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Add datasets notebook #23

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1 change: 1 addition & 0 deletions docs/source/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -5,4 +5,5 @@
:maxdepth: 2
:caption: Contents

./notebooks/datasets
```
197 changes: 197 additions & 0 deletions docs/source/notebooks/datasets.ipynb
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@@ -0,0 +1,197 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "033684fb-65b2-4586-a959-68c614741ca2",
"metadata": {},
"source": [
"# Datasets\n",
"\n",
"Here, we'll see how to work with LangSmith datasets."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6d272fbf-710e-4a49-a0da-67e010541905",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_benchmarks import clone_public_dataset, download_public_dataset"
]
},
{
"cell_type": "markdown",
"id": "18ee0f96-e5c4-4ae9-aebf-7d8b88c51662",
"metadata": {},
"source": [
"Let's first download the dataset to the local file system"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "58b94f6d-0c91-4361-9b22-f758ffaa150a",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fetching examples...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a2fad8c0c3549ec96a3b38fe8a002b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/21 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done fetching examples.\n"
]
}
],
"source": [
"download_public_dataset('https://api.smith.langchain.com/public/e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5/examples')"
]
},
{
"cell_type": "markdown",
"id": "841db832-b0d3-4fd1-8531-1154ec9b3caa",
"metadata": {},
"source": [
"we can take a look at the first two examples"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "664e90fc-af84-4c5f-a3dd-5d9ffe649650",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"created_at\": \"2023-11-15T15:26:53.511629\",\n",
" \"dataset_id\": \"9f73165c-d333-4d14-8f59-bd7eede5db08\",\n",
" \"id\": \"0703a989-2693-4039-a1f6-7281fc1b4cb0\",\n",
" \"inputs\": {\n",
" \"question\": \"do bob and alice live in the same city?\"\n",
" },\n",
" \"modified_at\": \"2023-11-15T15:26:53.511629\",\n",
" \"outputs\": {\n",
" \"expected_steps\": [\n",
" \"find_users_by_name\",\n",
" \"get_user_location\",\n",
" \"get_city_for_location\",\n",
" \"get_user_location\",\n",
" \"get_city_for_location\"\n",
" ],\n",
" \"order_matters\": false,\n",
" \"reference\": \"no\"\n",
" },\n",
" \"runs\": []\n",
" },\n",
" {\n",
" \"created_at\": \"2023-11-15T15:26:53.491359\",\n",
" \"dataset_id\": \"9f73165c-d333-4d14-8f59-bd7eede5db08\",\n",
" \"id\": \"b258b95a-9524-4da7-b758-c5481109322d\",\n",
" \"inputs\": {\n",
" \"question\": \"Is it likely that Donna is outside with an umbrella at this time?\"\n",
" },\n",
" \"modified_at\": \"2023-11-15T15:26:53.491359\",\n",
" \"outputs\": {\n",
" \"expected_steps\": [\n",
" \"find_users_by_name\",\n",
" \"get_user_location\",\n",
" \"get_current_time_for_location\",\n",
" \"get_current_weather_for_location\"\n",
" ],\n",
" \"order_matters\": false,\n",
" \"reference\": \"yes\"\n",
" },\n",
" \"runs\": []\n",
" }\n",
"]\n"
]
}
],
"source": [
"import json\n",
"with open('./e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5.json', 'r', encoding='utf-8') as f:\n",
" print(json.dumps(json.load(f)[:2], indent=2, sort_keys=True))"
]
},
{
"cell_type": "markdown",
"id": "2c6cf01f-466b-406d-b4c7-2395747780fd",
"metadata": {},
"source": [
"We can also clone the dataset to our local tenant"
]
},
{
"cell_type": "markdown",
"id": "e4dea4df-2f1c-436b-a71c-49ffb2295ccc",
"metadata": {},
"source": [
"Executing this command will clone the dataset to your local tenant. \n",
"For this to work you must have a langsmith account set up."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18d0b905-2a6a-4752-a7cb-8653bd9049e3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"clone_public_dataset('https://api.smith.langchain.com/public/e95d45da-aaa3-44b3-ba2b-7c15ff6e46f5/examples', dataset_name='Agent Dataset')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}