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trying to add docs
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{
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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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"# Apify Dataset\n",
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"\n",
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">[Apify Dataset](https://docs.apify.com/platform/storage/dataset) is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of [Apify Actors](https://apify.com/store)—serverless cloud programs for varius web scraping, crawling, and data extraction use cases.\n",
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"\n",
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"This notebook shows how to load Apify datasets to LangChain.\n",
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"\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify.html) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
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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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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#!pip install apify-client"
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]
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},
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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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"First, import `ApifyDatasetLoader` into your source code:"
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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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"source": [
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"from langchain.document_loaders import ApifyDatasetLoader\n",
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"from langchain.document_loaders.base import Document"
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]
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},
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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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"Then provide a function that maps Apify dataset record fields to LangChain `Document` format.\n",
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"\n",
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"For example, if your dataset items are structured like this:\n",
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"\n",
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"```json\n",
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"{\n",
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" \"url\": \"https://apify.com\",\n",
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" \"text\": \"Apify is the best web scraping and automation platform.\"\n",
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"}\n",
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"```\n",
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"\n",
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"The mapping function in the code below will convert them to LangChain `Document` format, so that you can use them further with any LLM model (e.g. for question answering)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"loader = ApifyDatasetLoader(\n",
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" dataset_id=\"your-dataset-id\",\n",
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" dataset_mapping_function=lambda dataset_item: Document(\n",
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" page_content=dataset_item[\"text\"], metadata={\"source\": dataset_item[\"url\"]}\n",
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" ),\n",
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")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"data = loader.load()"
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]
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},
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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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"## An example with question answering\n",
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"\n",
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"In this example, we use data from a dataset to answer a question."
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.docstore.document import Document\n",
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"from langchain.document_loaders import ApifyDatasetLoader\n",
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"from langchain.indexes import VectorstoreIndexCreator"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = ApifyDatasetLoader(\n",
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" dataset_id=\"your-dataset-id\",\n",
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" dataset_mapping_function=lambda item: Document(\n",
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" page_content=item[\"text\"] or \"\", metadata={\"source\": item[\"url\"]}\n",
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" ),\n",
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")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"index = VectorstoreIndexCreator().from_loaders([loader])"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What is Apify?\"\n",
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"result = index.query_with_sources(query)"
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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": 8,
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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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" Apify is a platform for developing, running, and sharing serverless cloud programs. It enables users to create web scraping and automation tools and publish them on the Apify platform.\n",
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"\n",
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"https://docs.apify.com/platform/actors, https://docs.apify.com/platform/actors/running/actors-in-store, https://docs.apify.com/platform/security, https://docs.apify.com/platform/actors/examples\n"
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]
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}
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],
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"source": [
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"print(result[\"answer\"])\n",
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"print(result[\"sources\"])"
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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