Source code for langchain_community.embeddings.johnsnowlabs

import os
import sys
from typing import Any, List

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

[docs]class JohnSnowLabsEmbeddings(BaseModel, Embeddings): """JohnSnowLabs embedding models To use, you should have the ``johnsnowlabs`` python package installed. Example: .. code-block:: python from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings embedding = JohnSnowLabsEmbeddings(model='embed_sentence.bert') output = embedding.embed_query("foo bar") """ # noqa: E501 model: Any = "embed_sentence.bert" def __init__( self, model: Any = "embed_sentence.bert", hardware_target: str = "cpu", **kwargs: Any, ): """Initialize the johnsnowlabs model.""" super().__init__(**kwargs) # 1) Check imports try: from johnsnowlabs import nlp from nlu.pipe.pipeline import NLUPipeline except ImportError as exc: raise ImportError( "Could not import johnsnowlabs python package. " "Please install it with `pip install johnsnowlabs`." ) from exc # 2) Start a Spark Session try: os.environ["PYSPARK_PYTHON"] = sys.executable os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable nlp.start(hardware_target=hardware_target) except Exception as exc: raise Exception("Failure starting Spark Session") from exc # 3) Load the model try: if isinstance(model, str): self.model = nlp.load(model) elif isinstance(model, NLUPipeline): self.model = model else: self.model = nlp.to_nlu_pipe(model) except Exception as exc: raise Exception("Failure loading model") from exc class Config: """Configuration for this pydantic object.""" extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a JohnSnowLabs transformer model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ df = self.model.predict(texts, output_level="document") emb_col = None for c in df.columns: if "embedding" in c: emb_col = c return [vec.tolist() for vec in df[emb_col].tolist()]
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using a JohnSnowLabs transformer model. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.embed_documents([text])[0]