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https://github.com/bytedance/deer-flow.git
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66b9e7f212
* feat: emit structured runtime metadata * fix: avoid subagent import cycle in replay gateway * fix: preserve legacy subtask result parsing * refactor: tighten runtime metadata contracts * fix(middleware): keep recovery hint on task exception wrapper content The structured-metadata stamp overwrote the wrapper text with the bare task-failure message, dropping the model-facing 'Continue with available context, or choose an alternative tool.' guidance that every other tool exception keeps. Append the shared hint after the formatted message. * fix(subagents): require lowercase hex for result_sha256 reader Length-only validation accepted any 64-char string; a faulty serializer or relaying wrapper could store a non-digest value in the delegation ledger. Enforce the producer's hexdigest shape with a fullmatch. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
"""Configuration for conversation summarization."""
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from typing import Literal
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from pydantic import BaseModel, Field
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ContextSizeType = Literal["fraction", "tokens", "messages"]
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DEFAULT_SKILL_FILE_READ_TOOL_NAMES: tuple[str, ...] = ("read_file", "read", "view", "cat")
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class ContextSize(BaseModel):
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"""Context size specification for trigger or keep parameters."""
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type: ContextSizeType = Field(description="Type of context size specification")
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value: int | float = Field(description="Value for the context size specification")
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def to_tuple(self) -> tuple[ContextSizeType, int | float]:
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"""Convert to tuple format expected by SummarizationMiddleware."""
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return (self.type, self.value)
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class SummarizationConfig(BaseModel):
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"""Configuration for automatic conversation summarization."""
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enabled: bool = Field(
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default=False,
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description="Whether to enable automatic conversation summarization",
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)
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model_name: str | None = Field(
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default=None,
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description="Model name to use for summarization (None = use a lightweight model)",
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)
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trigger: ContextSize | list[ContextSize] | None = Field(
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default=None,
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description="One or more thresholds that trigger summarization. When any threshold is met, summarization runs. "
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"Examples: {'type': 'messages', 'value': 50} triggers at 50 messages, "
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"{'type': 'tokens', 'value': 4000} triggers at 4000 tokens, "
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"{'type': 'fraction', 'value': 0.8} triggers at 80% of model's max input tokens",
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)
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keep: ContextSize = Field(
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default_factory=lambda: ContextSize(type="messages", value=20),
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description="Context retention policy after summarization. Specifies how much history to preserve. "
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"Examples: {'type': 'messages', 'value': 20} keeps 20 messages, "
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"{'type': 'tokens', 'value': 3000} keeps 3000 tokens, "
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"{'type': 'fraction', 'value': 0.3} keeps 30% of model's max input tokens",
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)
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trim_tokens_to_summarize: int | None = Field(
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default=4000,
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description="Maximum tokens to keep when preparing messages for summarization. Pass null to skip trimming.",
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)
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summary_prompt: str | None = Field(
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default=None,
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description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
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)
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skill_file_read_tool_names: list[str] = Field(
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default_factory=lambda: list(DEFAULT_SKILL_FILE_READ_TOOL_NAMES),
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description="Tool names treated as skill-file reads when capturing loaded skills into the durable skill_context channel.",
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)
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# Global configuration instance
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_summarization_config: SummarizationConfig = SummarizationConfig()
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def get_summarization_config() -> SummarizationConfig:
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"""Get the current summarization configuration."""
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return _summarization_config
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def set_summarization_config(config: SummarizationConfig) -> None:
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"""Set the summarization configuration."""
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global _summarization_config
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_summarization_config = config
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def load_summarization_config_from_dict(config_dict: dict) -> None:
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"""Load summarization configuration from a dictionary."""
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global _summarization_config
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_summarization_config = SummarizationConfig(**config_dict)
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