Vertex AI Vector Search - Mem0

Usage

To use Google Cloud Vertex AI Vector Search with mem0, you need to configure the vector_store in your mem0 config:

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

os.environ["GOOGLE_API_KEY"] = "sk-xx"

config = { "vector_store": { "provider": "vertex_ai_vector_search", "config": { "endpoint_id": "YOUR_ENDPOINT_ID", # Required: Vector Search endpoint ID "index_id": "YOUR_INDEX_ID", # Required: Vector Search index ID "deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID", # Required: Deployment-specific ID "project_id": "YOUR_PROJECT_ID", # Required: Google Cloud project ID "project_number": "YOUR_PROJECT_NUMBER", # Required: Google Cloud project number "region": "YOUR_REGION", # Required: Google Cloud region "credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS "vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations } } } m = Memory.from_config(config) m.add("Your text here", user_id="user", metadata={"category": "example"})


```typescript TypeScript
import { Memory } from "mem0ai/oss";

// Authenticate with GOOGLE_APPLICATION_CREDENTIALS in your environment,
// or pass credentialsPath / serviceAccountJson in the config below.
const config = {
  vectorStore: {
    provider: "vertex_ai_vector_search",
    config: {
      endpointId: "YOUR_ENDPOINT_ID", // Required: Vector Search endpoint ID
      indexId: "YOUR_INDEX_ID", // Required: Vector Search index ID
      deploymentIndexId: "YOUR_DEPLOYMENT_INDEX_ID", // Required: Deployment-specific ID
      projectId: "YOUR_PROJECT_ID", // Required: Google Cloud project ID
      projectNumber: "YOUR_PROJECT_NUMBER", // Required: Google Cloud project number
      region: "YOUR_REGION", // Required: Google Cloud region
      credentialsPath: "path/to/credentials.json", // Optional: defaults to GOOGLE_APPLICATION_CREDENTIALS
      vectorSearchApiEndpoint: "YOUR_API_ENDPOINT", // Required for search/get operations
    },
  },
};

const memory = new Memory(config);
await memory.add("Your text here", {
  userId: "user",
  metadata: { category: "example" },
});

</CodeGroup>

Required Parameters

<Tabs> <Tab title="Python"> | Parameter | Description | Required | |-----------|-------------|----------| | `endpoint_id` | Vector Search endpoint ID | Yes | | `index_id` | Vector Search index ID | Yes | | `deployment_index_id` | Deployment-specific index ID | Yes | | `project_id` | Google Cloud project ID | Yes | | `project_number` | Google Cloud project number | Yes | | `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) | | `region` | Google Cloud region | Yes | | `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) | | `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` | </Tab> <Tab title="TypeScript"> | Parameter | Description | Required | |-----------|-------------|----------| | `endpointId` | Vector Search endpoint ID | Yes | | `indexId` | Vector Search index ID | Yes | | `deploymentIndexId` | Deployment-specific index ID | Yes | | `projectId` | Google Cloud project ID | Yes | | `projectNumber` | Google Cloud project number | Yes | | `vectorSearchApiEndpoint` | Vector search API endpoint | Yes (for get operations) | | `region` | Google Cloud region | Yes | | `credentialsPath` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) | | `serviceAccountJson` | Service account credentials as an object (alternative to `credentialsPath`) | No | </Tab> </Tabs>