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Apify

This notebook shows how to use the Apify integration for LangChain.

Apify is a cloud platform for web scraping and data extraction, which provides an ecosystem of more than a thousand ready-made apps called Actors for various web scraping, crawling, and data extraction use cases. For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.

In this example, we'll use the Website Content Crawler Actor, which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs, and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.

%pip install --upgrade --quiet  apify-client langchain-community langchain-openai langchain

First, import ApifyWrapper into your source code:

from langchain.indexes import VectorstoreIndexCreator
from langchain_community.utilities import ApifyWrapper
from langchain_core.documents import Document
from langchain_openai import OpenAI
from langchain_openai.embeddings import OpenAIEmbeddings

Initialize it using your Apify API token and for the purpose of this example, also with your OpenAI API key:

import os

os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
os.environ["APIFY_API_TOKEN"] = "Your Apify API token"

apify = ApifyWrapper()

Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.

Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you'll also find the explanation of the dataset_mapping_function, which is used to map fields from the Apify dataset records to LangChain Document fields.

loader = apify.call_actor(
    actor_id="apify/website-content-crawler",
    run_input={"startUrls": [{"url": "https://python.langchain.com"}]},
    dataset_mapping_function=lambda item: Document(
        page_content=item["text"] or "", metadata={"source": item["url"]}
    ),
)

Initialize the vector index from the crawled documents:

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

And finally, query the vector index:

query = "What is LangChain?"
result = index.query_with_sources(query, llm=OpenAI())
print(result["answer"])
print(result["sources"])
 LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.

https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html

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