This n8n workflow automates the process of collecting, formatting, embedding, and storing web data for use in language learning models (LLMs). It is designed for creating AI-ready vector datasets from web content, utilizing Bright Data for web crawling, Google Gemini for embeddings, and Pinecone for storage.
The workflow begins with a manual trigger, allowing users to specify a target URL and webhook for data output. It then makes a web request to Bright Data’s API to fetch raw web content from the specified URL. The response data is formatted into a structured JSON schema, emphasizing items like titles, points, and comments.
Next, the extracted content undergoes detailed analysis using an AI agent, which extracts relevant information from the search results. This processed data is then formatted again for consistency before being embedded into vector representations using Google Gemini’s embeddings model.
The embeddings, along with the formatted data, are stored in Pinecone’s vector database, making the data AI-ready for downstream applications like semantic search or personalized NLP tasks. Additional sticky notes within the workflow provide documentation and notes for users.
This workflow is highly practical for developers or data scientists looking to automate web data collection for large language model training, enabling efficient creation of high-quality, searchable datasets that can be used for various AI applications.
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