**Title:** Issue with Qdrant Vector Store Node in n8n – “Bad Request” when searching
**Body:**
Hello n8n Community,
I’m facing an issue while using the **Qdrant Vector Store** node connected to an AI Agent workflow. Whenever I try to perform a search, I get the following error:
```json
{
"errorMessage": "Bad Request",
"errorDescription": "Bad Request",
"errorDetails": {},
"n8nDetails": {
"time": "05/09/2025, 19:28:34",
"n8nVersion": "1.108.2 (Cloud)",
"binaryDataMode": "filesystem"
}
}
```
Here is the setup of the nodes involved in the workflow:
```json
{
"nodes": [
{
"parameters": {
"model": "nomic-embed-text:latest"
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"typeVersion": 1,
"position": [1680,544],
"id": "c6ee250a-0d5a-4273-8399-8ba43ee3733b",
"name": "Embeddings Ollama1",
"credentials": { "ollamaApi": { "id": "7vYFQDKOLB2g4N6k", "name": "Ollama account" } }
},
{
"parameters": { "name": "retriever", "description": "Search for products in the semantic database", "topK": 3 },
"id": "b204047f-281f-45a0-96de-1b04e9764505",
"name": "Vector Store Tool",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [1856,256],
"typeVersion": 1
},
{
"parameters": { "options": {} },
"id": "547fb95c-d6cd-489f-bd2b-d7106f904f3e",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [2112,432],
"typeVersion": 1,
"credentials": { "openAiApi": { "id": "EAmihlyEFu3UqJRi", "name": "OpenAi account" } }
},
{
"parameters": {
"qdrantCollection": { "__rl": true, "value": "universal_semantic_pipeline", "mode": "list", "cachedResultName": "universal_semantic_pipeline" },
"options": { "searchFilterJson": "{\n \"vectorName\": \"shrouf_vector\",\n \"scoreThreshold\": 0.2\n}" }
},
"id": "de58cd40-7bf1-45d1-a248-f3e63998e6a9",
"name": "Qdrant Vector Store2",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [1680,400],
"typeVersion": 1,
"credentials": { "qdrantApi": { "id": "OgAYcIrfiNxVCECc", "name": "QdrantApi account" } }
}
],
"connections": {
"Embeddings Ollama1": { "ai_embedding": [[{ "node": "Qdrant Vector Store2", "type": "ai_embedding", "index": 0 }]] },
"Vector Store Tool": { "ai_tool": [[]] },
"OpenAI Chat Model": { "ai_languageModel": [[{ "node": "Vector Store Tool", "type": "ai_languageModel", "index": 0 }]] },
"Qdrant Vector Store2": { "ai_vectorStore": [[{ "node": "Vector Store Tool", "type": "ai_vectorStore", "index": 0 }]] }
}
}
```
For reference, here are the Qdrant collection settings and vector info:
```json
{
"status": "green",
"optimizer_status": "ok",
"indexed_vectors_count": 1,
"points_count": 1,
"segments_count": 2,
"config": {
"params": {
"vectors": {
"shrouf_vector": { "size": 768, "distance": "Dot" }
},
"shard_number": 1,
"replication_factor": 1,
"write_consistency_factor": 1,
"on_disk_payload": true
},
"hnsw_config": {
"m": 16,
"ef_construct": 100,
"full_scan_threshold": 10000,
"max_indexing_threads": 0,
"on_disk": false
}
}
}
```
**Problem:**
* The search in Qdrant node returns a **“Bad Request”** error.
* I have specified `vectorName` in the `searchFilterJson`, but it still fails.
* The vector exists in the collection and is correctly indexed with size 768.
**Question:**
* Is there a specific way to configure **vectorName** in n8n Qdrant Vector Store node for custom vectors?
* Are there any known issues with the latest n8n Cloud version regarding named vectors?
* Any guidance on how to properly connect **Embeddings Ollama → Qdrant → Vector Store Tool → AI Agent** would be greatly appreciated.
Thank you in advance for any help!
---
