Automate AI-Ready Vector Dataset Creation for LLMs
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Streamline the creation of high-quality, AI-ready vector datasets for your Large Language Models. This workflow automates data acquisition, formatting, and ingestion into vector databases like Pinecone using Gemini's AI capabilities.
About This Workflow
Unlock the full potential of your Large Language Models with this n8n workflow designed to automate the creation of sophisticated, AI-ready vector datasets. By leveraging Bright Data's web scraping capabilities, coupled with Google Gemini's powerful AI for data formatting and extraction, you can effortlessly transform raw web data into structured, searchable embeddings. The workflow then seamlessly integrates these embeddings into Pinecone, a leading vector database, ensuring your LLMs have access to precisely curated and optimized data for advanced applications. This solution significantly reduces manual effort and accelerates your AI development lifecycle.
Key Features
- Automated Data Acquisition: Effortlessly scrape data from specified URLs using Bright Data's web unlocker.
- Intelligent Data Formatting: Utilize Google Gemini AI to extract, structure, and format raw web content into a usable schema.
- Vector Database Integration: Directly ingest embeddings into Pinecone for efficient similarity search and LLM querying.
- Flexible Data Splitting: Employ the Recursive Character Text Splitter for optimal chunking of text data.
- Pre-built AI Components: Leverages n8n's Langchain nodes for seamless integration of LLM functionalities.
How To Use
- Configure Bright Data: Ensure your Bright Data credentials and zone are set up correctly within n8n.
- Set Target URL: Update the
urlparameter in the 'Set Fields - URL and Webhook URL' node with the website you wish to scrape. - Connect Pinecone: Provide your Pinecone API credentials and specify the target index name (e.g., 'hacker-news').
- Set Google Gemini Credentials: Authenticate your Google Gemini API account in n8n.
- Trigger Workflow: Initiate the workflow by clicking 'Test workflow' or by setting up an appropriate trigger.
- Monitor Execution: Observe the workflow's progress, paying attention to data extraction, formatting, and Pinecone ingestion.
Apps Used
Workflow JSON
{
"id": "484db45f-ffe1-48e6-9eae-9a683866665a",
"name": "Automate AI-Ready Vector Dataset Creation for LLMs",
"nodes": 10,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 484db45f-ffe1...
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