base

Base classes for the augmented step.

Classes:

• AugmentedBase –

  Define the base structure for Augmented classes.

AugmentedBase

AugmentedBase(
    model_name: Optional[str] = None,
    db: DBBase = FaissDB(),
    top_k: Optional[int] = None,
    api_key: str = '',
    cache: Optional[Cache] = None,
    logs: dict[str, Any] = DEFAULT_LOGS,
)

Bases: RagoBase

Define the base structure for Augmented classes.

Methods:

• get_embedding –

  Retrieve the embedding for a given text using OpenAI API.

• search –

  Search an encoded query into vector database.

Source code in src/rago/augmented/base.py

def __init__(
    self,
    model_name: Optional[str] = None,
    db: DBBase = FaissDB(),
    top_k: Optional[int] = None,
    api_key: str = '',
    cache: Optional[Cache] = None,
    logs: dict[str, Any] = DEFAULT_LOGS,
) -> None:
    """Initialize AugmentedBase."""
    if logs is DEFAULT_LOGS:
        logs = {}
    super().__init__(api_key=api_key, cache=cache, logs=logs)

    self.db = db

    self.top_k = top_k if top_k is not None else self.default_top_k
    self.model_name = (
        model_name if model_name is not None else self.default_model_name
    )
    self.model = None

    self._validate()
    self._setup()

get_embedding

get_embedding(content: list[str]) -> EmbeddingType

Retrieve the embedding for a given text using OpenAI API.

Source code in src/rago/augmented/base.py

def get_embedding(self, content: list[str]) -> EmbeddingType:
    """Retrieve the embedding for a given text using OpenAI API."""
    raise Exception('Method not implemented.')

search abstractmethod

search(
    query: str, documents: Any, top_k: int = 0
) -> list[str]

Search an encoded query into vector database.

Source code in src/rago/augmented/base.py

@abstractmethod
def search(
    self,
    query: str,
    documents: Any,
    top_k: int = 0,
) -> list[str]:
    """Search an encoded query into vector database."""
    ...