langchain.llms.symblai_nebula.Nebula¶
class langchain.llms.symblai_nebula.Nebula[source]¶
Bases: LLM
Nebula Service models.
To use, you should have the environment variable NEBULA_SERVICE_URL,
NEBULA_SERVICE_PATH and NEBULA_API_KEY set with your Nebula
Service, or pass it as a named parameter to the constructor.
Example
from langchain.llms import Nebula
nebula = Nebula(
    nebula_service_url="NEBULA_SERVICE_URL",
    nebula_service_path="NEBULA_SERVICE_PATH",
    nebula_api_key="NEBULA_API_KEY",
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param max_new_tokens: Optional[int] = 128¶
param max_retries: Optional[int] = 10¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: Optional[str] = None¶
param model_kwargs: Optional[dict] = None¶
Optional
param nebula_api_key: Optional[str] = None¶
param nebula_service_path: Optional[str] = None¶
param nebula_service_url: Optional[str] = None¶
param penalty_alpha: Optional[float] = 0.0¶
param repetition_penalty: Optional[float] = 1.0¶
param stop_sequences: Optional[List[str]] = None¶
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.