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feat(core): update version and enhance configuration management (#164)

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jinliyl 2026-03-18 15:16:10 +08:00 • committed by GitHub
parent 8d7cc4bbd6
commit d313ae6e52
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6 changed files with 33 additions and 58 deletions
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2
reme/__init__.py
View file

@ -6,7 +6,7 @@ from . import extension
from . import memory
from .reme import ReMe
__version__ = "0.3.0.8"
__version__ = "0.3.0.9"
__all__ = [
"config",

24
reme/core/application.py
View file

@ -15,7 +15,7 @@ from .registry_factory import R
from .schema import Response, ServiceConfig
from .service_context import ServiceContext
from .token_counter import BaseTokenCounter
from .utils import execute_stream_task, PydanticConfigParser, init_logger, MCPClient, print_logo, get_logger
from .utils import execute_stream_task, PydanticConfigParser, init_logger, MCPClient, print_logo, get_logger, load_env
from .vector_store import BaseVectorStore
logger = get_logger()
@ -46,12 +46,16 @@ class Application:
default_file_watcher_config: dict | None = None,
**kwargs,
):
load_env()
self.llm_api_key = llm_api_key or os.getenv("LLM_API_KEY", "")
self.llm_base_url = llm_base_url or os.getenv("LLM_BASE_URL", "")
self.embedding_api_key = embedding_api_key or os.getenv("EMBEDDING_API_KEY", "")
self.embedding_base_url = embedding_base_url or os.getenv("EMBEDDING_BASE_URL", "")
self.service_context = ServiceContext(
*args,
llm_api_key=llm_api_key,
llm_base_url=llm_base_url,
embedding_api_key=embedding_api_key,
embedding_base_url=embedding_base_url,
service_config=None,
parser=parser,
working_dir=working_dir,
@ -158,11 +162,11 @@ class Application:
else:
config_dict = config.model_dump(exclude={"backend"})
if not config_dict.get("api_key", ""):
config_dict["api_key"] = os.getenv("LLM_API_KEY", "")
config_dict["api_key"] = self.llm_api_key
if "client_kwargs" not in config_dict:
config_dict["client_kwargs"] = {}
if not config_dict["client_kwargs"].get("base_url", ""):
config_dict["client_kwargs"]["base_url"] = os.getenv("LLM_BASE_URL", "")
config_dict["client_kwargs"]["base_url"] = self.llm_base_url
self.service_context.as_llms[name] = R.as_llms[config.backend](**config_dict)
for name, config in self.service_config.as_llm_formatters.items():
@ -184,6 +188,8 @@ class Application:
logger.warning(f"LLM backend {config.backend} is not supported.")
else:
config_dict = config.model_dump(exclude={"backend"})
config_dict.setdefault("api_key", self.llm_api_key)
config_dict.setdefault("base_url", self.llm_base_url)
self.service_context.llms[name] = R.llms[config.backend](**config_dict)
await self.service_context.llms[name].start()
@ -192,7 +198,9 @@ class Application:
logger.warning(f"Embedding model backend {config.backend} is not supported.")
else:
config_dict = config.model_dump(exclude={"backend"})
config_dict["cache_dir"] = working_path / "embedding_cache"
config_dict.setdefault("api_key", self.embedding_api_key)
config_dict.setdefault("base_url", self.embedding_base_url)
config_dict.setdefault("cache_dir", working_path / "embedding_cache")
self.service_context.embedding_models[name] = R.embedding_models[config.backend](**config_dict)
await self.service_context.embedding_models[name].start()

13
reme/core/as_token_counter/reme_token_counter.py
View file

@ -47,9 +47,18 @@ class ReMeTokenCounter(HuggingFaceTokenCounter):
# Set HuggingFace endpoint for mirror support
if use_mirror:
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
mirror = "https://hf-mirror.com"
else:
os.environ.pop("HF_ENDPOINT", None)
mirror = "https://huggingface.co"
os.environ["HF_ENDPOINT"] = mirror
# if the huggingface is already imported in other dependencies,
# we need to set the endpoint manually
import huggingface_hub.constants
huggingface_hub.constants.ENDPOINT = mirror
huggingface_hub.constants.HUGGINGFACE_CO_URL_TEMPLATE = mirror + "/{repo_id}/resolve/{revision}/{filename}"
try:
super().__init__(

15
reme/core/embedding/base_embedding_model.py
View file

@ -6,7 +6,6 @@ Defines the abstract base class and standard API for all embedding model impleme
import asyncio
import hashlib
import json
import os
import time
from abc import ABC
from collections import OrderedDict
@ -56,8 +55,8 @@ class BaseEmbeddingModel(ABC):
enable_cache: Whether to enable embedding cache
**kwargs: Additional model-specific parameters
"""
self._api_key: str = api_key
self._base_url: str = base_url
self.api_key: str = api_key
self.base_url: str = base_url
self.model_name = model_name
self.dimensions = dimensions
self.use_dimensions = use_dimensions
@ -78,16 +77,6 @@ class BaseEmbeddingModel(ABC):
self.cache_path: Path = Path(self.cache_dir)
self.cache_path.mkdir(parents=True, exist_ok=True)
@property
def api_key(self) -> str | None:
"""Get API key from environment variable."""
return os.getenv("EMBEDDING_API_KEY") or self._api_key
@property
def base_url(self) -> str | None:
"""Get base URL from environment variable."""
return os.getenv("EMBEDDING_BASE_URL") or self._base_url
def _truncate_text(self, text: str) -> str:
"""Truncate text to max_input_length if it exceeds the limit."""
if len(text) > self.max_input_length:

15
reme/core/llm/base_llm.py
View file

@ -2,7 +2,6 @@
import asyncio
import json
import os
import time
from abc import ABC, abstractmethod
from typing import Callable, Generator, AsyncGenerator, Any
@ -36,8 +35,8 @@ class BaseLLM(ABC):
request_interval: Minimum seconds between requests (default: 0.0)
**kwargs: Additional model-specific parameters
"""
self._api_key: str = api_key
self._base_url: str = base_url
self.api_key: str = api_key
self.base_url: str = base_url
self.model_name: str = model_name
self.max_retries: int = max_retries
self.raise_exception: bool = raise_exception
@ -47,16 +46,6 @@ class BaseLLM(ABC):
self._last_request_time: float = 0.0
self._request_lock: asyncio.Lock = asyncio.Lock()
@property
def api_key(self) -> str | None:
"""Get API key from environment variable."""
return os.getenv("LLM_API_KEY") or self._api_key
@property
def base_url(self) -> str | None:
"""Get base URL from environment variable."""
return os.getenv("LLM_BASE_URL") or self._base_url
@staticmethod
def _accumulate_tool_call_chunk(tool_call, ret_tools: list[ToolCall]):
"""Assemble incremental tool call chunks into complete ToolCall objects."""

22
reme/core/service_context.py
View file

@ -1,6 +1,5 @@
"""Service context."""
import os
from concurrent.futures import ThreadPoolExecutor
from typing import TYPE_CHECKING
@ -8,7 +7,7 @@ from loguru import logger
from .base_dict import BaseDict
from .schema import ServiceConfig
from .utils import load_env, PydanticConfigParser
from .utils import PydanticConfigParser
if TYPE_CHECKING:
from agentscope.model import ChatModelBase
@ -29,10 +28,6 @@ class ServiceContext(BaseDict):
def __init__(
self,
*args,
llm_api_key: str | None = None,
llm_base_url: str | None = None,
embedding_api_key: str | None = None,
embedding_base_url: str | None = None,
service_config: ServiceConfig | None = None,
parser: type[PydanticConfigParser] | None = None,
working_dir: str | None = None,
@ -52,15 +47,6 @@ class ServiceContext(BaseDict):
):
super().__init__()
# Load environment variables
load_env()
# Update common environment variables for LLM and embedding services.
self.update_env("LLM_API_KEY", llm_api_key)
self.update_env("LLM_BASE_URL", llm_base_url)
self.update_env("EMBEDDING_API_KEY", embedding_api_key)
self.update_env("EMBEDDING_BASE_URL", embedding_base_url)
if service_config is None:
parser_class = parser if parser is not None else PydanticConfigParser
parser_instance = parser_class(ServiceConfig)
@ -114,12 +100,6 @@ class ServiceContext(BaseDict):
self.flows: dict[str, "BaseFlow"] = {}
self.mcp_server_mapping: dict[str, dict] = {}
@staticmethod
def update_env(key: str, value: str | None):
"""Update environment variable if value is provided."""
if value:
os.environ[key] = value
@staticmethod
def _update_section_config(config: dict, section_name: str, **kwargs):
"""Update a specific section of the service config with new values."""