📊 Classify LinkedIn posts as quality or slop with OpenAI and Qdrant

⚡ 46 views · 📊 Market Research & Insights

💡 Pro Tip — If you’re pulling LinkedIn data through HTTP requests or dealing with API restrictions, there’s a community node called ScraperNode that handles this natively. It has dedicated scrapers for profiles, companies, jobs, and people search — you just pass a URL and get structured data back.

Description

Purpose

This workflow is the official backend for the StopSlopIn Chrome extension – it classifies LinkedIn posts as quality or slop using a strict LLM quality gate and learns from user votes over time via a Qdrant vector store.

What this is for

This runs the webhook that powers the StopSlopIn Chrome extension on the Chrome Web Store. The extension sends LinkedIn posts here for analysis and user votes here for training – everything stays on your own n8n instance.

Setup

• Add your OpenAI credentials to the chat model and embeddings nodes
• Add your Qdrant credentials to both vector store nodes, pointing to a collection named stopslopin
• Activate the workflow, copy the webhook URL, and paste it into the StopSlopIn Chrome extension settings
• Follow the instructions on the yellow sticky notes for anything else

How it works

A single webhook exposes two actions, selected via a ?action= query parameter: analyze for classification, vote for training.

1. A Switch node routes incoming requests based on the action parameter
2. On analyze: each post is enriched with similar prior-rated posts pulled from Qdrant (RAG), batched together, and sent to the LLM with a strict quality-gate system prompt
3. The LLM returns a structured JSON array of pass / fail results, which is sent back to the caller
4. On vote: the post is embedded and stored in Qdrant along with the user’s “good” or “slop” rating as metadata
5. Every new vote becomes in-context reference material for future classifications, so the filter gradually adapts to personal taste

Customization

• LLM – swap the OpenAI Chat Model for any LangChain-compatible chat model (Claude, Ollama, etc.)
• Prompt – edit the system prompt inside the Basic LLM Chain node to match your own feed taste
• Similarity threshold – change the value in the Filter node (default 0.7) to make RAG examples looser or stricter

Compatibility

• n8n Version 2.17.0 or above
• Cloud or Self-Hosted
• Requires: OpenAI account, Qdrant instance

Note: post contents sent through this workflow are forwarded to the configured LLM and embeddings provider (OpenAI by default). Swap those nodes for a local or alternative provider if that is a concern.

🔗 Nodes Used

Webhook, Filter, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model, Structured Output Parser

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup