langchain/docs/versioned_docs/version-0.2.x/integrations/document_loaders/apify_dataset.ipynb

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   "source": [
    "# Apify Dataset\n",
    "\n",
    ">[Apify Dataset](https://docs.apify.com/platform/storage/dataset) is a scalable 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 various web scraping, crawling, and data extraction use cases.\n",
    "\n",
    "This notebook shows how to load Apify datasets to LangChain.\n",
    "\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "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) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "%pip install --upgrade --quiet  apify-client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import `ApifyDatasetLoader` into your source code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import ApifyDatasetLoader\n",
    "from langchain_core.documents import Document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then provide a function that maps Apify dataset record fields to LangChain `Document` format.\n",
    "\n",
    "For example, if your dataset items are structured like this:\n",
    "\n",
    "```json\n",
    "{\n",
    "    \"url\": \"https://apify.com\",\n",
    "    \"text\": \"Apify is the best web scraping and automation platform.\"\n",
    "}\n",
    "```\n",
    "\n",
    "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = ApifyDatasetLoader(\n",
    "    dataset_id=\"your-dataset-id\",\n",
    "    dataset_mapping_function=lambda dataset_item: Document(\n",
    "        page_content=dataset_item[\"text\"], metadata={\"source\": dataset_item[\"url\"]}\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example with question answering\n",
    "\n",
    "In this example, we use data from a dataset to answer a question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.docstore.document import Document\n",
    "from langchain.indexes import VectorstoreIndexCreator\n",
    "from langchain_community.document_loaders import ApifyDatasetLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = ApifyDatasetLoader(\n",
    "    dataset_id=\"your-dataset-id\",\n",
    "    dataset_mapping_function=lambda item: Document(\n",
    "        page_content=item[\"text\"] or \"\", metadata={\"source\": item[\"url\"]}\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = VectorstoreIndexCreator().from_loaders([loader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What is Apify?\"\n",
    "result = index.query_with_sources(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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",
      "\n",
      "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"
     ]
    }
   ],
   "source": [
    "print(result[\"answer\"])\n",
    "print(result[\"sources\"])"
   ]
  }
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