mem0ai/mem0 · critical · Error

Vector operations failed. Please ensure: 1. The vector exten

Error message

Vector operations failed. Please ensure:
1. The vector extension is enabled
2. The table "${this.tableName}" exists with correct schema
3. The match_vectors function is created
4. Row Level Security policies allow the configured Supabase key to read the table

RUN THE FOLLOWING SQL IN YOUR SUPABASE SQL EDITOR:

-- Enable the vector extension
create extension if not exists vector;

-- Create the memories table
create table if not exists memories (
  id text primary key,
  embedding vector(1536),
  metadata jsonb,
  created_at timestamp with time zone default timezone('utc', now()),
  updated_at timestamp with time zone default timezone('utc', now())
);

-- Create the memory migrations table
create table if not exists memory_migrations (
  user_id text primary key,
  created_at timestamp with time zone default timezone('utc', now())
);

-- Create the vector similarity search function
create or replace function match_vectors(
  query_embedding vector(1536),
  match_count int,
  filter jsonb default '{}'::jsonb
)
returns table (
  id text,
  similarity float,
  metadata jsonb
)
language plpgsql
as $$
begin
  return query
  select
    t.id::text,
    1 - (t.embedding <=> query_embedding) as similarity,
    t.metadata
  from memories t
  where case
    when filter::text = '{}'::text then true
    else t.metadata @> filter
  end
  order by t.embedding <=> query_embedding
  limit match_count;
end;
$$;

See the SQL migration instructions in the code comments.

What it means

During initialization, the Supabase vector store probes the configured table with a one-row select on the embedding column. If Supabase returns an error (missing table, missing vector extension, RLS blocking the key, wrong column), the store throws this aggregate error with ready-to-run SQL that creates the memories table, memory_migrations table, and match_vectors similarity function. It is a setup/diagnostic error, not a transient failure.

Source

Thrown at mem0-ts/src/oss/src/vector_stores/supabase.ts:135

  async initialize(): Promise<void> {
    if (!this._initPromise) {
      this._initPromise = this._doInitialize();
    }
    return this._initPromise;
  }

  private async _doInitialize(): Promise<void> {
    await this.ensureClient();
    try {
      const { error: probeError } = await this.client
        .from(this.tableName)
        .select(this.embeddingColumnName)
        .limit(1);

      if (probeError) {
        console.error("Table probe error:", probeError);
        throw new Error(
          `Vector operations failed. Please ensure:
1. The vector extension is enabled
2. The table "${this.tableName}" exists with correct schema
3. The match_vectors function is created
4. Row Level Security policies allow the configured Supabase key to read the table

RUN THE FOLLOWING SQL IN YOUR SUPABASE SQL EDITOR:

-- Enable the vector extension
create extension if not exists vector;

-- Create the memories table
create table if not exists memories (
  id text primary key,
  embedding vector(1536),
  metadata jsonb,
  created_at timestamp with time zone default timezone('utc', now()),
  updated_at timestamp with time zone default timezone('utc', now())

View on GitHub (pinned to 001c235229)

Solutions

1. Open the Supabase SQL editor and run the exact SQL block embedded in the error message (extension, tables, match_vectors function).
2. Verify the API key has access: either use the service_role key or add RLS SELECT policies for the authenticated/anon role on the table.
3. Check config.tableName and the embedding column name match your actual schema (default 'memories' / 'embedding', vector(1536)).
4. Adjust the vector(1536) literal in the SQL if your embedding model outputs a different dimension, then recreate.

Example fix

-- run in Supabase SQL editor (from the error message)
create extension if not exists vector;
create table if not exists memories (
  id text primary key,
  embedding vector(1536),
  metadata jsonb,
  created_at timestamptz default timezone('utc', now()),
  updated_at timestamptz default timezone('utc', now())
);
-- plus memory_migrations table and match_vectors function from the error text

// pre-flight: verify table probe succeeds before creating Memory
const { createClient } = await import('@supabase/supabase-js');
const sb = createClient(url, key);
const { error } = await sb.from('memories').select('embedding').limit(1);
if (error) throw new Error(`Supabase not ready — run the setup SQL first: ${error.message}`);

try { const memory = new Memory({ vectorStore: { provider: 'supabase', config } }); } catch (e) { if (e instanceof Error && e.message.includes('RUN THE FOLLOWING SQL')) { await runMigrations(); /* then retry construction */ } else throw e; }

When it happens

Trigger: First use of the Supabase vector store against a fresh Supabase project where the vector extension, memories table, or match_vectors function does not exist; using an anon key whose RLS policies deny SELECT on the table; renaming tableName or embeddingColumnName in config to values that don't exist in the schema.

Common situations: New Supabase project without migrations applied; using service-role vs anon key with restrictive RLS; config.tableName pointing at a custom table that was never created; pgvector extension disabled on the project.

Related errors

• Databricks vector store requires accessToken or clientId/cli
• Invalid ${label} '${name}': only letters, digits, and unders
• Invalid filter key '${key}': only letters, digits, and under
• Unsupported filter operator: ${op}
• PGVector requires either connectionString or ${missingFields

AI-assisted analysis of mem0ai/mem0@001c235229 (2026-08-15). Data as JSON: /api/errors/ef821d5cc8499679. Report an issue: GitHub.