Files
deer-flow/backend/src/config/summarization_config.py
2026-01-19 16:17:31 +08:00

75 lines
2.9 KiB
Python

"""Configuration for conversation summarization."""
from typing import Literal
from pydantic import BaseModel, Field
ContextSizeType = Literal["fraction", "tokens", "messages"]
class ContextSize(BaseModel):
"""Context size specification for trigger or keep parameters."""
type: ContextSizeType = Field(description="Type of context size specification")
value: int | float = Field(description="Value for the context size specification")
def to_tuple(self) -> tuple[ContextSizeType, int | float]:
"""Convert to tuple format expected by SummarizationMiddleware."""
return (self.type, self.value)
class SummarizationConfig(BaseModel):
"""Configuration for automatic conversation summarization."""
enabled: bool = Field(
default=False,
description="Whether to enable automatic conversation summarization",
)
model_name: str | None = Field(
default=None,
description="Model name to use for summarization (None = use a lightweight model)",
)
trigger: ContextSize | list[ContextSize] | None = Field(
default=None,
description="One or more thresholds that trigger summarization. When any threshold is met, summarization runs. "
"Examples: {'type': 'messages', 'value': 50} triggers at 50 messages, "
"{'type': 'tokens', 'value': 4000} triggers at 4000 tokens, "
"{'type': 'fraction', 'value': 0.8} triggers at 80% of model's max input tokens",
)
keep: ContextSize = Field(
default_factory=lambda: ContextSize(type="messages", value=20),
description="Context retention policy after summarization. Specifies how much history to preserve. "
"Examples: {'type': 'messages', 'value': 20} keeps 20 messages, "
"{'type': 'tokens', 'value': 3000} keeps 3000 tokens, "
"{'type': 'fraction', 'value': 0.3} keeps 30% of model's max input tokens",
)
trim_tokens_to_summarize: int | None = Field(
default=4000,
description="Maximum tokens to keep when preparing messages for summarization. Pass null to skip trimming.",
)
summary_prompt: str | None = Field(
default=None,
description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
)
# Global configuration instance
_summarization_config: SummarizationConfig = SummarizationConfig()
def get_summarization_config() -> SummarizationConfig:
"""Get the current summarization configuration."""
return _summarization_config
def set_summarization_config(config: SummarizationConfig) -> None:
"""Set the summarization configuration."""
global _summarization_config
_summarization_config = config
def load_summarization_config_from_dict(config_dict: dict) -> None:
"""Load summarization configuration from a dictionary."""
global _summarization_config
_summarization_config = SummarizationConfig(**config_dict)