Interacting with Red Hat Developer Lightspeed for Red Hat Developer Hub
Leverage Artificial Intelligence (AI)-driven expertise of the Red Hat Developer Lightspeed for Red Hat Developer Hub (Developer Lightspeed for RHDH) virtual assistant to help you use Red Hat Developer Hub (RHDH)
Abstract
- 1. Interacting with Red Hat Developer Lightspeed for Red Hat Developer Hub
- 1.1. About Developer Lightspeed for RHDH
- 1.2. Supported architecture for Red Hat Developer Lightspeed for Red Hat Developer Hub
- 1.3. Retrieval augmented generation (RAG) embeddings
- 1.4. Installing and configuring Red Hat Developer Lightspeed for Red Hat Developer Hub
- 1.5. Customizing Developer Lightspeed for RHDH
- 1.6. Using Developer Lightspeed for RHDH
- 1.7. Appendix: LLM requirements
- 1.8. Appendix About user data security
1. Interacting with Red Hat Developer Lightspeed for Red Hat Developer Hub
1.1. About Developer Lightspeed for RHDH
This section describes Developer Preview features in the Red Hat Developer Lightspeed for Red Hat Developer Hub plugin. Developer Preview features are not supported by Red Hat in any way and are not functionally complete or production-ready. Do not use Developer Preview features for production or business-critical workloads. Developer Preview features provide early access to functionality in advance of possible inclusion in a Red Hat product offering. Customers can use these features to test functionality and provide feedback during the development process. Developer Preview features might not have any documentation, are subject to change or removal at any time, and have received limited testing. Red Hat might provide ways to submit feedback on Developer Preview features without an associated SLA.
For more information about the support scope of Red Hat Developer Preview features, see Developer Preview Support Scope.
This early access program enables customers to share feedback on the user experience, features, capabilities, and any issues encountered. Your input helps ensure that Developer Lightspeed for RHDH better meets your needs when it is officially released and made generally available.
Red Hat Developer Lightspeed for Red Hat Developer Hub (Developer Lightspeed for RHDH) is a virtual assistant powered by generative Artificial Intelligence (AI) designed for Red Hat Developer Hub(RHDH). The assistant offers in-depth insights into RHDH, including its wide range of capabilities. You can interact with this assistant to explore and learn more about RHDH in greater detail.
Developer Lightspeed for RHDH provides a natural language interface within the RHDH console, helping you easily find information about the product, understand its features, and get answers to your questions.
You can experience Developer Lightspeed for RHDH Developer Preview by installing the Developer Lightspeed for Red Hat Developer Hub plugin within an existing RHDH instance. Alternatively, if you prefer to test it locally first, you can try Developer Lightspeed for RHDH using RHDH Local.

Additional resources
1.2. Supported architecture for Red Hat Developer Lightspeed for Red Hat Developer Hub
Developer Lightspeed for RHDH is available as a plugin on all platforms that host RHDH. It requires two sidecar containers: the Lightspeed Core Service (LCS) and the Llama Stack service.
The LCS container acts as the intermediary layer, which interfaces with and manages the Llama Stack service.

Additional resources
1.2.1. About Lightspeed Core Service and Llama Stack
The Lightspeed Core Service and Llama Stack deploy together as sidecar containers to augment RHDH functionality.
The Llama Stack delivers the augmented functionality by integrating and managing core components, which include:
- Large language model (LLM) inference providers
- Model Context Protocol (MCP) or Retrieval Augmented Generation (RAG) tool runtime providers
- Safety providers
- Vector database settings
The Lightspeed Core Service serves as the Llama Stack service intermediary. It manages the operational configuration and key data, specifically:
- User feedback collection
- MCP server configuration
- Conversation history
Llama Stack provides the inference functionality that LCS uses to process requests. For more information, see What is Llama Stack.
The Red Hat Developer Lightspeed for Red Hat Developer Hub plugin in RHDH sends prompts and receives LLM responses through the LCS sidecar. LCS then uses the Llama Stack sidecar service to perform inference and MCP or RAG tool calling.
Red Hat Developer Lightspeed for Red Hat Developer Hub is a Developer Preview release. You must manually deploy the Lightspeed Core Service and Llama Stack sidecar containers, and install the Red Hat Developer Lightspeed for Red Hat Developer Hub plugin on your RHDH instance.
1.3. Retrieval augmented generation (RAG) embeddings
The Red Hat Developer Hub documentation serves as the Retrieval-Augmented Generation (RAG) data source.
RAG initialization occurs through an initialization container, which copies the RAG data to a shared volume. The Llama Stack sidecar then mounts this shared volume to access the RAG data. The Llama Stack service uses the resulting RAG embeddings in the vector database as a reference. This allows the service to provide citations to production documentation during the inference process.
1.4. Installing and configuring Red Hat Developer Lightspeed for Red Hat Developer Hub
Developer Lightspeed for RHDH consists of several components which work together to deliver virtual assistant (chat) functionality to your developers. The following list main components:
- Llama stack server (container sidecar)
- Based on open source Llama Stack, this service operates as the main gateway to your LLM inferencing provider for chat services. Its modular architecture supports the integration of other services, such as Model Context Protocol (MCP). To support the chat functionality of Developer Lightspeed for RHDH, you must integrate your LLM provider with the Llama Stack server. This dependency on external LLM providers is called Bring Your Own Model or BYOM.
- Lightspeed Core Service (LCS) (container sidecar)
- Based on the open source Lightspeed Core, this service extends the Llama Stack server by providing features such as chat history maintenance and user feedback gathering.
- Red Hat Developer Lightspeed for Red Hat Developer Hub (dynamic plugins)
- These plugins are required to enable the Developer Lightspeed for RHDH user interface within your RHDH instance.
Configuring these components to initialise correctly and communicate with each other is essential in order to provide Developer Lightspeed for RHDH to your users.
If you are upgrading from the previous Developer Lightspeed for RHDH Developer Preview with Road-Core Service, you must remove all existing Developer Lightspeed for RHDH configurations and settings before you reinstall.
To prevent or resolve upgrade inconsistencies, first drop and recreate the dynamic plugins volume.
This reinstallation is required due to the following fundamental architectural changes:
- The previous release used Road-Core Service as a sidecar container for interfacing with LLM providers.
- The updated architecture replaces {rcs-short} with the new Lightspeed Core Service and Llama Stack server, which necessitates new configurations for the plugins, volumes, containers, and secrets.
Prerequisites
- You are logged in to your OpenShift Container Platform account.
- You have an RHDH instance installed using either the Operator or the Helm chart.
- You have created a custom dynamic plugins ConfigMap.
Procedure
You must manually install and configure the Developer Lightspeed for RHDH plugin, the Lightspeed Core Service (LCS) sidecar container, and the Llama Stack sidecar container.
Create the Lightspeed Core Service (LCS) ConfigMap: The LCS ConfigMap stores the configuration for the Lightspeed Core Service and is mounted to the LCS container.
- In the OpenShift Container Platform web console, navigate to your RHDH instance and select the ConfigMaps tab.
- Click Create ConfigMaps.
From the Create ConfigMap page, select the YAML view option and edit the file using the following structure. This example demonstrates the configuration for the LCS ConfigMap, typically named
lightspeed-stack, which connects to the Llama Stack service locally on port8321:kind: ConfigMap apiVersion: v1 metadata: name: lightspeed-stack data: lightspeed-stack.yaml: | name: Lightspeed Core Service (LCS) service: host: 0.0.0.0 # Use ${LIGHTSPEED_SERVICE_PORT} if you are not running the Service on port `8080` # port: ${LIGHTSPEED_SERVICE_PORT} auth_enabled: false workers: 1 color_log: true access_log: true llama_stack: use_as_library_client: false url: http://localhost:8321 user_data_collection: feedback_enabled: true feedback_storage: "/tmp/data/feedback" transcripts_enabled: true transcripts_storage: "/tmp/data/transcripts" authentication: module: "noop" conversation_cache: type: "sqlite" sqlite: db_path: "/tmp/data/conversations/lcs_cache.db"where:
RHDH_HOST- Enter the hostname for RHDH.
- Click Create.
Create the Developer Lightspeed for RHDH ConfigMap: Create a dedicated Developer Lightspeed for RHDH ConfigMap (
lightspeed-app-config) to hold specific plugin configurations.- In the OpenShift Container Platform web console, navigate to your RHDH instance and select the ConfigMaps tab.
- Click Create ConfigMap.
From the Create ConfigMap page, select the YAML view option and add the following example:
kind: ConfigMap apiVersion: v1 metadata: name: lightspeed-app-config namespace: <__namespace__> # Enter your RHDH instance namespace data: app-config.yaml: |- backend: csp: upgrade-insecure-requests: false img-src: - "'self'" - "data:" - https://img.freepik.com - https://cdn.dribbble.com - https://avatars.githubusercontent.com # This is to load GitHub avatars in the UI script-src: - "'self'" - "'unsafe-eval'" - https://cdn.jsdelivr.net lightspeed: # OPTIONAL: Custom users prompts displayed to users # If not provided, the plugin uses built-in default prompts prompts: - title: `Getting Started with Red Hat Developer Hub` message: Can you guide me through the first steps to start using {product-short} as a developer, like exploring the Software Catalog and adding my service? # OPTIONAL: Port for lightspeed service (default: 8080) # servicePort: ${LIGHTSPEED_SERVICE_PORT} # OPTIONAL: Override default RHDH system prompt # systemPrompt: "You are a helpful assistant focused on Red Hat Developer Hub development."- Click Create.
Create Llama Stack Secret file (
llama-stack-secrets): This file holds sensitive configuration data for your LLM provider and Llama Stack environment variables.NoteYou must provide an LLM provider that conforms to the OpenAI API specification. For more information, see Changing your LLM provider.
- In the OpenShift Container Platform web console, go to Secrets.
- Click Create → Key/value secret.
In the Create key/value secret page, select the YAML view option and add the following example:
apiVersion: v1 kind: Secret metadata: name: llama-stack-secrets type: Opaque stringData: VLLM_URL: "" VLLM_API_KEY: "" VLLM_MAX_TOKENS: "" VLLM_TLS_VERIFY: "" VALIDATION_PROVIDER: "" VALIDATION_MODEL_NAME: ""
where:
VLLM_URL- Required for remote services: Set this to the API endpoint URL of your preferred LLM provider, if it is compatible with the OpenAI API specification (Examples: OpenAI, Red Hat OpenShift AI, vLLM)
VLLM_API_KEY- Required for remote services: Set this to the API key or token required for authentication with your remote LLM provider, if it is compatible with the OpenAI API specification
VLLM_MAX_TOKENS- Optional
VLLM_TLS_VERIFY- Optional
VALIDATION_PROVIDER-
Set this as
vllm,ollama,openai, depending on the key you have set in this configuration file VALIDATION_MODEL_NAME- Set the name of the model you want to use for validation
- Click Create.
Update the dynamic plugins ConfigMap: Add the Developer Lightspeed for RHDH plugin image to your existing dynamic plugins ConfigMap (
dynamic-plugins-rhdh).includes: - dynamic-plugins.default.yaml plugins: - package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed:bs_1.42.5__1.0.3!red-hat-developer-hub-backstage-plugin-lightspeed disabled: false pluginConfig: lightspeed: # OPTIONAL: Custom users prompts displayed to users prompts: - title: 'Getting Started with Red Hat Developer Hub' message: Can you guide me through the first steps to start using Developer Hub as a developer, like exploring the Software Catalog and adding my service? dynamicPlugins: frontend: red-hat-developer-hub.backstage-plugin-lightspeed: translationResources: - importName: lightspeedTranslations module: Alpha ref: lightspeedTranslationRef appIcons: - name: LightspeedIcon module: LightspeedPlugin importName: LightspeedIcon dynamicRoutes: - path: /lightspeed importName: LightspeedPage module: LightspeedPlugin menuItem: icon: LightspeedIcon text: Lightspeed - package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed-backend:bs_1.42.5__1.0.3!red-hat-developer-hub-backstage-plugin-lightspeed-backend disabled: falseUpdate your deployment configuration: Update the deployment configuration based on how your RHDH instance was installed. You must add two sidecar containers:
llama-stackandlightspeed-core.For an Operator-installed RHDH instance (Update Backstage Custom Resource (CR)):
In the
spec.application.appConfig.configMapssection of your Backstage CR, add the Developer Lightspeed for RHDH custom app configuration:appConfig: configMaps: - name: lightspeed-app-configUpdate the
spec.deployment.patch.spec.template.spec.volumesspecification to include volumes for LCS configuration (lightspeed-stack), shared storage for feedback (shared-storage), and RAG data (rag-data-volume):volumes: - configMap: name: lightspeed-stack name: lightspeed-stack - emptyDir: {} name: shared-storage - emptyDir: {} name: rag-data-volumeAdd the
initContainerssection to initialize RAG data:initContainers: - name: init-rag-data image: 'quay.io/redhat-ai-dev/rag-content:release-1.8-lcs' command: - "sh" - "-c" - "echo 'Copying RAG data...'; cp -r /rag/vector_db/rhdh_product_docs /data/ && cp -r /rag/embeddings_model /data/ && echo 'Copy complete.'" volumeMounts: - mountPath: /data name: rag-data-volumeAdd the Llama Stack and the LCS containers to the
spec.deployment.patch.spec.template.spec.containerssection:spec: application: - extraEnvs: secrets: - name: lightspeed-secrets containers: # ... Your existing RHDH container definition ... - envFrom: - secretRef: name: llama-stack-secrets image: 'quay.io/redhat-ai-dev/llama-stack:0.1.1' # Llama Stack image name: llama-stack volumeMounts: - mountPath: /app-root/.llama name: shared-storage - mountPath: /app-root/embeddings_model name: rag-data-volume subPath: embeddings_model - mountPath: /app-root/vector_db/rhdh_product_docs name: rag-data-volume subPath: rhdh_product_docs - image: 'quay.io/lightspeed-core/lightspeed-stack:dev-20251021-ee9f08f' # Lightspeed Core Service image name: lightspeed-core volumeMounts: - mountPath: /app-root/lightspeed-stack.yaml name: lightspeed-stack subPath: lightspeed-stack.yaml - mountPath: /tmp/data/feedback name: shared-storage - mountPath: /tmp/data/transcripts name: shared-storage - mountPath: /tmp/data/conversations name: shared-storage- Click Save. The Pods are automatically restarted.
For a Helm-installed RHDH instance (Update the Helm chart):
-
Add your dynamic plugins configuration in the
global.dynamicproperty. Add your Developer Lightspeed for RHDH custom app config file to
extraAppConfig:extraAppConfig: - configMapRef: lightspeed-app-config filename: app-config.yamlAdd the Llama Stack Secret file to
extraEnvVarsSecrets:extraEnvVarsSecrets: - llama-stack-secretsUpdate the
extraVolumessection to include the LCS ConfigMap (lightspeed-stack), shared storage, and RAG data volume:extraVolumes: - configMap: name: lightspeed-stack name: lightspeed-stack - emptyDir: {} name: shared-storage - emptyDir: {} name: rag-data-volumeUpdate the
initContainerssection (if supported by your Helm chart structure) to initialize RAG data.initContainers: - name: init-rag-data image: 'quay.io/redhat-ai-dev/rag-content:release-1.8-lcs' command: - "sh" - "-c" - "echo 'Copying RAG data...'; cp -r /rag/vector_db/rhdh_product_docs /data/ && cp -r /rag/embeddings_model /data/ && echo 'Copy complete.'" volumeMounts: - mountPath: /data name: rag-data-volumeAdd the Llama Stack and LCS container definitions to
extraContainersNoteIf you have Road-Core Service installed from the previous Red Hat Developer Lightspeed for Red Hat Developer Hub configuration, you must replace the older single container configuration found in source with the two sidecars.
extraContainers: # Llama Stack Container - envFrom: - secretRef: name: llama-stack-secrets image: 'quay.io/redhat-ai-dev/llama-stack:0.1.1' name: llama-stack volumeMounts: - mountPath: /app-root/.llama name: shared-storage - mountPath: /app-root/embeddings_model name: rag-data-volume subPath: embeddings_model - mountPath: /app-root/vector_db/rhdh_product_docs name: rag-data-volume subPath: rhdh_product_docs # Lightspeed Core Service Container - image: 'quay.io/lightspeed-core/lightspeed-stack:dev-20251021-ee9f08f' name: lightspeed-core volumeMounts: - mountPath: /app-root/lightspeed-stack.yaml name: lightspeed-stack subPath: lightspeed-stack.yaml - mountPath: /tmp/data/feedback name: shared-storage - mountPath: /tmp/data/transcripts name: shared-storage - mountPath: /tmp/data/conversations name: shared-storage- Click Save and then Helm upgrade.
-
Add your dynamic plugins configuration in the
Optional: Manage authorization (RBAC): If you have users who are not administrators, you must define permissions and roles for them to use Developer Lightspeed for RHDH. The Lightspeed Backend plugin uses Backstage RBAC for authorization.
For an Operator-installed RHDH instance:
Configure the required RBAC permission by defining an
rbac-policies.csvfile, includinglightspeed.chat.read,lightspeed.chat.create, andlightspeed.chat.deletepermissions:p, role:default/_<your_team>_, lightspeed.chat.read, read, allow p, role:default/_<your_team>_, lightspeed.chat.create, create, allow p, role:default/_<your_team>_, lightspeed.chat.delete, delete, allow g, user:default/_<your_user>_, role:default/_<your_team>_
Upload your
rbac-policies.csvfile to anrbac-policiesConfigMap in your OpenShift Container Platform project containing RHDH and update your Backstage CR:apiVersion: rhdh.redhat.com/v1alpha3 kind: Backstage spec: application: extraFiles: mountPath: /opt/app-root/src configMaps: - name: rbac-policies
For a Helm-installed RHDH instance:
Configure the required RBAC permission by defining an
rbac-policies.csvfile:p, role:default/_<your_team>_, lightspeed.chat.read, read, allow p, role:default/_<your_team>_, lightspeed.chat.create, create, allow p, role:default/_<your_team>_, lightspeed.chat.delete, delete, allow g, user:default/_<your_user>_, role:default/_<your_team>_
Optional: Declare policy administrators by editing your custom RHDH ConfigMap (
app-config.yaml) and adding the following code to enable selected authenticated users to configure RBAC policies through the REST API or Web UI:permission: enabled: true rbac: policies-csv-file: /opt/app-root/src/rbac-policies.csv policyFileReload: true admin: users: - name: user:default/<your_policy_administrator_name>
Verification
- Log in to your RHDH instance.
- In your RHDH navigation menu, you are able to see and access the Lightspeed menu item. Clicking this menu item takes you to the Developer Lightspeed for RHDH screen.
1.5. Customizing Developer Lightspeed for RHDH
You can customize Developer Lightspeed for RHDH functionalities such as gathering feedback, storing chat history in PostgreSQL, and configuring Model Context Protocol (MCP) tools.
1.5.1. Gathering feedback in Developer Lightspeed for RHDH
Feedback collection is an optional feature configured on the LCS. This feature gathers user feedback by providing thumbs-up/down ratings and text comments directly from the chat window.
LCS collects the feedback, the user’s query, and the response of the model, storing the data as a JSON file on the local file system of the Pod. A platform administrator must later collect and analyze this data to assess model performance and improve the user experience.
The collected data resides in the cluster where RHDH and LCS are deployed, making it accessible only to platform administrators for that cluster. For data removal, users must request this action from their platform administrator, as Red Hat neither collects nor accesses this data.
Procedure
To enable feedback collection, in the LCS configuration file (
lightspeed-stack.yaml), add the following settings:user_data_collection: feedback_enabled: true feedback_storage: "/tmp/data/feedback" transcripts_enabled: true transcripts_storage: "/tmp/data/transcripts"To disable feedback collection, in the LCS configuration file (
lightspeed-stack.yaml), add the following settings:user_data_collection: feedback_enabled: false feedback_storage: "/tmp/data/feedback" transcripts_enabled: true transcripts_storage: "/tmp/data/transcripts"
1.5.2. Updating the system prompt in Developer Lightspeed for RHDH
You can override the default system prompt that Developer Lightspeed for RHDH uses to better frame queries to your LLM. Customizing the system prompt allows you to refine the context, personality, and instructions that the LLM receives, improving the relevance and accuracy of the responses it creates for your specific environment.
Procedure
To set a custom system prompt, in your Developer Lightspeed for RHDH app config file, add or modify the
lightspeed.systemPromptkey and set its value to your preferred prompt string as shown in the following example:lightspeed: # ... other lightspeed configurations systemPrompt: "You are a helpful assistant focused on Red Hat Developer Hub development."
Set systemPrompt to prefix all queries sent by Developer Lightspeed for RHDH to the LLM with this instruction, guiding the model to generate more tailored responses.
1.5.3. Customizing the chat history storage in Developer Lightspeed for RHDH
By default, the Developer Lightspeed for RHDH service stores chat history in a non-persistent local SQL database within in the LCS container. This means that chat history is lost if you create and use a new LCS sidecar. You can manually configure Developer Lightspeed for RHDH to store the chat history persistently as a long-term backup with PostgreSQL by updating your LCS service configuration.
+
Configuring Developer Lightspeed for RHDH to use PostgreSQL records prompts and responses, which platform administrators can review. You must assess any data privacy and security implications if user chat history contains private, sensitive, or confidential information. For users that wish to have their chat data removed, they must request their respective platform administrator to perform this action. Red Hat does not collect or access this chat history data.
Procedure
Configure the chat history storage type in the LCS configuration file (
lightspeed-stack.yaml) using any of the relevant options:To enable persistent storage with PostgreSQL, add the following configuration:
conversation_cache: type: postgres postgres: host: _<your_database_host>_ port: _<your_database_port>_ db: _<your_database_name>_ user: _<your_user_name>_ password: _<postgres_password>_To retain the default, non-persistent SQLite storage, make sure the configuration is set as shown in the following example:
conversation_cache: type: "sqlite" sqlite: db_path: "/tmp/data/conversations/lcs_cache.db"
- Restart your LCS service to apply the new configuration.
1.6. Using Developer Lightspeed for RHDH
Red Hat Developer Lightspeed for Red Hat Developer Hub is designed to support you when performing various tasks during your development workflow.
The Question Validation feature is enabled by default if you are using the quay.io/redhat-ai-dev/llama-stack image without overriding the run.yaml configuration file in the image. To disable Question Validation, you must mount a run.yaml file to the container with the following sections removed:
-
Safety -
Shields -
External_providers_dirset tonull
With Question Validation enabled, you can ask Developer Lightspeed for RHDH the following types of questions:
- “Tell me about Red Hat Developer Hub.”
- “What are the benefits of RHDH?”
- “Can I use RHDH on an OpenShift Container Platform?”
- “How do I install plugins on Red Hat Developer Hub?”
With Question Validation disabled, the scope of prompts you can put to Developer Lightspeed for RHDH is much broader. This allows Developer Lightspeed for RHDH to support you in a much more varied range of work situations as described in the following examples:
- “Analyze this log for me…”
- “Suggest libraries and frameworks I can use to build Event Driven Architecture microservices.”
- “I’m not familiar with this language, so explain to me what this code snippet is doing…”
- “Create a Kubernetes deployment for this service…”
- “Create a test plan for the following scenarios and conditions…”
- “Create a Jira record that describes the following feature…”
- “Draft the end-user documentation describing how to use the following cli command…”
1.6.1. Using Developer Lightspeed for RHDH to start a chat for the first time
You can start a chat with Developer Lightspeed for RHDH for quick answers on a number of topics depending on your settings. You can manually start a chat with the Developer Lightspeed for RHDH or use the following sample prompts we have provided to help you get started:
- Getting Started with Red Hat Developer Hub
- Deploy with Tekton
- Create an OpenShift Deployment
Prerequisites
- You have the Developer Lightspeed for RHDH plugin configured in your RHDH instance.
Procedure
- In your RHDH navigation menu, click Lightspeed.
You can start a chat in either of the following ways:
To manually start a chat, in the Send a message text box, you can do any of the following tasks:
- Type your query and press Enter.
To attach a file in the chat, click the Attach icon or drag and drop the file in your chat.
NoteThe following file types are supported:
yaml,json, andtxt.- Click Open.
- To start a chat using the existing prompts, in the Developer Lightspeed for RHDH virtual assistant interface, click any of the relevant prompt tiles.
1.6.2. Using Developer Lightspeed for RHDH to create new chats after the first chat
After you have started an initial chat with the Developer Lightspeed for RHDH, you can begin a chat on a new topic at any time. Even if you log out and log back in, your previous chats are still available in your chat history for you to view.
Prerequisites
- You have the Developer Lightspeed for RHDH plugin configured in your RHDH instance.
Procedure
- In your RHDH navigation menu, click Lightspeed.
- In the Developer Lightspeed for RHDH virtual assistant interface, click New chat.
1.6.3. Using Developer Lightspeed for RHDH to view chat history
Your chats with Developer Lightspeed for RHDH are automatically saved in your RHDH instance. You can easily revisit your chat history at any time, switch between chats, and revisit any previous chats. Each chat remains active, enabling you to go back to any of your previous chats and continue from where you left off.
Prerequisites
- You have the Developer Lightspeed for RHDH plugin configured in your RHDH instance.
Procedure
- In your RHDH navigation menu, click Lightspeed. Developer Lightspeed for RHDH opens with your previous chat.
In the Developer Lightspeed for RHDH virtual assistant interface, do any of the following tasks:
- Select a chat title to open and view the full chat.
- In Search previous chats…, enter the text that you want to find from the earlier chats.
1.6.4. Using Developer Lightspeed for RHDH to delete a chat
Prerequisites
- You have the Developer Lightspeed for RHDH plugin configured in your RHDH instance.
Procedure
- In your RHDH navigation menu, click Lightspeed. Developer Lightspeed for RHDH opens with your previous chat.
- In Developer Lightspeed for RHDH, select the vertical ellipsis for the chat title of the chat that you want to delete.
- Select Delete.
1.7. Appendix: LLM requirements
1.7.1. Large language model (LLM) requirements
Developer Lightspeed for RHDH follows a Bring Your Own Model approach. This model means that to function, Developer Lightspeed for RHDH requires access to a large language model (LLM) which you must provide. An LLM is a type of generative AI that interprets natural language and generates human-like text or audio responses. When an LLM is used as a virtual assistant, the LLM can interpret questions and provide answers in a conversational manner.
LLMs are usually provided by a service or server. Because Developer Lightspeed for RHDH does not provide an LLM for you, you must configure your preferred LLM provider during installation. You can configure the underlying Llama Stack server to integrate with a number of LLM providers that offer compatibility with the OpenAI API including the following inference providers:
- OpenAI (cloud-based inference service)
- Red Hat OpenShift AI (enterprise model builder and inference server)
- Red Hat Enterprise Linux AI (enterprise inference server)
- Ollama (popular desktop inference server)
- vLLM (popular enterprise inference server)
1.7.2. OpenAI
OpenAI offers a range of generative AI models, such as GPT 5, which can be used to provide inference services for applications like Developer Lightspeed for RHDH.
To use OpenAI with Developer Lightspeed for RHDH, you need access to the OpenAI API platform. For more information, see the OpenAI developer platform documentation.
1.7.3. Ollama
Ollama is a powerful and easy-to-use open-source project that simplifies the process of running large language models (LLMs) locally on your computer. It provides a simple command-line interface for downloading, managing, and running a wide variety of open-source models, such as Llama 3, Mistral, and many others, all without requiring a dedicated server or cloud service. By abstracting away the complex setup and dependencies, Ollama makes it accessible for developers, researchers, and enthusiasts to experiment with, build on, and integrate state-of-the-art LLMs into their applications directly from their personal machines.
The open source Ollama server in container form provides a convenient local testbed for LLM models that is very accessible and easily controlled.
Additional resources
1.7.4. vLLM
vLLM is an open-source, high-throughput serving engine for large language models (LLMs) that significantly improves upon traditional serving systems. It achieves this by introducing several key optimizations to reduce memory usage and eliminate redundant computations. vLLM prominently increases the number of concurrent requests an LLM can handle, making it a powerful tool for deploying and scaling LLM-based applications.
1.8. Appendix About user data security
1.8.1. About data use
Developer Lightspeed for RHDH is a virtual assistant you interact with using natural language. Using the Developer Lightspeed for RHDH interface, you send chat messages that Developer Lightspeed for RHDH transforms and sends to the large language model (LLM) provider you have configured for your environment. These messages could potentially contain information provided by your users about themselves, your cluster, cluster resources, or other aspects of your business or working environment.
Developer Lightspeed for RHDH has limited capabilities to filter or redact the information you provide to the LLM. Do not enter information into Developer Lightspeed for RHDH that you do not want to send to the LLM provider. To remind end users not to share private or confidential information, Developer Lightspeed for RHDH begins each new chat with an 'Important' message asking them not to “include personal or sensitive information” in their chat messages.
1.8.2. About feedback collection
Developer Lightspeed for RHDH collects feedback from users who engage with the feedback feature in the virtual assistant interface. If a user submits feedback, the feedback score (thumbs up or down), text feedback (if entered), the user query, and the LLM provider response are stored locally in the file system of the Pod. Red Hat does not have access to the collected feedback data.
1.8.3. About Bring Your Own Model
Developer Lightspeed for RHDH does not provide its own inference services, but uses a Bring Your Own Model approach. This means that you can configure the Lightspeed Core Service to talk to the inference server or service of your choice. This also means that you are responsible for ensuring that the configured service meets your particular company policies and legal requirements, including any applicable terms with the third-party model provider. The only technical requirements for inference services are:
- The service must conform to the OpenAI API specification.
- The service must be configured correctly following the installation and configuration instructions. There are many commercial and open source inference services that support the OpenAI API specification for chat completions. The cost, performance, and security of these services can differ and it is up to you to choose, through evaluation and testing, the inference service that best meets your company’s needs.
Additional resources
1.8.4. Your responsibility
All of the information your users share in their questions and responses with Developer Lightspeed for RHDH are shared with the LLM inference service you configured. You are responsible for ensuring compliance with your company’s policies regarding the sharing of data with your chosen inference service.