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"""Cohere embeddings."""
from typing import Iterable, cast
import cohere
import numpy as np
from typing_extensions import override
from ..config import CONFIG
from ..schema import Item, RichData
from ..signals.signal import TextEmbeddingSignal
from ..signals.splitters.chunk_splitter import split_text
from .embedding import compute_split_embeddings
NUM_PARALLEL_REQUESTS = 10
COHERE_BATCH_SIZE = 96
class Cohere(TextEmbeddingSignal):
"""Computes embeddings using Cohere's embedding API.
<br>**Important**: This will send data to an external server!
<br>To use this signal, you must get a Cohere API key from
[cohere.com/embed](https://cohere.com/embed) and add it to your .env.local.
<br>For details on pricing, see: https://cohere.com/pricing.
"""
name = 'cohere'
display_name = 'Cohere Embeddings'
_model: cohere.Client
@override
def setup(self) -> None:
api_key = CONFIG.get('COHERE_API_KEY')
if not api_key:
raise ValueError('`COHERE_API_KEY` environment variable not set.')
self._model = cohere.Client(api_key, max_retries=10)
@override
def compute(self, docs: Iterable[RichData]) -> Iterable[Item]:
"""Compute embeddings for the given documents."""
def embed_fn(texts: list[str]) -> list[np.ndarray]:
return self._model.embed(texts, truncate='END').embeddings
docs = cast(Iterable[str], docs)
split_fn = split_text if self._split else None
yield from compute_split_embeddings(
docs, COHERE_BATCH_SIZE, embed_fn, split_fn, num_parallel_requests=NUM_PARALLEL_REQUESTS)
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