Gobble Bot

Side project

All-to-1 scraper for GPTs

Year started

What is Gobble Bot

An all-in-one scraper that converts various types of content into a single text file, helping with the creation of custom GPTs or training various LLM-based chatbots. It can handle diverse data types including YouTube videos, websites, and multiple file formats. Ensures data security with on-device processing.

Who Can Benefit from Gobble Bot

Individuals looking to create custom GPT chatbots from content spread across websites, videos, and numerous file types.

What Makes Gobble Bot Unique

The broad range of data it can process and the promise of data security with processing occurring on the user's device. Lightweight tool with focus on quickly scraping large amounts of content.

Use Cases

  • Create a GPT that can answer questions from a YouTube video
  • Crawl and scrape content from a website and use it to train a GPT


  • Read files in various formats: PDF, TXT, DOC and more
  • Crawl and scrape any website
  • Fetch transcripts of YouTube videos
  • Create one text file ready for training a GPT

Why scrape data for creating chatbots with RAG

Scraping diverse data for RAG-based (Retriever-Augmented Generation) chatbot creation allows for more nuanced and contextually aware responses. As the RAG model retrieves relevant documents from a large corpus before generating a response, a richer and more varied dataset can lead to more accurate and engaging interactions.

Vector databases are a type of database designed to store and process high-dimensional vector data efficiently. A vector database can handle operations like nearest neighbor search, which is crucial in many AI applications. For instance, it's used in recommendation systems, image recognition, and text retrieval systems.

Retriever-Augmented Generation (RAG) is a retrieval-based method used in natural language processing. The RAG approach combines the benefits of pre-trained language models with the power of document retrieval. It uses a retriever to fetch relevant documents from a large corpus and then feeds these documents into a generator model to construct a response.

The RAG retrieval approach can benefit significantly from vector databases. In the RAG retrieval process, the model must search through potentially millions of documents to find the most relevant ones. This is where vector databases come in - they can efficiently handle such large-scale nearest neighbor searches, significantly speeding up the retrieval step in RAG.

In conclusion, vector databases provide the infrastructure needed for efficient large-scale vector operations, while RAG retrieval leverages this infrastructure to deliver highly relevant responses in natural language processing tasks.