/lightspeed-service

Core repository for an AI-powered OCP assistant service

Primary LanguagePythonApache License 2.0Apache-2.0

About The Project

Built With

this project is built using IBM watson machine learning

Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

  • python
  • BAM account
    • BAM_API_KEY
    • BAM_URL

Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

  1. Get a BAM API Key at https://ibmmid.com
  2. Clone the repo 
    git clone <project gti>
  3. Install python packages 
    pip install -r requirement.txt
  4. Create a new file .env from the default.env example and enter your BAM_API_KEY in the top line

Usage

Local Deployment

Run the server

in order to run the API service

uvicorn app.main:app --reload

Query the server

To send a request to the server you can use the following curl command:

curl -X 'POST' 'http://127.0.0.1:8080/ols' -H2 'accept: application/json' -H 'Content-Type: application/json' -d '{"query": "write a deployment yaml for the mongodb image"}'

Gradio UI

There is a minimal Gradio UI you can use when running the OLS server locally. To use it, first start the OLS server per Run the server and then browse to the built in gradio interface at http://localhost:8080/ui

By default this interface will ask the OLS server to retain and use your conversation history for subsequent interactions. To disable this behavior, expand the Additional Inputs configuration at the bottom of the page and uncheck the Use history checkbox. When not using history each message you submit to OLS will be treated independently with no context of previous interactions.

In Cluster Deployment

Deploying OLS on an openshift cluster is fairly easy with the configuration files we have in manifests folder.

You can use the existing image built from the latest code via this image pullspec: quay.io/openshift/lightspeed-service-api:latest

If you need to build your own image, you can use the following commands:

podman build -f Containerfile -t=<your-image-pullspec> .
podman push <your-image-pullspec>

Once we have our image ready, export it as an ENV and use the below kustomize command to deploy resources.

export OLS_IMAGE=<image-pullspec>
kustomize build . | envsubst | oc apply -f -

This should deploy ols fronting with a kube-rbac-proxy along with a sample client that makes requests to one of the ols endpoints demonstrating client usage of our service.

Roadmap

  • [ ]

See the open issues for a full list of proposed features (and known issues).

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Contributing

See contributors guide.

License

Published under the Apache 2.0 License