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edwardtheharris/agent

By edwardtheharris

Updated about 2 years ago

A simple agent for operating large language models on Kubernetes.

Image
Machine learning & AI
0

220

edwardtheharris/agent repository overview

Quality Gate Status Coverage Status

Kubernetes Operator Agent

Development

This project uses Skaffold and Helm for development. Please make sure you have them installed on your machine.

Python

The main code is written in Python, which may be formatted with either PyDantic or Black.

Prerequisites
  1. Access to a running Kubernetes cluster or the ability to create one.
  2. Python
  3. LangChain
  4. Helm
Bare Metal Deployment

The process for this follows.

  1. Build the agent image and push it to a public or local image repository.

    docker build -t dockerhubuser/agent:0.0.1 .
    
  2. Configure the redis-stack application's required storage class settings.

    # values.yaml
    # This is necessary only if you need to override the default `hostpath` storageClass.
    redis-stack-server:
      redis_stack_server:
        storage_class: csi-lvm-linear
    
  3. Configure the image repository and tag settings.

    # values.yaml
    image:
      repository: dockerhubuser/agent
      tag: 0.0.1
    
  4. Obtain an OpenAPI API key and configure its value for the deployment.

    # secrets/values.yaml
    envVars:
      OPENAI_API_KEY: base64-encoded-api-key
    
  5. Create a namespace for the agent.

    kubectl create ns agent
    
  6. Deploy the agent with Helm using the values shown above.

    helm upgrade --install -n agent agent deployment/helm/k8s-agent -f secrets/values.yaml -f values.yaml
    

Tag summary

Content type

Image

Digest

sha256:be10aeb80

Size

505.6 MB

Last updated

about 2 years ago

docker pull edwardtheharris/agent:0.0.1