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chore: import upstream snapshot with attribution
2026-07-13 13:02:24 +08:00

56 行
1.7 KiB
Python

from functools import lru_cache
from typing import Any
from pydantic_settings import BaseSettings, SettingsConfigDict
from cognee.infrastructure.data.chunking.DefaultChunkEngine import DefaultChunkEngine
from cognee.shared.data_models import ChunkEngine, ChunkStrategy
class ChunkConfig(BaseSettings):
"""
Manage configuration settings for chunk processing.
"""
chunk_size: int = 1500
chunk_overlap: int = 10
chunk_strategy: ChunkStrategy = ChunkStrategy.PARAGRAPH
chunk_engine: ChunkEngine = ChunkEngine.DEFAULT_ENGINE
model_config = SettingsConfigDict(env_file=".env", extra="allow")
def to_dict(self) -> dict[str, Any]:
"""
Convert the chunk settings to a dictionary format.
Returns:
--------
- dict: A dictionary representation of the chunk configuration settings.
"""
return {
"chunk_size": self.chunk_size,
"chunk_overlap": self.chunk_overlap,
"chunk_strategy": self.chunk_strategy,
"chunk_engine": self.chunk_engine,
}
@lru_cache
def get_chunk_config() -> ChunkConfig:
"""
Retrieve the configuration for chunking data, caching the result for efficiency.
This function creates an instance of the ChunkConfig class, which contains settings such
as chunk size, overlap, strategy, and engine. The use of lru_cache ensures that
subsequent calls to this function will return the cached instance, improving performance
by avoiding re-creation of the object.
Returns:
--------
- ChunkConfig: An instance of the ChunkConfig class containing the chunking
configuration settings.
"""
return ChunkConfig()