langchain_community.embeddings.llamacpp.LlamaCppEmbeddings¶
- class langchain_community.embeddings.llamacpp.LlamaCppEmbeddings[source]¶
Bases:
BaseModel,Embeddingsllama.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_community.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¶