chenmingxuan
2ddec53954
Localize the web console and persist custom model and capability settings. Use OpenAI Responses exclusively and move runtime limits into environment configuration. Bootstrap uv and the virtual environment on Windows, support optional China mirrors, and package source distributions without environments, build output, or credentials. Update regression tests and repository quality checks for the new behavior.
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Single transport for all Artemis models: OpenAI Responses API."""
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from enum import StrEnum
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import hashlib
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import os
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from typing import Any
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from langchain_core.language_models.chat_models import BaseChatModel
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from pydantic import BaseModel, Field
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from artemis.config.settings import settings
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class ModelProvider(StrEnum):
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OPENAI = "openai"
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@classmethod
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def from_string(cls, val: Any) -> "ModelProvider":
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if val is None or str(val).strip().lower() in ("", "openai"):
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return cls.OPENAI
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raise ValueError(f"Unsupported LLM provider {val!r}; only OpenAI Responses is supported")
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class ModelEndpoint(BaseModel):
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provider: ModelProvider = ModelProvider.OPENAI
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model_name: str = Field(default_factory=lambda: settings.OPENAI_MODEL)
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api_key: str | None = Field(default=None, repr=False)
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api_base: str | None = None
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temperature: float | None = None
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max_tokens: int | None = Field(default=None, gt=0)
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timeout_seconds: float = Field(default_factory=lambda: settings.OPENAI_TIMEOUT_SECONDS, gt=0)
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max_retries: int = Field(default_factory=lambda: settings.OPENAI_MAX_RETRIES, ge=0)
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is_multimodal: bool = True
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reasoning_effort: str | None = None
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enable_grounding: bool = False
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def cache_key(self) -> tuple:
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key = self.api_key or (
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settings.OPENAI_API_KEY.get_secret_value() if settings.OPENAI_API_KEY else ""
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)
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return (
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self.provider.value,
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self.model_name,
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self.temperature,
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self.max_tokens,
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self.timeout_seconds,
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self.max_retries,
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self.reasoning_effort,
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self.enable_grounding,
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self.api_base or settings.OPENAI_BASE_URL,
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hashlib.sha256(key.encode()).hexdigest(),
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)
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class ModelFactory:
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_cache: dict[tuple, BaseChatModel] = {}
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@classmethod
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def get_model(cls, endpoint: ModelEndpoint) -> BaseChatModel:
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key = endpoint.cache_key()
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if key not in cls._cache:
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cls._cache[key] = cls.create_model(endpoint)
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return cls._cache[key]
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@classmethod
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def create_model(cls, endpoint: ModelEndpoint) -> BaseChatModel:
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ModelProvider.from_string(endpoint.provider)
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if os.environ.get("ARTEMIS_FAKE_LLM") == "1":
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from artemis.llm.fake_model import FakeChatModel
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return FakeChatModel(
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delay_s=float(os.environ.get("ARTEMIS_FAKE_LLM_DELAY_S", "0") or 0)
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)
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from langchain_openai import ChatOpenAI
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api_key = endpoint.api_key or (
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settings.OPENAI_API_KEY.get_secret_value() if settings.OPENAI_API_KEY else None
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)
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if not api_key:
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raise ValueError("请在环境变量或 .env 中配置 OPENAI_API_KEY")
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kwargs = {
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"model": endpoint.model_name,
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"api_key": api_key,
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"base_url": endpoint.api_base or settings.OPENAI_BASE_URL or None,
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"timeout": endpoint.timeout_seconds,
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"max_retries": endpoint.max_retries,
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"temperature": endpoint.temperature,
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"max_tokens": endpoint.max_tokens,
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"reasoning_effort": endpoint.reasoning_effort,
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"use_responses_api": True,
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"output_version": "responses/v1",
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"store": False,
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}
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return ChatOpenAI(**{k: v for k, v in kwargs.items() if v is not None})
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