mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-20 15:11:09 +00:00
DanielWalnut
19fa1e97c3
* feat: implement backend logic * feat: implement api/config endpoint * rename the symbol * feat: re-implement configuration at client-side * feat: add client-side of deep thinking * fix backend bug * feat: add reasoning block * docs: update readme * fix: translate into English * fix: change icon to lightbulb * feat: ignore more bad cases * feat: adjust thinking layout, and implement auto scrolling * docs: add comments --------- Co-authored-by: Henry Li <henry1943@163.com>
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import os
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from dataclasses import dataclass, field, fields
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from typing import Any, Optional
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from langchain_core.runnables import RunnableConfig
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from src.rag.retriever import Resource
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from src.config.report_style import ReportStyle
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@dataclass(kw_only=True)
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class Configuration:
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"""The configurable fields."""
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resources: list[Resource] = field(
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default_factory=list
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) # Resources to be used for the research
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max_plan_iterations: int = 1 # Maximum number of plan iterations
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max_step_num: int = 3 # Maximum number of steps in a plan
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max_search_results: int = 3 # Maximum number of search results
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mcp_settings: dict = None # MCP settings, including dynamic loaded tools
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report_style: str = ReportStyle.ACADEMIC.value # Report style
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enable_deep_thinking: bool = False # Whether to enable deep thinking
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@classmethod
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def from_runnable_config(
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cls, config: Optional[RunnableConfig] = None
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) -> "Configuration":
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"""Create a Configuration instance from a RunnableConfig."""
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configurable = (
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config["configurable"] if config and "configurable" in config else {}
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)
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values: dict[str, Any] = {
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f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
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for f in fields(cls)
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if f.init
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}
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return cls(**{k: v for k, v in values.items() if v})
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