Basic config examples for AI Semantic Prompt Guard - Plugin - unreleased

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Basic configuration examples

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The following examples provide some typical configurations for enabling the ai-semantic-prompt-guard plugin on a service.

Kong Admin API

Konnect API

Kubernetes

Declarative (YAML)

Konnect Terraform

Make the following request:

curl -X POST http://localhost:8001/services/{serviceName|Id}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
  "name": "ai-semantic-prompt-guard",
  "config": {
    "embeddings": {
      "auth": {
        "header_name": "Authorization",
        "header_value": "Bearer <OPENAI_API_TOKEN>"
      },
      "model": {
        "name": "text-embedding-3-small",
        "provider": "openai"
      }
    },
    "search": {
      "threshold": 0.7
    },
    "vectordb": {
      "strategy": "redis",
      "distance_metric": "cosine",
      "threshold": 0.5,
      "dimensions": 1024,
      "redis": {
        "host": "localhost",
        "port": 6379
      }
    },
    "rules": {
      "match_all_conversation_history": true,
      "allow_prompts": [
        "Questions about Kong"
      ]
    }
  }
}
    '

Replace SERVICE_NAME|ID with the id or name of the service that this plugin configuration will target.

Make the following request, substituting your own access token, region, control plane ID, and service ID:

curl -X POST \
https://{us|eu}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/services/{serviceId}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer TOKEN" \
    --data '{"name":"ai-semantic-prompt-guard","config":{"embeddings":{"auth":{"header_name":"Authorization","header_value":"Bearer <OPENAI_API_TOKEN>"},"model":{"name":"text-embedding-3-small","provider":"openai"}},"search":{"threshold":0.7},"vectordb":{"strategy":"redis","distance_metric":"cosine","threshold":0.5,"dimensions":1024,"redis":{"host":"localhost","port":6379}},"rules":{"match_all_conversation_history":true,"allow_prompts":["Questions about Kong"]}}}'

See the Konnect API reference to learn about region-specific URLs and personal access tokens.

First, create a KongPlugin resource:

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-semantic-prompt-guard-example
plugin: ai-semantic-prompt-guard
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: Bearer <OPENAI_API_TOKEN>
    model:
      name: text-embedding-3-small
      provider: openai
  search:
    threshold: 0.7
  vectordb:
    strategy: redis
    distance_metric: cosine
    threshold: 0.5
    dimensions: 1024
    redis:
      host: localhost
      port: 6379
  rules:
    match_all_conversation_history: true
    allow_prompts:
    - Questions about Kong
" | kubectl apply -f -

Next, apply the KongPlugin resource to an ingress by annotating the service as follows:

kubectl annotate service SERVICE_NAME konghq.com/plugins=ai-semantic-prompt-guard-example

Replace SERVICE_NAME with the name of the service that this plugin configuration will target. You can see your available ingresses by running kubectl get service.

Note: The KongPlugin resource only needs to be defined once and can be applied to any service, consumer, or route in the namespace. If you want the plugin to be available cluster-wide, create the resource as a KongClusterPlugin instead of KongPlugin.

Add this section to your declarative configuration file:

plugins:
- name: ai-semantic-prompt-guard
  service: SERVICE_NAME|ID
  config:
    embeddings:
      auth:
        header_name: Authorization
        header_value: Bearer <OPENAI_API_TOKEN>
      model:
        name: text-embedding-3-small
        provider: openai
    search:
      threshold: 0.7
    vectordb:
      strategy: redis
      distance_metric: cosine
      threshold: 0.5
      dimensions: 1024
      redis:
        host: localhost
        port: 6379
    rules:
      match_all_conversation_history: true
      allow_prompts:
      - Questions about Kong

Replace SERVICE_NAME|ID with the id or name of the service that this plugin configuration will target.

Prerequisite: Configure your Personal Access Token

terraform {
  required_providers {
    konnect = {
      source  = "kong/konnect"
    }
  }
}

provider "konnect" {
  personal_access_token = "kpat_YOUR_TOKEN"
  server_url            = "https://us.api.konghq.com/"
}

Add the following to your Terraform configuration to create a Konnect Gateway Plugin:

resource "konnect_gateway_plugin_ai_semantic_prompt_guard" "my_ai_semantic_prompt_guard" {
  enabled = true

  config = {
    embeddings = {
      auth = {
        header_name = "Authorization"
        header_value = "Bearer <OPENAI_API_TOKEN>"
      }
      model = {
        name = "text-embedding-3-small"
        provider = "openai"
      }
    }
    search = {
      threshold = 0.7
    }
    vectordb = {
      strategy = "redis"
      distance_metric = "cosine"
      threshold = 0.5
      dimensions = 1024
      redis = {
        host = "localhost"
        port = 6379
      }
    }
    rules = {
      match_all_conversation_history = true
      allow_prompts = ["Questions about Kong"]
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
  service = {
    id = konnect_gateway_service.my_service.id
  }
}

The following examples provide some typical configurations for enabling the ai-semantic-prompt-guard plugin on a route.

Kong Admin API

Konnect API

Kubernetes

Declarative (YAML)

Konnect Terraform

Make the following request:

curl -X POST http://localhost:8001/routes/{routeName|Id}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
  "name": "ai-semantic-prompt-guard",
  "config": {
    "embeddings": {
      "auth": {
        "header_name": "Authorization",
        "header_value": "Bearer <OPENAI_API_TOKEN>"
      },
      "model": {
        "name": "text-embedding-3-small",
        "provider": "openai"
      }
    },
    "search": {
      "threshold": 0.7
    },
    "vectordb": {
      "strategy": "redis",
      "distance_metric": "cosine",
      "threshold": 0.5,
      "dimensions": 1024,
      "redis": {
        "host": "localhost",
        "port": 6379
      }
    },
    "rules": {
      "match_all_conversation_history": true,
      "allow_prompts": [
        "Questions about Kong"
      ]
    }
  }
}
    '

Replace ROUTE_NAME|ID with the id or name of the route that this plugin configuration will target.

Make the following request, substituting your own access token, region, control plane ID, and route ID:

curl -X POST \
https://{us|eu}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/routes/{routeId}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer TOKEN" \
    --data '{"name":"ai-semantic-prompt-guard","config":{"embeddings":{"auth":{"header_name":"Authorization","header_value":"Bearer <OPENAI_API_TOKEN>"},"model":{"name":"text-embedding-3-small","provider":"openai"}},"search":{"threshold":0.7},"vectordb":{"strategy":"redis","distance_metric":"cosine","threshold":0.5,"dimensions":1024,"redis":{"host":"localhost","port":6379}},"rules":{"match_all_conversation_history":true,"allow_prompts":["Questions about Kong"]}}}'

See the Konnect API reference to learn about region-specific URLs and personal access tokens.

First, create a KongPlugin resource:

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-semantic-prompt-guard-example
plugin: ai-semantic-prompt-guard
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: Bearer <OPENAI_API_TOKEN>
    model:
      name: text-embedding-3-small
      provider: openai
  search:
    threshold: 0.7
  vectordb:
    strategy: redis
    distance_metric: cosine
    threshold: 0.5
    dimensions: 1024
    redis:
      host: localhost
      port: 6379
  rules:
    match_all_conversation_history: true
    allow_prompts:
    - Questions about Kong
" | kubectl apply -f -

Next, apply the KongPlugin resource to an ingress by annotating the ingress as follows:

kubectl annotate ingress INGRESS_NAME konghq.com/plugins=ai-semantic-prompt-guard-example

Replace INGRESS_NAME with the name of the ingress that this plugin configuration will target. You can see your available ingresses by running kubectl get ingress.

Note: The KongPlugin resource only needs to be defined once and can be applied to any service, consumer, or route in the namespace. If you want the plugin to be available cluster-wide, create the resource as a KongClusterPlugin instead of KongPlugin.

Add this section to your declarative configuration file:

plugins:
- name: ai-semantic-prompt-guard
  route: ROUTE_NAME|ID
  config:
    embeddings:
      auth:
        header_name: Authorization
        header_value: Bearer <OPENAI_API_TOKEN>
      model:
        name: text-embedding-3-small
        provider: openai
    search:
      threshold: 0.7
    vectordb:
      strategy: redis
      distance_metric: cosine
      threshold: 0.5
      dimensions: 1024
      redis:
        host: localhost
        port: 6379
    rules:
      match_all_conversation_history: true
      allow_prompts:
      - Questions about Kong

Replace ROUTE_NAME|ID with the id or name of the route that this plugin configuration will target.

Prerequisite: Configure your Personal Access Token

terraform {
  required_providers {
    konnect = {
      source  = "kong/konnect"
    }
  }
}

provider "konnect" {
  personal_access_token = "kpat_YOUR_TOKEN"
  server_url            = "https://us.api.konghq.com/"
}

Add the following to your Terraform configuration to create a Konnect Gateway Plugin:

resource "konnect_gateway_plugin_ai_semantic_prompt_guard" "my_ai_semantic_prompt_guard" {
  enabled = true

  config = {
    embeddings = {
      auth = {
        header_name = "Authorization"
        header_value = "Bearer <OPENAI_API_TOKEN>"
      }
      model = {
        name = "text-embedding-3-small"
        provider = "openai"
      }
    }
    search = {
      threshold = 0.7
    }
    vectordb = {
      strategy = "redis"
      distance_metric = "cosine"
      threshold = 0.5
      dimensions = 1024
      redis = {
        host = "localhost"
        port = 6379
      }
    }
    rules = {
      match_all_conversation_history = true
      allow_prompts = ["Questions about Kong"]
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
  route = {
    id = konnect_gateway_route.my_route.id
  }
}

The following examples provide some typical configurations for enabling the ai-semantic-prompt-guard plugin on a consumer.

Kong Admin API

Konnect API

Kubernetes

Declarative (YAML)

Konnect Terraform

Make the following request:

curl -X POST http://localhost:8001/consumers/{consumerName|Id}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
  "name": "ai-semantic-prompt-guard",
  "config": {
    "embeddings": {
      "auth": {
        "header_name": "Authorization",
        "header_value": "Bearer <OPENAI_API_TOKEN>"
      },
      "model": {
        "name": "text-embedding-3-small",
        "provider": "openai"
      }
    },
    "search": {
      "threshold": 0.7
    },
    "vectordb": {
      "strategy": "redis",
      "distance_metric": "cosine",
      "threshold": 0.5,
      "dimensions": 1024,
      "redis": {
        "host": "localhost",
        "port": 6379
      }
    },
    "rules": {
      "match_all_conversation_history": true,
      "allow_prompts": [
        "Questions about Kong"
      ]
    }
  }
}
    '

Replace CONSUMER_NAME|ID with the id or name of the consumer that this plugin configuration will target.

Make the following request, substituting your own access token, region, control plane ID, and consumer ID:

curl -X POST \
https://{us|eu}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/consumers/{consumerId}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer TOKEN" \
    --data '{"name":"ai-semantic-prompt-guard","config":{"embeddings":{"auth":{"header_name":"Authorization","header_value":"Bearer <OPENAI_API_TOKEN>"},"model":{"name":"text-embedding-3-small","provider":"openai"}},"search":{"threshold":0.7},"vectordb":{"strategy":"redis","distance_metric":"cosine","threshold":0.5,"dimensions":1024,"redis":{"host":"localhost","port":6379}},"rules":{"match_all_conversation_history":true,"allow_prompts":["Questions about Kong"]}}}'

See the Konnect API reference to learn about region-specific URLs and personal access tokens.

First, create a KongPlugin resource:

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-semantic-prompt-guard-example
plugin: ai-semantic-prompt-guard
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: Bearer <OPENAI_API_TOKEN>
    model:
      name: text-embedding-3-small
      provider: openai
  search:
    threshold: 0.7
  vectordb:
    strategy: redis
    distance_metric: cosine
    threshold: 0.5
    dimensions: 1024
    redis:
      host: localhost
      port: 6379
  rules:
    match_all_conversation_history: true
    allow_prompts:
    - Questions about Kong
" | kubectl apply -f -

Next, apply the KongPlugin resource to an ingress by annotating the KongConsumer object as follows:

kubectl annotate KongConsumer CONSUMER_NAME konghq.com/plugins=ai-semantic-prompt-guard-example

Replace CONSUMER_NAME with the name of the consumer that this plugin configuration will target. You can see your available consumers by running kubectl get KongConsumer.

To learn more about KongConsumer objects, see Provisioning Consumers and Credentials.

Note: The KongPlugin resource only needs to be defined once and can be applied to any service, consumer, or route in the namespace. If you want the plugin to be available cluster-wide, create the resource as a KongClusterPlugin instead of KongPlugin.

Add this section to your declarative configuration file:

plugins:
- name: ai-semantic-prompt-guard
  consumer: CONSUMER_NAME|ID
  config:
    embeddings:
      auth:
        header_name: Authorization
        header_value: Bearer <OPENAI_API_TOKEN>
      model:
        name: text-embedding-3-small
        provider: openai
    search:
      threshold: 0.7
    vectordb:
      strategy: redis
      distance_metric: cosine
      threshold: 0.5
      dimensions: 1024
      redis:
        host: localhost
        port: 6379
    rules:
      match_all_conversation_history: true
      allow_prompts:
      - Questions about Kong

Replace CONSUMER_NAME|ID with the id or name of the consumer that this plugin configuration will target.

Prerequisite: Configure your Personal Access Token

terraform {
  required_providers {
    konnect = {
      source  = "kong/konnect"
    }
  }
}

provider "konnect" {
  personal_access_token = "kpat_YOUR_TOKEN"
  server_url            = "https://us.api.konghq.com/"
}

Add the following to your Terraform configuration to create a Konnect Gateway Plugin:

resource "konnect_gateway_plugin_ai_semantic_prompt_guard" "my_ai_semantic_prompt_guard" {
  enabled = true

  config = {
    embeddings = {
      auth = {
        header_name = "Authorization"
        header_value = "Bearer <OPENAI_API_TOKEN>"
      }
      model = {
        name = "text-embedding-3-small"
        provider = "openai"
      }
    }
    search = {
      threshold = 0.7
    }
    vectordb = {
      strategy = "redis"
      distance_metric = "cosine"
      threshold = 0.5
      dimensions = 1024
      redis = {
        host = "localhost"
        port = 6379
      }
    }
    rules = {
      match_all_conversation_history = true
      allow_prompts = ["Questions about Kong"]
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
  consumer = {
    id = konnect_gateway_consumer.my_consumer.id
  }
}

The following examples provide some typical configurations for enabling the ai-semantic-prompt-guard plugin on a consumer group.

Kong Admin API

Konnect API

Kubernetes

Declarative (YAML)

Konnect Terraform

Make the following request:

curl -X POST http://localhost:8001/consumer_groups/{consumerGroupName|Id}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
  "name": "ai-semantic-prompt-guard",
  "config": {
    "embeddings": {
      "auth": {
        "header_name": "Authorization",
        "header_value": "Bearer <OPENAI_API_TOKEN>"
      },
      "model": {
        "name": "text-embedding-3-small",
        "provider": "openai"
      }
    },
    "search": {
      "threshold": 0.7
    },
    "vectordb": {
      "strategy": "redis",
      "distance_metric": "cosine",
      "threshold": 0.5,
      "dimensions": 1024,
      "redis": {
        "host": "localhost",
        "port": 6379
      }
    },
    "rules": {
      "match_all_conversation_history": true,
      "allow_prompts": [
        "Questions about Kong"
      ]
    }
  }
}
    '

Replace CONSUMER_GROUP_NAME|ID with the id or name of the consumer group that this plugin configuration will target.

Make the following request, substituting your own access token, region, control plane ID, and consumer group ID:

curl -X POST \
https://{us|eu}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/consumer_groups/{consumerGroupId}/plugins \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer TOKEN" \
    --data '{"name":"ai-semantic-prompt-guard","config":{"embeddings":{"auth":{"header_name":"Authorization","header_value":"Bearer <OPENAI_API_TOKEN>"},"model":{"name":"text-embedding-3-small","provider":"openai"}},"search":{"threshold":0.7},"vectordb":{"strategy":"redis","distance_metric":"cosine","threshold":0.5,"dimensions":1024,"redis":{"host":"localhost","port":6379}},"rules":{"match_all_conversation_history":true,"allow_prompts":["Questions about Kong"]}}}'

See the Konnect API reference to learn about region-specific URLs and personal access tokens.

First, create a KongPlugin resource:

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-semantic-prompt-guard-example
plugin: ai-semantic-prompt-guard
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: Bearer <OPENAI_API_TOKEN>
    model:
      name: text-embedding-3-small
      provider: openai
  search:
    threshold: 0.7
  vectordb:
    strategy: redis
    distance_metric: cosine
    threshold: 0.5
    dimensions: 1024
    redis:
      host: localhost
      port: 6379
  rules:
    match_all_conversation_history: true
    allow_prompts:
    - Questions about Kong
" | kubectl apply -f -

Next, apply the KongPlugin resource to an ingress by annotating the KongConsumerGroup object as follows:

kubectl annotate KongConsumerGroup CONSUMER_GROUP_NAME konghq.com/plugins=ai-semantic-prompt-guard-example

Replace CONSUMER_GROUP_NAME with the name of the consumer group that this plugin configuration will target. You can see your available consumer groups by running kubectl get KongConsumerGroup.

Note: The KongPlugin resource only needs to be defined once and can be applied to any service, consumer, consumer group, or route in the namespace. If you want the plugin to be available cluster-wide, create the resource as a KongClusterPlugin instead of KongPlugin.

Add this section to your declarative configuration file:

plugins:
- name: ai-semantic-prompt-guard
  consumer_group: CONSUMER_GROUP_NAME|ID
  config:
    embeddings:
      auth:
        header_name: Authorization
        header_value: Bearer <OPENAI_API_TOKEN>
      model:
        name: text-embedding-3-small
        provider: openai
    search:
      threshold: 0.7
    vectordb:
      strategy: redis
      distance_metric: cosine
      threshold: 0.5
      dimensions: 1024
      redis:
        host: localhost
        port: 6379
    rules:
      match_all_conversation_history: true
      allow_prompts:
      - Questions about Kong

Replace CONSUMER_GROUP_NAME|ID with the id or name of the consumer group that this plugin configuration will target.

Prerequisite: Configure your Personal Access Token

terraform {
  required_providers {
    konnect = {
      source  = "kong/konnect"
    }
  }
}

provider "konnect" {
  personal_access_token = "kpat_YOUR_TOKEN"
  server_url            = "https://us.api.konghq.com/"
}

Add the following to your Terraform configuration to create a Konnect Gateway Plugin:

resource "konnect_gateway_plugin_ai_semantic_prompt_guard" "my_ai_semantic_prompt_guard" {
  enabled = true

  config = {
    embeddings = {
      auth = {
        header_name = "Authorization"
        header_value = "Bearer <OPENAI_API_TOKEN>"
      }
      model = {
        name = "text-embedding-3-small"
        provider = "openai"
      }
    }
    search = {
      threshold = 0.7
    }
    vectordb = {
      strategy = "redis"
      distance_metric = "cosine"
      threshold = 0.5
      dimensions = 1024
      redis = {
        host = "localhost"
        port = 6379
      }
    }
    rules = {
      match_all_conversation_history = true
      allow_prompts = ["Questions about Kong"]
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
  consumer_group = {
    id = konnect_gateway_consumer_group.my_consumer_group.id
  }
}

A plugin which is not associated to any service, route, consumer, or consumer group is considered global, and will be run on every request.

In self-managed Kong Gateway Enterprise, the plugin applies to every entity in a given workspace.
In self-managed Kong Gateway (OSS), the plugin applies to your entire environment.
In Konnect, the plugin applies to every entity in a given control plane.

Read the Plugin Reference and the Plugin Precedence sections for more information.

The following examples provide some typical configurations for enabling the AI Semantic Prompt Guard plugin globally.

Kong Admin API

Konnect API

Kubernetes

Declarative (YAML)

Konnect Terraform

Make the following request:

curl -X POST http://localhost:8001/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
  "name": "ai-semantic-prompt-guard",
  "config": {
    "embeddings": {
      "auth": {
        "header_name": "Authorization",
        "header_value": "Bearer <OPENAI_API_TOKEN>"
      },
      "model": {
        "name": "text-embedding-3-small",
        "provider": "openai"
      }
    },
    "search": {
      "threshold": 0.7
    },
    "vectordb": {
      "strategy": "redis",
      "distance_metric": "cosine",
      "threshold": 0.5,
      "dimensions": 1024,
      "redis": {
        "host": "localhost",
        "port": 6379
      }
    },
    "rules": {
      "match_all_conversation_history": true,
      "allow_prompts": [
        "Questions about Kong"
      ]
    }
  }
}
    '

Make the following request, substituting your own access token, region, and control plane ID:

curl -X POST \
https://{us|eu}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer TOKEN" \
    --data '{"name":"ai-semantic-prompt-guard","config":{"embeddings":{"auth":{"header_name":"Authorization","header_value":"Bearer <OPENAI_API_TOKEN>"},"model":{"name":"text-embedding-3-small","provider":"openai"}},"search":{"threshold":0.7},"vectordb":{"strategy":"redis","distance_metric":"cosine","threshold":0.5,"dimensions":1024,"redis":{"host":"localhost","port":6379}},"rules":{"match_all_conversation_history":true,"allow_prompts":["Questions about Kong"]}}}'

See the Konnect API reference to learn about region-specific URLs and personal access tokens.

Create a KongClusterPlugin resource and label it as global:

apiVersion: configuration.konghq.com/v1
kind: KongClusterPlugin
metadata:
  name: <global-ai-semantic-prompt-guard>
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: "true"
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: Bearer <OPENAI_API_TOKEN>
    model:
      name: text-embedding-3-small
      provider: openai
  search:
    threshold: 0.7
  vectordb:
    strategy: redis
    distance_metric: cosine
    threshold: 0.5
    dimensions: 1024
    redis:
      host: localhost
      port: 6379
  rules:
    match_all_conversation_history: true
    allow_prompts:
    - Questions about Kong
plugin: ai-semantic-prompt-guard

Add a plugins entry in the declarative configuration file:

plugins:
- name: ai-semantic-prompt-guard
  config:
    embeddings:
      auth:
        header_name: Authorization
        header_value: Bearer <OPENAI_API_TOKEN>
      model:
        name: text-embedding-3-small
        provider: openai
    search:
      threshold: 0.7
    vectordb:
      strategy: redis
      distance_metric: cosine
      threshold: 0.5
      dimensions: 1024
      redis:
        host: localhost
        port: 6379
    rules:
      match_all_conversation_history: true
      allow_prompts:
      - Questions about Kong

Prerequisite: Configure your Personal Access Token

terraform {
  required_providers {
    konnect = {
      source  = "kong/konnect"
    }
  }
}

provider "konnect" {
  personal_access_token = "kpat_YOUR_TOKEN"
  server_url            = "https://us.api.konghq.com/"
}

Add the following to your Terraform configuration to create a Konnect Gateway Plugin:

resource "konnect_gateway_plugin_ai_semantic_prompt_guard" "my_ai_semantic_prompt_guard" {
  enabled = true

  config = {
    embeddings = {
      auth = {
        header_name = "Authorization"
        header_value = "Bearer <OPENAI_API_TOKEN>"
      }
      model = {
        name = "text-embedding-3-small"
        provider = "openai"
      }
    }
    search = {
      threshold = 0.7
    }
    vectordb = {
      strategy = "redis"
      distance_metric = "cosine"
      threshold = 0.5
      dimensions = 1024
      redis = {
        host = "localhost"
        port = 6379
      }
    }
    rules = {
      match_all_conversation_history = true
      allow_prompts = ["Questions about Kong"]
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
}

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