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About The Project

OpenShift LightSpeed (OLS) is an AI powered assistant that runs on OpenShift and provides answers to product questions using backend LLM services.

Prerequisites

  • Python 3.11
  • Git, pip and PDM
  • An LLM api key, currently BAM (IBM's research environment), OpenAI, Azure OpenAI, and Watsonx are supported as backends.

Installation

  1. Clone the repo

    git clone https://github.com/openshift/lightspeed-service.git
    cd lightspeed-service
  2. Install python packages

    make install-deps
  3. Get API keys

    a. BAM provided LLM

    1. Get a BAM API Key at https://bam.res.ibm.com
      • Login with your IBM W3 Id credentials.
      • Copy the API Key from the Documentation section. BAM API Key
    2. BAM API URL: https://bam-api.res.ibm.com

    b. OpenAI provided LLM (OpenAI api key)

  4. Store local copies of API keys securely

    Here is a proposed scheme for storing API keys on your development workstation. It is similar to how private keys are stored for OpenSSH. It keeps copies of files containing API keys from getting scattered around and forgotten:

    $ cd <lightspeed-service local git repo root>
    $ find ~/.openai -ls
    72906922      0 drwx------   1 username username        6 Feb  6 16:45 /home/username/.openai
    72906953      4 -rw-------   1 username username       52 Feb  6 16:45 /home/username/.openai/key
    $ ls -l openai_api_key.txt
    lrwxrwxrwx. 1 username username 26 Feb  6 17:41 openai_api_key.txt -> /home/username/.openai/key
    $ grep openai_api_key.txt olsconfig.yaml
     credentials_path: openai_api_key.txt
    
  5. Configure OpenShift LightSpeed (OLS)

    OLS configuration is in YAML format. It is loaded from a file referred to by the OLS_CONFIG_FILE environment variable and defaults to olsconfig.yaml in the current directory. You can find a example configuration in the examples/olsconfig.yaml file in this repository.

    API credentials are in turn loaded from files specified in the config YAML by the credentials_path attributes. If these paths are relative, they are relative to the current working directory. To use the example olsconfig.yaml as is, place your BAM API Key into a file named bam_api_key.txt in your working directory.

    The example configuration file defines providers for four LLM providers: BAM, OpenAI, Azure OpenAI, and Watsonx, but defines BAM as the default provider. If you prefer to use different LLM provider than BAM, such as OpenAI, ensure that the provider definition points to file containing a valid OpenAI, Watsonx etc. API key, and change the default_model and default_provider values to reference the selected provider and model.

  6. Configure OLS Authentication

    This section provides guidance on how to configure authentication within OLS. It includes instructions on enabling or disabling authentication, configuring authentication through OCP RBAC, overriding authentication configurations, and specifying a static authentication token in development environments.

    1. Enabling and Disabling Authentication

      Authentication is enabled by default in OLS. To disable authentication, modify the dev_config in your configuration file as shown below:

         dev_config:
            disable_auth: true
    2. Configuring Authentication with OCP RBAC

      OLS utilizes OCP RBAC for authentication, necessitating connectivity to an OCP cluster. It automatically selects the configuration from the first available source, either an in-cluster configuration or a KubeConfig file.

    3. Overriding Authentication Configuration

      You can customize the authentication configuration by overriding the default settings. The configurable options include:

      • Kubernetes Cluster API URL (k8s_cluster_api): The URL of the K8S/OCP API server where tokens are validated.
      • CA Certificate Path (k8s_ca_cert_path): Path to a CA certificate for clusters with self-signed certificates.
      • Skip TLS Verification (skip_tls_verification): If true, the Kubernetes client skips TLS certificate validation for the OCP cluster.

      To apply any of these overrides, update your configuration file as follows:

         ols_config:
            authentication_config:
               k8s_cluster_api: "https://api.example.com:6443"
               k8s_ca_cert_path: "/Users/home/ca.crt"
               skip_tls_verification: false
    4. Providing a Static Authentication Token in Development Environments

      For development environments, you may wish to use a static token for authentication purposes. This can be configured in the dev_config section of your configuration file:

         dev_config:
            k8s_auth_token: your-user-token

      Note: using static token will require you to set the k8s_cluster_api mentioned in section 6.4, as this will disable the loading of OCP config from in-cluster/kubeconfig.

  7. Configure OLS TLS communication

    This section provides instructions on configuring TLS (Transport Layer Security) for the OLS Application, enabling secure connections via HTTPS. TLS is enabled by default; however, if necessary, it can be disabled through the dev_config settings.

    1. Enabling and Disabling TLS

      By default, TLS is enabled in OLS. To disable TLS, adjust the dev_config in your configuration file as shown below:

         dev_config:
            disable_tls: false
    2. Configuring TLS in local Environments:

      1. Generate Self-Signed Certificates: To generate self-signed certificates, run the following command from the project's root directory:
           ./scripts/generate-certs.sh
      2. Update OLS Configuration: Modify your config.yaml to include paths to your certificate and its private key:
           ols_config:
              tls_config:
                 tls_certificate_path: /full/path/to/certs/cert.pem
                 tls_key_path: /full/path/to/certs/key.pem
      3. Launch OLS with HTTPS: After applying the above configurations, OLS will run over HTTPS.
    3. Configuring OLS in OpenShift:

      For deploying in OpenShift, Service-Served Certificates can be utilized. Update your ols-config.yaml as shown below, based on the example provided in the examples directory:

         ols_config:
            tls_config:
               tls_certificate_path: /app-root/certs/cert.pem
               tls_key_path: /app-root/certs/key.pem
    4. Using a Private Key with a Password If your private key is encrypted with a password, specify a path to a file that contains the key password as follows:

         ols_config:
            tls_config:
               tls_key_password_path: /app-root/certs/password.txt
  8. (Optional) Configure the local document store

    make get-rag
  9. (Optional) Configure conversation cache Conversation cache can be stored in memory (it's content will be lost after shutdown) or in PostgreSQL database. It is possible to specify storage type in olsconfig.yaml configuration file.

  10. Cache stored in memory:

    ols_config:
      conversation_cache:
        type: memory
        memory:
          max_entries: 1000
  11. Cache stored in PostgreSQL:

    conversation_cache:
      type: postgres
      postgres:
        host: "foobar.com"
        port: "1234"
        dbname: "test"
        user: "user"
        password_path: postgres_password.txt
        ca_cert_path: postgres_cert.crt
        ssl_mode: "require"

    In this case, file postgres_password.txt contains password required to connect to PostgreSQL. Also CA certificate can be specified using postgres_ca_cert.crt to verify trusted TLS connection with the server. All these files needs to be accessible.

Usage

Local Deployment

Run the server

in order to run the API service

make run

Optionally run with podman

There is an all-in-one image that has the document store included already.

  1. Follow steps above to create your config yaml and your API key file(s).

  2. Place your config yaml and your API key file(s) in a known location (eg: /path/to/config)

  3. Make sure your config yaml references the config folder for the path to your key file(s) (eg: credentials_path: config/openai_api_key.txt)

  4. Run the all-in-one-container. Example invocation:

     podman run -it --rm -v `/path/to/config:/app-root/config:Z \
     -e OLS_CONFIG_FILE=/app-root/config/olsconfig.yaml -p 8080:8080 \
     quay.io/openshift-lightspeed/lightspeed-service-api:latest

Optionally run inside an OpenShift environment

In the examples folder is a set of YAML manifests, openshift-lightspeed.yaml. This includes all the resources necessary to get OpenShift Lightspeed running in a cluster. It is configured expecting to only use OpenAI as the inference endpoint, but you can easily modify these manifests, looking at the olsconfig.yaml to see how to alter it to work with BAM as the provider.

There is a commented-out OpenShift Route with TLS Edge termination available if you wish to use it.

To deploy, assuming you already have an OpenShift environment to target and that you are logged in with sufficient permissions:

  1. Make the change to your API keys and/or provider configuration in the manifest file
  2. Create a namespace/project to hold OLS
  3. oc apply -f examples/openshift-lightspeed-tls.yaml -n created-namespace

Once deployed, it is probably easiest to oc port-forward into the pod where OLS is running so that you can access it from your local machine.

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/v1/query' -H 'accept: application/json' -H 'Content-Type: application/json' -d '{"query": "write a deployment yaml for the mongodb image"}'

Swagger UI

Web page with Swagger UI has the standard /docs endpoint. If the service is running on localhost on port 8080, Swagger UI can be accessed on address http://localhost:8080/docs.

OpenAPI

OpenAPI schema is available docs/openapi.json. It is possible to re-generate the document with schema by using:

make schema

When the OLS service is started OpenAPI schema is available on /openapi.json endpoint. For example, for service running on localhost on port 8080, it can be accessed and pretty printed by using following command:

curl 'http://127.0.0.1:8080/openapi.json' | jq .

Metrics

Service exposes metrics in Prometheus format on /metrics endpoint. Scraping them is straightforward:

curl 'http://127.0.0.1:8080/metrics'

Gradio UI

There is a minimal Gradio UI you can use when running the OLS server locally. To use it, it is needed to enable UI in olsconfig.yaml file:

dev_config:
  enable_dev_ui: true

Then 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.

Swagger UI

OLS API documentation is available at http://localhost:8080/docs

Deploying OLS on OpenShift

A Helm chart is available for installing the service in OpenShift.

Before installing the chart, you must configure the auth.key parameter in the Values file

To install the chart with the release name ols-release in the namespace openshift-lightspeed:

helm upgrade --install ols-release helm/ --create-namespace --namespace openshift-lightspeed

The command deploys the service in the default configuration.

The default configuration contains OLS fronting with a kube-rbac-proxy.

To uninstall/delete the chart with the release name ols-release:

helm delete ols-release --namespace openshift-lightspeed

Chart customization is available using the Values file.

Project structure

  1. REST API handlers
  2. Configuration loader
  3. LLM loader
  4. Doc retriever from vector storage
  5. Question validator
  6. Docs summarizer
  7. Conversation cache
  8. (Local) Web-based user interface

Sequence diagram

Sequence of operations performed when user asks a question:

Sequence diagram

Contributing

License

Published under the Apache 2.0 License

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Core repository for an AI-powered OCP assistant service

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