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93 lines
2.0 KiB
Plaintext
93 lines
2.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get embeddings\n",
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"\n",
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"The function `get_embedding` will give us an embedding for an input text."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1536"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import openai\n",
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"\n",
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"embedding = openai.Embedding.create(\n",
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" input=\"Your text goes here\", model=\"text-embedding-ada-002\"\n",
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")[\"data\"][0][\"embedding\"]\n",
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"len(embedding)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1536\n"
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]
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}
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],
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"source": [
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"import openai\n",
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"from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
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"\n",
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"\n",
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"@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))\n",
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"def get_embedding(text: str, model=\"text-embedding-ada-002\") -> list[float]:\n",
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" return openai.Embedding.create(input=[text], model=model)[\"data\"][0][\"embedding\"]\n",
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"\n",
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"\n",
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"embedding = get_embedding(\"Your text goes here\", model=\"text-embedding-ada-002\")\n",
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"print(len(embedding))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.9 ('openai')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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