AI Proxy Advanced: Embeddings route - Plugin

Embeddings routev3.11+

Access the embeddings endpoint to generate text embeddings using OpenAI’s text-embedding-3-large model. Use this route for embedding-based semantic search, similarity, or other vector-based AI features.

Prerequisites

OpenAI account

Set up the plugin

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    config:
      genai_category: text/embeddings
      targets:
      - route_type: llm/v1/embeddings
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        model:
          provider: openai
          name: text-embedding-3-large
          options:
            max_tokens: 512
            temperature: 1.0

Make the following request:

curl -i -X POST http://localhost:8001/plugins/ \
    --header "Accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make the following request:

curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

region: Geographic region where your Kong Konnect is hosted and operates.
controlPlaneId: The id of the control plane.
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account.

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

echo "
apiVersion: configuration.konghq.com/v1
kind: KongClusterPlugin
metadata:
  name: ai-proxy-advanced
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: 'true'
config:
  genai_category: text/embeddings
  targets:
  - route_type: llm/v1/embeddings
    auth:
      header_name: Authorization
      header_value: Bearer ${{ env 'DECK_OPENAI_API_KEY' }}
    model:
      provider: openai
      name: text-embedding-3-large
      options:
        max_tokens: 512
        temperature: 1.0
plugin: ai-proxy-advanced
" | kubectl apply -f -

Prerequisite: Configure your Personal Access Token

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

provider "konnect" {
  personal_access_token = "$KONNECT_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_proxy_advanced" "my_ai_proxy_advanced" {
  enabled = true

  config = {
    genai_category = "text/embeddings"
    targets = [
      {
        route_type = "llm/v1/embeddings"

        auth = {
          header_name = "Authorization"
          header_value = "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
        }

        model = {
          provider = "openai"
          name = "text-embedding-3-large"

          options = {
            max_tokens = 512
            temperature = 1.0
          }
        }
      }    ]
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
}

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    service: serviceName|Id
    config:
      genai_category: text/embeddings
      targets:
      - route_type: llm/v1/embeddings
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        model:
          provider: openai
          name: text-embedding-3-large
          options:
            max_tokens: 512
            temperature: 1.0

Make sure to replace the following placeholders with your own values:

serviceName|Id: The id or name of the service the plugin configuration will target.

Make the following request:

curl -i -X POST http://localhost:8001/services/{serviceName|Id}/plugins/ \
    --header "Accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

serviceName|Id: The id or name of the service the plugin configuration will target.

Make the following request:

curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/services/{serviceId}/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

region: Geographic region where your Kong Konnect is hosted and operates.
controlPlaneId: The id of the control plane.
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account.
serviceId: The id of the service the plugin configuration will target.

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

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-proxy-advanced
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  genai_category: text/embeddings
  targets:
  - route_type: llm/v1/embeddings
    auth:
      header_name: Authorization
      header_value: Bearer ${{ env 'DECK_OPENAI_API_KEY' }}
    model:
      provider: openai
      name: text-embedding-3-large
      options:
        max_tokens: 512
        temperature: 1.0
plugin: ai-proxy-advanced
" | kubectl apply -f -

Next, apply the KongPlugin resource by annotating the service resource:

kubectl annotate -n kong service SERVICE_NAME konghq.com/plugins=ai-proxy-advanced

Prerequisite: Configure your Personal Access Token

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

provider "konnect" {
  personal_access_token = "$KONNECT_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_proxy_advanced" "my_ai_proxy_advanced" {
  enabled = true

  config = {
    genai_category = "text/embeddings"
    targets = [
      {
        route_type = "llm/v1/embeddings"

        auth = {
          header_name = "Authorization"
          header_value = "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
        }

        model = {
          provider = "openai"
          name = "text-embedding-3-large"

          options = {
            max_tokens = 512
            temperature = 1.0
          }
        }
      }    ]
  }

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

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    route: routeName|Id
    config:
      genai_category: text/embeddings
      targets:
      - route_type: llm/v1/embeddings
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        model:
          provider: openai
          name: text-embedding-3-large
          options:
            max_tokens: 512
            temperature: 1.0

Make sure to replace the following placeholders with your own values:

routeName|Id: The id or name of the route the plugin configuration will target.

Make the following request:

curl -i -X POST http://localhost:8001/routes/{routeName|Id}/plugins/ \
    --header "Accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

routeName|Id: The id or name of the route the plugin configuration will target.

Make the following request:

curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/routes/{routeId}/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

region: Geographic region where your Kong Konnect is hosted and operates.
controlPlaneId: The id of the control plane.
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account.
routeId: The id of the route the plugin configuration will target.

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

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-proxy-advanced
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  genai_category: text/embeddings
  targets:
  - route_type: llm/v1/embeddings
    auth:
      header_name: Authorization
      header_value: Bearer ${{ env 'DECK_OPENAI_API_KEY' }}
    model:
      provider: openai
      name: text-embedding-3-large
      options:
        max_tokens: 512
        temperature: 1.0
plugin: ai-proxy-advanced
" | kubectl apply -f -

Next, apply the KongPlugin resource by annotating the httproute or ingress resource:

kubectl annotate -n kong httproute  konghq.com/plugins=ai-proxy-advanced

kubectl annotate -n kong ingress  konghq.com/plugins=ai-proxy-advanced

Prerequisite: Configure your Personal Access Token

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

provider "konnect" {
  personal_access_token = "$KONNECT_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_proxy_advanced" "my_ai_proxy_advanced" {
  enabled = true

  config = {
    genai_category = "text/embeddings"
    targets = [
      {
        route_type = "llm/v1/embeddings"

        auth = {
          header_name = "Authorization"
          header_value = "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
        }

        model = {
          provider = "openai"
          name = "text-embedding-3-large"

          options = {
            max_tokens = 512
            temperature = 1.0
          }
        }
      }    ]
  }

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

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    consumer: consumerName|Id
    config:
      genai_category: text/embeddings
      targets:
      - route_type: llm/v1/embeddings
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        model:
          provider: openai
          name: text-embedding-3-large
          options:
            max_tokens: 512
            temperature: 1.0

Make sure to replace the following placeholders with your own values:

consumerName|Id: The id or name of the consumer the plugin configuration will target.

Make the following request:

curl -i -X POST http://localhost:8001/consumers/{consumerName|Id}/plugins/ \
    --header "Accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

consumerName|Id: The id or name of the consumer the plugin configuration will target.

Make the following request:

curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/consumers/{consumerId}/plugins/ \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

region: Geographic region where your Kong Konnect is hosted and operates.
controlPlaneId: The id of the control plane.
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account.
consumerId: The id of the consumer the plugin configuration will target.

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

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-proxy-advanced
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  genai_category: text/embeddings
  targets:
  - route_type: llm/v1/embeddings
    auth:
      header_name: Authorization
      header_value: Bearer ${{ env 'DECK_OPENAI_API_KEY' }}
    model:
      provider: openai
      name: text-embedding-3-large
      options:
        max_tokens: 512
        temperature: 1.0
plugin: ai-proxy-advanced
" | kubectl apply -f -

Next, apply the KongPlugin resource by annotating the KongConsumer resource:

kubectl annotate -n kong  CONSUMER_NAME konghq.com/plugins=ai-proxy-advanced

Prerequisite: Configure your Personal Access Token

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

provider "konnect" {
  personal_access_token = "$KONNECT_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_proxy_advanced" "my_ai_proxy_advanced" {
  enabled = true

  config = {
    genai_category = "text/embeddings"
    targets = [
      {
        route_type = "llm/v1/embeddings"

        auth = {
          header_name = "Authorization"
          header_value = "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
        }

        model = {
          provider = "openai"
          name = "text-embedding-3-large"

          options = {
            max_tokens = 512
            temperature = 1.0
          }
        }
      }    ]
  }

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

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy-advanced
    consumer_group: consumerGroupName|Id
    config:
      genai_category: text/embeddings
      targets:
      - route_type: llm/v1/embeddings
        auth:
          header_name: Authorization
          header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
        model:
          provider: openai
          name: text-embedding-3-large
          options:
            max_tokens: 512
            temperature: 1.0

Make sure to replace the following placeholders with your own values:

consumerGroupName|Id: The id or name of the consumer group the plugin configuration will target.

Make the following request:

curl -i -X POST http://localhost:8001/consumer_groups/{consumerGroupName|Id}/plugins/ \
    --header "Accept: application/json" \
    --header "Content-Type: application/json" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

consumerGroupName|Id: The id or name of the consumer group the plugin configuration will target.

Make the following request:

curl -X POST https://{region}.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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy-advanced",
      "config": {
        "genai_category": "text/embeddings",
        "targets": [
          {
            "route_type": "llm/v1/embeddings",
            "auth": {
              "header_name": "Authorization",
              "header_value": "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
            },
            "model": {
              "provider": "openai",
              "name": "text-embedding-3-large",
              "options": {
                "max_tokens": 512,
                "temperature": 1.0
              }
            }
          }
        ]
      }
    }
    '

Make sure to replace the following placeholders with your own values:

region: Geographic region where your Kong Konnect is hosted and operates.
controlPlaneId: The id of the control plane.
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account.
consumerGroupId: The id of the consumer group the plugin configuration will target.

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

echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
  name: ai-proxy-advanced
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  genai_category: text/embeddings
  targets:
  - route_type: llm/v1/embeddings
    auth:
      header_name: Authorization
      header_value: Bearer ${{ env 'DECK_OPENAI_API_KEY' }}
    model:
      provider: openai
      name: text-embedding-3-large
      options:
        max_tokens: 512
        temperature: 1.0
plugin: ai-proxy-advanced
" | kubectl apply -f -

Next, apply the KongPlugin resource by annotating the KongConsumerGroup resource:

kubectl annotate -n kong  CONSUMERGROUP_NAME konghq.com/plugins=ai-proxy-advanced

Prerequisite: Configure your Personal Access Token

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

provider "konnect" {
  personal_access_token = "$KONNECT_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_proxy_advanced" "my_ai_proxy_advanced" {
  enabled = true

  config = {
    genai_category = "text/embeddings"
    targets = [
      {
        route_type = "llm/v1/embeddings"

        auth = {
          header_name = "Authorization"
          header_value = "Bearer ${{ env \"DECK_OPENAI_API_KEY\" }}"
        }

        model = {
          provider = "openai"
          name = "text-embedding-3-large"

          options = {
            max_tokens = 512
            temperature = 1.0
          }
        }
      }    ]
  }

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

AI Proxy Advanced

Embeddings routev3.11+

Prerequisites

Set up the plugin

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