Fast, reliable data for ChatGPT and LLMs
Extract text content from the web to feed your vector databases, fine-tune or train your large language models (LLMs) such as ChatGPT or LLaMA.
POWERING THE WORLD'S TOP DATA-DRIVEN TEAMS
Generative AI is powered by web scraping
Data is the fuel for AI, and web is the largest source of data ever created. Today's most popular language models like ChatGPT or LLaMA were all trained on data scraped from the web. Apify gives you the same superpowers and brings the vast amounts of data from the web to your fingertips.
Extract documents from the web and load them to vector databases for querying and prompt generation.
Extract text and images from the web to generate training datasets for your new AI models.
Use domain-specific data extracted from the web with the OpenAI fine-tuning API or other models.
Customer service and support is a major area where generative AI and large language models (LLMs) in particular are starting to unlock huge amounts of customer value. Read about how Intercom's new AI chatbot is already using web scraping to answer customer queries.
Enrich your LLM with your own data or data from the web to deliver accurate responses. Unlock the power of real-time information, ensuring your chatbot is always up-to-date and relevant.
Provide your chatbot with data from external sources like forums, review sites or social media so it can give you real-time insights, sentiment analysis, and actionable feedback about your brand.
Make your chatbot more intelligent and accurate by integrating your own and external online sources. Impress users with precise, reliable, and personal interactions.
Effortlessly stay informed with a chatbot that aggregates and condenses the latest news. Gauge public sentiment, grasp prevailing opinions, and make informed decisions.
Read about AI and web scraping
Find out how we think generative AI and machine learning will transform web scraping.
Web scraping for AI: how to collect data for LLMs
A tutorial that shows you how to crawl, extract, and process web data to feed, fine-tune, or train large language models.
AI and copyright: the legal landscape
Is AI-generated content protected by copyright law? And can copyrighted content be used to train AI? We explore the legal landscape for answers.
Applications of ChatGPT and other large language models in web scraping
Many people are wondering and (un)happily speculating on when and how large language models will change their work and industry. So what about AI and web scraping?
How to use LangChain with OpenAI, Pinecone, and Apify
Use LangChain, Pinecone, and Apify to extend the capabilities of OpenAI's ChatGPT and answer questions based on data after 2021 or from any dataset.
Generative AI is a type of deep learning model focused on generating text, images, audio, video, code, and other data types in response to text prompts. Examples of generative AI models are ChatGPT, MidJourney, and BARD.
AI is a field of computer science that aims to create intelligent machines or systems that can perform tasks that typically require human intelligence. Generative AI is a subfield of AI focused on creating systems capable of generating new content, such as images, text, music, or video.
Large language models, or LLMs, are a form of generative AI. They are typically transformer models that use deep learning methods to understand and generate text in a human-like fashion. Examples of LLMs are ChatGPT, LLaMA, LLaMDA, and BARD.
Data ingestion is the process of collecting, processing, and preparing data for analysis or machine learning. In the context of LLMs, data ingestion involves collecting text data (web scraping), preprocessing it (cleaning, normalization, tokenization), and preparing it for training (feature engineering).
Web scraping allows you to collect reliable, up-to-date information that can be used to feed, fine-tune, or train large language models (LLMs) or provide context for prompts for ChatGPT. In return, the model will answer questions based on your or your customer's websites and content.
Vector databases are designed to handle the unique structure of vector embeddings, which are dense vectors of numbers that represent text. They are used in machine learning to index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another.
LangChain is an open-source framework for developing applications powered by language models. It connects to the AI models you want to use and links them with outside sources. That means you can chain commands together so the AI model can know what it needs to do to produce the answers or perform the tasks you require.
Pinecone is a popular vector database that lets you provide long-term memory for high-performance AI applications. It is used for semantic search, similarity search for images and audio, recommendation systems, record matching, anomaly detection, and natural language processing.
- Data collection: use a tool like Apify's Website Content Crawler to scrape web data. Configure the crawler settings like start URLs, crawler type, HTML processing, and data cleaning to tailor the data to what you need.
- Data processing: clean and process the scraped data by removing unnecessary HTML elements, duplications, and transforming it into a usable format (e.g. JSON, CSV).
- Integration and training: integrate the cleaned and processed data with tools like LangChain or Pinecone and feed it into your LLM to fine-tune or train the model according to your specific requirements. Check out this full step-by-step tutorial on how to collect data for LLMs with web scraping.