Build AI-Ready Vector Datasets with Bright Data, Gemini & Pinecone
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This workflow automates the creation of high-quality, AI-ready vector datasets by seamlessly integrating web scraping, large language model processing, and vector database storage. Extract valuable information from any website, enrich it with Gemini, and store it efficiently in Pinecone for advanced RAG applications.
About This Workflow
Unlock the full potential of your LLM applications with this robust n8n workflow. It begins by utilizing Bright Data's Web Unlocker to perform advanced web scraping, fetching structured and unstructured data from target URLs. The scraped content is then processed through Google Gemini's powerful chat models and an AI agent for intelligent information extraction and formatting. A structured output parser ensures data consistency, while Gemini Embeddings transform text into dense vector representations. Finally, these AI-ready vectors are efficiently stored in Pinecone, enabling rapid similarity searches and powering sophisticated retrieval-augmented generation (RAG) systems for your LLMs.
Key Features
- Automated Web Scraping: Leverage Bright Data's Web Unlocker for reliable and scalable data extraction from any website.
- Gemini-Powered AI Processing: Utilize Google Gemini chat models for advanced information extraction, formatting, and content summarization.
- Structured Data Output: Ensure data consistency with a structured output parser for defined schema (ID, title, summary, keywords, topics).
- Vector Embeddings & Storage: Generate high-quality text embeddings using Google Gemini and store them in Pinecone for efficient semantic search and retrieval.
- Modular & Extensible: Built with LangChain nodes, offering flexibility to adapt and expand your AI data pipeline.
How To Use
- Set Your Target URL: In the 'Set Fields - URL and Webhook URL' node, update the
urlfield with the website you wish to scrape. (e.g.,https://example.com) - Configure Bright Data Credentials: Ensure your Bright Data Web Unlocker credentials are set up for the 'Make a web request' node.
- Configure Google Gemini (PaLM) Credentials: Provide your API key for the 'Google Gemini Chat Model' and 'Embeddings Google Gemini' nodes.
- Configure Pinecone Credentials & Index: In the 'Pinecone Vector Store' node, connect your Pinecone API credentials and verify or set the
pineconeIndexto your desired index (e.g.,hacker-news). - Test the Workflow: Click 'Test workflow' to see the scraped data processed by Gemini and inserted into Pinecone.
Apps Used
Workflow JSON
{
"id": "5eebdd2a-c17a-4198-b6dc-ea6093ab4e0a",
"name": "Build AI-Ready Vector Datasets with Bright Data, Gemini & Pinecone",
"nodes": 26,
"category": "Engineering",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 5eebdd2a-c17a...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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