View Vector Repository Management API Reference

Overview

The View Vector Repository management system provides comprehensive configuration for vector database storage and AI-powered semantic search capabilities. Vector repositories define the properties and connections by which vector embeddings are stored, enabling advanced search, similarity matching, and AI-driven data processing within the View platform.

Key Features

Vector Database Integration: Seamless integration with PostgreSQL with pgvector extension
Embedding Storage: Efficient storage and retrieval of high-dimensional vector embeddings
Model Configuration: Support for various language models and embedding dimensions
Database Management: Complete database connection and configuration management
Schema Support: Flexible schema and table configuration for vector storage
Security: Secure database credentials and connection management
Scalability: Support for high-dimensional vectors and large-scale embedding storage
AI Integration: Foundation for AI-powered search and semantic data processing

Supported Operations

Create: Create new vector repository configurations with database connections
Read: Retrieve individual vector repository configurations and metadata
Enumerate: List all vector repositories with pagination support
Update: Modify existing vector repository configurations and settings
Delete: Remove vector repository configurations and associated data
Existence Check: Verify vector repository presence without retrieving details

API Endpoints

Vector repositories are managed via the Configuration server API at [http|https]://[hostname]:[port]/v1.0/tenants/[tenant-guid]/vectorrepositories

Supported HTTP Methods: GET, HEAD, PUT, DELETE

Important: All vector repository operations require appropriate authentication tokens.

Vector Repository Object Structure

Vector repository objects contain comprehensive configuration for vector database connections and embedding storage. Here's the complete structure:

{
    "GUID": "example-vector-repository",
    "TenantGUID": "default",
    "Name": "My knowledgebase",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "pgvector",
    "DatabaseName": "vectordb",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "password",
    "SchemaName": "public",
    "CreatedUtc": "2024-07-10T05:09:32.000000Z"
}

Field Descriptions

GUID (GUID): Globally unique identifier for the vector repository object
TenantGUID (GUID): Globally unique identifier for the tenant
Name (string): Display name for the vector repository
RepositoryType (string): Type of vector repository (currently only "Pgvector")
Model (string): Name of the language model used to generate embeddings stored in this repository
Dimensionality (integer): Dimensionality of the embeddings (number of array elements)
DatabaseHostname (string): Hostname of the vector database server
DatabaseName (string): Name of the database containing vector data
DatabaseTable (string): Table name where vector data is stored
DatabasePort (integer): Port number for database connection
DatabaseUser (string): Username for database authentication
DatabasePassword (string): Password for database authentication
SchemaName (string): Database schema name for vector storage
CreatedUtc (datetime): UTC timestamp when the repository was created

Create Vector Repository

Creates a new vector repository configuration using PUT /v1.0/tenants/[tenant-guid]/vectorrepositories. This endpoint allows you to configure vector database connections for storing and retrieving high-dimensional embeddings.

Request Parameters

Required Parameters

Name (string, Body, Required): Display name for the vector repository
RepositoryType (string, Body, Required): Type of vector repository (currently only "Pgvector")
Model (string, Body, Required): Name of the language model used to generate embeddings
Dimensionality (integer, Body, Required): Dimensionality of the embeddings (number of array elements)
DatabaseHostname (string, Body, Required): Hostname of the vector database server
DatabaseName (string, Body, Required): Name of the database containing vector data
DatabaseTable (string, Body, Required): Table name where vector data is stored
DatabasePort (integer, Body, Required): Port number for database connection
DatabaseUser (string, Body, Required): Username for database authentication
DatabasePassword (string, Body, Required): Password for database authentication

Optional Parameters

SchemaName (string, Body, Optional): Database schema name for vector storage (defaults to "public")

Important Notes

Database Compatibility: Ensure the target database has pgvector extension installed
Model Consistency: Use consistent model names across related vector repositories
Dimensionality Matching: Ensure dimensionality matches the embedding model output
Connection Testing: Verify database connectivity before creating the repository

curl --location --request PUT 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories' \
--header 'content-type: application/json' \
--header 'Authorization: ••••••' \
--data '{
    "Name": "My vector repository",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "localhost",
    "DatabaseName": "vectordb",
    "SchemaName": "public",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "password"
}'


import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const createVectorRepository = async () => {
  try {
    const response = await api.VectorRepository.create({
      Name: "My vector repository",
      RepositoryType: "Pgvector",
      Model: "all-MiniLM-L6-v2",
      Dimensionality: 384,
      DatabaseHostname: "localhost",
      DatabaseName: "vectordb",
      SchemaName: "public",
      DatabaseTable: "minilm",
      DatabasePort: 5432,
      DatabaseUser: "postgres",
      DatabasePassword: "*****rd",
    });
    console.log(response, "Vector repository created successfully");
  } catch (err) {
    console.log("Error creating Vector repository:", err);
  }
};

createVectorRepository();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def createVectorRepository():
    vectorRepository = configuration.VectorRepository.create(
        Name="My vector repository",
        RepositoryType="Pgvector",
        Model="all-MiniLM-L6-v2",
        Dimensionality=384,
        DatabaseHostname="localhost",
        DatabaseName="vectordb",
        SchemaName="public",
        DatabaseTable="minilm",
        DatabasePort=5432,
        DatabaseUser="postgres",
        DatabasePassword="******rd"
    )
    print(vectorRepository)

createVectorRepository()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://locahost:8000/");
            
VectorRepository request = new VectorRepository
{
    Name = "My vector repository",
    RepositoryType = VectorRepositoryTypeEnum.Pgvector,
    Model = "all-MiniLM-L6-v2",
    Dimensionality = 384,
    DatabaseHostname = "localhost",
    DatabaseName = "vectordb",
    SchemaName = "public",
    DatabaseTable = "minilm",
    DatabasePort = 5432,
    DatabaseUser = "postgres",
    DatabasePassword = "*****rd",
};

VectorRepository response = await sdk.VectorRepository.Create(request);

Response

Returns the created vector repository object with all configuration details:

{
    "GUID": "example-vector-repository",
    "TenantGUID": "default",
    "Name": "My vector repository",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "localhost",
    "DatabaseName": "vectordb",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "***word",
    "SchemaName": "public",
    "CreatedUtc": "2024-07-10T05:09:32.000000Z"
}

Enumerate Vector Repositories

Retrieves a paginated list of all vector repository objects in the tenant using GET /v2.0/tenants/[tenant-guid]/vectorrepositories. This endpoint provides comprehensive enumer

ation with pagination support for managing multiple vector repository configurations.

Request Parameters

No additional parameters required beyond authentication.

curl --location 'http://localhost:8000/v2.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/' \
--header 'Authorization: ••••••'



import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const enumerateVectorRepositories = async () => {
  try {
    const response = await api.VectorRepository.enumerate();
    console.log(response, "Vector repositories fetched successfully");
  } catch (err) {
    console.log("Error fetching Vector repositories:", err);
  }
};

enumerateVectorRepositories();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def enumerateVectorRepositories():
    vectorRepositories = configuration.VectorRepository.enumerate()
    print(vectorRepositories)

enumerateVectorRepositories()

using View.Sdk;
using View.Sdk.Configuration;


ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");
            
EnumerationResult<VectorRepository> response = await sdk.VectorRepository.Enumerate();

Response

Returns a paginated list of vector repository objects:

{
    "Success": true,
    "Timestamp": {
        "Start": "2024-10-21T02:36:37.677751Z",
        "TotalMs": 23.58,
        "Messages": {}
    },
    "MaxResults": 10,
    "IterationsRequired": 1,
    "EndOfResults": true,
    "RecordsRemaining": 0,
    "Objects": [
        {
            "GUID": "example-vector-repository",
            "TenantGUID": "default",
            "Name": "My vector repository",
            "RepositoryType": "Pgvector",
            "Model": "all-MiniLM-L6-v2",
            "Dimensionality": 384,
            "DatabaseHostname": "localhost",
            "DatabaseName": "vectordb",
            "DatabaseTable": "minilm",
            "DatabasePort": 5432,
            "DatabaseUser": "postgres",
            "DatabasePassword": "***word",
            "SchemaName": "public",
            "CreatedUtc": "2024-07-10T05:09:32.000000Z"
        }
    ],
    "ContinuationToken": null
}

Read Vector Repository

Retrieves vector repository configuration and metadata by GUID using GET /v1.0/tenants/[tenant-guid]/vectorrepositories/[vectorrepository-guid]. Returns the complete vector repository configuration including all database connection details and metadata. If the repository doesn't exist, a 404 error is returned.

Request Parameters

vectorrepository-guid (string, Path, Required): GUID of the vector repository object to retrieve

curl --location 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/00000000-0000-0000-0000-000000000000' \
--header 'Authorization: ••••••'

import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const getVectorRepository = async () => {
  try {
    const response = await api.VectorRepository.read(
      "<embeddingsrule-guid>"
    );
    console.log(response, "Vector repository fetched successfully");
  } catch (err) {
    console.log("Error fetching Vector repository:", err);
  }
};
getVectorRepository();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def readVectorRepository():
    vectorRepository = configuration.VectorRepository.retrieve("<embeddingsrule-guid>")
    print(vectorRepository)

readVectorRepository()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");
            
VectorRepository response = await sdk.VectorRepository.Retrieve(Guid.Parse("<vectorrepository-guid>"));

Response

Returns the complete vector repository configuration:

{
    "GUID": "example-vector-repository",
    "TenantGUID": "default",
    "Name": "My vector repository",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "localhost",
    "DatabaseName": "vectordb",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "***word",
    "SchemaName": "public",
    "CreatedUtc": "2024-07-10T05:09:32.000000Z"
}

Note: the HEAD method can be used as an alternative to get to simply check the existence of the object. HEAD requests return either a 200/OK in the event the object exists, or a 404/Not Found if not. No response body is returned with a HEAD request.

Read All Vector Repositories

Retrieves all vector repository objects in the tenant using GET /v1.0/tenants/[tenant-guid]/vectorrepositories/. Returns an array of vector repository objects with complete configuration details for all repositories in the tenant.

Request Parameters

No additional parameters required beyond authentication.

curl --location 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/' \
--header 'Authorization: ••••••'

import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const getAllVectorRepositories = async () => {
  try {
    const response = await api.VectorRepository.readAll();
    console.log(response, "All vector repositories fetched successfully");
  } catch (err) {
    console.log("Error fetching Vector repositories:", err);
  }
};

getAllVectorRepositories();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def readAllVectorRepositories():
    vectorRepositories = configuration.VectorRepository.retrieve_all()
    print(vectorRepositories)

readAllVectorRepositories()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");
            
List<VectorRepository> response = await sdk.VectorRepository.RetrieveMany();

Response

Returns an array of all vector repository objects:

[
    {
        "GUID": "example-vector-repository",
        "TenantGUID": "default",
        "Name": "My vector repository",
        "RepositoryType": "Pgvector",
        "Model": "all-MiniLM-L6-v2",
        "Dimensionality": 384,
        "DatabaseHostname": "localhost",
        "DatabaseName": "vectordb",
        "DatabaseTable": "minilm",
        "DatabasePort": 5432,
        "DatabaseUser": "postgres",
        "DatabasePassword": "***word",
        "SchemaName": "public",
        "CreatedUtc": "2024-07-10T05:09:32.000000Z"
    },
    {
        "GUID": "another-vector-repository",
        "TenantGUID": "default",
        "Name": "Production vector repository",
        "RepositoryType": "Pgvector",
        "Model": "sentence-transformers/all-mpnet-base-v2",
        "Dimensionality": 768,
        "DatabaseHostname": "prod-db.example.com",
        "DatabaseName": "production_vectordb",
        "DatabaseTable": "mpnet_embeddings",
        "DatabasePort": 5432,
        "DatabaseUser": "vector_user",
        "DatabasePassword": "***word",
        "SchemaName": "public",
        "CreatedUtc": "2024-07-11T10:15:30.123456Z"
    }
]

Update Vector Repository

Updates an existing vector repository configuration using PUT /v1.0/tenants/[tenant-guid]/vectorrepositories/[vectorrepository-guid]. This endpoint allows you to modify vector repository parameters while preserving certain immutable fields.

Request Parameters

vectorrepository-guid (string, Path, Required): GUID of the vector repository object to update

Updateable Fields

All configuration parameters can be updated except for:

GUID: Immutable identifier
TenantGUID: Immutable tenant association
CreatedUtc: Immutable creation timestamp

Important Notes

Field Preservation: Certain fields cannot be modified and will be preserved across updates
Complete Object: Provide a fully populated object in the request body
Database Validation: All database connection parameters will be validated before applying changes
Connection Testing: Updated connections should be tested to ensure proper functionality

Request body:

curl --location --request PUT 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/00000000-0000-0000-0000-000000000000' \
--header 'content-type: application/json' \
--header 'Authorization: ••••••' \
--data '{
    "Name": "My updated vector repository",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "localhost",
    "DatabaseName": "vectordb",
    "SchemaName": "public",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "password"
}'

import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const updateVectorRepository = async () => {
  try {
    const response = await api.VectorRepository.update({
      GUID: "<vectorrepository-guid>",
      TenantGUID: "<tenant-guid>",
      Name: "My vector repository ash updated",
      RepositoryType: "Pgvector",
      Model: "all-MiniLM-L6-v2",
      Dimensionality: 384,
      DatabaseHostname: "localhost",
      DatabaseName: "vectordb",
      SchemaName: "public",
      DatabaseTable: "minilm",
      DatabasePort: 5432,
      DatabaseUser: "postgres",
      DatabasePassword: "******rd",
      CreatedUtc: "2025-03-26T10:00:43.978210Z",
    });
    console.log(response, "Vector repository updated successfully");
  } catch (err) {
    console.log("Error updating Vector repository:", err);
  }
};

updateVectorRepository();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def updateVectorRepository():
    vectorRepository = configuration.VectorRepository.update("<vectorrepository-guid>",
        Name="My vector repository [updated]",
        RepositoryType="Pgvector",
        Model="all-MiniLM-L6-v2",
        Dimensionality=384,
        DatabaseHostname="localhost",
        DatabaseName="vectordb",
        SchemaName="public",
        DatabaseTable="minilm",
        DatabasePort=5432,
        DatabaseUser="postgres",
        DatabasePassword="******rd"
    )
    print(vectorRepository)

updateVectorRepository()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");
            
var request = new VectorRepository
{
     GUID = Guid.Parse("<vectorrepository-guid>"),
     TenantGUID = Guid.Parse("<tenant-guid>"),
     Name = "My vector repository updated",
     RepositoryType = VectorRepositoryTypeEnum.Pgvector,
     Model = "all-MiniLM-L6-v2",
     Dimensionality = 384,
     DatabaseHostname = "localhost",
     DatabaseName = "vectordb",
     SchemaName = "public",
     DatabaseTable = "minilm",
     DatabasePort = 5432,
     DatabaseUser = "postgres",
     DatabasePassword = "******rd",
};

VectorRepository response = await sdk.VectorRepository.Update(request);

Response

Returns the updated vector repository object with all configuration details:

{
    "GUID": "example-vector-repository",
    "TenantGUID": "default",
    "Name": "My updated vector repository",
    "RepositoryType": "Pgvector",
    "Model": "all-MiniLM-L6-v2",
    "Dimensionality": 384,
    "DatabaseHostname": "localhost",
    "DatabaseName": "vectordb",
    "DatabaseTable": "minilm",
    "DatabasePort": 5432,
    "DatabaseUser": "postgres",
    "DatabasePassword": "***word",
    "SchemaName": "public",
    "CreatedUtc": "2024-07-10T05:09:32.000000Z"
}

Delete Vector Repository

Deletes a vector repository object by GUID using DELETE /v1.0/tenants/[tenant-guid]/vectorrepositories/[vectorrepository-guid]. This operation permanently removes the vector repository configuration from the system. Use with caution as this action cannot be undone.

Important Note: Ensure no active embeddings or AI processes are using this repository before deletion.

Request Parameters

vectorrepository-guid (string, Path, Required): GUID of the vector repository object to delete

curl --location --request DELETE 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/00000000-0000-0000-0000-000000000000' \
--header 'Authorization: ••••••' \

import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const deleteVectorRepository = async () => {
  try {
    const response = await api.VectorRepository.delete(
      "<vectorrepository-guid>"
    );
    console.log(response, "Vector repository deleted successfully");
  } catch (err) {
    console.log("Error deleting Vector repository:", err);
  }
};

deleteVectorRepository();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def deleteVectorRepository():
    vectorRepository = configuration.VectorRepository.delete("<vectorrepository-guid>")
    print(vectorRepository)

deleteVectorRepository()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");

bool deleted = await sdk.VectorRepository.Delete(Guid.Parse("<vectorrepository-guid>"));

Response

Returns 204 No Content on successful deletion. No response body is returned.

Check Vector Repository Existence

Verifies if a vector repository object exists without retrieving its configuration using HEAD /v1.0/tenants/[tenant-guid]/vectorrepositories/[vectorrepository-guid]. This is an efficient way to check repository presence before performing operations.

Request Parameters

vectorrepository-guid (string, Path, Required): GUID of the vector repository object to check

curl --location --head 'http://localhost:8000/v1.0/tenants/00000000-0000-0000-0000-000000000000/vectorrepositories/00000000-0000-0000-0000-000000000000' \
--header 'Authorization: ••••••'

import { ViewConfigurationSdk } from "view-sdk";

const api = new ViewConfigurationSdk(
  "http://localhost:8000/", //endpoint
  "default", //tenant Id
  "default" //access key
 );

const vectorRepositoryExists = async () => {
  try {
    const response = await api.VectorRepository.exists(
      "<vectorrepository-guid>"
    );
    console.log(response, "Vector repository exists");
  } catch (err) {
    console.log("Error checking Vector repository:", err);
  }
};

vectorRepositoryExists();

import view_sdk
from view_sdk import configuration
from view_sdk.sdk_configuration import Service

sdk = view_sdk.configure(
    access_key="default",
    base_url="localhost", 
    tenant_guid="default",
    service_ports={Service.DEFAULT: 8000},
)

def existsVectorRepository():
    vectorRepository = configuration.VectorRepository.exists("<vectorrepository-guid>")
    print(vectorRepository)

existsVectorRepository()

using View.Sdk;
using View.Sdk.Configuration;

ViewConfigurationSdk sdk = new ViewConfigurationSdk(Guid.Parse("<tenant-guid>"),"default", "http://localhost:8000/");

bool exists = await sdk.VectorRepository.Exists(Guid.Parse("<vectorrepository-guid>"));

Response

200 No Content: Vector repository exists
404 Not Found: Vector repository does not exist
No response body: Only HTTP status code is returned

Note: HEAD requests do not return a response body, only the HTTP status code indicating whether the vector repository exists.

Best Practices

When managing vector repositories in the View platform, consider the following recommendations for optimal vector database configuration and AI integration:

Database Security: Use secure database connections (SSL/TLS) and regularly rotate database credentials
Model Consistency: Maintain consistent embedding models and dimensionality across related repositories
Connection Testing: Always test database connectivity before creating or updating vector repositories
Resource Planning: Plan for appropriate database resources based on expected vector storage and query loads
Backup Strategy: Implement proper backup and recovery procedures for vector database data

Next Steps

After successfully configuring vector repositories, you can:

Embeddings Rules: Create embeddings rules to automatically generate and store vector embeddings
AI Search: Implement semantic search capabilities using the configured vector repositories
Data Processing: Set up automated data processing pipelines for vector generation
Performance Optimization: Monitor and optimize vector database performance for large-scale operations
Integration: Integrate vector repositories with other View platform services for comprehensive AI workflows