompletion(gemini_messages)
|
||||
assert resp.text == default_resp.text
|
||||
|
||||
resp = await gemini_gpt.aask(prompt_msg, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await gemini_gpt.acompletion_text(messages, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await gemini_gpt.acompletion_text(messages, stream=True)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await gemini_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
await llm_general_chat_funcs_test(gemini_llm, prompt, gemini_messages, resp_cont)
|
||||
|
|
|
|||
|
|
@ -9,12 +9,15 @@ import pytest
|
|||
|
||||
from metagpt.provider.ollama_api import OllamaLLM
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
llm_general_chat_funcs_test,
|
||||
messages,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
prompt_msg = "who are you"
|
||||
messages = [{"role": "user", "content": prompt_msg}]
|
||||
|
||||
resp_content = "I'm ollama"
|
||||
default_resp = {"message": {"role": "assistant", "content": resp_content}}
|
||||
resp_cont = resp_cont_tmpl.format(name="ollama")
|
||||
default_resp = {"message": {"role": "assistant", "content": resp_cont}}
|
||||
|
||||
|
||||
async def mock_ollama_arequest(self, stream: bool = False, **kwargs) -> Tuple[Any, Any, bool]:
|
||||
|
|
@ -41,19 +44,12 @@ 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(mock_llm_config)
|
||||
ollama_llm = OllamaLLM(mock_llm_config)
|
||||
|
||||
resp = await ollama_gpt.acompletion(messages)
|
||||
resp = await ollama_llm.acompletion(messages)
|
||||
assert resp["message"]["content"] == default_resp["message"]["content"]
|
||||
|
||||
resp = await ollama_gpt.aask(prompt_msg, stream=False)
|
||||
assert resp == resp_content
|
||||
resp = await ollama_llm.aask(prompt, stream=False)
|
||||
assert resp == resp_cont
|
||||
|
||||
resp = await ollama_gpt.acompletion_text(messages, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await ollama_gpt.acompletion_text(messages, stream=True)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await ollama_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
await llm_general_chat_funcs_test(ollama_llm, prompt, messages, resp_cont)
|
||||
|
|
|
|||
|
|
@ -3,53 +3,26 @@
|
|||
# @Desc :
|
||||
|
||||
import pytest
|
||||
from openai.types.chat.chat_completion import (
|
||||
ChatCompletion,
|
||||
ChatCompletionMessage,
|
||||
Choice,
|
||||
)
|
||||
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
|
||||
from openai.types.chat.chat_completion_chunk import Choice as AChoice
|
||||
from openai.types.chat.chat_completion_chunk import ChoiceDelta
|
||||
from openai.types.completion_usage import CompletionUsage
|
||||
|
||||
from metagpt.provider.open_llm_api import OpenLLM
|
||||
from metagpt.utils.cost_manager import Costs
|
||||
from metagpt.utils.cost_manager import CostManager, Costs
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config
|
||||
|
||||
resp_content = "I'm llama2"
|
||||
default_resp = ChatCompletion(
|
||||
id="cmpl-a6652c1bb181caae8dd19ad8",
|
||||
model="llama-v2-13b-chat",
|
||||
object="chat.completion",
|
||||
created=1703302755,
|
||||
choices=[
|
||||
Choice(
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
message=ChatCompletionMessage(role="assistant", content=resp_content),
|
||||
logprobs=None,
|
||||
)
|
||||
],
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
get_openai_chat_completion,
|
||||
get_openai_chat_completion_chunk,
|
||||
llm_general_chat_funcs_test,
|
||||
messages,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
default_resp_chunk = ChatCompletionChunk(
|
||||
id=default_resp.id,
|
||||
model=default_resp.model,
|
||||
object="chat.completion.chunk",
|
||||
created=default_resp.created,
|
||||
choices=[
|
||||
AChoice(
|
||||
delta=ChoiceDelta(content=resp_content, role="assistant"),
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
logprobs=None,
|
||||
)
|
||||
],
|
||||
)
|
||||
name = "llama2-7b"
|
||||
resp_cont = resp_cont_tmpl.format(name=name)
|
||||
default_resp = get_openai_chat_completion(name)
|
||||
|
||||
prompt_msg = "who are you"
|
||||
messages = [{"role": "user", "content": prompt_msg}]
|
||||
default_resp_chunk = get_openai_chat_completion_chunk(name)
|
||||
|
||||
|
||||
async def mock_openai_acompletions_create(self, stream: bool = False, **kwargs) -> ChatCompletionChunk:
|
||||
|
|
@ -68,25 +41,16 @@ 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(mock_llm_config)
|
||||
openllm_gpt.model = "llama-v2-13b-chat"
|
||||
openllm_llm = OpenLLM(mock_llm_config)
|
||||
openllm_llm.model = "llama-v2-13b-chat"
|
||||
|
||||
openllm_gpt._update_costs(usage=CompletionUsage(prompt_tokens=100, completion_tokens=100, total_tokens=200))
|
||||
assert openllm_gpt.get_costs() == Costs(
|
||||
openllm_llm.cost_manager = CostManager()
|
||||
openllm_llm._update_costs(usage=CompletionUsage(prompt_tokens=100, completion_tokens=100, total_tokens=200))
|
||||
assert openllm_llm.get_costs() == Costs(
|
||||
total_prompt_tokens=100, total_completion_tokens=100, total_cost=0, total_budget=0
|
||||
)
|
||||
|
||||
resp = await openllm_gpt.acompletion(messages)
|
||||
assert resp.choices[0].message.content in resp_content
|
||||
resp = await openllm_llm.acompletion(messages)
|
||||
assert resp.choices[0].message.content in resp_cont
|
||||
|
||||
resp = await openllm_gpt.aask(prompt_msg, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await openllm_gpt.acompletion_text(messages, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await openllm_gpt.acompletion_text(messages, stream=True)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await openllm_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
await llm_general_chat_funcs_test(openllm_llm, prompt, messages, resp_cont)
|
||||
|
|
|
|||
|
|
@ -4,12 +4,18 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from metagpt.config2 import Config
|
||||
from metagpt.provider.spark_api import GetMessageFromWeb, SparkLLM
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config
|
||||
from tests.metagpt.provider.mock_llm_config import (
|
||||
mock_llm_config,
|
||||
mock_llm_config_spark,
|
||||
)
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
llm_general_chat_funcs_test,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
prompt_msg = "who are you"
|
||||
resp_content = "I'm Spark"
|
||||
resp_cont = resp_cont_tmpl.format(name="Spark")
|
||||
|
||||
|
||||
class MockWebSocketApp(object):
|
||||
|
|
@ -23,7 +29,7 @@ class MockWebSocketApp(object):
|
|||
def test_get_msg_from_web(mocker):
|
||||
mocker.patch("websocket.WebSocketApp", MockWebSocketApp)
|
||||
|
||||
get_msg_from_web = GetMessageFromWeb(prompt_msg, mock_llm_config)
|
||||
get_msg_from_web = GetMessageFromWeb(prompt, mock_llm_config)
|
||||
assert get_msg_from_web.gen_params()["parameter"]["chat"]["domain"] == "mock_domain"
|
||||
|
||||
ret = get_msg_from_web.run()
|
||||
|
|
@ -31,34 +37,26 @@ def test_get_msg_from_web(mocker):
|
|||
|
||||
|
||||
def mock_spark_get_msg_from_web_run(self) -> str:
|
||||
return resp_content
|
||||
return resp_cont
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_spark_aask():
|
||||
llm = SparkLLM(Config.from_home("spark.yaml").llm)
|
||||
async def test_spark_aask(mocker):
|
||||
mocker.patch("metagpt.provider.spark_api.GetMessageFromWeb.run", mock_spark_get_msg_from_web_run)
|
||||
|
||||
llm = SparkLLM(mock_llm_config_spark)
|
||||
|
||||
resp = await llm.aask("Hello!")
|
||||
print(resp)
|
||||
assert resp == resp_cont
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_spark_acompletion(mocker):
|
||||
mocker.patch("metagpt.provider.spark_api.GetMessageFromWeb.run", mock_spark_get_msg_from_web_run)
|
||||
|
||||
spark_gpt = SparkLLM(mock_llm_config)
|
||||
spark_llm = SparkLLM(mock_llm_config)
|
||||
|
||||
resp = await spark_gpt.acompletion([])
|
||||
assert resp == resp_content
|
||||
resp = await spark_llm.acompletion([])
|
||||
assert resp == resp_cont
|
||||
|
||||
resp = await spark_gpt.aask(prompt_msg, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await spark_gpt.acompletion_text([], stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await spark_gpt.acompletion_text([], stream=True)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await spark_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
await llm_general_chat_funcs_test(spark_llm, prompt, prompt, resp_cont)
|
||||
|
|
|
|||
|
|
@ -6,22 +6,24 @@ import pytest
|
|||
|
||||
from metagpt.provider.zhipuai_api import ZhiPuAILLM
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config_zhipu
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
get_part_chat_completion,
|
||||
llm_general_chat_funcs_test,
|
||||
messages,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
prompt_msg = "who are you"
|
||||
messages = [{"role": "user", "content": prompt_msg}]
|
||||
|
||||
resp_content = "I'm chatglm-turbo"
|
||||
default_resp = {
|
||||
"choices": [{"finish_reason": "stop", "index": 0, "message": {"content": resp_content, "role": "assistant"}}],
|
||||
"usage": {"completion_tokens": 22, "prompt_tokens": 19, "total_tokens": 41},
|
||||
}
|
||||
name = "ChatGLM-4"
|
||||
resp_cont = resp_cont_tmpl.format(name=name)
|
||||
default_resp = get_part_chat_completion(name)
|
||||
|
||||
|
||||
async def mock_zhipuai_acreate_stream(self, **kwargs):
|
||||
class MockResponse(object):
|
||||
async def _aread(self):
|
||||
class Iterator(object):
|
||||
events = [{"choices": [{"index": 0, "delta": {"content": resp_content, "role": "assistant"}}]}]
|
||||
events = [{"choices": [{"index": 0, "delta": {"content": resp_cont, "role": "assistant"}}]}]
|
||||
|
||||
async def __aiter__(self):
|
||||
for event in self.events:
|
||||
|
|
@ -46,22 +48,12 @@ async def test_zhipuai_acompletion(mocker):
|
|||
mocker.patch("metagpt.provider.zhipuai.zhipu_model_api.ZhiPuModelAPI.acreate", mock_zhipuai_acreate)
|
||||
mocker.patch("metagpt.provider.zhipuai.zhipu_model_api.ZhiPuModelAPI.acreate_stream", mock_zhipuai_acreate_stream)
|
||||
|
||||
zhipu_gpt = ZhiPuAILLM(mock_llm_config_zhipu)
|
||||
zhipu_llm = ZhiPuAILLM(mock_llm_config_zhipu)
|
||||
|
||||
resp = await zhipu_gpt.acompletion(messages)
|
||||
assert resp["choices"][0]["message"]["content"] == resp_content
|
||||
resp = await zhipu_llm.acompletion(messages)
|
||||
assert resp["choices"][0]["message"]["content"] == resp_cont
|
||||
|
||||
resp = await zhipu_gpt.aask(prompt_msg, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await zhipu_gpt.acompletion_text(messages, stream=False)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await zhipu_gpt.acompletion_text(messages, stream=True)
|
||||
assert resp == resp_content
|
||||
|
||||
resp = await zhipu_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
await llm_general_chat_funcs_test(zhipu_llm, prompt, messages, resp_cont)
|
||||
|
||||
|
||||
def test_zhipuai_proxy():
|
||||
|
|
|
|||
|
|
@ -1,7 +0,0 @@
|
|||
llm:
|
||||
api_type: "spark"
|
||||
app_id: "xxx"
|
||||
api_key: "xxx"
|
||||
api_secret: "xxx"
|
||||
domain: "generalv2"
|
||||
base_url: "wss://spark-api.xf-yun.com/v3.1/chat"
|
||||
Loading…
Add table
Add a link
Reference in a new issue