Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone

Who this is for? This workflow enables automated, scalable collection of high-quality, AI-ready data from websites using Bright Data’s Web Unlocker, with a focus on preparing that data for LLM training. Leveraging LLM Chains and AI agents, the system formats and extracts key information, then stores the structured embeddings in a Pinecone vector database. This workflow is tailored for:​ ML Engineers & Researchers building or fine-tuning domain-specific LLMs. AI Startups needing clean, structured content for product training. Data Teams preparing knowledge bases for enterprise-grade AI apps. LLM-as-a-Service Providers sourcing dynamic web content across niches. What problem is this workflow s

Help me set up the n8n workflow "Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone" (https://n8n.io/workflows/3542). It uses: HTTP Request, AI Agent, Basic LLM Chain, Structured Output Parser, Recursive Character Text Splitter, Pinecone Vector Store, Default Data Loader, Embeddings Google Gemini, Google Gemini Chat Model, Information Extractor. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.