Source code for langchain_community.embeddings.llamacpp

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator


[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_community.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.""" n_threads: Optional[int] = Field(None, alias="n_threads") """Number of threads to use. If None, the number of threads is automatically determined.""" n_batch: Optional[int] = Field(512, alias="n_batch") """Number of tokens to process in parallel. Should be a number between 1 and n_ctx.""" n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers") """Number of layers to be loaded into gpu memory. Default None.""" verbose: bool = Field(True, alias="verbose") """Print verbose output to stderr.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that llama-cpp-python library is installed.""" model_path = values["model_path"] model_param_names = [ "n_ctx", "n_parts", "seed", "f16_kv", "logits_all", "vocab_only", "use_mlock", "n_threads", "n_batch", "verbose", ] model_params = {k: values[k] for k in model_param_names} # For backwards compatibility, only include if non-null. if values["n_gpu_layers"] is not None: model_params["n_gpu_layers"] = values["n_gpu_layers"] try: from llama_cpp import Llama values["client"] = Llama(model_path, embedding=True, **model_params) except ImportError: raise ImportError( "Could not import llama-cpp-python library. " "Please install the llama-cpp-python library to " "use this embedding model: pip install llama-cpp-python" ) except Exception as e: raise ValueError( f"Could not load Llama model from path: {model_path}. " f"Received error {e}" ) return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of documents using the Llama model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings = [self.client.embed(text) for text in texts] return [list(map(float, e)) for e in embeddings]
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using the Llama model. Args: text: The text to embed. Returns: Embeddings for the text. """ embedding = self.client.embed(text) return list(map(float, embedding))