AI Semantic Cache: Set up with OpenAI and PGVector - Plugin

Set up with OpenAI and PGVectorv3.8+

Enable AI Semantic Caching with OpenAI embeddings API and a PGVector database.

If you use the text-embedding-ada-002 as an embedding model, you must set a fixed dimension of 1536, as required by the official model specification. Alternatively, use the text-embedding-3-small model, which supports dynamic dimensions and works without specifying a fixed value.

Prerequisites

The AI Proxy or AI Proxy Advanced plugin is enabled
An OpenAI account
A PostgreSQL server with the PGVector extension installed

Environment variables

OPENAI_API_KEY: Your OpenAI API key

Set up the plugin

Add this section to your kong.yaml configuration file:

_format_version: "3.0"
plugins:
  - name: ai-semantic-cache
    config:
      embeddings:
        auth:
          header_name: Authorization
          header_value: ${{ 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: pgvector
        threshold: 0.1
        pgvector:
          host: pgvector.pgvector.svc.cluster.local
          port: 5432
          database: kong
          user: kong

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": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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-semantic-cache
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: 'true'
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: '$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: pgvector
    threshold: 0.1
    pgvector:
      host: pgvector.pgvector.svc.cluster.local
      port: 5432
      database: kong
      user: kong
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 = 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 = "pgvector"
      threshold = 0.1

      pgvector = {
        host = "pgvector.pgvector.svc.cluster.local"
        port = 5432
        database = "kong"
        user = "kong"
      }
    }
  }

  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 "openai_api_key" {
  type = string
}

Add this section to your kong.yaml configuration file:

_format_version: "3.0"
plugins:
  - name: ai-semantic-cache
    service: serviceName|Id
    config:
      embeddings:
        auth:
          header_name: Authorization
          header_value: ${{ 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: pgvector
        threshold: 0.1
        pgvector:
          host: pgvector.pgvector.svc.cluster.local
          port: 5432
          database: kong
          user: kong

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-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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-semantic-cache
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: '$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: pgvector
    threshold: 0.1
    pgvector:
      host: pgvector.pgvector.svc.cluster.local
      port: 5432
      database: kong
      user: kong
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 = 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 = "pgvector"
      threshold = 0.1

      pgvector = {
        host = "pgvector.pgvector.svc.cluster.local"
        port = 5432
        database = "kong"
        user = "kong"
      }
    }
  }

  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 "openai_api_key" {
  type = string
}

Add this section to your kong.yaml configuration file:

_format_version: "3.0"
plugins:
  - name: ai-semantic-cache
    route: routeName|Id
    config:
      embeddings:
        auth:
          header_name: Authorization
          header_value: ${{ 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: pgvector
        threshold: 0.1
        pgvector:
          host: pgvector.pgvector.svc.cluster.local
          port: 5432
          database: kong
          user: kong

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-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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-semantic-cache
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: '$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: pgvector
    threshold: 0.1
    pgvector:
      host: pgvector.pgvector.svc.cluster.local
      port: 5432
      database: kong
      user: kong
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 = 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 = "pgvector"
      threshold = 0.1

      pgvector = {
        host = "pgvector.pgvector.svc.cluster.local"
        port = 5432
        database = "kong"
        user = "kong"
      }
    }
  }

  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 "openai_api_key" {
  type = string
}

Add this section to your kong.yaml configuration file:

_format_version: "3.0"
plugins:
  - name: ai-semantic-cache
    consumer: consumerName|Id
    config:
      embeddings:
        auth:
          header_name: Authorization
          header_value: ${{ 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: pgvector
        threshold: 0.1
        pgvector:
          host: pgvector.pgvector.svc.cluster.local
          port: 5432
          database: kong
          user: kong

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-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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-semantic-cache
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: '$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: pgvector
    threshold: 0.1
    pgvector:
      host: pgvector.pgvector.svc.cluster.local
      port: 5432
      database: kong
      user: kong
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 = 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 = "pgvector"
      threshold = 0.1

      pgvector = {
        host = "pgvector.pgvector.svc.cluster.local"
        port = 5432
        database = "kong"
        user = "kong"
      }
    }
  }

  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 "openai_api_key" {
  type = string
}

Add this section to your kong.yaml configuration file:

_format_version: "3.0"
plugins:
  - name: ai-semantic-cache
    consumer_group: consumerGroupName|Id
    config:
      embeddings:
        auth:
          header_name: Authorization
          header_value: ${{ 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: pgvector
        threshold: 0.1
        pgvector:
          host: pgvector.pgvector.svc.cluster.local
          port: 5432
          database: kong
          user: kong

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-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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 $KONNECT_TOKEN" \
    --data '
    {
      "name": "ai-semantic-cache",
      "config": {
        "embeddings": {
          "auth": {
            "header_name": "Authorization",
            "header_value": "'$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": "pgvector",
          "threshold": 0.1,
          "pgvector": {
            "host": "pgvector.pgvector.svc.cluster.local",
            "port": 5432,
            "database": "kong",
            "user": "kong"
          }
        }
      }
    }
    '

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-semantic-cache
  namespace: kong
  annotations:
    kubernetes.io/ingress.class: kong
config:
  embeddings:
    auth:
      header_name: Authorization
      header_value: '$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: pgvector
    threshold: 0.1
    pgvector:
      host: pgvector.pgvector.svc.cluster.local
      port: 5432
      database: kong
      user: kong
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 = 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 = "pgvector"
      threshold = 0.1

      pgvector = {
        host = "pgvector.pgvector.svc.cluster.local"
        port = 5432
        database = "kong"
        user = "kong"
      }
    }
  }

  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 "openai_api_key" {
  type = string
}

AI Semantic Cache

Set up with OpenAI and PGVectorv3.8+

Prerequisites

Environment variables

Set up the plugin

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