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https://github.com/langchain-ai/langchain-benchmarks.git
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2 Commits
| Author | SHA1 | Date | |
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| 93c3d0b729 | |||
| 8e6c84cc88 |
@@ -5,4 +5,5 @@
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:maxdepth: 2
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:caption: Contents
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./notebooks/datasets
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```
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@@ -0,0 +1,197 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "033684fb-65b2-4586-a959-68c614741ca2",
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"metadata": {},
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"source": [
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"# Datasets\n",
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"\n",
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"Here, we'll see how to work with LangSmith datasets."
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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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"id": "6d272fbf-710e-4a49-a0da-67e010541905",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain_benchmarks import clone_public_dataset, download_public_dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18ee0f96-e5c4-4ae9-aebf-7d8b88c51662",
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"metadata": {},
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"source": [
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"Let's first download the dataset to the local file system"
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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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"id": "58b94f6d-0c91-4361-9b22-f758ffaa150a",
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"metadata": {
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"tags": []
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},
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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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"Fetching examples...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5a2fad8c0c3549ec96a3b38fe8a002b0",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/21 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"Done fetching examples.\n"
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]
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}
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],
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"source": [
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"download_public_dataset('https://api.smith.langchain.com/public/e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5/examples')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "841db832-b0d3-4fd1-8531-1154ec9b3caa",
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"metadata": {},
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"source": [
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"we can take a look at the first two examples"
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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": 3,
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"id": "664e90fc-af84-4c5f-a3dd-5d9ffe649650",
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"metadata": {
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"tags": []
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},
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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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"[\n",
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" {\n",
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" \"created_at\": \"2023-11-15T15:26:53.511629\",\n",
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" \"dataset_id\": \"9f73165c-d333-4d14-8f59-bd7eede5db08\",\n",
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" \"id\": \"0703a989-2693-4039-a1f6-7281fc1b4cb0\",\n",
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" \"inputs\": {\n",
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" \"question\": \"do bob and alice live in the same city?\"\n",
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" },\n",
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" \"modified_at\": \"2023-11-15T15:26:53.511629\",\n",
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" \"outputs\": {\n",
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" \"expected_steps\": [\n",
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" \"find_users_by_name\",\n",
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" \"get_user_location\",\n",
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" \"get_city_for_location\",\n",
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" \"get_user_location\",\n",
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" \"get_city_for_location\"\n",
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" ],\n",
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" \"order_matters\": false,\n",
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" \"reference\": \"no\"\n",
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" },\n",
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" \"runs\": []\n",
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" },\n",
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" {\n",
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" \"created_at\": \"2023-11-15T15:26:53.491359\",\n",
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" \"dataset_id\": \"9f73165c-d333-4d14-8f59-bd7eede5db08\",\n",
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" \"id\": \"b258b95a-9524-4da7-b758-c5481109322d\",\n",
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" \"inputs\": {\n",
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" \"question\": \"Is it likely that Donna is outside with an umbrella at this time?\"\n",
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" },\n",
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" \"modified_at\": \"2023-11-15T15:26:53.491359\",\n",
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" \"outputs\": {\n",
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" \"expected_steps\": [\n",
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" \"find_users_by_name\",\n",
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" \"get_user_location\",\n",
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" \"get_current_time_for_location\",\n",
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" \"get_current_weather_for_location\"\n",
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" ],\n",
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" \"order_matters\": false,\n",
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" \"reference\": \"yes\"\n",
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" },\n",
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" \"runs\": []\n",
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" }\n",
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"]\n"
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]
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}
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],
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"source": [
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"import json\n",
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"with open('./e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5.json', 'r', encoding='utf-8') as f:\n",
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" print(json.dumps(json.load(f)[:2], indent=2, sort_keys=True))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2c6cf01f-466b-406d-b4c7-2395747780fd",
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"metadata": {},
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"source": [
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"We can also clone the dataset to our local tenant"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4dea4df-2f1c-436b-a71c-49ffb2295ccc",
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"metadata": {},
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"source": [
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"Executing this command will clone the dataset to your local tenant. \n",
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"For this to work you must have a langsmith account set up."
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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": null,
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"id": "18d0b905-2a6a-4752-a7cb-8653bd9049e3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"clone_public_dataset('https://api.smith.langchain.com/public/e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5/examples', dataset_name='Agent Dataset')"
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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 (ipykernel)",
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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": "3.9.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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