Ride-Share Surge Predictor: Unlock Smarter Travel
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Leverage AI to predict ride-share surge pricing and optimize your travel plans. This workflow intelligently processes data to provide timely surge insights, ensuring you get the best value.
About This Workflow
The Ride-Share Surge Predictor is an intelligent automation workflow designed to provide users with real-time insights into ride-sharing surge pricing. By integrating with AI models and vector stores, it analyzes relevant data to anticipate demand spikes. This allows users to make informed decisions, saving time and money on their commutes. The workflow begins by receiving data via a webhook, which is then processed, embedded, and stored for efficient querying. It utilizes a powerful language model to understand and respond to queries, ultimately logging predictions to a Google Sheet for easy access and further analysis.
Key Features
- AI-Powered Prediction: Utilizes advanced AI models for accurate surge price forecasting.
- Real-Time Data Processing: Efficiently handles incoming data through webhooks for up-to-the-minute insights.
- Intelligent Data Storage: Employs vector databases for optimized storage and retrieval of prediction data.
- Automated Logging: Seamlessly logs surge predictions to a Google Sheet for easy review and historical tracking.
How To Use
- Webhook Setup: Configure the 'Webhook' node to receive incoming ride-share data. Set the
pathtoride-share_surge_predictor. - Text Splitting: Utilize the 'Splitter' node to break down incoming text data into manageable chunks for AI processing. Adjust
chunkSizeandchunkOverlapas needed. - AI Embeddings: Connect the 'Embeddings' node to convert text chunks into numerical representations using your chosen AI model (e.g., Cohere).
- Data Storage: Configure the 'Insert' node to store the embeddings in your Supabase vector store. Specify the
indexNamefor organization. - Querying Data: Set up the 'Query' node, also targeting your Supabase vector store with the same
indexName, to retrieve relevant data for predictions. - Tool Integration: Use the 'Tool' node to make your vector store accessible as a tool for the AI agent.
- Memory Management: Configure the 'Memory' node to maintain conversational context for the AI agent.
- AI Chat Integration: Connect the 'Chat' node to an AI language model (e.g., Anthropic) to power the agent's responses.
- Agent Configuration: Set up the 'Agent' node, defining its
promptTypeas 'define' and passing in the necessary inputs from previous nodes. - Logging Predictions: Connect the 'Agent' node to the 'Sheet' node to append the AI's predictions to your designated Google Sheet ('Log' sheet).
Apps Used
Workflow JSON
{
"id": "bc5d51c4-fe1f-41b1-9a9f-0e1c83713a84",
"name": "Ride-Share Surge Predictor: Unlock Smarter Travel",
"nodes": 17,
"category": "Operations",
"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: bc5d51c4-fe1f...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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