from __future__ import annotations
from enum import Enum
from typing import Any, Dict, Iterator, List, Optional, Union
from camel_converter import to_snake
from camel_converter.pydantic_base import CamelBase
[docs]
class IndexStats:
__dict: Dict
def __init__(self, doc: Dict[str, Any]) -> None:
self.__dict = doc
for key, val in doc.items():
key = to_snake(key)
if isinstance(val, dict):
setattr(self, key, IndexStats(val))
else:
setattr(self, key, val)
def __getattr__(self, attr: str) -> Any:
if attr in self.__dict.keys():
return attr
raise AttributeError(f"{self.__class__.__name__} object has no attribute {attr}")
def __iter__(self) -> Iterator:
return iter(self.__dict__.items())
[docs]
class Faceting(CamelBase):
max_values_per_facet: int
sort_facet_values_by: Optional[Dict[str, str]] = None
[docs]
class MinWordSizeForTypos(CamelBase):
one_typo: Optional[int] = None
two_typos: Optional[int] = None
[docs]
class TypoTolerance(CamelBase):
enabled: bool = True
disable_on_attributes: Optional[List[str]] = None
disable_on_words: Optional[List[str]] = None
min_word_size_for_typos: Optional[MinWordSizeForTypos] = None
[docs]
class ProximityPrecision(str, Enum):
BY_WORD = "byWord"
BY_ATTRIBUTE = "byAttribute"
[docs]
class OpenAiEmbedder(CamelBase):
source: str = "openAi"
model: Optional[str] = None # Defaults to text-embedding-ada-002
dimensions: Optional[int] = None # Uses the model default
api_key: Optional[str] = None # Can be provided through a CLI option or environment variable
document_template: Optional[str] = None
[docs]
class HuggingFaceEmbedder(CamelBase):
source: str = "huggingFace"
model: Optional[str] = None # Defaults to BAAI/bge-base-en-v1.5
revision: Optional[str] = None
document_template: Optional[str] = None
[docs]
class UserProvidedEmbedder(CamelBase):
source: str = "userProvided"
dimensions: int
[docs]
class Embedders(CamelBase):
embedders: Dict[str, Union[OpenAiEmbedder, HuggingFaceEmbedder, UserProvidedEmbedder]]