Multilingual Metadata Generation for Content
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Generate structured metadata in both English and Chinese from incoming data, and categorize emails.
About This Workflow
This workflow demonstrates how to process incoming data (potentially from a webhook or email) and generate structured metadata. It also showcases two distinct AI-powered use cases: categorizing incoming emails using language models and interacting with a PDF document to answer questions, and booking appointments using an AI agent with access to calendar tools. The workflow is designed to handle data in English and generate corresponding metadata in Chinese, facilitating international content management and user interaction.
Key Features
- Multilingual Metadata Generation: Process data and output structured metadata in both English and Chinese.
- AI-Powered Email Categorization: Automatically classify incoming emails into predefined categories (e.g., 'automation', 'music') using a text classifier.
- PDF Question Answering: Ingest PDF documents, create embeddings, store them in a vector database (Pinecone), and allow users to ask questions answered by an AI model that references the document's content.
- AI Appointment Booking Agent: Utilize an AI agent to manage calendar appointments, checking availability and booking meetings via Google Calendar API.
- Webhook Integration: Receive data via a webhook to trigger the workflow.
- Email Processing: Trigger email-related actions based on specific conditions.
How To Use
-
**Email Categorization (Example #1):
- The
On new email to nathan's inbox(Gmail Trigger) node can be enabled to receive emails. - The
OpenAI Chat Model1(GPT-4o) is used by theAssign label with AInode to classify the email text. - Based on the classification,
Add automation labelorAdd music label(Gmail nodes) apply the relevant labels. - The
WebhookandWhether email contains n8nnodes demonstrate a separate path for handling incoming webhook data, potentially related to email processing or other triggers.
- The
-
**PDF Question Answering (Example #2):
- The
PDFs to downloadnode serves as a placeholder for PDF data. Download PDF(HTTP Request) fetches the PDF.Recursive Character Text Splitterbreaks the PDF text into manageable chunks.Embeddings OpenAIgenerates embeddings for the text chunks.Insert into Pinecone vector storestores these embeddings and associated metadata (document title, publish year, author) in Pinecone.Read Pinecone Vector Storeretrieves relevant information based on a query.OpenAI Chat Model(GPT-4o) andVector Store Retrieverare used byQuestion and Answer Chainto answer user queries based on the PDF content.
- The
-
**Appointment Booking (Example #3):
- The
When chat message receivednode (Chat Trigger) initiates the appointment booking process. - The
Appointment booking agentorchestrates the interaction. - It uses
Anthropic Chat Modelfor conversational AI andWindow Buffer Memoryto maintain context. Get calendar availability(Google Calendar API Tool) checks for free slots.Book appointment(Google Calendar API Tool) creates the event if a slot is available.- The agent is configured with a
systemMessageproviding instructions and context.
- The
Apps Used
Workflow JSON
{
"id": "2eb53f27-baa8-4095-9214-3f51b154548c",
"name": "Multilingual Metadata Generation for Content",
"nodes": 28,
"category": "AI & Language Models",
"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: 2eb53f27-baa8...
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