vercel/ai · error · TooManyEmbeddingValuesForCallError

AI_TooManyEmbeddingValuesForCallError

AI_TooManyEmbeddingValuesForCallError

Error message

Too many values for a single embedding call. The ${provider} model "${modelId}" can only embed up to ${maxEmbeddingsPerCall} values per call, but ${values.length} values were provided.

What it means

Bedrock embedding models accept only maxEmbeddingsPerCall values per doEmbed request. When you pass more values than the model's per-call limit, the SDK throws AI_TooManyEmbeddingValuesForCallError instead of silently truncating. The `embedMany` helper normally batches automatically; this error surfaces when calling the model's doEmbed directly or when batch size is forced.

Source

Thrown at packages/amazon-bedrock/src/amazon-bedrock-embedding-model.ts:79

  constructor(
    readonly modelId: AmazonBedrockEmbeddingModelId,
    private readonly config: AmazonBedrockEmbeddingConfig,
  ) {}

  private getUrl(modelId: string): string {
    const encodedModelId = encodeURIComponent(modelId);
    return `${this.config.baseUrl()}/model/${encodedModelId}/invoke`;
  }

  async doEmbed({
    values,
    headers,
    abortSignal,
    providerOptions,
  }: Parameters<EmbeddingModelV4['doEmbed']>[0]): Promise<DoEmbedResponse> {
    if (values.length > this.maxEmbeddingsPerCall) {
      throw new TooManyEmbeddingValuesForCallError({
        provider: this.provider,
        modelId: this.modelId,
        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,
        values,
      });
    }

    // Parse provider options. Prefer `amazonBedrock`; fall back to legacy
    // `bedrock` key for backward compatibility.
    const amazonBedrockOptions =
      (await parseProviderOptions({
        provider: 'amazonBedrock',
        providerOptions,
        schema: amazonBedrockEmbeddingModelOptionsSchema,
      })) ??
      (await parseProviderOptions({
        provider: 'bedrock',
        providerOptions,

View on GitHub (pinned to 69428b1f8b)

Solutions

1. Split your inputs into chunks no larger than the model's maxEmbeddingsPerCall (embedMany does this automatically — pass the full array to embedMany rather than calling doEmbed directly)
2. Check model.maxEmbeddingsPerCall before calling doEmbed and slice your values array accordingly
3. Use the top-level embed()/embedMany() API instead of the low-level doEmbed method
4. If you need larger batches, pick a Bedrock embedding model with a higher per-call limit

Example fix

// before
await model.doEmbed({ values: docs }); // 500 docs, limit 32
// after
const BATCH = model.maxEmbeddingsPerCall;
for (let i = 0; i < docs.length; i += BATCH) {
  await model.doEmbed({ values: docs.slice(i, i + BATCH) });
}

const max = model.maxEmbeddingsPerCall;
if (values.length > max) throw new Error(`Chunk inputs to <= ${max} values per call`);
// or: chunk and call per chunk

try {
  await embedMany({ model, values });
} catch (e) {
  if (TooManyEmbeddingValuesForCallError.isInstance(e)) {
    // chunk values by e.maxEmbeddingsPerCall and retry
  }
}

When it happens

Trigger: Calling embedMany/embed with more input values than the model's maxEmbeddingsPerCall (Bedrock Titan models commonly cap at 1–32), or calling model.doEmbed directly with an oversized array.

Common situations: Batching thousands of documents into a single embed call; copying code from another provider whose models allow 2048 inputs per call; overriding maxEmbeddingsPerCall or using maxCallsPerRound/maxEntriesPerCall options incorrectly.

Related errors

• AI_UnsupportedFunctionalityError
• Incomplete Amazon Bedrock event-stream frame: ${buffer.lengt
• AI_EmptyResponseBodyError
• Unsupported task type: ${taskType}
• Amazon Bedrock request was moderated: ${reasons.join(', ')}

AI-assisted analysis of vercel/ai@69428b1f8b (2026-08-30). Data as JSON: /api/errors/8319981ad2c580a3. Report an issue: GitHub.