Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone
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Build AI-ready vector datasets for LLMs by extracting and embedding data from web sources using Bright Data, Google Gemini, and Pinecone.
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About This Workflow
Overview
This workflow demonstrates how to create AI-ready vector datasets suitable for Large Language Models (LLMs). It leverages Bright Data for web scraping, Google Gemini for text embedding and data formatting, and Pinecone as a vector database for storage and retrieval.
Key Features
- Web scraping of specified URLs using Bright Data.
- Data formatting and extraction using AI agents powered by Google Gemini.
- Text embedding for creating vector representations of the data.
- Storing embeddings in Pinecone for efficient similarity search.
How To Use
- Configure Bright Data credentials and the target URL in the 'Set Fields - URL and Webhook URL' node.
- Set up Google Gemini API credentials.
- Configure Pinecone credentials and specify the index name.
- Trigger the workflow manually by clicking 'Test workflow'.
Apps Used
Workflow JSON
{
"id": "befdc807-25f2-4d25-ac6c-f18e20b4a89e",
"name": "Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone",
"nodes": 0,
"category": "Data Integration & AI",
"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: befdc807-25f2...
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