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"""OpenAI embeddings."""
from typing import TYPE_CHECKING, Iterable, cast
import numpy as np
from tenacity import retry, stop_after_attempt, wait_random_exponential
from typing_extensions import override
from ..config import env
from ..schema import Item, RichData
from ..signals.signal import TextEmbeddingSignal
from ..signals.splitters.chunk_splitter import split_text
from .embedding import compute_split_embeddings
if TYPE_CHECKING:
import openai
NUM_PARALLEL_REQUESTS = 10
OPENAI_BATCH_SIZE = 128
EMBEDDING_MODEL = 'text-embedding-ada-002'
class OpenAI(TextEmbeddingSignal):
"""Computes embeddings using OpenAI's embedding API.
<br>**Important**: This will send data to an external server!
<br>To use this signal, you must get an OpenAI API key from
[platform.openai.com](https://platform.openai.com/) and add it to your .env.local.
<br>For details on pricing, see: https://openai.com/pricing.
"""
name = 'openai'
display_name = 'OpenAI Embeddings'
_model: 'openai.Embedding'
@override
def setup(self) -> None:
api_key = env('OPENAI_API_KEY')
if not api_key:
raise ValueError('`OPENAI_API_KEY` environment variable not set.')
try:
import openai
openai.api_key = api_key
self._model = openai.Embedding
except ImportError:
raise ImportError('Could not import the "openai" python package. '
'Please install it with `pip install openai`.')
@override
def compute(self, docs: Iterable[RichData]) -> Iterable[Item]:
"""Compute embeddings for the given documents."""
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10))
def embed_fn(texts: list[str]) -> list[np.ndarray]:
# Replace newlines, which can negatively affect performance.
# See https://github.com/search?q=repo%3Aopenai%2Fopenai-python+replace+newlines&type=code
texts = [text.replace('\n', ' ') for text in texts]
response = self._model.create(input=texts, model=EMBEDDING_MODEL)
return [np.array(embedding['embedding'], dtype=np.float32) for embedding in response['data']]
docs = cast(Iterable[str], docs)
split_fn = split_text if self._split else None
yield from compute_split_embeddings(
docs, OPENAI_BATCH_SIZE, embed_fn, split_fn, num_parallel_requests=NUM_PARALLEL_REQUESTS)
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