Qdrant Store AI Tools icon

Qdrant Store AI Tools

Embed text and binary files into a Qdrant vector store using a connected embedding model. Binary files are sent directly to the embedder (multimodal support with automatic fallback). Connect an Embedding model to the "Embedding" input and the "Tool" output to an AI Agent.

Qdrant Store AI Tools

AI-generated

Summary

Expose a set of AI agent tools for embedding and storing text and binary data into a Qdrant vector store, including creating and deleting Qdrant collections.

Inputs

  • Default Collection (required) — Name of the default Qdrant collection to store vectors in; must exist or be created beforehand.
  • Tools to Enable (required) — Select which tools to expose to the AI Agent, options include storing single text, storing a binary file, batch storing texts, creating a collection, and deleting a collection.

Output shape

a list of AI tools compatible with LangChain's DynamicStructuredTool, each accepting JSON input parameters according to their function (e.g., text to embed, binary property name, collection name). The node outputs these tools via the 'Tool' output to be used by AI Agent nodes.

The tools require a connected Embedding model sub-node on the 'Embedding' input. Binary data is accepted as buffer or base64 fallback. Tools include: 'qdrant_store_text' (embed and store one text), 'qdrant_store_binary_file' (embed and store binary files with chunking support for text files), 'qdrant_batch_store_texts' (embed and store multiple texts in batch), 'qdrant_create_collection' (create a vector collection with specified config), and 'qdrant_delete_collection' (delete a collection permanently). The 'Default Collection' parameter specifies the collection used when not overridden. This node is designed to be used in conjunction with AI Agent nodes in n8n workflows.

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