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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "-DBXBd1Q6SFF"
},
"outputs": [],
"source": [
"import requests\n",
"from typing import List, Dict, Any, Iterator\n",
"\n",
"class DatasetSearchClient:\n",
" def __init__(self, base_url: str = \"https://librarian-bots-dataset-column-search-api.hf.space\"):\n",
" self.base_url = base_url\n",
"\n",
" def search(self,\n",
" columns: List[str],\n",
" match_all: bool = False,\n",
" page_size: int = 100) -> Iterator[Dict[str, Any]]:\n",
" \"\"\"\n",
" Search datasets using the provided API, automatically handling pagination.\n",
"\n",
" Args:\n",
" columns (List[str]): List of column names to search for.\n",
" match_all (bool, optional): If True, match all columns. If False, match any column. Defaults to False.\n",
" page_size (int, optional): Number of results per page. Defaults to 100.\n",
"\n",
" Yields:\n",
" Dict[str, Any]: Each dataset result from all pages.\n",
"\n",
" Raises:\n",
" requests.RequestException: If there's an error with the HTTP request.\n",
" ValueError: If the API returns an unexpected response format.\n",
" \"\"\"\n",
" page = 1\n",
" total_results = None\n",
"\n",
" while total_results is None or (page - 1) * page_size < total_results:\n",
" params = {\n",
" \"columns\": columns,\n",
" \"match_all\": str(match_all).lower(),\n",
" \"page\": page,\n",
" \"page_size\": page_size\n",
" }\n",
"\n",
" try:\n",
" response = requests.get(f\"{self.base_url}/search\", params=params)\n",
" response.raise_for_status()\n",
" data = response.json()\n",
"\n",
" if not {\"total\", \"page\", \"page_size\", \"results\"}.issubset(data.keys()):\n",
" raise ValueError(\"Unexpected response format from the API\")\n",
"\n",
" if total_results is None:\n",
" total_results = data['total']\n",
"\n",
" for dataset in data['results']:\n",
" yield dataset\n",
"\n",
" page += 1\n",
"\n",
" except requests.RequestException as e:\n",
" raise requests.RequestException(f\"Error connecting to the API: {str(e)}\")\n",
" except ValueError as e:\n",
" raise ValueError(f\"Error processing API response: {str(e)}\")\n",
"\n",
"# Create an instance of the client\n",
"client = DatasetSearchClient()"
]
},
{
"cell_type": "code",
"source": [
"results = list(client.search(['tools'],match_all=True))\n",
"len(results)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9yupgFYx6Sqx",
"outputId": "ac6d7c15-2267-4bbd-ceaa-1d98faee188b"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"38"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"results[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "atL-PQq76VrV",
"outputId": "f357fe16-a1f9-4bb2-ca3d-767f3ac6508d"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'hub_id': 'llamafactory/glaive_toolcall_en',\n",
" 'likes': 1,\n",
" 'downloads': 1151,\n",
" 'tags': ['task_categories:text-generation',\n",
" 'task_categories:question-answering',\n",
" 'language:en',\n",
" 'license:apache-2.0',\n",
" 'size_categories:1K<n<10K',\n",
" 'json',\n",
" 'text',\n",
" 'datasets',\n",
" 'mlcroissant',\n",
" 'region:us',\n",
" 'llama-factory',\n",
" 'croissant'],\n",
" 'created_at': 1715955540,\n",
" 'last_modified': 1717785919,\n",
" 'license': ['apache-2.0'],\n",
" 'language': ['en'],\n",
" 'config_name': 'default',\n",
" 'column_names': ['conversations', 'tools'],\n",
" 'features': [{'name': 'conversations',\n",
" 'list': [{'name': 'from', 'dtype': 'string'},\n",
" {'name': 'value', 'dtype': 'string'}]},\n",
" {'name': 'tools', 'dtype': 'string'}],\n",
" 'match_count': 1}"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import create_collection, add_collection_item"
],
"metadata": {
"id": "pXKtgF3r7GSK"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"collection = create_collection(\"Probably function calling datasets\", namespace=\"librarian-bots\",)"
],
"metadata": {
"id": "MzkGofqF7M0i"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"collection.slug"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "rAGoahvb7Ucp",
"outputId": "c5f7b158-85cb-49be-903f-7caaa98f7b74"
},
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'librarian-bots/probably-function-calling-datasets-6683d24da13a7bb7efee7464'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"for item in results:\n",
" add_collection_item(collection.slug, item['hub_id'], item_type=\"dataset\")"
],
"metadata": {
"id": "LR6nJyCL7ZZK"
},
"execution_count": 13,
"outputs": []
}
]
} |