You can use the OpenTelemetry plugin with Jaeger to send Gen AI analytics and monitoring data to Jaeger dashboards. Set KONG_TRACING_INSTRUMENTATIONS=all and KONG_TRACING_SAMPLING_RATE=1.0. Enable the OTEL plugin with your Jaeger tracing endpoint, and specify the name you want to track the traces by in resource_attributes.service.name.
Set the following Jaeger tracing variables before you configure the Data Plane:
export KONG_TRACING_INSTRUMENTATIONS=all
export KONG_TRACING_SAMPLING_RATE=1.0
This is a Konnect tutorial and requires a Konnect personal access token.
Create a new personal access token by opening the Konnect PAT page and selecting Generate Token.
Export your token to an environment variable:
export KONNECT_TOKEN='YOUR_KONNECT_PAT'
Run the quickstart script to automatically provision a Control Plane and Data Plane, and configure your environment:
curl -Ls https://get.konghq.com/quickstart | bash -s -- -k $KONNECT_TOKEN -e KONG_TRACING_INSTRUMENTATIONS -e KONG_TRACING_SAMPLING_RATE --deck-output
This sets up a Konnect Control Plane named quickstart, provisions a local Data Plane, and prints out the following environment variable exports:
export DECK_KONNECT_TOKEN=$KONNECT_TOKEN
export DECK_KONNECT_CONTROL_PLANE_NAME=quickstart
export KONNECT_CONTROL_PLANE_URL=https://us.api.konghq.com
export KONNECT_PROXY_URL='http://localhost:8000'
Copy and paste these into your terminal to configure your session.
This tutorial requires Kong Gateway Enterprise. If you don’t have Kong Gateway set up yet, you can use the quickstart script with an enterprise license to get an instance of Kong Gateway running almost instantly.
Export your license to an environment variable:
export KONG_LICENSE_DATA='LICENSE-CONTENTS-GO-HERE'
Run the quickstart script:
curl -Ls https://get.konghq.com/quickstart | bash -s -- -e KONG_LICENSE_DATA \
-e KONG_TRACING_INSTRUMENTATIONS \
-e KONG_TRACING_SAMPLING_RATE
Once Kong Gateway is ready, you will see the following message:
Kong Gateway Ready
decK is a CLI tool for managing Kong Gateway declaratively with state files. To complete this tutorial, install decK version 1.43 or later.
This guide uses deck gateway apply, which directly applies entity configuration to your Gateway instance.
We recommend upgrading your decK installation to take advantage of this tool.
You can check your current decK version with deck version.
For this tutorial, you’ll need Kong Gateway entities, like Gateway Services and Routes, pre-configured. These entities are essential for Kong Gateway to function but installing them isn’t the focus of this guide. Follow these steps to pre-configure them:
Run the following command:
echo '
_format_version: "3.0"
services:
- name: example-service
url: http://httpbin.konghq.com/anything
routes:
- name: example-route
paths:
- "/anything"
service:
name: example-service
' | deck gateway apply -
To learn more about entities, you can read our entities documentation.
This tutorial uses OpenAI:
Create a decK variable with the API key:
export DECK_OPENAI_API_KEY='YOUR OPENAI API KEY'
This tutorial requires you to install Jaeger.
In a new terminal window, deploy a Jaeger instance with Docker in all-in-one mode:
docker run --rm --name jaeger \
-e COLLECTOR_OTLP_ENABLED=true \
-p 16686:16686 \
-p 4317:4317 \
-p 4318:4318 \
-p 5778:5778 \
-p 9411:9411 \
jaegertracing/jaeger:2.5.0
The COLLECTOR_OTLP_ENABLED environment variable must be set to true to enable the OpenTelemetry Collector.
In this tutorial, we’re using host.docker.internal as our host instead of the localhost that Jaeger is using because Kong Gateway is running in a container that has a different localhost to you. Export the host as an environment variable in the terminal window you used to set the other Kong Gateway environment variables:
export DECK_JAEGER_HOST=host.docker.internal
The AI Proxy plugin routes LLM requests to external providers like OpenAI. To observe these interactions in detail, enable the plugin’s logging capabilities, which instrument requests and responses as OpenTelemetry spans.
Configure AI Proxy to route traffic to OpenAI and enable trace logging:
echo '
_format_version: "3.0"
plugins:
- name: ai-proxy
config:
route_type: llm/v1/chat
auth:
header_name: Authorization
header_value: Bearer ${{ env "DECK_OPENAI_API_KEY" }}
model:
provider: openai
name: gpt-4o
options:
max_tokens: 512
temperature: 1.0
logging:
log_statistics: true
log_payloads: true
' | deck gateway apply -
The logging configuration controls what the AI Proxy plugin records:
log_statistics: Captures token usage, latency, and model metadatalog_payloads: Records the complete request prompts and LLM responsesThese logs become OpenTelemetry span attributes when the OpenTelemetry plugin is enabled.
The OpenTelemetry plugin instruments Kong Gateway to export distributed traces. This allows you to observe request flows, measure latency, and inspect AI proxy operations including the prompts sent to LLMs and the responses received.
Configure the plugin to send traces to your Jaeger collector:
echo '
_format_version: "3.0"
plugins:
- name: opentelemetry
config:
traces_endpoint: http://${{ env "DECK_JAEGER_HOST" }}:4318/v1/traces
resource_attributes:
service.name: kong-dev
' | deck gateway apply -
The traces_endpoint points to Jaeger’s OTLP HTTP receiver on port 4318. The service.name attribute identifies this Kong Gateway instance in the Jaeger UI, allowing you to filter traces by service.
For more information about the ports Jaeger uses, see API Ports in the Jaeger documentation.
curl -X POST "$KONNECT_PROXY_URL/anything" \
--no-progress-meter --fail-with-body \
-H "Accept: application/json"\
-H "Content-Type: application/json" \
--json '{
"messages": [
{
"role": "system",
"content": "You are a historian"
},
{
"role": "user",
"content": "Who was the last emperor of the Byzantine empire?"
}
]
}'
curl -X POST "http://localhost:8000/anything" \
--no-progress-meter --fail-with-body \
-H "Accept: application/json"\
-H "Content-Type: application/json" \
--json '{
"messages": [
{
"role": "system",
"content": "You are a historian"
},
{
"role": "user",
"content": "Who was the last emperor of the Byzantine empire?"
}
]
}'
gen_ai traces in JaegerVerify that the trace includes the expected span attributes for LLM operations.
http://localhost:16686/.kong-dev.kong-dev service.kong.access.plugin.ai-proxy.kong.gen_ai.gen_ai.operation.name: Set to chat
gen_ai.provider.name: Set to openai
gen_ai.request.model: The model identifier (for example, gpt-4o)gen_ai.request.max_tokens: Maximum token limit (for example, 512)gen_ai.request.temperature: Sampling temperature (for example, 1)gen_ai.input.messages: Array of messages sent to the LLM with role and content fieldsgen_ai.output.type: Set to json
gen_ai.output.messages: Complete API response including choices, usage statistics, and metadatagen_ai.response.idgen_ai.response.model: Actual model version used (for example, gpt-4o-2024-08-06)gen_ai.response.finish_reasons: Array of finish reasons (for example, ["stop"])gen_ai.usage.input_tokensgen_ai.usage.output_tokensIf you created a new control plane and want to conserve your free trial credits or avoid unnecessary charges, delete the new control plane used in this tutorial.
curl -Ls https://get.konghq.com/quickstart | bash -s -- -d
What if I’m using an incompatible OpenTelemetry APM vendor? How do I configure the OTEL plugin then?
Create a config file (otelcol.yaml) for the OpenTelemetry Collector:
receivers:
otlp:
protocols:
grpc:
http:
processors:
batch:
exporters:
logging:
loglevel: debug
zipkin:
endpoint: "http://some.url:9411/api/v2/spans"
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [logging, zipkin]
logs:
receivers: [otlp]
processors: [batch]
exporters: [logging]
Run the OpenTelemetry Collector with Docker:
docker run --name opentelemetry-collector \
-p 4317:4317 \
-p 4318:4318 \
-p 55679:55679 \
-v $(pwd)/otelcol.yaml:/etc/otel-collector-config.yaml \
otel/opentelemetry-collector-contrib:0.52.0 \
--config=/etc/otel-collector-config.yaml
See the OpenTelemetry Collector documentation for more information. Now you can enable the OTEL plugin.