dify/api/services/entities/knowledge_entities/rag_pipeline_entities.py
2025-09-23 16:40:26 +08:00

131 lines
2.7 KiB
Python

from typing import Literal
from pydantic import BaseModel, field_validator
class IconInfo(BaseModel):
icon: str
icon_background: str | None = None
icon_type: str | None = None
icon_url: str | None = None
class PipelineTemplateInfoEntity(BaseModel):
name: str
description: str
icon_info: IconInfo
class RagPipelineDatasetCreateEntity(BaseModel):
name: str
description: str
icon_info: IconInfo
permission: str
partial_member_list: list[str] | None = None
yaml_content: str | None = None
class RerankingModelConfig(BaseModel):
"""
Reranking Model Config.
"""
reranking_provider_name: str | None = ""
reranking_model_name: str | None = ""
class VectorSetting(BaseModel):
"""
Vector Setting.
"""
vector_weight: float
embedding_provider_name: str
embedding_model_name: str
class KeywordSetting(BaseModel):
"""
Keyword Setting.
"""
keyword_weight: float
class WeightedScoreConfig(BaseModel):
"""
Weighted score Config.
"""
vector_setting: VectorSetting | None
keyword_setting: KeywordSetting | None
class EmbeddingSetting(BaseModel):
"""
Embedding Setting.
"""
embedding_provider_name: str
embedding_model_name: str
class EconomySetting(BaseModel):
"""
Economy Setting.
"""
keyword_number: int
class RetrievalSetting(BaseModel):
"""
Retrieval Setting.
"""
search_method: Literal["semantic_search", "full_text_search", "keyword_search", "hybrid_search"]
top_k: int
score_threshold: float | None = 0.5
score_threshold_enabled: bool = False
reranking_mode: str | None = "reranking_model"
reranking_enable: bool | None = True
reranking_model: RerankingModelConfig | None = None
weights: WeightedScoreConfig | None = None
class IndexMethod(BaseModel):
"""
Knowledge Index Setting.
"""
indexing_technique: Literal["high_quality", "economy"]
embedding_setting: EmbeddingSetting
economy_setting: EconomySetting
class KnowledgeConfiguration(BaseModel):
"""
Knowledge Base Configuration.
"""
chunk_structure: str
indexing_technique: Literal["high_quality", "economy"]
embedding_model_provider: str = ""
embedding_model: str = ""
keyword_number: int | None = 10
retrieval_model: RetrievalSetting
@field_validator("embedding_model_provider", mode="before")
@classmethod
def validate_embedding_model_provider(cls, v):
if v is None:
return ""
return v
@field_validator("embedding_model", mode="before")
@classmethod
def validate_embedding_model(cls, v):
if v is None:
return ""
return v