n8n-io/n8n · error · OperationalError

NVIDIA embeddings API returned ${data.length} embeddings for

Error message

NVIDIA embeddings API returned ${data.length} embeddings for a batch of ${expected} inputs

What it means

An OperationalError raised while flattening NVIDIA embeddings batch responses: each batch must return exactly one embedding per input so the caller can zip them by position. If data.length !== expected for any batch, embedding alignment would be corrupted, so the helper fails loudly instead of silently shifting vectors.

Source

Thrown at packages/@n8n/nodes-langchain/nodes/embeddings/EmbeddingsNvidia/helpers.ts:70

				model: this.model,
				input: batch,
				input_type: inputType,
			};
			if (this.dimensions) params.dimensions = this.dimensions;
			if (this.encodingFormat) params.encoding_format = this.encodingFormat;
			const { data } = await this.embeddingWithRetry(params);
			return { expected: batch.length, data };
		});

		const batchResponses = await Promise.all(batchRequests);

		// The caller maps each input text to the embedding at the same position, so every batch must
		// return exactly one embedding per input. Flatten by input position (like the base class) and
		// fail loudly on a malformed response rather than silently dropping or shifting embeddings.
		const embeddings: number[][] = [];
		for (const { expected, data } of batchResponses) {
			if (data.length !== expected) {
				throw new OperationalError(
					`NVIDIA embeddings API returned ${data.length} embeddings for a batch of ${expected} inputs`,
				);
			}
			for (const entry of data) {
				embeddings.push(entry.embedding);
			}
		}
		return embeddings;
	}
}

View on GitHub (pinned to 5ac6606e81)

Solutions

1. Reduce the batch size (chunk documents before calling embedDocuments) to stay under the NVIDIA model's limit.
2. Sanitise inputs — remove empty/whitespace strings and de-duplicate before embedding, then re-expand the result.
3. Confirm the NVIDIA model id is an embeddings model (e.g. NV-Embed-QA, nvidia/embedqa-4).
4. Retry once; if intermittent, lower concurrency or add maxRetries/timeout options.

// Chunk inputs to a safe batch size before calling embedDocuments
const MAX_BATCH = 16; // consult NVIDIA model docs
for (let i = 0; i < documents.length; i += MAX_BATCH) {
  const batch = documents.slice(i, i + MAX_BATCH).filter(d => d.trim().length > 0);
  // ... call embedWithRetry(batch)
}

const matchesBatchSize = (data: unknown[], expected: number): boolean =>
  Array.isArray(data) && data.length === expected;

try {
  return embedBatch(batch);
} catch (e) {
  if (/returned \d+ embeddings for a batch of/i.test((e as Error).message)) {
    // retry with batch size 1 to localise the failing input
    return batch.map(one => embedBatch([one])[0]);
  }
  throw e;
}

When it happens

Trigger: NVIDIA's /v1/embeddings endpoint returns fewer or more entries than the number of input texts in a batch — caused by the API dropping empty inputs, deduplicating identical texts, a rate-limit returning a partial body, or a model cap on batch size that truncates the response.

Common situations: Sending a batch larger than the model's max batch size; sending duplicate or whitespace-only strings the API collapses; endpoint misbehaviour under load; using a non-embedding NVIDIA model that returns a single aggregate vector.

Related errors

• Unexpected embedding response from Bedrock
• Cannot embed empty or undefined text
• Documents must be an array
• Model output doesn't fit required format
• Model output doesn't fit required format

AI-assisted analysis of n8n-io/n8n@5ac6606e81 (2026-08-12). Data as JSON: /api/errors/05bd1b56db80f3b5. Report an issue: GitHub.