Qdrant AI Tools
AI-generatedSummary
Expose a suite of Qdrant vector store operations as AI tools for use by an n8n AI Agent, optionally leveraging connected embedding and reranker sub-nodes for semantic search and improved result relevance.
Inputs
- Default Collection — Sets the default Qdrant collection name used when no explicit collection is specified in tool calls (default: 'documents').
- Tools to Enable — Select which Qdrant operations to expose to the AI Agent, such as semantic search, scrolling points, CRUD on points, collection management, and snapshots.
- Default Search Limit — Specifies the default maximum number of results returned by semantic search or recommendation queries when no explicit limit is provided.
- Include Payload in Results — Determines whether to include stored metadata and text payloads in results from search, scroll, and retrieval operations by default.
- Include Vectors in Results — Determines whether to include raw vector data in search and retrieval results, which can increase response size if enabled.
Output shape
a collection of DynamicStructuredTool instances representing enabled Qdrant operations, each callable by the AI Agent with parameters per operation
The node outputs tools consumable by an AI Agent via the 'Tool' output. Semantic search tools require an Embedding input sub-node to compute query vectors; when a Reranker input sub-node is connected, search results are automatically reranked for improved relevance. Each tool accepts operation-specific parameters, with sensible defaults such as the default collection name and result limits. Errors during tool execution return JSON error objects. The node itself is not intended for direct execution outside the AI Agent context.
Examples
Example 1: Enable semantic search, scrolling points, and collection management tools for an AI Agent to interact with Qdrant.
Set Default Collection to 'documents', enable tools including 'search', 'scroll', 'listCollections', 'createCollection', etc., and optionally connect an embedding model to the Embedding input and a reranker model to the Reranker input to enhance search quality.