AI Proxy: OpenAI SDK: Proxy multiple models deployed in the same Azure instance - Plugin

OpenAI SDK: Proxy multiple models deployed in the same Azure instancev3.6+

Configure one route to proxy multiple models deployed in the same Azure instance.

For this plugin to work properly, you need a Gateway Route with the following configuration:
routes:
 - name: azure-chat
   paths:
     - "~/openai/deployments/(?<azure_instance>[^#?/]+)/chat/completions"
   methods:
     - POST

When you apply this configuration, you can set the SDK endpoint to http://localhost:8000/azure. When the Azure instance parameter is set to my-gpt-3-5, the Python SDK produces the URL http://localhost:8000/openai/deployments/my-gpt-3-5/chat/completions and is directed to the respective Azure deployment ID and model.

Prerequisites

Cohere account
Mistral account

Environment variables

AZURE_API_KEY: The API key used to authenticate requests to Cohere.

Set up the plugin

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy
    config:
      route_type: llm/v1/chat
      auth:
        header_name: api-key
        header_value: ${{ env "DECK_AZURE_API_KEY" }}
      logging:
        log_statistics: true
        log_payloads: false
      model:
        provider: azure
        name: "$(uri_captures.azure_instance)"
        options:
          azure_instance: my-openai-instace
          azure_deployment_id: "$(uri_captures.azure_instance)"

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",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: 'true'
config:
  route_type: llm/v1/chat
  auth:
    header_name: api-key
    header_value: '$AZURE_API_KEY'
  logging:
    log_statistics: true
    log_payloads: false
  model:
    provider: azure
    name: '$(uri_captures.azure_instance)'
    options:
      azure_instance: my-openai-instace
      azure_deployment_id: '$(uri_captures.azure_instance)'
plugin: ai-proxy
" | 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" "my_ai_proxy" {
  enabled = true

  config = {
    route_type = "llm/v1/chat"

    auth = {
      header_name = "api-key"
      header_value = var.azure_api_key
    }

    logging = {
      log_statistics = true
      log_payloads = false
    }

    model = {
      provider = "azure"
      name = "$(uri_captures.azure_instance)"

      options = {
        azure_instance = "my-openai-instace"
        azure_deployment_id = "$(uri_captures.azure_instance)"
      }
    }
  }

  control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
}

This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "azure_api_key" {
  type = string
}

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy
    service: serviceName|Id
    config:
      route_type: llm/v1/chat
      auth:
        header_name: api-key
        header_value: ${{ env "DECK_AZURE_API_KEY" }}
      logging:
        log_statistics: true
        log_payloads: false
      model:
        provider: azure
        name: "$(uri_captures.azure_instance)"
        options:
          azure_instance: my-openai-instace
          azure_deployment_id: "$(uri_captures.azure_instance)"

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",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  route_type: llm/v1/chat
  auth:
    header_name: api-key
    header_value: '$AZURE_API_KEY'
  logging:
    log_statistics: true
    log_payloads: false
  model:
    provider: azure
    name: '$(uri_captures.azure_instance)'
    options:
      azure_instance: my-openai-instace
      azure_deployment_id: '$(uri_captures.azure_instance)'
plugin: ai-proxy
" | 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

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" "my_ai_proxy" {
  enabled = true

  config = {
    route_type = "llm/v1/chat"

    auth = {
      header_name = "api-key"
      header_value = var.azure_api_key
    }

    logging = {
      log_statistics = true
      log_payloads = false
    }

    model = {
      provider = "azure"
      name = "$(uri_captures.azure_instance)"

      options = {
        azure_instance = "my-openai-instace"
        azure_deployment_id = "$(uri_captures.azure_instance)"
      }
    }
  }

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

This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "azure_api_key" {
  type = string
}

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy
    route: routeName|Id
    config:
      route_type: llm/v1/chat
      auth:
        header_name: api-key
        header_value: ${{ env "DECK_AZURE_API_KEY" }}
      logging:
        log_statistics: true
        log_payloads: false
      model:
        provider: azure
        name: "$(uri_captures.azure_instance)"
        options:
          azure_instance: my-openai-instace
          azure_deployment_id: "$(uri_captures.azure_instance)"

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",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  route_type: llm/v1/chat
  auth:
    header_name: api-key
    header_value: '$AZURE_API_KEY'
  logging:
    log_statistics: true
    log_payloads: false
  model:
    provider: azure
    name: '$(uri_captures.azure_instance)'
    options:
      azure_instance: my-openai-instace
      azure_deployment_id: '$(uri_captures.azure_instance)'
plugin: ai-proxy
" | 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

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

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" "my_ai_proxy" {
  enabled = true

  config = {
    route_type = "llm/v1/chat"

    auth = {
      header_name = "api-key"
      header_value = var.azure_api_key
    }

    logging = {
      log_statistics = true
      log_payloads = false
    }

    model = {
      provider = "azure"
      name = "$(uri_captures.azure_instance)"

      options = {
        azure_instance = "my-openai-instace"
        azure_deployment_id = "$(uri_captures.azure_instance)"
      }
    }
  }

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

This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "azure_api_key" {
  type = string
}

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy
    consumer: consumerName|Id
    config:
      route_type: llm/v1/chat
      auth:
        header_name: api-key
        header_value: ${{ env "DECK_AZURE_API_KEY" }}
      logging:
        log_statistics: true
        log_payloads: false
      model:
        provider: azure
        name: "$(uri_captures.azure_instance)"
        options:
          azure_instance: my-openai-instace
          azure_deployment_id: "$(uri_captures.azure_instance)"

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",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  route_type: llm/v1/chat
  auth:
    header_name: api-key
    header_value: '$AZURE_API_KEY'
  logging:
    log_statistics: true
    log_payloads: false
  model:
    provider: azure
    name: '$(uri_captures.azure_instance)'
    options:
      azure_instance: my-openai-instace
      azure_deployment_id: '$(uri_captures.azure_instance)'
plugin: ai-proxy
" | kubectl apply -f -

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

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

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" "my_ai_proxy" {
  enabled = true

  config = {
    route_type = "llm/v1/chat"

    auth = {
      header_name = "api-key"
      header_value = var.azure_api_key
    }

    logging = {
      log_statistics = true
      log_payloads = false
    }

    model = {
      provider = "azure"
      name = "$(uri_captures.azure_instance)"

      options = {
        azure_instance = "my-openai-instace"
        azure_deployment_id = "$(uri_captures.azure_instance)"
      }
    }
  }

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

This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "azure_api_key" {
  type = string
}

Add this section to your declarative configuration file:

_format_version: "3.0"
plugins:
  - name: ai-proxy
    consumer_group: consumerGroupName|Id
    config:
      route_type: llm/v1/chat
      auth:
        header_name: api-key
        header_value: ${{ env "DECK_AZURE_API_KEY" }}
      logging:
        log_statistics: true
        log_payloads: false
      model:
        provider: azure
        name: "$(uri_captures.azure_instance)"
        options:
          azure_instance: my-openai-instace
          azure_deployment_id: "$(uri_captures.azure_instance)"

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",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-proxy",
      "config": {
        "route_type": "llm/v1/chat",
        "auth": {
          "header_name": "api-key",
          "header_value": "'$AZURE_API_KEY'"
        },
        "logging": {
          "log_statistics": true,
          "log_payloads": false
        },
        "model": {
          "provider": "azure",
          "name": "$(uri_captures.azure_instance)",
          "options": {
            "azure_instance": "my-openai-instace",
            "azure_deployment_id": "$(uri_captures.azure_instance)"
          }
        }
      }
    }
    '

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
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  route_type: llm/v1/chat
  auth:
    header_name: api-key
    header_value: '$AZURE_API_KEY'
  logging:
    log_statistics: true
    log_payloads: false
  model:
    provider: azure
    name: '$(uri_captures.azure_instance)'
    options:
      azure_instance: my-openai-instace
      azure_deployment_id: '$(uri_captures.azure_instance)'
plugin: ai-proxy
" | kubectl apply -f -

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

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

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" "my_ai_proxy" {
  enabled = true

  config = {
    route_type = "llm/v1/chat"

    auth = {
      header_name = "api-key"
      header_value = var.azure_api_key
    }

    logging = {
      log_statistics = true
      log_payloads = false
    }

    model = {
      provider = "azure"
      name = "$(uri_captures.azure_instance)"

      options = {
        azure_instance = "my-openai-instace"
        azure_deployment_id = "$(uri_captures.azure_instance)"
      }
    }
  }

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

This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "azure_api_key" {
  type = string
}

AI Proxy

OpenAI SDK: Proxy multiple models deployed in the same Azure instancev3.6+

Prerequisites

Environment variables

Set up the plugin

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