Source code for gptcache.embedding.cohere
import numpy as np
from gptcache.utils import import_cohere
from gptcache.embedding.base import BaseEmbedding
import_cohere()
import cohere # pylint: disable=C0413
[docs]class Cohere(BaseEmbedding):
"""Generate text embedding for given text using Cohere.
:param model: model name (size), defaults to 'large'.
:type model: str
:param api_key: Cohere API Key.
:type api_key: str
Example:
.. code-block:: python
from gptcache.embedding import Cohere
test_sentence = 'Hello, world.'
encoder = Cohere(model='small', api_key='your_cohere_key')
embed = encoder.to_embeddings(test_sentence)
"""
def __init__(self, model: str = "large", api_key: str = None):
self.co = cohere.Client(api_key)
self.model = model
if model in self.dim_dict():
self.__dimension = self.dim_dict()[model]
else:
self.__dimension = None
[docs] def to_embeddings(self, data, **_):
"""Generate embedding given text input
:param data: text in string.
:type data: str
:return: a text embedding in shape of (dim,).
"""
if not isinstance(data, list):
data = [data]
response = self.co.embed(texts=data, model=self.model)
embeddings = response.embeddings
return np.array(embeddings).astype("float32").squeeze(0)
@property
def dimension(self):
"""Embedding dimension.
:return: embedding dimension
"""
if not self.__dimension:
foo_emb = self.to_embeddings("foo")
self.__dimension = len(foo_emb)
return self.__dimension
[docs] @staticmethod
def dim_dict():
return {"large": 4096, "small": 1024}