Azure

You can easily deploy gecholog in your own Azure subscription. The recommended method is to use our One-click deployment to set up a pre-configured gecholog container, storage for logs, and a powerful Azure Log Analytics Dashboard. Our deployment template is publicly available on GitHub and is easy to customize for expert users.

It is also possible to deploy gecholog as a Standalone container on Azure.

"One click"-deployment

1. Click to deploy

2. Create a new resource group with a name of your choice, something like gecholog-eval

3. The AISERVICE_API_BASE url

Add your AISERVICE_API_BASE to the deployment form.

4. Proceed to Deploy

Review and create the deployment. Within a few minutes, all the resources will be deployed, and the gecholog container will be connected, up, and running.

What's included in the deployment?

The deployment consists of:

1. A pre-built but customizable gecholog Dashboard DevOps.
2. The standard gecholog/gecholog:latest container from the public Docker Hub repository.
3. An Azure Log Analytics workspace for ingesting logs.
4. An Azure Storage Account.

Send your first api calls via gecholog

The Azure "One-click" deployment utilizes managed Azure Container Instances (ACI) to host the gecholog container. ACI is configured to generate a fully qualified domain name (FQDN) for the container's endpoint, allowing immediate use of the gecholog service. Locate the FQDN of the gecholog container in the Overview section:

Follow the steps from Test You Configuration to send your first API call via gecholog. It can look like this if you use Azure OpenAI:

setx OPENAI_API_KEY "your_api_key"              
setx DEPLOYMENT "your_azure_deployment"         

export OPENAI_API_KEY=your_api_key             
export DEPLOYMENT=your_azure_deployment         

Make the request

curl -X POST ^
     -H "api-key: %OPENAI_API_KEY%" ^
     -H "Content-Type: application/json" ^
     -d "{\"messages\": [{\"role\": \"system\",\"content\": \"Assistant is a large language model trained by OpenAI.\"},{\"role\": \"user\",\"content\": \"Who are the founders of Microsoft?\"}],\"max_tokens\": 15}" ^
     http://gecholog-y0urun1qu3url.northeurope.azurecontainer.io:5380/service/standard/openai/deployments/%DEPLOYMENT%/chat/completions?api-version=2023-12-01-preview

curl -X POST -H "api-key: $OPENAI_API_KEY" -H "Content-Type: application/json" -d '{
    "messages": [
      {
        "role": "system",
        "content": "Assistant is a large language model trained by OpenAI."
      },
      {
        "role": "user",
        "content": "Who are the founders of Microsoft?"
      }
    ],
    "max_tokens": 15
  }' "http://gecholog-y0urun1qu3url.northeurope.azurecontainer.io:5380/service/standard/openai/deployments/$DEPLOYMENT/chat/completions?api-version=2023-12-01-preview"

Replace gecholog-y0urun1qu3url.northeurope.azurecontainer.io with your FQDN.

Powerful Dashboard

One of the resources deployed is the pre-built yet customizable gecholog Dashboard DevOps. This is an Azure Log Analytics Workbook featuring a set of pre-made charts:

• Count Histogram
• Consumption Statistics
• Token Consumption Histogram
• Average Duration Histogram
• Duration Distribution
• Processor Executions
• Transaction Finder

Selecting different time periods and slicing the data by various fields is straightforward using the workbook parameters at the top.

Example of visualizations in the Dashboard:

Patience with azure log analytics

Azure Log Analytics will automatically generate a custom log table named gecholog_CL. This should appear within a few minutes after your first API call is processed through gecholog. However, the creation of the index and custom fields can take some time. Until the index is fully established, the dashboard will not render. In rare cases, we have observed delays of up to 120 minutes from the time of the first API call. A notable advantage of Azure Log Analytics is its ability to add new fields as they are received. This flexibility can be quite beneficial compared to other log analytics tools with more rigid initial schemas. However, it's important to note that the time from when a new field is created to when it becomes available in the schema can sometimes be lengthy.

Gecholog storage account

The gechologstorage storage account contains two fileshares, each mounted to the gecholog container.

The conf file share is mounted to the /app/conf directory within the gecholog container. This setup provides a straightforward method to access all gecholog configuration files. For the monitored configuration files (see Automatic updates) just update the configuration files and hit save to active your new settings.

The log file share is mounted to /app/log. This is the destination where nats2file writes the logs. You have the option to download these logs, establish a backup routine, or use this fileshare for third-party log file ingestion.

Updating a configuration

Prerequisites

• An Azure Subscription.
• Registration of the Microsoft.insights Resource Provider.
• Authorization to execute deployments from a template.
• Authorization to create a new resource group.
• Access to the following services in your chosen region:
  • Storage Account & file shares
  • Log Analytics Workspace
  • Log Analytics Workbook
  • Azure Container Instances

Azure CLI Deployment

You can deploy the new-gecholog-resource-group.json package using the Azure CLI, which is especially useful if you wish to modify the template before deployment. Below is a brief guide on deploying the resources using Azure CLI. To access to both the ARM and .bicep templates, visit our GitHub resources page.

1. Create a parameters.json File

{
    "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#",
    "contentVersion": "1.0.0.0",
    "parameters": {
        "aiServiceApiBase": {
            "value": "http://your.openai.azure.com/"
        },
        "natsToken": {
            "value": "changeme"
        }
    }
}

2. Download the ARM template

1. Use the following link to download the ARM template. Right-click and select Save link as...
   Gecholog ARM template on github

2. Save the file as new-gecholog-resource-group.json on your local machine.

3. Deploy using Azure CLI

az deployment group create --name gecholog-deployment --resource-group gecholog-eval --template-file new-gecholog-resource-group.json --parameters @parameters.json

NOTE: On our Github page you can also find a new-gecholog-resource-group.bicep file for the same deployment.

Deploying gecholog into an existing resource group

If you want to redeploy only the gecholog container, for example if you want to redeploy a newer image or change any of the environment variables (environment variables cannot be changed after deployment) this can easily be achieved using Azure CLI. Use the gecholog-container-only.bicep available on GitHub and run

az deployment group create --name redeploy --resource-group <resourceGroupName> --template-file gecholog-container-only.bicep