Guidance on integrating N8N with Qdrant Vector Store to fix vector name errors

[aroberts]

I’m facing issues while trying to set up a straightforward N8N workflow using the Qdrant Vector Store. Every time I attempt to execute the workflow, I receive this error message:

Error in node 'Qdrant Vector Store'
400 Bad Request: Invalid input: Non-existent vector name:

I’ve followed the initial guide, but it looks like there are some essential instructions missing regarding the configuration of the Qdrant collection. The guide does not clarify how to set up the collection on the Qdrant dashboard to make it compatible with N8N.

Here are my questions:

• How should I create a Qdrant collection that works with N8N?
• What collection settings do I need?
• Are there any particular settings required for the vectors?

Here’s what my current workflow setup looks like:

{
  "name": "SampleWorkflow",
  "nodes": [
    {
      "parameters": {
        "model": "text-embedding-ada-002",
        "options": {}
      },
      "id": "embed-node-001",
      "name": "OpenAI Embeddings",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "typeVersion": 1,
      "position": [900, 200]
    },
    {
      "parameters": {
        "repository": "https://github.com/example/sample-docs",
        "additionalOptions": {}
      },
      "id": "loader-node-002",
      "name": "Git Document Loader",
      "type": "@n8n/n8n-nodes-langchain.documentGithubLoader",
      "typeVersion": 1,
      "position": [1000, 200]
    },
    {
      "parameters": {
        "mode": "insert",
        "qdrantCollection": {
          "__rl": true,
          "value": "my_documents",
          "mode": "list"
        }
      },
      "id": "vector-store-003",
      "name": "Qdrant Vector Database",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "typeVersion": 1,
      "position": [1100, 100]
    }
  ]
}

I created the collection using the Qdrant interface, but I might be missing critical settings. Has anyone successfully implemented this? What are the optimal collection settings I should use?

[JackWolf69]

This error occurs when your Qdrant collection’s vector configuration does not align with N8N’s expectations. By default, N8N looks for a vector named ‘content’, which may not exist or could be named differently in your setup. To resolve this, access your Qdrant dashboard and create a vector titled ‘content’ with a size that corresponds to your embedding model—1536 for ‘text-embedding-ada-002’. It’s also advisable to set the distance metric to ‘Cosine’, as it complements OpenAI embeddings effectively. I experienced a similar issue until I identified the vector name as the root cause. Ensure your collection settings are compatible with N8N’s requirements.

[Hermione_Book]

Had the exact same issue when I started using N8N with Qdrant. The problem is creating collections through Qdrant’s web interface - it doesn’t set up the vector config N8N needs. You’ve got to create the collection via API or make sure the vector name matches what N8N expects. I had to recreate mine with vector name ‘content’ and 1536 dimensions for OpenAI embeddings. Don’t forget the vectors config block with the right distance metric. Just use Qdrant’s API directly or check your collection’s vector setup in the dashboard (vectors tab) to confirm it matches your embedding model’s dimensions.

[Mandy_45Photography]

Check if your Qdrant collection has vectors set up right. The dashboard sometimes screws up vector fields when you create collections manually. Delete the collection and remake it with proper config - you need 1536 dimensions for OpenAI’s ada model and cosine distance. Also make sure the vector name matches what n8n’s looking for.