langchain.schema.language_model.BaseLanguageModel¶
class langchain.schema.language_model.BaseLanguageModel[source]¶
Bases: Serializable, Runnable[Union[PromptValue, str, List[BaseMessage]], LanguageModelOutput], ABC
Abstract base class for interfacing with language models.
All language model wrappers inherit from BaseLanguageModel.
Exposes three main methods:
- generate_prompt: generate language model outputs for a sequence of prompt
values. A prompt value is a model input that can be converted to any language
model input format (string or messages).
predict: pass in a single string to a language model and return a stringprediction.
predict_messages: pass in a sequence of BaseMessages (corresponding to a singlemodel call) to a language model and return a BaseMessage prediction.
Each of these has an equivalent asynchronous method.
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.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
abstract async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any) → LLMResult[source]¶
Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters