Source code for gptcache.embedding.langchain

import numpy as np

from gptcache.embedding.base import BaseEmbedding
from gptcache.utils import import_langchain


from langchain.embeddings.base import Embeddings  # pylint: disable=C0413

[docs]class LangChain(BaseEmbedding): """Generate text embedding for given text using LangChain :param embeddings: the LangChain Embeddings object. :type embeddings: Embeddings :param dimension: The vector dimension after embedding is calculated by calling embed once by default. If you confirm the dimension, you can assign a value to this parameter to reduce this request. :type dimension: int Example: .. code-block:: python from gptcache.embedding import LangChain from langchain.embeddings.openai import OpenAIEmbeddings test_sentence = 'Hello, world.' embeddings = OpenAIEmbeddings(model="your-embeddings-deployment-name") encoder = LangChain(embeddings=embeddings) embed = encoder.to_embeddings(test_sentence) """ def __init__(self, embeddings: Embeddings, dimension: int = 0): self._embeddings: Embeddings = embeddings self._dimension: int = ( dimension if dimension != 0 else len(self._embeddings.embed_query("foo")) )
[docs] def to_embeddings(self, data, **kwargs): vector_data = self._embeddings.embed_query(data) return np.array(vector_data).astype("float32")
@property def dimension(self) -> int: return self._dimension