deerflow2/src/config/configuration.py

76 lines
2.5 KiB
Python

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT
import logging
import os
from dataclasses import dataclass, field, fields
from typing import Any, Optional
from langchain_core.runnables import RunnableConfig
from src.rag.retriever import Resource
from src.config.report_style import ReportStyle
logger = logging.getLogger(__name__)
def get_recursion_limit(default: int = 25) -> int:
"""Get the recursion limit from environment variable or use default.
Args:
default: Default recursion limit if environment variable is not set or invalid
Returns:
int: The recursion limit to use
"""
try:
env_value_str = os.getenv("AGENT_RECURSION_LIMIT", str(default))
parsed_limit = int(env_value_str)
if parsed_limit > 0:
logger.info(f"Recursion limit set to: {parsed_limit}")
return parsed_limit
else:
logger.warning(
f"AGENT_RECURSION_LIMIT value '{env_value_str}' (parsed as {parsed_limit}) is not positive. "
f"Using default value {default}."
)
return default
except ValueError:
raw_env_value = os.getenv("AGENT_RECURSION_LIMIT")
logger.warning(
f"Invalid AGENT_RECURSION_LIMIT value: '{raw_env_value}'. "
f"Using default value {default}."
)
return default
@dataclass(kw_only=True)
class Configuration:
"""The configurable fields."""
resources: list[Resource] = field(
default_factory=list
) # Resources to be used for the research
max_plan_iterations: int = 1 # Maximum number of plan iterations
max_step_num: int = 3 # Maximum number of steps in a plan
max_search_results: int = 3 # Maximum number of search results
mcp_settings: dict = None # MCP settings, including dynamic loaded tools
report_style: str = ReportStyle.ACADEMIC.value # Report style
enable_deep_thinking: bool = False # Whether to enable deep thinking
@classmethod
def from_runnable_config(
cls, config: Optional[RunnableConfig] = None
) -> "Configuration":
"""Create a Configuration instance from a RunnableConfig."""
configurable = (
config["configurable"] if config and "configurable" in config else {}
)
values: dict[str, Any] = {
f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
for f in fields(cls)
if f.init
}
return cls(**{k: v for k, v in values.items() if v})