Qdrant is an open source vector database built for scale. It rips through billions of vectors with no sweat and can handle hybrid queries, filtering, and multimodal data.
When you combine n8n and Qdrant, developers and non-developers alike can build AI-powered automations—like chatbots that recall past interactions or recommend relevant documents—using only visual workflows.
Qdrant enables storing and searching unstructured data (like video, photos, PDFs, or code snippets) through semantic similarity, rather than just keywords. This makes it possible to create hybrid workflows that combine traditional automation with context-aware intelligence, all without writing a single line of code.
MCP Server: https://github.com/qdrant/mcp-server-qdrant
Sparse Neural Model (miniCOIL): https://github.com/qdrant/miniCOIL
https://www.youtube.com/watch?v=BF02jULGCfo
https://www.youtube.com/watch?v=mXNrhyw4q84