OpenAI SDK: Proxy multiple models deployed in the same Azure instance
Configure one route to proxy multiple models deployed in the same Azure instance.
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.
For this configuration to work properly, you need a Route with the following configuration:
routes: - name: azure-chat paths: - "~/openai/deployments/(?<azure_instance>[^#?/]+)/chat/completions" methods: - POSTCopied!
Prerequisites
-
Cohere account
-
Mistral account
Environment variables
-
AZURE_API_KEY: The API key used to authenticate requests to Azure.
Add this section to your kong.yaml 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)"
}
}
},
"tags": []
}
'
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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane.
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
konghq.com/tags: ''
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)"
}
}
}
tags = []
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 kong.yaml 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: "$(uri_captures.azure_instance)"
Make sure to replace the following placeholders with your own values:
-
serviceName|Id: Theidornameof the service the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/services/{serviceName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy",
"config": {
"route_type": "llm/v1/chat",
"auth": {
"header_name": "api-key",
"header_value": "'$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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
serviceName|Id: Theidornameof 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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
serviceId: Theidof 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
konghq.com/tags: ''
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)"
}
}
}
tags = []
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 kong.yaml 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: "$(uri_captures.azure_instance)"
Make sure to replace the following placeholders with your own values:
-
routeName|Id: Theidornameof the route the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/routes/{routeName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy",
"config": {
"route_type": "llm/v1/chat",
"auth": {
"header_name": "api-key",
"header_value": "'$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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
routeName|Id: Theidornameof 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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
routeId: Theidof 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
konghq.com/tags: ''
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)"
}
}
}
tags = []
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 kong.yaml 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: "$(uri_captures.azure_instance)"
Make sure to replace the following placeholders with your own values:
-
consumerName|Id: Theidornameof the consumer the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumers/{consumerName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy",
"config": {
"route_type": "llm/v1/chat",
"auth": {
"header_name": "api-key",
"header_value": "'$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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
consumerName|Id: Theidornameof 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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
consumerId: Theidof 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
konghq.com/tags: ''
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)"
}
}
}
tags = []
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 kong.yaml 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: "$(uri_captures.azure_instance)"
Make sure to replace the following placeholders with your own values:
-
consumerGroupName|Id: Theidornameof the consumer group the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumer_groups/{consumerGroupName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy",
"config": {
"route_type": "llm/v1/chat",
"auth": {
"header_name": "api-key",
"header_value": "'$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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
consumerGroupName|Id: Theidornameof 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)"
}
}
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
consumerGroupId: Theidof 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
konghq.com/tags: ''
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)"
}
}
}
tags = []
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
}