Automation library · Setup and learning

Build your own Qdrant vector store MCP server with OpenAI

Started by an event in a connected app, and works across Qdrant. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 37 steps, 3 branches: large, and worth reading before you trust it with anything live.

LargeCalls a model: OpenAIRetrieval

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37 stepsLarge: read it before trusting it with anything live
3Branches: places the flow splits
0Loops or waits
1Service it connects to

The flow

Starts it 3Model 11Logic 16Service 7

The steps in the order they run, from what starts it.

  1. 1InsertTool WorkflowModel
  2. 2SearchTool WorkflowModel
  3. 3RecommendTool WorkflowModel
  4. 4When Executed by Another WorkflowExecute Workflow triggerStarts it
  5. 5CompareTool WorkflowModel
  6. 6Embeddings OpenAIEmbeddings Open AiModel
  7. 7Recursive Character Text SplitterText Splitter Recursive Character Text SplitterModel
  8. 8Embeddings OpenAI1Embeddings Open AiModel
  9. 9ListCompaniesTool WorkflowModel
  10. 10When clicking ‘Test workflow’Manual triggerStarts it
  11. 11Qdrant MCP ServerMcp triggerStarts it
  12. 12OperationSwitchLogic
  13. 13Insert ReviewsVector Store QdrantModel
  14. 14Default Data LoaderDocument Default Data LoaderModel
  15. 15Search ReviewsVector Store QdrantModel
  16. 16Create CollectionHttp RequestService
  17. 17List by Facet APIHttp RequestService
  18. 18Get Embeddings1Http RequestService
  19. 19Preferences to ItemsCodeLogic
  20. 20Get Insert ResponseSetLogic
  21. 21Get Search ResponseAggregateLogic
  22. 22Create Facet IndexHttp RequestService
  23. 23Aggregate Embeddings1AggregateLogic
  24. 24Get EmbeddingsHttp RequestService
  25. 25Group Search APIHttp RequestService
  26. 26Aggregate EmbeddingsAggregateLogic
  27. 27Split Out CompaniesSplit OutLogic
  28. 28Recommend APIHttp RequestService
  29. 29Simplify Group ResultsSetLogic
  30. 30Simplify Recommend ResponseSetLogic
  31. 31Filter By CompanyIdFilterLogic
  32. 32Has Results?1IfLogic
  33. 33Has Results?IfLogic
  34. 34Aggregate Recommend ResponseAggregateLogic
  35. 35Empty Compare Response1SetLogic
  36. 36Aggregate Compare ResponseAggregateLogic
  37. 37Empty Compare ResponseSetLogic

How it's built

Starts on
An event in a connected app
Steps
37, with 3 branches
Size
Large: read it before trusting it with anything live
Connects to
Qdrant
Model
OpenAI
Retrieval
Answers are drawn from documents you supply, not from the model alone

Questions

What does it need to run?

An n8n account, plus your own Qdrant account, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on an event in a connected app.

Does it use AI?

Yes. It calls OpenAI. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Take your time. At 37 steps it is large: read it before trusting it with anything live.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

Before you run it

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 37 steps before you point it at anything that matters.