Apify 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—serverless cloud programs for various web scraping, crawling, and data extraction use cases.
This notebook shows how to load Apify datasets to LangChain.

​
Prerequisites

You need to have an existing dataset on the Apify platform. This example shows how to load a dataset produced by the Website Content Crawler. 
%pip install --upgrade --quiet langchain langchain-apify langchain-openai
First, import ApifyDatasetLoader into your source code: 
from langchain_apify import ApifyDatasetLoader
from langchain_core.documents import Document
Find your Apify API token and OpenAI API key and initialize these into environment variable: 
import os

os.environ["APIFY_API_TOKEN"] = "your-apify-api-token"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
Then provide a function that maps Apify dataset record fields to LangChain Document format. For example, if your dataset items are structured like this: 
{
    "url": "https://apify.com",
    "text": "Apify is the best web scraping and automation platform."
}
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). 
loader = ApifyDatasetLoader(
    dataset_id="your-dataset-id",
    dataset_mapping_function=lambda dataset_item: Document(
        page_content=dataset_item["text"], metadata={"source": dataset_item["url"]}
    ),
)

data = loader.load()

​
An example with question answering

In this example, we use data from a dataset to answer a question. 
from langchain.indexes import VectorstoreIndexCreator
from langchain_apify import ApifyWrapper
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings

loader = ApifyDatasetLoader(
    dataset_id="your-dataset-id",
    dataset_mapping_function=lambda item: Document(
        page_content=item["text"] or "", metadata={"source": item["url"]}
    ),
)

index = VectorstoreIndexCreator(
    vectorstore_cls=InMemoryVectorStore, embedding=OpenAIEmbeddings()
).from_loaders([loader])

llm = ChatOpenAI(model="gpt-4o-mini")

query = "What is Apify?"
result = index.query_with_sources(query, llm=llm)

print(result["answer"])
print(result["sources"])

 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.

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