This article provides a complete, practical walkthrough of the Splitout Postgres Automation Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   From Gmail to Vector Intelligence: Automate Email Embeddings with n8n, PGVector, and Ollama
   
   Meta Description:
   Explore a powerful n8n workflow that fetches Gmail emails, stores structured metadata in PostgreSQL, and transforms email content into vector embeddings using Ollama’s AI models for advanced semantic search with PGVector.
   
   Keywords:
   n8n automation, Gmail API, email embeddings, vector search, PGVector, Ollama AI, semantic search, PostgreSQL automation, Langchain, email metadata, AI workflow automation
   
   Third-Party APIs and Services Used:
   
   1. Gmail API – To retrieve messages from a Gmail inbox.
   2. Ollama – To generate vector embeddings using the nomic-embed-text model.
   3. PostgreSQL – For structured data storage (emails_metadata) and vector storage (emails_embeddings) using PGVector integration.
   4. Langchain – Used within n8n to split text and load data for embedding generation.
   
   Article:
   
   Automating the Future of Email Search with AI: Gmail to Vector Embeddings via n8n, PGVector & Ollama
   
   In today's data-driven world, emails represent a goldmine of unstructured information. But how do you unlock actionable insights from your inbox? Enter the world of vector embeddings—a way to translate unstructured text into numerical representations for advanced search and recommendation capabilities. And now, with workflow orchestration tools like n8n, AI models from Ollama, and vector storage via PGVector, transforming emails into semantically searchable data has never been easier.
   
   This article explores a versatile automated workflow that transforms raw Gmail messages into vector embeddings for high-performance semantic search. Whether you're a developer, automation enthusiast, or data scientist, this n8n solution brings together some of the best in modern data processing.
   
   Overview of the Workflow
   
   The core of this workflow consists of a multi-stage data pipeline that performs the following tasks:
   
   - Automatically fetch emails from Gmail
   - Extract and store structured metadata in PostgreSQL
   - Tokenize and split email content into more manageable chunks
   - Convert plain text into vector embeddings using Ollama’s nomic-embed-text model
   - Store embeddings in a vector-capable Postgres table via PGVector
   - Tag emails with metadata to associate structured and vectorized data
   
   Let's break this down.
   
   1. Email Retrieval – Gmail Integration
   
   Using the Gmail Trigger and Gmail Get Messages nodes within n8n, the workflow taps into the Gmail API to fetch either historical or real-time messages. On initial setup, users can bulk-import emails starting from the date they created their Gmail account (which they specify in a simple code block). The Gmail Trigger runs every minute to check for new incoming emails.
   
   This step ensures that the workflow can be manually run for bulk imports or automatically for new content on the fly.
   
   2. Extracting and Structuring Metadata
   
   Once emails are fetched, the workflow uses n8n's Set node (named "Extract email fields") to pull out structured information such as:
   
   - Sender and recipient info
   - Subject line
   - Date and time
   - Message ID and thread ID
   - Attachments (by filename)
   
   This extracted data is passed to a PostgreSQL database table (emails_metadata), providing a structured foundation for future filtering, analytics, and joins.
   
   3. Text Splitting – Optimized for AI Processing
   
   Since AI models often have context size limits, large email bodies must be split intelligently. The workflow uses Langchain’s Recursive Character Text Splitter to divide the content into manageable chunks of 2,000 characters with a 50-character overlap—ideal for contextual integrity.
   
   4. Embedding Generation – Unlocking Meaning with Ollama
   
   The chunked text is then fed into the Ollama Embeddings node, which applies the nomic-embed-text model. This model translates each chunk of text into a high-dimensional vector that encodes semantic meaning.
   
   Why is this important? It enables capabilities like:
   
   - Semantic similarity search
   - Content recommendations
   - Topic clustering
   
   Think of it like Google Search on steroids—understanding meaning, not just keywords.
   
   5. Vector Storage – Semantic Retrieval at Scale with PGVector
   
   The final vectorized output, along with metadata tags (email ID and thread ID), is stored in a PostgreSQL table configured with PGVector, a popular extension for native vector similarity search.
   
   Now, users can perform operations like:
   
   - “Find emails similar to this one”
   - “Retrieve threads where this concept appears”
   - “Cluster emails by topic automatically”
   
   6. Resilience and Repeatability
   
   The workflow features "Manual Trigger" and "IF" logic nodes to distinguish manual runs (like bulk imports) from automated triggers, ensuring robust and reliable execution. There's also logic for looping over time intervals, allowing you to process archived emails week-by-week for scalability.
   
   Why This Workflow Matters
   
   For businesses and individuals drowning in email data, this solution makes it possible to repurpose inboxes into powerful knowledge engines. Instead of keyword-based search, which often leads to imprecise results, semantic search lets you ask nuanced questions like "show me all emails related to project delays."
   
   It’s a combination of smart data structuring (via PostgreSQL), intelligent indexing (PGVector), and cutting-edge language understanding (via Ollama), all orchestrated visually in n8n.
   
   Final Thoughts
   
   With just a few clicks and configuration tweaks, you can activate a production-grade NLP pipeline that handles your Gmail inbox like you've never seen before. The modularity of n8n ensures that the workflow is extendable—add Slack notifications, CRM logging, or sentiment scoring as needed.
   
   This isn’t just automation; it’s email intelligence, transformed.
   
   If you're curious to try it out, the full workflow is open to customization, including model swapping, filter tuning, and data enrichment. Whether you’re deploying a semantic support bot or reverse-engineering years of customer communication patterns, this solution lights the path toward smarter, AI-powered email ecosystems.
   
   So, plug in your Gmail account, turn on the workflow, and watch your inbox come alive with insights, all thanks to n8n, Ollama, and PGVector.
   
   — End —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.