Source code for langchain.embeddings.llamacpp
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class LlamaCppEmbeddings(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:
        .. code-block:: python
            from langchain.embeddings import LlamaCppEmbeddings
            llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
    """
    client: Any  #: :meta private:
    model_path: str
    n_ctx: int = Field(512, alias="n_ctx")
    """Token context window."""
    n_parts: int = Field(-1, alias="n_parts")
    """Number of parts to split the model into. 
    If -1, the number of parts is automatically determined."""
    seed: int = Field(-1, alias="seed")
    """Seed. If -1, a random seed is used."""
    f16_kv: bool = Field(False, alias="f16_kv")
    """Use half-precision for key/value cache."""
    logits_all: bool = Field(False, alias="logits_all")
    """Return logits for all tokens, not just the last token."""
    vocab_only: bool = Field(False, alias="vocab_only")
    """Only load the vocabulary, no weights."""
    use_mlock: bool = Field(False, alias="use_mlock")
    """Force system to keep model in RAM."""