Best n8n Qdrant Node & Integration: Workflows & Templates

Vision RAG and Image Embeddings using Cohere Models

Build an advanced Vision RAG solution in n8n. This n8n workflow uses Cohere Embed v4 for image embeddings and Cohere Command-A vision model to answer complex questions about graphical documents stored in Qdrant.

Contextual Retrieval RAG System with Kimi-K2, Gemini, and Qdrant

Build advanced RAG using contextual summaries. This n8n workflow leverages Kimi-K2 via Featherless.ai for unlimited tokens, Gemini embeddings, and Qdrant vector database storage.

Hybrid Search Indexing for Legal Datasets using Qdrant

Use this comprehensive n8n workflow to ingest, preprocess, and index legal Q&A datasets into Qdrant for highly efficient hybrid search (dense/sparse vectors). Includes options for Qdrant Cloud Inference and OpenAI embeddings.

Vision RAG and Image Embeddings using Cohere Models

Build an advanced Vision RAG solution in n8n. This n8n workflow uses Cohere Embed v4 for image embeddings and Cohere Command-A vision model to answer complex questions about graphical documents stored in Qdrant.

Hybrid Search Retrieval Evaluation for Legal AI RAG

Evaluate Hybrid Search quality using Qdrant and n8n. This n8n workflow calculates hits@1 performance using BM25 and semantic search for legal AI RAG systems.

Hybrid Search Indexing for Legal Datasets using Qdrant

Use this comprehensive n8n workflow to ingest, preprocess, and index legal Q&A datasets into Qdrant for highly efficient hybrid search (dense/sparse vectors). Includes options for Qdrant Cloud Inference and OpenAI embeddings.

Contextual Retrieval RAG System with Kimi-K2, Gemini, and Qdrant

Build advanced RAG using contextual summaries. This n8n workflow leverages Kimi-K2 via Featherless.ai for unlimited tokens, Gemini embeddings, and Qdrant vector database storage.

Contextual Retrieval RAG System with Kimi-K2, Gemini, and Qdrant

Build advanced RAG using contextual summaries. This n8n workflow leverages Kimi-K2 via Featherless.ai for unlimited tokens, Gemini embeddings, and Qdrant vector database storage.