How to do question answering from a PDF with Python and RAG
Build a Python pipeline that answers questions from PDFs and links each answer to its page, then check the citations before you trust them.
18 stories
Build a Python pipeline that answers questions from PDFs and links each answer to its page, then check the citations before you trust them.
How a developer who bids on Spanish public contracts filtered the PLACSP tender feed into something an AI agent can query, then connected his Actor to Claude Desktop through the Apify MCP server.
Bright Data and Apify both help builders get web data into AI agents and automated workflows. Their approaches differ in what they offer, how you use their tools, and how you pay for them.
A build log of adding MCP connector delivery to a pay-per-result Actor, from the input schema and argument mapping to a live delivery into Notion.
Learn what Model Context Protocol is, how MCP hosts, clients, and servers work together, and how to connect your first AI agent to Apify MCP.
Apify Store categories are being rebuilt around the job you're trying to do - a use-case tree plus filters for industry, site, output, and Actor type.
Compare 10 AI tools for real estate investors, from deal sourcing and rental analysis to valuations and CRE deal management, with features, reviews, and pricing.
An AI agent and an MCP server are two parts of one system. Here's what each one owns, what a connection costs in context, and which one you should build.
Google Trends scores every request against its own top term, so separate batches cannot be ranked together. Claude knew that, then guessed anyway. A calibrated Apify Actor fixed it.
Scrape a website into LlamaIndex with Apify, update the index without re-embedding unchanged pages, and stop partial crawls from deleting pages that still exist.
Connect a LangGraph agent to Apify through the official langchain-apify integration or Apify MCP Server, and give it real-time web search, scraping, and social media data.
I connected my Apify Actor to an AI agent over the Apify MCP server. It ran, returned data, and the data was garbage. Nothing was broken: every failure traced back to something my input schema never said. Four description rewrites later, the agent picks the right settings on its own.
Features, pricing, and crawl options side by side, plus a look at Firecrawl's new Alexandria library and when each platform is the better pick.
One of my popular Actors is a LinkedIn Sales Navigator scraper. It has dozens of users, but is also completely unusable by an AI agent, and I did not know that until I checked.
Build a tracker that scrapes a category on a schedule and logs every run into Google Sheets, no code required.
Choose your AI agent’s web tools based on actual evidence. Compare what Apify and Exa returned, what they missed, how long each task took, and how much it cost.
We tested Tavily against RAG Web Browser on real research queries. One retrieved 8.4x more evidence for $0.115 per 4 queries. See the results.
You need the data, but collecting it has become a chore. Let ChatGPT and Apify handle the work for you.
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