n8n-io/n8n · error · NodeOperationError

Azure AI Search API error (${statusCode}): ${errorMessage}

Error message

Azure AI Search API error (${statusCode}): ${errorMessage}

What it means

The populateVectorStore catch block handles Azure SDK RestError specially: it surfaces the HTTP statusCode, the SDK error code, and a multi-line description of common causes. RestError is thrown by @azure/core-rest-pipeline when a request fails or has no response.

Source

Thrown at packages/@n8n/nodes-langchain/nodes/vector_store/VectorStoreAzureAISearch/VectorStoreAzureAISearch.node.ts:539

				(error as any).statusCode === 403
			) {
				throw new NodeOperationError(
					context.getNode(),
					'Authorization failed - insufficient permissions for document upload.',
					{
						itemIndex,
						description:
							'The API Key does not have sufficient permissions for write operations. Ensure the key has the required access level.',
					},
				);
			}

			// Check for RestError (common Azure SDK error)
			if ((error as any).name === 'RestError' || error.message?.includes('RestError')) {
				const statusCode = (error as any).statusCode || 'unknown';
				const errorCode = (error as any).code || 'unknown';
				const errorMessage = error instanceof Error ? error.message : String(error);
				throw new NodeOperationError(
					context.getNode(),
					`Azure AI Search API error (${statusCode}): ${errorMessage}`,
					{
						itemIndex,
						description: `Error code: ${errorCode}\n\nCommon causes:\n- Invalid endpoint URL\n- Index doesn't exist\n- Authentication/authorization issues\n- API version mismatch\n\nCheck the console logs for detailed error information.`,
					},
				);
			}

			const errorMessage = error instanceof Error ? error.message : String(error);
			throw new NodeOperationError(context.getNode(), `Error: ${errorMessage}`, {
				itemIndex,
				description: 'Please check your Azure AI Search connection details and index configuration',
			});
		}
	},
}) {}

View on GitHub (pinned to 5ac6606e81)

Solutions

1. Read the statusCode and error code in the message/description to classify (404=index missing, 400=bad payload, 429=throttle, 5xx=transient).
2. Ensure the index exists and its vector field dimensions match the embeddings model.
3. For 429, reduce batch size / add retry with backoff.
4. Update the @azure/search-documents SDK if there is an API-version mismatch.

function isRestError(e: unknown): e is { statusCode?: number; code?: string; message: string } {
  return e instanceof Error && ((e as any).name === 'RestError' || /RestError/i.test(e.message));
}

for (const [attempt, delay] of [0, 500, 2000].entries()) {
  try { await vectorStore.addDocuments(docs); break; }
  catch (e) {
    if (isRestError(e) && (e.statusCode === 429 || (e.statusCode ?? 0) >= 500) && attempt < 2) {
      await sleep(delay); continue;
    }
    throw e;
  }
}

When it happens

Trigger: error.name === 'RestError' OR error.message includes 'RestError' during document upload — e.g. the REST call to Azure AI Search returned a non-2xx status (400 bad request, 404 index missing, 409 conflict, 429 throttled, 5xx).

Common situations: Index does not exist; API version mismatch between SDK and service; malformed document fields/vector dimensions; throttling (429); transient 5xx from Azure; wrong endpoint URL.

Related errors

• Authentication failed during document upload - invalid API k
• Authorization failed - insufficient permissions for document
• Parameter ${key} must be a string
• Azure AI Search endpoint is missing or invalid
• API Key is required for authentication

AI-assisted analysis of n8n-io/n8n@5ac6606e81 (2026-08-12). Data as JSON: /api/errors/9e3136640b9b9203. Report an issue: GitHub.