Classify LinkedIn posts as quality or slop with OpenAI and Qdrant
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 23 nodes and runs when an HTTP POST request hits its webhook URL. Along the way it connects Embeddings OpenAI, OpenAI Chat Model and Qdrant Vector Store.
How it runs
Trigger: When an HTTP POST request hits its webhook URL.
- Switch: routes items into 2 branches
- Split Out: splits a list into separate items
- Store post (Qdrant Vector Store): stores documents in the Qdrant vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
- Skip empty items (Filter): keeps only the items that match a condition
- Respond to Webhook: sends the response back to the webhook caller
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- Aggregate: combines items into one
- Retrieve similar posts (Qdrant Vector Store): searches the Qdrant vector store, with embeddings: Embeddings OpenAI
- Merge: merges the data from its input branches
- Analyze posts (Basic LLM Chain): sends a prompt to an LLM, with model: OpenAI Chat Model; output parser: Structured Output Parser
…plus 7 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (17)
- Webhook
n8n-nodes-base.webhook - Respond to Webhook
n8n-nodes-base.respondToWebhook - Split Out
n8n-nodes-base.splitOut - Aggregate
n8n-nodes-base.aggregate - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Merge
n8n-nodes-base.merge - Filter
n8n-nodes-base.filter - Switch
n8n-nodes-base.switch - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - If
n8n-nodes-base.if - Edit Fields (Set)
n8n-nodes-base.set - No Operation, do nothing
n8n-nodes-base.noOp - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm
From the author
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…