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https://github.com/carlossg/argo-rollouts-ai

Argo Rollouts Analysis with AI
https://github.com/carlossg/argo-rollouts-ai

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Argo Rollouts Analysis with AI

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# Argo Rollouts Analysis using AI



Example Argo Rollouts Analysis that uses AI services to do the canary analysis.



This job can be executed as a Argo Rollouts canary analysis job.

It will fetch the logs from pods with the label `role=stable` and `role=canary`

and use an AI model to compare both logs to decide whether the canary promotion

should continue or not.



The result is returned from the model as a json file like:



```json

{

    "text": "The canary version initially had issues pulling the required image, leading to Bad Requests. However, the issue seems to have been resolved and the canary started successfully later on.  Further monitoring is recommended to ensure stability.",

    "promote": true,

    "confidence": 80

}

```



or



```json

{

    "text": "Canary deployment encountered Bad Request errors (HTTP 400) due to image pulling failures, indicating a problem with the canary version's image or configuration.  The stable version did not exhibit these errors.",

    "promote": false,

    "confidence": 95

}

```



Tested with:



* Google gemini-1.5-pro-latest

* Google gemini-1.5-flash-latest



# Usage



Argo Rollouts allows different sources for analysis, one of them being the execution of a job.



In your `Rollout` object set the `spec.metrics.provider.job` to something like the following.

[Full example](examples/analysis-success-rate-job-ai.yaml).



```yaml

        metadata:

          annotations:

            service: "{{args.service}}"

          labels:

            service: "{{args.service}}"

        spec:

          backoffLimit: 0

          ttlSecondsAfterFinished: 1200

          template:

            metadata:

              labels:

                app: success-rate-job-ai

            spec:

              restartPolicy: Never

              containers:

              - name: ai

                image: us-central1-docker.pkg.dev/api-project-642841493686/github/argo-rollouts-ai:580503203f0460f299e1f4de569b5876c597a37b

                imagePullPolicy: IfNotPresent

                env:

                - name: GOOGLE_API_KEY

                  valueFrom:

                    secretKeyRef:

                      name: success-rate-job-ai

                      key: google_api_key

                volumeMounts:

                - name: empty

                  mountPath: /logs

                # - name: code

                #   mountPath: /app

                resources:

                  requests:

                    memory: "32Mi"

                    cpu: "250m"

                  limits:

                    memory: "64Mi"

                    cpu: "500m"

              volumes:

              - name: empty

                emptyDir: {}

              # - name: code

              #   configMap:

              #     name: canary-demo-6mm6k4ffck

```



Different models can be passed to the app, ie.



```shell

python app.py gemini-1.5-flash-latest

```



# Development Setup



```

python3 -m venv venv

source venv/bin/activate

pip install -r requirements.txt



export GOOGLE_API_KEY=...

```



## Adding dependencies



```

pip install langchain-google-genai

python3 -m pip freeze > requirements.txt

```



# Langchain



[tutorial](https://python.langchain.com/v0.2/docs/tutorials/llm_chain/)



# Running



```

python app.py https://example.com

```



# Running in Docker



```

make build

docker run -ti --rm \

    -e GOOGLE_API_KEY \

    csanchez/argo-rollouts-ai

```



# Building docker images



```shell

make build

```



# Notes



Google Cloud credits are provided for this project.



#AISprint