Qdrant integration with N8N workflow failing - vector name error

Other Questions N8n
1

I’m having trouble setting up a basic N8N workflow with Qdrant vector database. When I try to run my workflow, I keep getting this error message:

Error in node 'Vector Database Node'
400 Bad Request: Invalid input - vector name does not exist

I followed the basic setup guide but I think I’m missing some key configuration steps. The main issues I’m running into are:

  • How do I properly create a Qdrant collection that works with N8N workflows?
  • What vector configuration settings should I use?
  • Are there any specific naming conventions I need to follow?

Here’s my current workflow setup:

{
  "name": "DocumentProcessingFlow",
  "nodes": [
    {
      "parameters": {
        "model": "text-embedding-ada-002",
        "settings": {}
      },
      "id": "embed-node-001",
      "name": "OpenAI Embeddings",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [900, 200]
    },
    {
      "parameters": {
        "repositoryUrl": "https://github.com/example/sample-docs",
        "extraOptions": {}
      },
      "id": "loader-node-002",
      "name": "Git Document Reader",
      "type": "@n8n/n8n-nodes-langchain.documentGithubLoader",
      "position": [1000, 200]
    },
    {
      "parameters": {
        "mode": "upsert",
        "collectionName": {
          "value": "my_documents",
          "mode": "manual"
        }
      },
      "id": "vector-store-003",
      "name": "Vector Database Node",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [800, 100]
    }
  ]
}

I created my collection in the Qdrant dashboard but maybe I’m using wrong vector dimensions or distance metrics. Has anyone successfully connected these two tools? What am I missing in my setup?

2

Had this exact same problem last month and it drove me crazy for hours. The issue isn’t with your collection setup but with the node configuration order in your workflow. Your Vector Database Node is positioned before your embedding and document loader nodes, but N8N processes the vector operations before the embeddings are actually created. You need to connect the nodes properly so the embeddings flow into the vector store node. Also double-check your Qdrant credentials in N8N settings - I was using the wrong API endpoint which caused similar vector name errors. Try switching your workflow execution order and make sure the OpenAI Embeddings node output feeds directly into the Vector Database Node input. The collection name looks fine, just ensure your Qdrant instance is actually running and accessible from your N8N installation.

3

check if your qdrant collection vector size matches openai ada-002 dimensions (1536). also make sure the collection exists before running the workflow - n8n wont auto-create it. i had same issue and it was becuase my collection was set to 768 dims instead of 1536.

4

The vector name error usually happens when there’s a mismatch between your Qdrant collection configuration and what N8N expects. From your workflow config, I see you’re using “my_documents” as collection name but the error suggests the vector field itself might be misconfigured. When creating your collection in Qdrant dashboard, make sure you set the vector field name to “vector” (not “vectors” or anything else). N8N’s Qdrant node specifically looks for this field name by default. Also verify your distance metric is set to “Cosine” which works best with OpenAI embeddings. Another thing to check is your Qdrant connection settings in N8N - the host URL should include the port (usually :6333) and if you’re using Qdrant Cloud, make sure your API key has write permissions for the collection you’re trying to access.