greatmengqi
edf345cd72
- Freeze all config models (AppConfig + 15 sub-configs) with frozen=True - Purify from_file() — remove 9 load_*_from_dict() side-effect calls - Replace mtime/reload/push/pop machinery with single ContextVar + init_app_config() - Delete 10 sub-module globals and their getters/setters/loaders - Migrate 50+ consumers from get_*_config() to get_app_config().xxx - Expand DeerFlowContext: app_config + thread_id + agent_name (frozen dataclass) - Wire into Gateway runtime (worker.py) and DeerFlowClient via context= parameter - Remove sandbox_id from runtime.context — flows through ThreadState.sandbox only - Middleware/tools access runtime.context directly via Runtime[DeerFlowContext] generic - resolve_context() retained at server entry points for LangGraph Server fallback
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
"""Configuration for conversation summarization."""
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field
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ContextSizeType = Literal["fraction", "tokens", "messages"]
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class ContextSize(BaseModel):
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"""Context size specification for trigger or keep parameters."""
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model_config = ConfigDict(frozen=True)
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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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model_config = ConfigDict(frozen=True)
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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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