Gmail to Vector Embeddings with PGVector and Ollama
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Process emails from Gmail, generate vector embeddings, and store them alongside metadata in a PostgreSQL database.
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About This Workflow
Overview
This workflow automates the process of ingesting emails from Gmail, extracting relevant information, generating vector embeddings using Ollama's 'nomic-embed-text' model, and storing both the structured email data and their embeddings in a PostgreSQL database with PGVector extension. It enables efficient similarity searches on email content.
Key Features
- Trigger-based Gmail email fetching.
- Extraction and structuring of email fields (ID, thread ID, sender, recipient, subject, date, body).
- Generation of vector embeddings for email content using Ollama.
- Storage of email metadata in a structured table.
- Storage of vector embeddings linked to email IDs in a vector-aware table.
How To Use
- Import: Import this workflow into your n8n instance.
- Configure Credentials: Set up your Gmail credentials in n8n.
- Configure Ollama: Ensure Ollama is running and the 'nomic-embed-text' model is available.
- Configure PostgreSQL: Set up your PostgreSQL database with the PGVector extension enabled and configure the connection details in n8n.
- Activate: Run the workflow manually to start processing emails or activate the Gmail trigger for continuous processing.
Apps Used
Workflow JSON
{
"id": "a044e671-c5a8-43e2-be7c-a5097ef62517",
"name": "Gmail to Vector Embeddings with PGVector and Ollama",
"nodes": 0,
"category": "Email Automation",
"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: a044e671-c5a8...
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Building complex chains with OpenAI, Claude, and LangChain.
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