Supabase Vector Store (#2427)

docs/components/vectordbs/dbs/supabase.mdx

@@ -4,7 +4,8 @@ Create a [Supabase](https://supabase.com/dashboard/projects) account and project
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -32,10 +33,90 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript Typescript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    vectorStore: {
+      provider: "supabase",
+      config: {
+        collectionName: "memories",
+        embeddingModelDims: 1536,
+        supabaseUrl: process.env.SUPABASE_URL || "",
+        supabaseKey: process.env.SUPABASE_KEY || "",
+        tableName: "memories",
+      },
+    },
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
+### SQL Migrations for TypeScript Implementation
+
+The following SQL migrations are required to enable the vector extension and create the memories table:
+
+```sql
+-- 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 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 $$
+begin
+  return query
+    select
+      id,
+      similarity,
+      metadata
+    from memories
+    where case
+      when filter::text = '{}'::text then true
+      else metadata @> filter
+    end
+    order by embedding <=> query_embedding
+    limit match_count;
+end;
+$$;
+```
+
+Goto [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations inside the SQL Editor.
+
 ### Config
 
 Here are the parameters available for configuring Supabase:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `connection_string` | PostgreSQL connection string (required) | None |
@@ -43,6 +124,17 @@ Here are the parameters available for configuring Supabase:
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `index_method` | Vector index method to use | `auto` |
 | `index_measure` | Distance measure for similarity search | `cosine_distance` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | Name for the vector collection | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `supabaseUrl` | Supabase URL | None |
+| `supabaseKey` | Supabase key | None |
+| `tableName` | Name for the vector table | `memories` |
+</Tab>
+</Tabs>
 
 ### Index Methods
 

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "2.1.8",
+  "version": "2.1.9",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",
@@ -34,7 +34,8 @@
     "clean": "rimraf dist",
     "build": "npm run clean && npx prettier --check . && npx tsup",
     "dev": "npx nodemon",
-    "start": "npx ts-node src/oss/examples/basic.ts",
+    "start": "pnpm run example memory",
+    "example": "ts-node src/oss/examples/vector-stores/index.ts",
     "test": "jest",
     "test:ts": "jest --config jest.config.js",
     "test:watch": "jest --config jest.config.js --watch",
@@ -99,14 +100,15 @@
   "peerDependencies": {
     "@anthropic-ai/sdk": "0.18.0",
     "@qdrant/js-client-rest": "1.13.0",
+    "@supabase/supabase-js": "^2.49.1",
     "@types/jest": "29.5.14",
     "@types/pg": "8.11.0",
     "@types/sqlite3": "3.1.11",
     "groq-sdk": "0.3.0",
+    "ollama": "^0.5.14",
     "pg": "8.11.3",
     "redis": "4.7.0",
-    "sqlite3": "5.1.7",
-    "ollama": "^0.5.14"
+    "sqlite3": "5.1.7"
   },
   "peerDependenciesMeta": {
     "posthog-node": {

mem0-ts/src/oss/examples/utils/test-utils.ts

@@ -0,0 +1,99 @@
+import { Memory } from "../../src";
+
+export async function runTests(memory: Memory) {
+  try {
+    // Reset all memories
+    console.log("\nResetting all memories...");
+    await memory.reset();
+    console.log("All memories reset");
+
+    // Add a single memory
+    console.log("\nAdding a single memory...");
+    const result1 = await memory.add(
+      "Hi, my name is John and I am a software engineer.",
+      {
+        userId: "john",
+      },
+    );
+    console.log("Added memory:", result1);
+
+    // Add multiple messages
+    console.log("\nAdding multiple messages...");
+    const result2 = await memory.add(
+      [
+        { role: "user", content: "What is your favorite city?" },
+        { role: "assistant", content: "I love Paris, it is my favorite city." },
+      ],
+      {
+        userId: "john",
+      },
+    );
+    console.log("Added messages:", result2);
+
+    // Trying to update the memory
+    const result3 = await memory.add(
+      [
+        { role: "user", content: "What is your favorite city?" },
+        {
+          role: "assistant",
+          content: "I love New York, it is my favorite city.",
+        },
+      ],
+      {
+        userId: "john",
+      },
+    );
+    console.log("Updated messages:", result3);
+
+    // Get a single memory
+    console.log("\nGetting a single memory...");
+    if (result1.results && result1.results.length > 0) {
+      const singleMemory = await memory.get(result1.results[0].id);
+      console.log("Single memory:", singleMemory);
+    } else {
+      console.log("No memory was added in the first step");
+    }
+
+    // Updating this memory
+    const result4 = await memory.update(
+      result1.results[0].id,
+      "I love India, it is my favorite country.",
+    );
+    console.log("Updated memory:", result4);
+
+    // Get all memories
+    console.log("\nGetting all memories...");
+    const allMemories = await memory.getAll({
+      userId: "john",
+    });
+    console.log("All memories:", allMemories);
+
+    // Search for memories
+    console.log("\nSearching memories...");
+    const searchResult = await memory.search("What do you know about Paris?", {
+      userId: "john",
+    });
+    console.log("Search results:", searchResult);
+
+    // Get memory history
+    if (result1.results && result1.results.length > 0) {
+      console.log("\nGetting memory history...");
+      const history = await memory.history(result1.results[0].id);
+      console.log("Memory history:", history);
+    }
+
+    // Delete a memory
+    if (result1.results && result1.results.length > 0) {
+      console.log("\nDeleting a memory...");
+      await memory.delete(result1.results[0].id);
+      console.log("Memory deleted successfully");
+    }
+
+    // Reset all memories
+    console.log("\nResetting all memories...");
+    await memory.reset();
+    console.log("All memories reset");
+  } catch (error) {
+    console.error("Error:", error);
+  }
+}

mem0-ts/src/oss/examples/vector-stores/index.ts

@@ -0,0 +1,53 @@
+import dotenv from "dotenv";
+import { demoMemoryStore } from "./memory";
+import { demoSupabase } from "./supabase";
+// import { demoQdrant } from "./qdrant";
+// import { demoRedis } from "./redis";
+// import { demoPGVector } from "./pgvector";
+
+// Load environment variables
+dotenv.config();
+
+async function main() {
+  const args = process.argv.slice(2);
+  const selectedStore = args[0]?.toLowerCase();
+
+  const stores: Record<string, () => Promise<void>> = {
+    // memory: demoMemoryStore,
+    supabase: demoSupabase,
+    // Uncomment these as they are implemented
+    // qdrant: demoQdrant,
+    // redis: demoRedis,
+    // pgvector: demoPGVector,
+  };
+
+  if (selectedStore) {
+    const demo = stores[selectedStore];
+    if (demo) {
+      try {
+        await demo();
+      } catch (error) {
+        console.error(`\nError running ${selectedStore} demo:`, error);
+        if (selectedStore !== "memory") {
+          console.log("\nFalling back to memory store...");
+          await stores.memory();
+        }
+      }
+    } else {
+      console.log(`\nUnknown vector store: ${selectedStore}`);
+      console.log("Available stores:", Object.keys(stores).join(", "));
+    }
+    return;
+  }
+
+  // If no store specified, run all available demos
+  for (const [name, demo] of Object.entries(stores)) {
+    try {
+      await demo();
+    } catch (error) {
+      console.error(`\nError running ${name} demo:`, error);
+    }
+  }
+}
+
+main().catch(console.error);

mem0-ts/src/oss/examples/vector-stores/memory.ts

@@ -0,0 +1,38 @@
+import { Memory } from "../../src";
+import { runTests } from "../utils/test-utils";
+
+export async function demoMemoryStore() {
+  console.log("\n=== Testing In-Memory Vector Store ===\n");
+
+  const memory = new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "text-embedding-3-small",
+      },
+    },
+    vectorStore: {
+      provider: "memory",
+      config: {
+        collectionName: "memories",
+        dimension: 1536,
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "gpt-4-turbo-preview",
+      },
+    },
+    historyDbPath: "memory.db",
+  });
+
+  await runTests(memory);
+}
+
+if (require.main === module) {
+  demoMemoryStore();
+}

mem0-ts/src/oss/examples/vector-stores/pgvector.ts

@@ -0,0 +1,49 @@
+import { Memory } from "../../src";
+import { runTests } from "../utils/test-utils";
+
+export async function demoPGVector() {
+  console.log("\n=== Testing PGVector Store ===\n");
+
+  const memory = new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "text-embedding-3-small",
+      },
+    },
+    vectorStore: {
+      provider: "pgvector",
+      config: {
+        collectionName: "memories",
+        dimension: 1536,
+        dbname: process.env.PGVECTOR_DB || "vectordb",
+        user: process.env.PGVECTOR_USER || "postgres",
+        password: process.env.PGVECTOR_PASSWORD || "postgres",
+        host: process.env.PGVECTOR_HOST || "localhost",
+        port: parseInt(process.env.PGVECTOR_PORT || "5432"),
+        embeddingModelDims: 1536,
+        hnsw: true,
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "gpt-4-turbo-preview",
+      },
+    },
+    historyDbPath: "memory.db",
+  });
+
+  await runTests(memory);
+}
+
+if (require.main === module) {
+  if (!process.env.PGVECTOR_DB) {
+    console.log("\nSkipping PGVector test - environment variables not set");
+    process.exit(0);
+  }
+  demoPGVector();
+}

mem0-ts/src/oss/examples/vector-stores/qdrant.ts

@@ -0,0 +1,50 @@
+import { Memory } from "../../src";
+import { runTests } from "../utils/test-utils";
+
+export async function demoQdrant() {
+  console.log("\n=== Testing Qdrant Store ===\n");
+
+  const memory = new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "text-embedding-3-small",
+      },
+    },
+    vectorStore: {
+      provider: "qdrant",
+      config: {
+        collectionName: "memories",
+        embeddingModelDims: 1536,
+        url: process.env.QDRANT_URL,
+        apiKey: process.env.QDRANT_API_KEY,
+        path: process.env.QDRANT_PATH,
+        host: process.env.QDRANT_HOST,
+        port: process.env.QDRANT_PORT
+          ? parseInt(process.env.QDRANT_PORT)
+          : undefined,
+        onDisk: true,
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "gpt-4-turbo-preview",
+      },
+    },
+    historyDbPath: "memory.db",
+  });
+
+  await runTests(memory);
+}
+
+if (require.main === module) {
+  if (!process.env.QDRANT_URL && !process.env.QDRANT_HOST) {
+    console.log("\nSkipping Qdrant test - environment variables not set");
+    process.exit(0);
+  }
+  demoQdrant();
+}

mem0-ts/src/oss/examples/vector-stores/redis.ts

@@ -0,0 +1,45 @@
+import { Memory } from "../../src";
+import { runTests } from "../utils/test-utils";
+
+export async function demoRedis() {
+  console.log("\n=== Testing Redis Store ===\n");
+
+  const memory = new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "text-embedding-3-small",
+      },
+    },
+    vectorStore: {
+      provider: "redis",
+      config: {
+        collectionName: "memories",
+        embeddingModelDims: 1536,
+        redisUrl: process.env.REDIS_URL || "redis://localhost:6379",
+        username: process.env.REDIS_USERNAME,
+        password: process.env.REDIS_PASSWORD,
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "gpt-4-turbo-preview",
+      },
+    },
+    historyDbPath: "memory.db",
+  });
+
+  await runTests(memory);
+}
+
+if (require.main === module) {
+  if (!process.env.REDIS_URL) {
+    console.log("\nSkipping Redis test - environment variables not set");
+    process.exit(0);
+  }
+  demoRedis();
+}

mem0-ts/src/oss/examples/vector-stores/supabase.ts

@@ -0,0 +1,49 @@
+import { Memory } from "../../src";
+import { runTests } from "../utils/test-utils";
+import dotenv from "dotenv";
+
+// Load environment variables
+dotenv.config();
+
+export async function demoSupabase() {
+  console.log("\n=== Testing Supabase Vector Store ===\n");
+
+  const memory = new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "text-embedding-3-small",
+      },
+    },
+    vectorStore: {
+      provider: "supabase",
+      config: {
+        collectionName: "memories",
+        embeddingModelDims: 1536,
+        supabaseUrl: process.env.SUPABASE_URL || "",
+        supabaseKey: process.env.SUPABASE_KEY || "",
+        tableName: "memories",
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || "",
+        model: "gpt-4-turbo-preview",
+      },
+    },
+    historyDbPath: "memory.db",
+  });
+
+  await runTests(memory);
+}
+
+if (require.main === module) {
+  if (!process.env.SUPABASE_URL || !process.env.SUPABASE_KEY) {
+    console.log("\nSkipping Supabase test - environment variables not set");
+    process.exit(0);
+  }
+  demoSupabase();
+}

mem0-ts/src/oss/package.json

@@ -7,8 +7,8 @@
   "scripts": {
     "build": "tsc",
     "test": "jest",
-    "start": "ts-node examples/basic.ts",
-    "example": "ts-node examples/basic.ts",
+    "start": "pnpm run example memory",
+    "example": "ts-node examples/vector-stores/index.ts",
     "clean": "rimraf dist",
     "prepare": "npm run build"
   },

mem0-ts/src/oss/src/llms/ollama.ts

@@ -57,7 +57,7 @@ export class OllamaLLM implements LLM {
           arguments: JSON.stringify(call.function.arguments),
         })),
       };
-      }
+    }
 
     return response.content || "";
   }

mem0-ts/src/oss/src/utils/factory.ts

@@ -12,6 +12,8 @@ import { VectorStore } from "../vector_stores/base";
 import { Qdrant } from "../vector_stores/qdrant";
 import { RedisDB } from "../vector_stores/redis";
 import { OllamaLLM } from "../llms/ollama";
+import { SupabaseDB } from "../vector_stores/supabase";
+
 export class EmbedderFactory {
   static create(provider: string, config: EmbeddingConfig): Embedder {
     switch (provider.toLowerCase()) {
@@ -53,6 +55,8 @@ export class VectorStoreFactory {
         return new Qdrant(config as any); // Type assertion needed as config is extended
       case "redis":
         return new RedisDB(config as any); // Type assertion needed as config is extended
+      case "supabase":
+        return new SupabaseDB(config as any); // Type assertion needed as config is extended
       default:
         throw new Error(`Unsupported vector store provider: ${provider}`);
     }

mem0-ts/src/oss/src/vector_stores/supabase.ts

@@ -0,0 +1,290 @@
+import { createClient, SupabaseClient } from "@supabase/supabase-js";
+import { VectorStore } from "./base";
+import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
+
+interface VectorData {
+  id: string;
+  embedding: number[];
+  metadata: Record<string, any>;
+  [key: string]: any;
+}
+
+interface VectorQueryParams {
+  query_embedding: number[];
+  match_count: number;
+  filter?: SearchFilters;
+}
+
+interface VectorSearchResult {
+  id: string;
+  similarity: number;
+  metadata: Record<string, any>;
+  [key: string]: any;
+}
+
+interface SupabaseConfig extends VectorStoreConfig {
+  supabaseUrl: string;
+  supabaseKey: string;
+  tableName: string;
+  embeddingColumnName?: string;
+  metadataColumnName?: string;
+}
+
+/*
+SQL Migration to run in 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 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 $$
+begin
+  return query
+  select
+    t.id::text,
+    1 - (t.embedding <=> query_embedding) as similarity,
+    t.metadata
+  from memories t
+  where case
+    when filter::text = '{}'::text then true
+    else t.metadata @> filter
+  end
+  order by t.embedding <=> query_embedding
+  limit match_count;
+end;
+$$;
+*/
+
+export class SupabaseDB implements VectorStore {
+  private client: SupabaseClient;
+  private readonly tableName: string;
+  private readonly embeddingColumnName: string;
+  private readonly metadataColumnName: string;
+
+  constructor(config: SupabaseConfig) {
+    this.client = createClient(config.supabaseUrl, config.supabaseKey);
+    this.tableName = config.tableName;
+    this.embeddingColumnName = config.embeddingColumnName || "embedding";
+    this.metadataColumnName = config.metadataColumnName || "metadata";
+
+    this.initialize().catch((err) => {
+      console.error("Failed to initialize Supabase:", err);
+      throw err;
+    });
+  }
+
+  private async initialize(): Promise<void> {
+    try {
+      // Verify table exists and vector operations work by attempting a test insert
+      const testVector = Array(1536).fill(0);
+      const { error: testError } = await this.client
+        .from(this.tableName)
+        .insert({
+          id: "test_vector",
+          [this.embeddingColumnName]: testVector,
+          [this.metadataColumnName]: {},
+        })
+        .select();
+
+      if (testError) {
+        console.error("Test insert error:", testError);
+        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
+See the SQL migration instructions in the code comments.`,
+        );
+      }
+
+      // Clean up test vector
+      await this.client.from(this.tableName).delete().eq("id", "test_vector");
+
+      console.log("Connected to Supabase successfully");
+    } catch (error) {
+      console.error("Error during Supabase initialization:", error);
+      throw error;
+    }
+  }
+
+  async insert(
+    vectors: number[][],
+    ids: string[],
+    payloads: Record<string, any>[],
+  ): Promise<void> {
+    try {
+      const data = vectors.map((vector, idx) => ({
+        id: ids[idx],
+        [this.embeddingColumnName]: vector,
+        [this.metadataColumnName]: {
+          ...payloads[idx],
+          created_at: new Date().toISOString(),
+        },
+      }));
+
+      const { error } = await this.client.from(this.tableName).insert(data);
+
+      if (error) throw error;
+    } catch (error) {
+      console.error("Error during vector insert:", error);
+      throw error;
+    }
+  }
+
+  async search(
+    query: number[],
+    limit: number = 5,
+    filters?: SearchFilters,
+  ): Promise<VectorStoreResult[]> {
+    try {
+      const rpcQuery: VectorQueryParams = {
+        query_embedding: query,
+        match_count: limit,
+      };
+
+      if (filters) {
+        rpcQuery.filter = filters;
+      }
+
+      const { data, error } = await this.client.rpc("match_vectors", rpcQuery);
+
+      if (error) throw error;
+      if (!data) return [];
+
+      const results = data as VectorSearchResult[];
+      return results.map((result) => ({
+        id: result.id,
+        payload: result.metadata,
+        score: result.similarity,
+      }));
+    } catch (error) {
+      console.error("Error during vector search:", error);
+      throw error;
+    }
+  }
+
+  async get(vectorId: string): Promise<VectorStoreResult | null> {
+    try {
+      const { data, error } = await this.client
+        .from(this.tableName)
+        .select("*")
+        .eq("id", vectorId)
+        .single();
+
+      if (error) throw error;
+      if (!data) return null;
+
+      return {
+        id: data.id,
+        payload: data[this.metadataColumnName],
+      };
+    } catch (error) {
+      console.error("Error getting vector:", error);
+      throw error;
+    }
+  }
+
+  async update(
+    vectorId: string,
+    vector: number[],
+    payload: Record<string, any>,
+  ): Promise<void> {
+    try {
+      const { error } = await this.client
+        .from(this.tableName)
+        .update({
+          [this.embeddingColumnName]: vector,
+          [this.metadataColumnName]: {
+            ...payload,
+            updated_at: new Date().toISOString(),
+          },
+        })
+        .eq("id", vectorId);
+
+      if (error) throw error;
+    } catch (error) {
+      console.error("Error during vector update:", error);
+      throw error;
+    }
+  }
+
+  async delete(vectorId: string): Promise<void> {
+    try {
+      const { error } = await this.client
+        .from(this.tableName)
+        .delete()
+        .eq("id", vectorId);
+
+      if (error) throw error;
+    } catch (error) {
+      console.error("Error deleting vector:", error);
+      throw error;
+    }
+  }
+
+  async deleteCol(): Promise<void> {
+    try {
+      const { error } = await this.client
+        .from(this.tableName)
+        .delete()
+        .neq("id", ""); // Delete all rows
+
+      if (error) throw error;
+    } catch (error) {
+      console.error("Error deleting collection:", error);
+      throw error;
+    }
+  }
+
+  async list(
+    filters?: SearchFilters,
+    limit: number = 100,
+  ): Promise<[VectorStoreResult[], number]> {
+    try {
+      let query = this.client
+        .from(this.tableName)
+        .select("*", { count: "exact" })
+        .limit(limit);
+
+      if (filters) {
+        Object.entries(filters).forEach(([key, value]) => {
+          query = query.eq(`${this.metadataColumnName}->>${key}`, value);
+        });
+      }
+
+      const { data, error, count } = await query;
+
+      if (error) throw error;
+
+      const results = data.map((item: VectorData) => ({
+        id: item.id,
+        payload: item[this.metadataColumnName],
+      }));
+
+      return [results, count || 0];
+    } catch (error) {
+      console.error("Error listing vectors:", error);
+      throw error;
+    }
+  }
+}