langchain.embeddings.llamacpp.LlamaCppEmbeddings

class langchain.embeddings.llamacpp.LlamaCppEmbeddings[source]

Bases: BaseModel, Embeddings

llama.cpp embedding models.

To use, you should have the llama-cpp-python library installed, and provide the path to the Llama model as a named parameter to the constructor. Check out: https://github.com/abetlen/llama-cpp-python

Example

from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")

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 f16_kv: bool = False

Use half-precision for key/value cache.

param logits_all: bool = False

Return logits for all tokens, not just the last token.

param model_path: str [Required]
param n_batch: Optional[int] = 8

Number of tokens to process in parallel. Should be a number between 1 and n_ctx.

param n_ctx: int = 512

Token context window.

param n_gpu_layers: Optional[int] = None

Number of layers to be loaded into gpu memory. Default None.

param n_parts: int = -1

Number of parts to split the model into. If -1, the number of parts is automatically determined.

param n_threads: Optional[int] = None

Number of threads to use. If None, the number of threads is automatically determined.

param seed: int = -1

Seed. If -1, a random seed is used.

param use_mlock: bool = False

Force system to keep model in RAM.

param verbose: bool = True

Print verbose output to stderr.

param vocab_only: bool = False

Only load the vocabulary, no weights.

async aembed_documents(texts: List[str]) List[List[float]]

Asynchronous Embed search docs.

async aembed_query(text: str) List[float]

Asynchronous Embed query text.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed_documents(texts: List[str]) List[List[float]][source]

Embed a list of documents using the Llama model.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

embed_query(text: str) List[float][source]

Embed a query using the Llama model.

Parameters

text – The text to embed.

Returns

Embeddings for the text.

classmethod from_orm(obj: Any) Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model

Examples using LlamaCppEmbeddings