feat(vector-stores): add Pinecone provider to TypeScript SDK (#5802)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
2026-07-06 12:09:10 -04:00
parent 4c974c8fa8
commit 03b41ab00f
7 changed files with 1001 additions and 4 deletions
@@ -10,7 +10,8 @@ description: "Use Pinecone as a fully managed vector database in Mem0 with serve

 ### Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

@@ -44,10 +45,43 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Set OPENAI_API_KEY and PINECONE_API_KEY in your environment
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'testing',
+      embeddingModelDims: 1536, // Matches OpenAI's text-embedding-3-small
+      namespace: 'my-namespace', // Optional: specify a namespace for multi-tenancy
+      serverlessConfig: {
+        cloud: 'aws', // 'aws' | 'gcp' | 'azure'
+        region: 'us-east-1',
+      },
+      metric: 'cosine',
+    },
+  },
+};
+
+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 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>
+
 ### Config

 Here are the parameters available for configuring Pinecone:

+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | Name of the index/collection | Required |
@@ -61,11 +95,28 @@ Here are the parameters available for configuring Pinecone:
 | `metric` | Distance metric for vector similarity | `"cosine"` |
 | `batch_size` | Batch size for operations | `100` |
 | `namespace` | Namespace for the collection, useful for multi-tenancy. | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | Name of the index/collection | Required |
+| `embeddingModelDims` | Dimensions of the embedding model (must match your chosen embedding model) | `1536` |
+| `client` | Existing Pinecone client instance | `undefined` |
+| `apiKey` | API key for Pinecone | Environment variable: `PINECONE_API_KEY` |
+| `serverlessConfig` | Configuration for serverless deployment (`cloud`, `region`) | `undefined` |
+| `podConfig` | Configuration for pod-based deployment (`environment`, `podType`, `pods`, `replicas`, `shards`) | `undefined` |
+| `metric` | Distance metric for vector similarity (`cosine`, `dotproduct`, `euclidean`) | `"cosine"` |
+| `batchSize` | Batch size for insert operations | `100` |
+| `namespace` | Namespace for the collection, useful for multi-tenancy. | `undefined` |
+| `extraParams` | Extra parameters spread into the Pinecone `createIndex` call | `{}` |
+</Tab>
+</Tabs>

 > **Important**: You must choose either `serverless_config` or `pod_config` for your deployment, but not both.

 #### Serverless Config Example
-```python
+<CodeGroup>
+```python Python
 config = {
    "vector_store": {
        "provider": "pinecone",
@@ -82,8 +133,27 @@ config = {
 }
 ```

+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'memory_index',
+      embeddingModelDims: 1536, // For OpenAI's text-embedding-3-small
+      namespace: 'my-namespace', // Optional: custom namespace
+      serverlessConfig: {
+        cloud: 'aws', // 'gcp' | 'azure'
+        region: 'us-east-1', // Choose appropriate region
+      },
+    },
+  },
+};
+```
+</CodeGroup>
+
 #### Pod Config Example
-```python
+<CodeGroup>
+```python Python
 config = {
    "vector_store": {
        "provider": "pinecone",
@@ -99,4 +169,23 @@ config = {
        }
    }
 }
-```
+```
+
+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'memory_index',
+      embeddingModelDims: 1536, // For OpenAI's text-embedding-ada-002
+      namespace: 'my-namespace', // Optional: custom namespace
+      podConfig: {
+        environment: 'gcp-starter',
+        replicas: 1,
+        podType: 'starter',
+      },
+    },
+  },
+};
+```
+</CodeGroup>
@@ -115,6 +115,7 @@
    "@google/genai": "^1.40.0",
    "@langchain/core": "^1.1.47",
    "@mistralai/mistralai": "^1.5.2",
+    "@pinecone-database/pinecone": "^8.0.0",
    "@qdrant/js-client-rest": "^1.18.0",
    "@supabase/supabase-js": "^2.49.1",
    "@types/jest": "29.5.14",
@@ -53,6 +53,9 @@ importers:
      '@mistralai/mistralai':
        specifier: ^1.5.2
        version: 1.15.1
+      '@pinecone-database/pinecone':
+        specifier: ^8.0.0
+        version: 8.0.0
      '@qdrant/js-client-rest':
        specifier: ^1.18.0
        version: 1.18.0(typescript@5.5.4)
@@ -796,6 +799,10 @@ packages:
    resolution: {integrity: sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==}
    engines: {node: '>= 8'}

+  '@pinecone-database/pinecone@8.0.0':
+    resolution: {integrity: sha512-dItFqLdis2Pd5lC67aKn8HhvXajzlOz4+0RyK1CRcZMdSwG8YxUCBtH4yXPC8/6uLxfr2dvMWnRFuKgtPwwajQ==}
+    engines: {node: '>=20.0.0'}
+
  '@pkgjs/parseargs@0.11.0':
    resolution: {integrity: sha512-+1VkjdD0QBLPodGrJUeqarH8VAIvQODIbwh9XpP5Syisf7YoQgsJKPNFoqqLQlu+VQ/tVSshMR6loPMn8U+dPg==}
    engines: {node: '>=14'}
@@ -4403,6 +4410,8 @@ snapshots:
      '@nodelib/fs.scandir': 2.1.5
      fastq: 1.20.1

+  '@pinecone-database/pinecone@8.0.0': {}
+
  '@pkgjs/parseargs@0.11.0':
    optional: true

@@ -0,0 +1,529 @@
+// jest.mock is hoisted before variable declarations, so we cannot close over
+// variables declared with let/const. All shared mock functions are attached to
+// the module-level `__mocks__` object that is populated inside the factory so
+// that the hoisted mock can reach them via a stable reference.
+
+const __mocks__: {
+  upsert: jest.Mock;
+  query: jest.Mock;
+  fetch: jest.Mock;
+  deleteOne: jest.Mock;
+  namespace: jest.Mock;
+  describeIndexStats: jest.Mock;
+  index: jest.Mock;
+  listIndexes: jest.Mock;
+  createIndex: jest.Mock;
+  deleteIndex: jest.Mock;
+  Pinecone: jest.Mock;
+} = {} as any;
+
+jest.mock("@pinecone-database/pinecone", () => {
+  // These are created fresh inside the factory so hoisting is safe.
+  const upsert = jest.fn().mockResolvedValue(undefined);
+  const query = jest.fn().mockResolvedValue({ matches: [] });
+  const fetch = jest.fn().mockResolvedValue({ records: {} });
+  const deleteOne = jest.fn().mockResolvedValue(undefined);
+
+  const nsHandle = { upsert, query, fetch, deleteOne };
+  const namespace = jest.fn().mockReturnValue(nsHandle);
+
+  const describeIndexStats = jest
+    .fn()
+    .mockResolvedValue({ totalRecordCount: 0, namespaces: {} });
+
+  const indexHandle = {
+    namespace,
+    describeIndexStats,
+    // expose ops directly for the no-namespace path
+    upsert,
+    query,
+    fetch,
+    deleteOne,
+  };
+  const index = jest.fn().mockReturnValue(indexHandle);
+
+  const listIndexes = jest.fn().mockResolvedValue({ indexes: [] });
+  const createIndex = jest.fn().mockResolvedValue(undefined);
+  const deleteIndex = jest.fn().mockResolvedValue(undefined);
+
+  const Pinecone = jest.fn().mockImplementation(() => ({
+    listIndexes,
+    createIndex,
+    deleteIndex,
+    index,
+  }));
+
+  // Populate the shared reference so tests can reach the mocks.
+  Object.assign(__mocks__, {
+    upsert,
+    query,
+    fetch,
+    deleteOne,
+    namespace,
+    describeIndexStats,
+    index,
+    listIndexes,
+    createIndex,
+    deleteIndex,
+    Pinecone,
+  });
+
+  return { Pinecone };
+});
+
+import { PineconeDB } from "../vector_stores/pinecone";
+import { VectorStoreFactory } from "../utils/factory";
+
+// --- Helpers ---
+
+function makeDb(overrides: Record<string, any> = {}): PineconeDB {
+  return new PineconeDB({
+    collectionName: "test-index",
+    embeddingModelDims: 4,
+    apiKey: "test-api-key",
+    ...overrides,
+  } as any);
+}
+
+async function initDb(
+  overrides: Record<string, any> = {},
+): Promise<PineconeDB> {
+  const db = makeDb(overrides);
+  await db.initialize();
+  return db;
+}
+
+// --- Reset mocks between tests ---
+
+beforeEach(() => {
+  jest.clearAllMocks();
+
+  __mocks__.listIndexes.mockResolvedValue({ indexes: [] });
+  __mocks__.createIndex.mockResolvedValue(undefined);
+  __mocks__.deleteIndex.mockResolvedValue(undefined);
+  __mocks__.upsert.mockResolvedValue(undefined);
+  __mocks__.query.mockResolvedValue({ matches: [] });
+  __mocks__.fetch.mockResolvedValue({ records: {} });
+  __mocks__.deleteOne.mockResolvedValue(undefined);
+  __mocks__.describeIndexStats.mockResolvedValue({
+    totalRecordCount: 0,
+    namespaces: {},
+  });
+
+  const nsHandle = {
+    upsert: __mocks__.upsert,
+    query: __mocks__.query,
+    fetch: __mocks__.fetch,
+    deleteOne: __mocks__.deleteOne,
+  };
+  __mocks__.namespace.mockReturnValue(nsHandle);
+  __mocks__.index.mockReturnValue({
+    namespace: __mocks__.namespace,
+    describeIndexStats: __mocks__.describeIndexStats,
+    upsert: __mocks__.upsert,
+    query: __mocks__.query,
+    fetch: __mocks__.fetch,
+    deleteOne: __mocks__.deleteOne,
+  });
+  __mocks__.Pinecone.mockImplementation(() => ({
+    listIndexes: __mocks__.listIndexes,
+    createIndex: __mocks__.createIndex,
+    deleteIndex: __mocks__.deleteIndex,
+    index: __mocks__.index,
+  }));
+});
+
+// --- Test suites ---
+
+describe("VectorStoreFactory", () => {
+  it("returns a PineconeDB instance for provider 'pinecone'", async () => {
+    const db = VectorStoreFactory.create("pinecone", {
+      collectionName: "x",
+      embeddingModelDims: 4,
+      apiKey: "k",
+    } as any);
+    expect(db).toBeInstanceOf(PineconeDB);
+    await (db as any).initialize();
+  });
+});
+
+describe("Constructor", () => {
+  it("uses apiKey from config", async () => {
+    await initDb({ apiKey: "from-config" });
+    expect(__mocks__.Pinecone).toHaveBeenCalledWith({ apiKey: "from-config" });
+  });
+
+  it("falls back to PINECONE_API_KEY env var", async () => {
+    process.env.PINECONE_API_KEY = "env-key";
+    try {
+      const db = new PineconeDB({
+        collectionName: "test-index",
+        embeddingModelDims: 4,
+      } as any);
+      await db.initialize();
+      expect(__mocks__.Pinecone).toHaveBeenCalledWith({ apiKey: "env-key" });
+    } finally {
+      delete process.env.PINECONE_API_KEY;
+    }
+  });
+
+  it("throws when no API key is provided", () => {
+    delete process.env.PINECONE_API_KEY;
+    expect(
+      () =>
+        new PineconeDB({
+          collectionName: "test-index",
+          embeddingModelDims: 4,
+        } as any),
+    ).toThrow("Pinecone API key required");
+  });
+
+  it("accepts a pre-built client via config.client", async () => {
+    const fakeClient = {
+      listIndexes: __mocks__.listIndexes,
+      createIndex: __mocks__.createIndex,
+      deleteIndex: __mocks__.deleteIndex,
+      index: __mocks__.index,
+    };
+    const db = new PineconeDB({
+      collectionName: "test-index",
+      embeddingModelDims: 4,
+      client: fakeClient,
+    } as any);
+    await db.initialize();
+    expect(__mocks__.Pinecone).not.toHaveBeenCalled();
+    expect(__mocks__.listIndexes).toHaveBeenCalled();
+  });
+});
+
+describe("initialize", () => {
+  it("creates index with serverless default spec when index does not exist", async () => {
+    await initDb();
+    expect(__mocks__.createIndex).toHaveBeenCalledWith(
+      expect.objectContaining({
+        name: "test-index",
+        dimension: 4,
+        metric: "cosine",
+        spec: { serverless: { cloud: "aws", region: "us-east-1" } },
+        waitUntilReady: true,
+      }),
+    );
+  });
+
+  it("creates index with pod spec when podConfig provided", async () => {
+    await initDb({
+      podConfig: { environment: "us-east1-gcp", podType: "p1.x2", pods: 2 },
+    });
+    expect(__mocks__.createIndex).toHaveBeenCalledWith(
+      expect.objectContaining({
+        spec: {
+          pod: {
+            environment: "us-east1-gcp",
+            podType: "p1.x2",
+            pods: 2,
+            replicas: 1,
+            shards: 1,
+          },
+        },
+      }),
+    );
+  });
+
+  it("skips createIndex when index already exists", async () => {
+    __mocks__.listIndexes.mockResolvedValue({
+      indexes: [{ name: "test-index" }],
+    });
+    await initDb();
+    expect(__mocks__.createIndex).not.toHaveBeenCalled();
+  });
+
+  it("_initPromise is shared across concurrent calls (idempotent)", async () => {
+    // makeDb() fires initialize() in the constructor; calling it again before
+    // it resolves must reuse the same in-flight promise so createIndex runs only once.
+    const db = makeDb();
+    await Promise.all([db.initialize(), db.initialize(), db.initialize()]);
+    expect(__mocks__.createIndex).toHaveBeenCalledTimes(1);
+  });
+});
+
+describe("insert", () => {
+  it("upserts records with correct shape", async () => {
+    const db = await initDb();
+    await db.insert([[1, 2, 3, 4]], ["id-1"], [{ text: "hello" }]);
+    expect(__mocks__.upsert).toHaveBeenCalledWith({
+      records: [
+        { id: "id-1", values: [1, 2, 3, 4], metadata: { text: "hello" } },
+      ],
+    });
+  });
+
+  it("splits 150 records into two batches with batchSize=100", async () => {
+    const db = await initDb({ batchSize: 100 });
+    const vectors = Array.from({ length: 150 }, () => [0, 0, 0, 0]);
+    const ids = Array.from({ length: 150 }, (_, i) => `id-${i}`);
+    const payloads = Array.from({ length: 150 }, () => ({}));
+    await db.insert(vectors, ids, payloads);
+    expect(__mocks__.upsert).toHaveBeenCalledTimes(2);
+    expect(__mocks__.upsert.mock.calls[0][0].records).toHaveLength(100);
+    expect(__mocks__.upsert.mock.calls[1][0].records).toHaveLength(50);
+  });
+});
+
+describe("search", () => {
+  it("calls query with correct args", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 10);
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({
+        vector: [1, 2, 3, 4],
+        topK: 10,
+        includeMetadata: true,
+        includeValues: false,
+      }),
+    );
+  });
+
+  it("translates equality filter to Pinecone $eq", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { user_id: "alice" });
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({
+        filter: { user_id: { $eq: "alice" } },
+      }),
+    );
+  });
+
+  it("translates range filter to $gte/$lte", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { score: { gte: 0.5, lte: 1.0 } });
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({
+        filter: { score: { $gte: 0.5, $lte: 1.0 } },
+      }),
+    );
+  });
+
+  it("translates array filter to $in", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { tag: ["a", "b"] });
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({ filter: { tag: { $in: ["a", "b"] } } }),
+    );
+  });
+
+  it("omits wildcard '*' from filter", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { user_id: "*" });
+    const call = __mocks__.query.mock.calls[0][0];
+    expect(call.filter).toBeUndefined();
+  });
+
+  it("translates OR filter", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, {
+      OR: [{ tag: "x" }, { tag: "y" }],
+    });
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({
+        filter: {
+          $or: [{ tag: { $eq: "x" } }, { tag: { $eq: "y" } }],
+        },
+      }),
+    );
+  });
+
+  it("passes no filter when filters is empty", async () => {
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, {});
+    const call = __mocks__.query.mock.calls[0][0];
+    expect(call.filter).toBeUndefined();
+  });
+
+  it("warns and skips NOT operator (unsupported by Pinecone)", async () => {
+    const warnSpy = jest.spyOn(console, "warn").mockImplementation(() => {});
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { NOT: [{ tag: "x" }] });
+    expect(warnSpy).toHaveBeenCalledWith(expect.stringContaining("NOT"));
+    warnSpy.mockRestore();
+  });
+
+  it("warns and skips contains operator", async () => {
+    const warnSpy = jest.spyOn(console, "warn").mockImplementation(() => {});
+    const db = await initDb();
+    await db.search([1, 2, 3, 4], 5, { tag: { contains: "foo" } });
+    expect(warnSpy).toHaveBeenCalledWith(expect.stringContaining("contains"));
+    warnSpy.mockRestore();
+  });
+
+  it("throws on unsupported filter operator", async () => {
+    const db = await initDb();
+    await expect(
+      db.search([1, 2, 3, 4], 5, { tag: { regex: "^foo" } } as any),
+    ).rejects.toThrow();
+  });
+
+  it("maps response matches to VectorStoreResult shape", async () => {
+    __mocks__.query.mockResolvedValue({
+      matches: [
+        { id: "v1", metadata: { text: "hi" }, score: 0.9 },
+        { id: "v2", metadata: { text: "bye" }, score: 0.7 },
+      ],
+    });
+    const db = await initDb();
+    const results = await db.search([1, 2, 3, 4]);
+    expect(results).toEqual([
+      { id: "v1", payload: { text: "hi" }, score: 0.9 },
+      { id: "v2", payload: { text: "bye" }, score: 0.7 },
+    ]);
+  });
+
+  it("returns [] when matches is empty", async () => {
+    __mocks__.query.mockResolvedValue({ matches: [] });
+    const db = await initDb();
+    const results = await db.search([1, 2, 3, 4]);
+    expect(results).toEqual([]);
+  });
+});
+
+describe("get", () => {
+  it("returns VectorStoreResult when record found", async () => {
+    __mocks__.fetch.mockResolvedValue({
+      records: {
+        "vec-1": { id: "vec-1", metadata: { text: "foo" } },
+      },
+    });
+    const db = await initDb();
+    const result = await db.get("vec-1");
+    expect(result).toEqual({ id: "vec-1", payload: { text: "foo" } });
+  });
+
+  it("returns null when record not found", async () => {
+    __mocks__.fetch.mockResolvedValue({ records: {} });
+    const db = await initDb();
+    const result = await db.get("missing");
+    expect(result).toBeNull();
+  });
+});
+
+describe("update", () => {
+  it("upserts a single record", async () => {
+    const db = await initDb();
+    await db.update("vec-1", [1, 2, 3, 4], { text: "updated" });
+    expect(__mocks__.upsert).toHaveBeenCalledWith({
+      records: [
+        { id: "vec-1", values: [1, 2, 3, 4], metadata: { text: "updated" } },
+      ],
+    });
+  });
+});
+
+describe("delete", () => {
+  it("calls deleteOne with the vectorId", async () => {
+    const db = await initDb();
+    await db.delete("vec-1");
+    expect(__mocks__.deleteOne).toHaveBeenCalledWith({ id: "vec-1" });
+  });
+});
+
+describe("deleteCol", () => {
+  it("calls deleteIndex and resets internal state so re-init creates fresh index", async () => {
+    const db = await initDb();
+    await db.deleteCol();
+    expect(__mocks__.deleteIndex).toHaveBeenCalledWith("test-index");
+    // After deleteCol, _index and _initPromise reset; next initialize triggers createIndex again.
+    __mocks__.listIndexes.mockResolvedValue({ indexes: [] });
+    await db.initialize();
+    expect(__mocks__.createIndex).toHaveBeenCalledTimes(2);
+  });
+});
+
+describe("list", () => {
+  it("passes zero vector to query", async () => {
+    const db = await initDb({ embeddingModelDims: 4 });
+    await db.list();
+    expect(__mocks__.query).toHaveBeenCalledWith(
+      expect.objectContaining({ vector: [0, 0, 0, 0] }),
+    );
+  });
+
+  it("returns the number of matches as the count", async () => {
+    __mocks__.query.mockResolvedValue({
+      matches: [
+        { id: "a", metadata: {}, score: 0 },
+        { id: "b", metadata: {}, score: 0 },
+      ],
+    });
+    const db = await initDb();
+    const [results, count] = await db.list();
+    expect(results).toHaveLength(2);
+    expect(count).toBe(2);
+  });
+
+  it("does not make an extra describeIndexStats round-trip", async () => {
+    __mocks__.query.mockResolvedValue({ matches: [] });
+    const db = await initDb();
+    await db.list();
+    expect(__mocks__.describeIndexStats).not.toHaveBeenCalled();
+  });
+});
+
+describe("getUserId", () => {
+  it("returns existing user_id from migrations namespace", async () => {
+    __mocks__.fetch.mockResolvedValue({
+      records: {
+        "mem0-user-id": {
+          id: "mem0-user-id",
+          metadata: { user_id: "u-123" },
+        },
+      },
+    });
+    const db = await initDb();
+    const uid = await db.getUserId();
+    expect(uid).toBe("u-123");
+    expect(__mocks__.namespace).toHaveBeenCalledWith("__mem0_migrations__");
+  });
+
+  it("generates and upserts a new user_id when absent", async () => {
+    __mocks__.fetch.mockResolvedValue({ records: {} });
+    const db = await initDb();
+    const uid = await db.getUserId();
+    expect(typeof uid).toBe("string");
+    expect(uid.length).toBeGreaterThan(0);
+    expect(__mocks__.upsert).toHaveBeenCalledWith(
+      expect.objectContaining({
+        records: expect.arrayContaining([
+          expect.objectContaining({
+            id: "mem0-user-id",
+            metadata: { user_id: uid },
+          }),
+        ]),
+      }),
+    );
+    expect(__mocks__.namespace).toHaveBeenCalledWith("__mem0_migrations__");
+  });
+});
+
+describe("setUserId", () => {
+  it("upserts with correct id, zero vector, and metadata", async () => {
+    const db = await initDb({ embeddingModelDims: 4 });
+    await db.setUserId("u-456");
+    expect(__mocks__.upsert).toHaveBeenCalledWith({
+      records: [
+        {
+          id: "mem0-user-id",
+          values: [0, 0, 0, 0],
+          metadata: { user_id: "u-456" },
+        },
+      ],
+    });
+    expect(__mocks__.namespace).toHaveBeenCalledWith("__mem0_migrations__");
+  });
+});
+
+describe("keywordSearch", () => {
+  it("returns null", async () => {
+    const db = await initDb();
+    const result = await db.keywordSearch("hello", 5);
+    expect(result).toBeNull();
+  });
+});
@@ -41,6 +41,7 @@ import { LangchainEmbedder } from "../embeddings/langchain";
 import { LangchainVectorStore } from "../vector_stores/langchain";
 import { AzureAISearch } from "../vector_stores/azure_ai_search";
 import { PGVector } from "../vector_stores/pgvector";
+import { PineconeDB } from "../vector_stores/pinecone";
 import { S3Vectors } from "../vector_stores/s3_vectors";

 export class EmbedderFactory {
@@ -126,6 +127,8 @@ export class VectorStoreFactory {
        return new AzureAISearch(config as any);
      case "pgvector":
        return new PGVector(config as any);
+      case "pinecone":
+        return new PineconeDB(config as any);
      case "s3-vectors":
      case "s3_vectors":
        return new S3Vectors(config as any);
@@ -0,0 +1,365 @@
+import { Pinecone } from "@pinecone-database/pinecone";
+import type { Index } from "@pinecone-database/pinecone";
+import { VectorStore } from "./base";
+import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
+
+const MIGRATIONS_NAMESPACE = "__mem0_migrations__";
+const MIGRATIONS_RECORD_ID = "mem0-user-id";
+
+interface PineconeDBConfig extends VectorStoreConfig {
+  collectionName: string;
+  embeddingModelDims: number;
+  client?: Pinecone;
+  apiKey?: string;
+  serverlessConfig?: { cloud: string; region: string };
+  podConfig?: {
+    environment: string;
+    podType?: string;
+    pods?: number;
+    replicas?: number;
+    shards?: number;
+  };
+  metric?: "cosine" | "dotproduct" | "euclidean";
+  batchSize?: number;
+  namespace?: string;
+  extraParams?: Record<string, any>;
+}
+
+export class PineconeDB implements VectorStore {
+  private client: Pinecone;
+  private readonly collectionName: string;
+  private readonly dimension: number;
+  private readonly metric: "cosine" | "dotproduct" | "euclidean";
+  private readonly batchSize: number;
+  private readonly namespace: string;
+  private readonly serverlessConfig?: { cloud: string; region: string };
+  private readonly podConfig?: {
+    environment: string;
+    podType?: string;
+    pods?: number;
+    replicas?: number;
+    shards?: number;
+  };
+  private readonly extraParams: Record<string, any>;
+  private _index?: Index;
+  private _initPromise?: Promise<void>;
+
+  constructor(config: PineconeDBConfig) {
+    if (config.client) {
+      this.client = config.client;
+    } else {
+      const apiKey = config.apiKey || process.env.PINECONE_API_KEY;
+      if (!apiKey) {
+        throw new Error(
+          "Pinecone API key required: pass apiKey or set PINECONE_API_KEY env var",
+        );
+      }
+      this.client = new Pinecone({ apiKey });
+    }
+
+    this.collectionName = config.collectionName;
+    this.dimension = config.embeddingModelDims || config.dimension || 1536;
+    this.metric = config.metric || "cosine";
+    this.batchSize = config.batchSize || 100;
+    this.namespace = config.namespace || "";
+    this.serverlessConfig = config.serverlessConfig;
+    this.podConfig = config.podConfig;
+    this.extraParams = config.extraParams || {};
+
+    this.initialize().catch(console.error);
+  }
+
+  async initialize(): Promise<void> {
+    if (!this._initPromise) {
+      this._initPromise = this._doInitialize();
+    }
+    return this._initPromise;
+  }
+
+  private async _doInitialize(): Promise<void> {
+    await this._ensureIndex();
+    this._index = this.client.index({ name: this.collectionName });
+  }
+
+  private async _ensureIndex(): Promise<void> {
+    const indexList = await this.client.listIndexes();
+    const exists = ((indexList as any).indexes || []).some(
+      (idx: { name: string }) => idx.name === this.collectionName,
+    );
+    if (exists) return;
+
+    const spec: Record<string, any> = this.podConfig
+      ? {
+          pod: {
+            environment: this.podConfig.environment,
+            podType: this.podConfig.podType || "p1.x1",
+            pods: this.podConfig.pods || 1,
+            replicas: this.podConfig.replicas || 1,
+            shards: this.podConfig.shards || 1,
+          },
+        }
+      : {
+          serverless: this.serverlessConfig || {
+            cloud: "aws",
+            region: "us-east-1",
+          },
+        };
+
+    await this.client.createIndex({
+      name: this.collectionName,
+      dimension: this.dimension,
+      metric: this.metric,
+      spec,
+      waitUntilReady: true,
+      ...this.extraParams,
+    });
+  }
+
+  private index(): Index {
+    return this._index!;
+  }
+
+  private namespacedIndex(): Index {
+    return this.namespace
+      ? this.index().namespace(this.namespace)
+      : this.index();
+  }
+
+  private migrationsIndex(): Index {
+    return this.index().namespace(MIGRATIONS_NAMESPACE);
+  }
+
+  private createFilter(
+    filters?: SearchFilters,
+  ): Record<string, any> | undefined {
+    if (!filters || Object.keys(filters).length === 0) return undefined;
+
+    const result: Record<string, any> = {};
+
+    for (const [key, value] of Object.entries(filters)) {
+      if (value === undefined || value === null) continue;
+
+      if (key === "AND" || key === "$and") {
+        result["$and"] = (value as SearchFilters[]).map(
+          (sub) => this.createFilter(sub) || {},
+        );
+        continue;
+      }
+      if (key === "OR" || key === "$or") {
+        result["$or"] = (value as SearchFilters[]).map(
+          (sub) => this.createFilter(sub) || {},
+        );
+        continue;
+      }
+      if (key === "NOT" || key === "$not") {
+        console.warn(
+          "Filter operator 'NOT' is not supported by Pinecone metadata filters; skipping.",
+        );
+        continue;
+      }
+
+      if (value === "*") continue;
+
+      if (Array.isArray(value)) {
+        result[key] = { $in: value };
+        continue;
+      }
+
+      if (typeof value === "object" && value !== null) {
+        const pineconeOps: Record<string, any> = {};
+        for (const [op, opVal] of Object.entries(value)) {
+          switch (op) {
+            case "eq":
+              pineconeOps["$eq"] = opVal;
+              break;
+            case "ne":
+              pineconeOps["$ne"] = opVal;
+              break;
+            case "gt":
+              pineconeOps["$gt"] = opVal;
+              break;
+            case "gte":
+              pineconeOps["$gte"] = opVal;
+              break;
+            case "lt":
+              pineconeOps["$lt"] = opVal;
+              break;
+            case "lte":
+              pineconeOps["$lte"] = opVal;
+              break;
+            case "in":
+              pineconeOps["$in"] = opVal;
+              break;
+            case "nin":
+              pineconeOps["$nin"] = opVal;
+              break;
+            case "contains":
+            case "icontains":
+              console.warn(
+                `Filter operator '${op}' is not supported by Pinecone metadata filters; skipping.`,
+              );
+              break;
+            default:
+              throw new Error(
+                `Unsupported filter operator '${op}' for Pinecone`,
+              );
+          }
+        }
+        if (Object.keys(pineconeOps).length > 0) {
+          result[key] = pineconeOps;
+        }
+        continue;
+      }
+
+      result[key] = { $eq: value };
+    }
+
+    return Object.keys(result).length > 0 ? result : undefined;
+  }
+
+  async insert(
+    vectors: number[][],
+    ids: string[],
+    payloads: Record<string, any>[],
+  ): Promise<void> {
+    await this.initialize();
+    const records = vectors.map((values, i) => ({
+      id: ids[i],
+      values,
+      metadata: payloads[i] || {},
+    }));
+    for (let i = 0; i < records.length; i += this.batchSize) {
+      await this.namespacedIndex().upsert({
+        records: records.slice(i, i + this.batchSize),
+      });
+    }
+  }
+
+  async keywordSearch(
+    _query: string,
+    _topK?: number,
+    _filters?: SearchFilters,
+  ): Promise<VectorStoreResult[] | null> {
+    return null;
+  }
+
+  async search(
+    query: number[],
+    topK: number = 5,
+    filters?: SearchFilters,
+  ): Promise<VectorStoreResult[]> {
+    await this.initialize();
+    const filter = this.createFilter(filters);
+    const response = await this.namespacedIndex().query({
+      vector: query,
+      topK,
+      includeMetadata: true,
+      includeValues: false,
+      ...(filter ? { filter } : {}),
+    });
+    return (response.matches || []).map((match: any) => ({
+      id: match.id,
+      payload: (match.metadata as Record<string, any>) || {},
+      score: match.score,
+    }));
+  }
+
+  async get(vectorId: string): Promise<VectorStoreResult | null> {
+    await this.initialize();
+    const response = await this.namespacedIndex().fetch({ ids: [vectorId] });
+    const record = (response.records || {})[vectorId];
+    if (!record) return null;
+    return {
+      id: record.id,
+      payload: (record.metadata as Record<string, any>) || {},
+    };
+  }
+
+  async update(
+    vectorId: string,
+    vector: number[],
+    payload: Record<string, any>,
+  ): Promise<void> {
+    await this.initialize();
+    await this.namespacedIndex().upsert({
+      records: [{ id: vectorId, values: vector, metadata: payload }],
+    });
+  }
+
+  async delete(vectorId: string): Promise<void> {
+    await this.initialize();
+    await this.namespacedIndex().deleteOne({ id: vectorId });
+  }
+
+  async deleteCol(): Promise<void> {
+    if (this._initPromise) {
+      await this._initPromise.catch(() => {});
+    }
+    await this.client.deleteIndex(this.collectionName);
+    this._index = undefined;
+    this._initPromise = undefined;
+  }
+
+  async list(
+    filters?: SearchFilters,
+    topK: number = 100,
+  ): Promise<[VectorStoreResult[], number]> {
+    await this.initialize();
+    const zeroVector = new Array(this.dimension).fill(0);
+    const filter = this.createFilter(filters);
+    const response = await this.namespacedIndex().query({
+      vector: zeroVector,
+      topK,
+      includeMetadata: true,
+      includeValues: false,
+      ...(filter ? { filter } : {}),
+    });
+    const results = (response.matches || []).map((match: any) => ({
+      id: match.id,
+      payload: (match.metadata as Record<string, any>) || {},
+      score: match.score,
+    }));
+    return [results, results.length];
+  }
+
+  async getUserId(): Promise<string> {
+    await this.initialize();
+    try {
+      const response = await this.migrationsIndex().fetch({
+        ids: [MIGRATIONS_RECORD_ID],
+      });
+      const record = (response.records || {})[MIGRATIONS_RECORD_ID];
+      if (record?.metadata?.user_id) {
+        return record.metadata.user_id as string;
+      }
+    } catch {
+      // no record yet, fall through
+    }
+    const randomUserId =
+      Math.random().toString(36).substring(2, 15) +
+      Math.random().toString(36).substring(2, 15);
+    await this.migrationsIndex().upsert({
+      records: [
+        {
+          id: MIGRATIONS_RECORD_ID,
+          values: new Array(this.dimension).fill(0),
+          metadata: { user_id: randomUserId },
+        },
+      ],
+    });
+    return randomUserId;
+  }
+
+  async setUserId(userId: string): Promise<void> {
+    await this.initialize();
+    await this.migrationsIndex().upsert({
+      records: [
+        {
+          id: MIGRATIONS_RECORD_ID,
+          values: new Array(this.dimension).fill(0),
+          metadata: { user_id: userId },
+        },
+      ],
+    });
+  }
+}
@@ -10,6 +10,7 @@ const external = [
  "pg",
  "zod",
  "better-sqlite3",
+  "@pinecone-database/pinecone",
  "@qdrant/js-client-rest",
  "redis",
  "iovalkey",