Set up with OpenAI and Redisv3.8+
Enable AI Semantic Caching with OpenAI embeddings API and a Redis vector database. Configuring Kong to use OpenAI as upstream or configure AI Proxy or AI Proxy Adavnced plugin is required.
Environment variables
-
OPENAI_API_KEY: Your OpenAI API key -
REDIS_HOST: The host where your Redis instance runs
Add this section to your declarative configuration file:
_format_version: "3.0"
plugins:
- name: ai-semantic-cache
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: ${{ env "DECK_REDIS_HOST" }}
port: 6379
Make the following request:
curl -i -X POST http://localhost:8001/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
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 $KONNECT_TOKEN" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
controlPlaneId: Theidof 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-semantic-cache
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
labels:
global: 'true'
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer $OPENAI_API_KEY
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: '$REDIS_HOST'
port: 6379
plugin: ai-semantic-cache
" | 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_semantic_cache" "my_ai_semantic_cache" {
enabled = true
config = {
embeddings = {
auth = {
header_name = "Authorization"
header_value = "Bearer var.openai_api_key"
}
model = {
provider = "openai"
name = "text-embedding-3-large"
options = {
upstream_url = "https://api.openai.com/v1/embeddings"
}
}
}
vectordb = {
dimensions = 3072
distance_metric = "cosine"
strategy = "redis"
threshold = 0.1
redis = {
host = var.redis_host
port = 6379
}
}
}
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 "redis_host" {
type = string
}
Add this section to your declarative configuration file:
_format_version: "3.0"
plugins:
- name: ai-semantic-cache
service: serviceName|Id
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: ${{ env "DECK_REDIS_HOST" }}
port: 6379
Make sure to replace the following placeholders with your own values:
-
serviceName|Id: Theidornameof the service the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/services/{serviceName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
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 $KONNECT_TOKEN" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
controlPlaneId: Theidof the control plane. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
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-semantic-cache
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer $OPENAI_API_KEY
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: '$REDIS_HOST'
port: 6379
plugin: ai-semantic-cache
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the service resource:
kubectl annotate -n kong service SERVICE_NAME konghq.com/plugins=ai-semantic-cache
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_semantic_cache" "my_ai_semantic_cache" {
enabled = true
config = {
embeddings = {
auth = {
header_name = "Authorization"
header_value = "Bearer var.openai_api_key"
}
model = {
provider = "openai"
name = "text-embedding-3-large"
options = {
upstream_url = "https://api.openai.com/v1/embeddings"
}
}
}
vectordb = {
dimensions = 3072
distance_metric = "cosine"
strategy = "redis"
threshold = 0.1
redis = {
host = var.redis_host
port = 6379
}
}
}
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 "redis_host" {
type = string
}
Add this section to your declarative configuration file:
_format_version: "3.0"
plugins:
- name: ai-semantic-cache
route: routeName|Id
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: ${{ env "DECK_REDIS_HOST" }}
port: 6379
Make sure to replace the following placeholders with your own values:
-
routeName|Id: Theidornameof the route the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/routes/{routeName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
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 $KONNECT_TOKEN" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
controlPlaneId: Theidof the control plane. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
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-semantic-cache
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer $OPENAI_API_KEY
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: '$REDIS_HOST'
port: 6379
plugin: ai-semantic-cache
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the httproute or ingress resource:
kubectl annotate -n kong httproute konghq.com/plugins=ai-semantic-cache
kubectl annotate -n kong ingress konghq.com/plugins=ai-semantic-cache
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_semantic_cache" "my_ai_semantic_cache" {
enabled = true
config = {
embeddings = {
auth = {
header_name = "Authorization"
header_value = "Bearer var.openai_api_key"
}
model = {
provider = "openai"
name = "text-embedding-3-large"
options = {
upstream_url = "https://api.openai.com/v1/embeddings"
}
}
}
vectordb = {
dimensions = 3072
distance_metric = "cosine"
strategy = "redis"
threshold = 0.1
redis = {
host = var.redis_host
port = 6379
}
}
}
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 "redis_host" {
type = string
}
Add this section to your declarative configuration file:
_format_version: "3.0"
plugins:
- name: ai-semantic-cache
consumer: consumerName|Id
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: ${{ env "DECK_REDIS_HOST" }}
port: 6379
Make sure to replace the following placeholders with your own values:
-
consumerName|Id: Theidornameof the consumer the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumers/{consumerName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
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 $KONNECT_TOKEN" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
controlPlaneId: Theidof the control plane. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
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-semantic-cache
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer $OPENAI_API_KEY
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: '$REDIS_HOST'
port: 6379
plugin: ai-semantic-cache
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the KongConsumer resource:
kubectl annotate -n kong CONSUMER_NAME konghq.com/plugins=ai-semantic-cache
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_semantic_cache" "my_ai_semantic_cache" {
enabled = true
config = {
embeddings = {
auth = {
header_name = "Authorization"
header_value = "Bearer var.openai_api_key"
}
model = {
provider = "openai"
name = "text-embedding-3-large"
options = {
upstream_url = "https://api.openai.com/v1/embeddings"
}
}
}
vectordb = {
dimensions = 3072
distance_metric = "cosine"
strategy = "redis"
threshold = 0.1
redis = {
host = var.redis_host
port = 6379
}
}
}
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 "redis_host" {
type = string
}
Add this section to your declarative configuration file:
_format_version: "3.0"
plugins:
- name: ai-semantic-cache
consumer_group: consumerGroupName|Id
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: ${{ env "DECK_REDIS_HOST" }}
port: 6379
Make sure to replace the following placeholders with your own values:
-
consumerGroupName|Id: Theidornameof the consumer group the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumer_groups/{consumerGroupName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
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 $KONNECT_TOKEN" \
--data '
{
"name": "ai-semantic-cache",
"config": {
"embeddings": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer '$OPENAI_API_KEY'"
},
"model": {
"provider": "openai",
"name": "text-embedding-3-large",
"options": {
"upstream_url": "https://api.openai.com/v1/embeddings"
}
}
},
"vectordb": {
"dimensions": 3072,
"distance_metric": "cosine",
"strategy": "redis",
"threshold": 0.1,
"redis": {
"host": "'$REDIS_HOST'",
"port": 6379
}
}
}
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
controlPlaneId: Theidof the control plane. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
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-semantic-cache
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
config:
embeddings:
auth:
header_name: Authorization
header_value: Bearer $OPENAI_API_KEY
model:
provider: openai
name: text-embedding-3-large
options:
upstream_url: https://api.openai.com/v1/embeddings
vectordb:
dimensions: 3072
distance_metric: cosine
strategy: redis
threshold: 0.1
redis:
host: '$REDIS_HOST'
port: 6379
plugin: ai-semantic-cache
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the KongConsumerGroup resource:
kubectl annotate -n kong CONSUMERGROUP_NAME konghq.com/plugins=ai-semantic-cache
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_semantic_cache" "my_ai_semantic_cache" {
enabled = true
config = {
embeddings = {
auth = {
header_name = "Authorization"
header_value = "Bearer var.openai_api_key"
}
model = {
provider = "openai"
name = "text-embedding-3-large"
options = {
upstream_url = "https://api.openai.com/v1/embeddings"
}
}
}
vectordb = {
dimensions = 3072
distance_metric = "cosine"
strategy = "redis"
threshold = 0.1
redis = {
host = var.redis_host
port = 6379
}
}
}
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 "redis_host" {
type = string
}