Quick overview
Quick overview This workflow exposes a POST webhook that authenticates a tenant, embeds the user query with OpenAI, searches tenant-scoped vectors in Qdrant, and uses GPT-4o to return a grounded answer with bracketed citations and usage metrics. How it works Receives a POST request via a webhook with a user query and tenant identifiers (for example, x-tenant-id in headers). Validates that the tenant ID and query are present and routes invalid requests to an HTTP error response. Sends the query to OpenAI Embeddings (text-embedding-3-small) to generate a vector. Builds a Qdrant search request that enforces multi-tenant isolation using payload filters (required tenant_id, optional workspace_id and category) and applies a similarity threshold. Queries Qdrant for the top matching chunks, then formats the retrieved payload into a single context block and a structured citations list. Sends the query and formatted context to OpenAI Chat Completions (GPT-4o) to generate an answer that cites sources like [Source 1] using only the provided context. Assembles a final JSON response (answer, citations, similarity metrics, and token usage) and returns it to the webhook caller. Setup Provide an OpenAI API key (used for embeddings and GPT-4o), either by setting OPENAI_API_KEY as an environment variable or replacing the placeholder in the HTTP Authorization headers. Set up a Qdrant instance and configure QDRANT_HOST, QDRANT_API_KEY (if required), and QDRANT_COLLECTION (defaults to enterprise_rag_vectors). Ensure your Qdrant collection stores payload fields used for filtering and citations (at minimum tenant_id, and optionally workspace_id, category, plus fields like content/text, title/filename, and document_id). Copy the webhook URL from the Webhook node and configure your client to POST a JSON body containing query and to include x-tenant-id (and optionally x-workspace-id) headers. Requirements • OpenAI API Account (with text-embedding-3-small and GPT-4o access) • Qdrant Cluster (Self-hosted Docker on VPS or Qdrant Cloud Free Tier) • n8n instance (Cloud or Self-hosted v1.0+) Customization • Swap text-embedding-3-small with local Ollama / BGE embeddings for completely air-gapped on-premise deployments. • Adjust the cosine similarity threshold (default 0.68) inside the Code node to fine-tune precision vs recall. • Connect the output response to Slack, WhatsApp, or customer support chatbots for automated enterprise document search. An n8n automation workflow template by Alfaz Mahmud Rizve.
Alfaz Mahmud Rizve
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