mem0ai/mem0 · error · Error

Vector at index is empty. Expected dimension .

Error message

Vector at index ${index} is empty. Expected dimension ${this.embeddingModelDims}.

What it means

Before inserting into OpenSearch, each vector is checked for a non-zero length. An empty array means the embedding call returned no components (for example an empty-string input or a broken provider response), and OpenSearch would reject the document with a less clear mapping error, so the store raises this explicit message naming the index position and expected dimension.

Solutions

1. Skip or reject empty documents before embedding so providers never receive them.
2. Validate embedding responses: if emb.length === 0 throw or retry at the embedding layer.
3. Log the failing index from the message and inspect that input document.

Example fix

// before
const embs = docs.map(d => embed(d)); // some empty string -> []
await store.insert(embs, ids, payloads);

// after
const embs = docs.map(d => d.trim() ? embed(d) : null);
if (embs.some(e => !e || e.length === 0)) throw new Error('empty embedding');

if (vectors.some((v) => Array.isArray(v) && v.length === 0)) {
  throw new Error('Empty embedding vector produced; check embedding inputs');
}

const isNonEmptyVector = (v: unknown): v is number[] => Array.isArray(v) && v.length > 0;

When it happens

Trigger: Passing vectors[i] === [] — e.g. an embedding provider returned an empty vector for an empty/whitespace document, or a placeholder was never filled.

Common situations: Embedding empty strings or skipped documents; test fixtures with empty arrays; providers that return [] on rate-limit or content-filter instead of throwing.

Related errors

• Vector at index is null or undefined.
• Cannot update with an empty vector.
• Filter value for ' ' must be an array.
• Invalid memory action
• filter value must be an array.

AI-assisted analysis of mem0ai/mem0@001c235229 (2026-08-15). Data as JSON: /api/errors/fa2bb4d691f8950e. Report an issue: GitHub.

Appendix: source

Thrown at mem0-ts/src/oss/src/vector_stores/opensearch.ts:216

    await this.client.indices.create({
      index: "memory_migrations",
      body: {
        mappings: {
          properties: {
            user_id: { type: "keyword" },
          },
        },
      },
    });
  }

  private validateVector(vector: number[], index: number): void {
    if (!vector) {
      throw new Error(`Vector at index ${index} is null or undefined.`);
    }
    if (vector.length === 0) {
      throw new Error(
        `Vector at index ${index} is empty. Expected dimension ${this.embeddingModelDims}.`,
      );
    }
    if (vector.length !== this.embeddingModelDims) {
      throw new Error(
        `Vector at index ${index} has dimension ${vector.length}, but index ` +
          `'${this.collectionName}' expects dimension ${this.embeddingModelDims}.`,
      );
    }
  }

  async insert(
    vectors: number[][],
    ids: string[],
    payloads: Record<string, any>[],
  ): Promise<void> {
    await this.initialize();
    vectors.forEach((vector, index) => this.validateVector(vector, index));

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