Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]¶
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
classmethod from_orm(obj: Any) → Model¶
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → LLM¶
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to the model 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,