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. Get AI-assisted help for your development tasks
- 1.7. Appendix: LLM requirements
- 1.8. Appendix About user data security
1. Interacting with Red Hat Developer Lightspeed for Red Hat Developer Hub
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.
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.
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 RHDH 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.
Developer Lightspeed for RHDH uses a FAB instead of a sidebar navigation item. If your environment uses another global FAB, you must move the existing button or disable it to prevent interface elements from overlapping. In your dynamic plugin configuration file, make the following update:
- package: ./dynamic-plugins/dist/red-hat-developer-hub-backstage-plugin-bulk-import
disabled: true
pluginConfig:
dynamicPlugins:
frontend:
red-hat-developer-hub.backstage-plugin-bulk-import:
mountPoints:
- mountPoint: global.floatingactionbutton/config
importName: BulkImportPage # Example
config:
slot: 'bottom-left'
icon: BulkImportIcon
label: 'Bulk import'
toolTip: 'Register multiple repositories in bulk'
to: /bulk-import
translationResources:
- importName: bulkImportTranslations
module: Alpha
ref: bulkImportTranslationRef
appIcons:
- name: bulkImportIcon
importName: BulkImportIcon
dynamicRoutes:
- path: /bulk-import
importName: BulkImportPage
menuItem:
icon: bulkImportIcon
text: Bulk import
textKey: menuItem.bulkImport
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
ImportantYou must verify that your model supports tool calling before you enable Model Context Protocol (MCP) features. Using an incompatible model results in error messages.
- 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 includes several components that work together to provide virtual assistant (chat) functionality:
- Llama stack server (container sidecar)
- Based on open source Llama Stack, this service is the gateway to your LLM inferencing provider for chat services. Its modular architecture supports integrating other services, such as Model Context Protocol (MCP). To enable chat functionality, you must integrate your LLM provider with the Llama Stack server using 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 maintaining chat history and gathering user feedback.
- Red Hat Developer Lightspeed for Red Hat Developer Hub (dynamic plugins)
- These plugins enable the Developer Lightspeed for RHDH user interface within your RHDH instance.
To provide Developer Lightspeed for RHDH to your users, you must configure these components to communicate with each other.
- 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 before reinstalling.
- To prevent or resolve upgrade inconsistencies, 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 requires 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.
- You have administrative access to the RHDH configuration files.
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 to store the service configuration:
- In the OpenShift Container Platform web console, navigate to your RHDH instance and select the ConfigMaps tab.
- Click Create ConfigMaps.
Select YAML view 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: ${LIGHTSPEED_SERVICE_PORT} auth_enabled: false workers: 1 color_log: true access_log: true llama_stack: use_as_library_client: false url: http://localhost:8321 user_data_collection: feedback_enabled: true feedback_storage: "/tmp/data/feedback" transcripts_enabled: true transcripts_storage: "/tmp/data/transcripts" authentication: module: "noop" conversation_cache: type: "sqlite" sqlite: db_path: "/tmp/data/conversations/lcs_cache.db"ImportantLCS uses Llama Stack expansion syntax for environment variables. To make sure the service correctly parses variables in the
lightspeed-stack.yamlfile, you must use the${env.VAR}format. Variable names are case-sensitive and must be uppercase. The syntax${env.var}is not supported. For example:model_config: api_key: ${env.VAR}- Click Create.
Create the Developer Lightspeed for RHDH ConfigMap (
lightspeed-app-config) for plugin configurations:- In the OpenShift Container Platform web console, navigate to your RHDH instance and select the ConfigMaps tab.
- Click Create ConfigMap.
Select the YAML view option and add the following configuration:
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) for LLM provider credentials:ImportantRed Hat Developer Hub 1.9 configures the Llama Stack image to use the vLLM provider exclusively. The vLLM provider supports the OpenAI API schema but does not support connecting directly to the official OpenAI service (
api.openai.com). Do not use the official OpenAI API URL or token with vLLM in this release. Attempting this might result in errors or improper responses. Native OpenAI provider support is planned for future releases.- In the OpenShift Container Platform web console, navigate to Secrets.
- Click Create → Key/value secret.
Select the YAML view option and add the following structure:
apiVersion: v1 kind: Secret metadata: name: llama-stack-secrets type: Opaque stringData: ENABLE_VLLM: "true" ENABLE_VERTEX_AI: "" ENABLE_OPENAI: "" ENABLE_OLLAMA: "" VLLM_URL: "_<api_endpoint>_" VLLM_API_KEY: "_<api_key>_" VLLM_MAX_TOKENS: "" VLLM_TLS_VERIFY: "" OPENAI_API_KEY: "" VERTEX_AI_PROJECT: "" VERTEX_AI_LOCATION: "" GOOGLE_APPLICATION_CREDENTIALS: "" OLLAMA_URL: "" SAFETY_MODEL: "llama-guard3:8b" SAFETY_URL: "http://localhost:#####/v1" SAFETY_API_KEY: ""
where:
ENABLE_VLLM- Enables the vLLM provider. Set to true to activate.
ENABLE_OPENAI- Enables the OpenAI provider. Set to true to activate.
ENABLE_VERTEX_AI- Enables the Vertex AI provider. Set to true to activate.
ENABLE_OLLAMA- Enables the Ollama provider. Set to true to activate.
To disable a provider, leave the value blank. (for example: ENABLE_VLLM="")
VLLM_URL- The API endpoint URL for the LLM provider. The provider must comply with the OpenAI API specification. Examples include OpenAI, Red Hat OpenShift AI, and 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. The maximum number of tokens the model generates.
VLLM_TLS_VERIFY- Optional. Specifies whether the system verifies the TLS certificate for the vLLM endpoint.
OPENAI_API_KEY- The API key required to access OpenAI models through the OpenAI API.
VERTEX_AI_PROJECT- The Google Cloud project ID required to access Gemini through Vertex AI.
VERTEX_AI_LOCATION- The Google Cloud region required to access Gemini through Vertex AI.
GOOGLE_APPLICATION_CREDENTIALS- The credentials required to authenticate with Google Cloud to access Vertex AI.
OLLAMA_URL- The URL for the Ollama service.
SAFETY_MODEL- The identifier for the safety model. The recommended value is llama-guard3:8b.
SAFETY_URL-
The URL hosting the safety model. You must append
/v1to the host address (for example:localhost:#/v1). SAFETY_API_KEYThe API key required to authenticate with the safety model, if applicable.
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: - disabled: false package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed:bs_1.45.3__1.2.3 pluginConfig: dynamicPlugins: frontend: red-hat-developer-hub.backstage-plugin-lightspeed: translationResources: - importName: lightspeedTranslations module: Alpha ref: lightspeedTranslationRef dynamicRoutes: - path: /lightspeed importName: LightspeedPage mountPoints: - mountPoint: application/listener importName: LightspeedFAB - mountPoint: application/provider importName: LightspeedDrawerProvider - mountPoint: application/internal/drawer-state importName: LightspeedDrawerStateExposer config: id: lightspeed - mountPoint: application/internal/drawer-content importName: LightspeedChatContainer config: id: lightspeed priority: 100 - disabled: false package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed-backend:bs_1.45.3__1.2.3Required only if your environment uses another global floating action button (FAB): You must move the existing FAB or disable it to prevent interface elements from overlapping by updating your dynamic plugin configuration file with the following changes:
- package: ./dynamic-plugins/dist/red-hat-developer-hub-backstage-plugin-bulk-import disabled: true pluginConfig: dynamicPlugins: frontend: red-hat-developer-hub.backstage-plugin-bulk-import: mountPoints: # START: If the user has an existing BulkImportPage FAB, move the FAB to the left - mountPoint: global.floatingactionbutton/config importName: BulkImportPage config: slot: 'bottom-left' # END icon: BulkImportIcon label: 'Bulk import' toolTip: 'Register multiple repositories in bulk' to: /bulk-import translationResources: - importName: bulkImportTranslations module: Alpha ref: bulkImportTranslationRef appIcons: - name: bulkImportIcon importName: BulkImportIcon dynamicRoutes: - path: /bulk-import importName: BulkImportPage menuItem: icon: bulkImportIcon text: Bulk import textKey: menuItem.bulkImportUpdate 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-volume-
Add the
initContainerssection to initialize RAG data:
+
----
initContainers:
- name: init-rag-data
image: 'quay.io/redhat-ai-dev/rag-content:release-1.9-lcs'
command:
- "sh"
- "-c"
- "echo 'Copying RAG data...'; cp -r /rag/vector_db/rhdh_product_docs /rag-content/ && cp -r /rag/embeddings_model /rag-content/ && echo 'Copy complete.'"
volumeMounts:
- mountPath: /rag-content
name: rag-data-volume
----
... Add the Llama Stack and the {lcs-short} containers to the `spec.deployment.patch.spec.template.spec.containers` section:+
containers:
# ... Your existing RHDH container definition ...
- envFrom:
- secretRef:
name: llama-stack-secrets
image: 'quay.io/redhat-ai-dev/llama-stack:0.1.4' # Llama Stack image
name: llama-stack
volumeMounts:
- mountPath: /app-root/.llama
name: shared-storage
- mountPath: /rag-content
name: rag-data-volume
- image: 'quay.io/lightspeed-core/lightspeed-stack:0.4.0' # 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-storageClick 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.global: dynamic: plugins: - package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed:bs_1.45.3__1.2.3 pluginConfig: dynamicPlugins: frontend: red-hat-developer-hub.backstage-plugin-lightspeed: translationResources: - importName: lightspeedTranslations module: Alpha ref: lightspeedTranslationRef dynamicRoutes: - path: /lightspeed importName: LightspeedPage mountPoints: - mountPoint: application/listener importName: LightspeedFAB - mountPoint: application/provider importName: LightspeedDrawerProvider - mountPoint: application/internal/drawer-state importName: LightspeedDrawerStateExposer config: id: lightspeed - mountPoint: application/internal/drawer-content importName: LightspeedChatContainer config: id: lightspeed priority: 100 - package: oci://ghcr.io/redhat-developer/rhdh-plugin-export-overlays/red-hat-developer-hub-backstage-plugin-lightspeed-backend:bs_1.45.3__1.2.3Add 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-1.9-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
extraContainers.NoteIf 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:0.1.4' name: llama-stack volumeMounts: - mountPath: /app-root/.llama name: shared-storage - mountPath: /rag-content/embeddings_model name: rag-data-volume subPath: embeddings_model - mountPath: /rag-content/vector_db/rhdh_product_docs name: rag-data-volume subPath: rhdh_product_docs # Lightspeed Core Service Container - image: 'quay.io/lightspeed-core/lightspeed-stack:0.4.0' 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-storageClick Save and then Helm upgrade.
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/v1alpha5 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.
- Verify that you can see and access the Lightspeed FAB in the home page.
- Select the Lightspeed FAB and verify the chat screen loads.
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 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. Get AI-assisted help for your development tasks
To assist with development tasks, platform questions, and debugging, use Red Hat Developer Lightspeed for Red Hat Developer Hub, a generative AI virtual assistant integrated directly into Red Hat Developer Hub (RHDH). You can use the conversational interface to ask platform-specific questions, analyze logs, generate code, and create test plans, which reduces the time spent searching through official documentation or disparate tools.
1.6.1. Configure safety guards in Red Hat Developer Hub
To protect users from insecure or harmful AI model outputs, Red Hat Developer Hub (RHDH) uses Llama Guard as a default safety shield. You must configure these guards to align with your organization’s security policies.
Default safety guard configuration-
The system uses Llama Guard as the default safety shield. Override these settings in the
run.yamlfile.
The external_providers_dir parameter defaults to null and is no longer required in your configuration.
Overriding safety guards-
To implement custom security layers or different safety shields, you must define a new safety provider within a custom
run.yamlfile. Disabling safety guards-
To run RHDH without safety guards, you must use the
run-no-guard.yamlconfiguration file.
Running without safety guards increases the risk of unvalidated model output. Only use this configuration in secure development environments.
Applying the no-guard configuration- To run the system without a safety guard, perform these steps:
Procedure
Add the following YAML file as a config map to your namespace:
version: 2 image_name: redhat-ai-dev-llama-stack-no-guard apis: - agents - inference - safety - tool_runtime - vector_io - files container_image: external_providers_dir: providers: agents: - config: persistence: agent_state: namespace: agents backend: kv_default responses: table_name: responses backend: sql_default provider_id: meta-reference provider_type: inline::meta-reference inference: - provider_id: ${env.ENABLE_VLLM:+vllm} provider_type: remote::vllm config: url: ${env.VLLM_URL:=} api_token: ${env.VLLM_API_KEY:=} max_tokens: ${env.VLLM_MAX_TOKENS:=4096} tls_verify: ${env.VLLM_TLS_VERIFY:=true} - provider_id: ${env.ENABLE_OLLAMA:+ollama} provider_type: remote::ollama config: url: ${env.OLLAMA_URL:=http://localhost:11434} - provider_id: ${env.ENABLE_OPENAI:+openai} provider_type: remote::openai config: api_key: ${env.OPENAI_API_KEY:=} - provider_id: ${env.ENABLE_VERTEX_AI:+vertexai} provider_type: remote::vertexai config: project: ${env.VERTEX_AI_PROJECT:=} location: ${env.VERTEX_AI_LOCATION:=us-central1} - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: {} tool_runtime: - provider_id: model-context-protocol provider_type: remote::model-context-protocol config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} vector_io: - provider_id: faiss provider_type: inline::faiss config: persistence: namespace: vector_io::faiss backend: faiss_kv files: - provider_id: localfs provider_type: inline::localfs config: storage_dir: /tmp/llama-stack-files metadata_store: table_name: files_metadata backend: sql_files storage: backends: kv_default: type: kv_sqlite db_path: /tmp/kvstore.db sql_default: type: sql_sqlite db_path: /tmp/sql_store.db sql_files: type: sql_sqlite db_path: /rag-content/vector_db/rhdh_product_docs/1.9/files_metadata.db faiss_kv: type: kv_sqlite db_path: /rag-content/vector_db/rhdh_product_docs/1.9/faiss_store.db stores: metadata: namespace: registry backend: faiss_kv inference: table_name: inference_store backend: sql_default max_write_queue_size: 10000 num_writers: 4 conversations: table_name: openai_conversations backend: sql_default registered_resources: models: - model_id: sentence-transformers/all-mpnet-base-v2 metadata: embedding_dimension: 768 model_type: embedding provider_id: sentence-transformers provider_model_id: /rag-content/embeddings_model tool_groups: - provider_id: rag-runtime toolgroup_id: builtin::rag vector_dbs: - vector_db_id: rhdh-product-docs-1_8 embedding_model: sentence-transformers/all-mpnet-base-v2 embedding_dimension: 768 provider_id: faiss server: auth: host: port: 8321 quota: tls_cafile: tls_certfile: tls_keyfile:Mount the config map to your Llama Stack container at
/app-root/run.yamlto make sure it overrides the default image file:name: llama-stack volumeMounts: - mountPath: /app-root/run.yaml subPath: run.yaml name: llama-stack-config
Configure the required volume:
volumes: - name: llama-stack-config configMap: name: llama-stack-configwhere:
llama-stack-config- The config map where you added the new no-guard configuration file.
- Restart the deployment if it does not trigger an automatic rollout.
Prerequisites
- Your platform engineer has installed and configured the Developer Lightspeed for RHDH plugin in your RHDH instance.
1.6.2. Get the best results for assistant queries
To resolve technical blockers and accelerate development tasks, you must structure your queries to provide specific context to the AI assistant. Using precise prompts ensures that Developer Lightspeed for RHDH generates relevant code snippets, architectural advice, or platform-specific instructions.
Use the following strategies to improve the accuracy of the assistant’s output during your development workflow:
- Specify technologies
- Instead of asking "How do I use templates?", ask "How do I create a Software Template that scaffolds a Node.js service with a CI/CD pipeline".
- Provide context
- Include details about your environment, such as "I am deploying to OpenShift; how do I configure my catalog-info.yaml to show pod health?".
- Leverage conversation context
- Ask follow-up questions to refine a previous answer. For example, if the assistant provides a code snippet, you can ask "Now rewrite that using TypeScript interfaces."
- Validate with citations
- Examine the provided documentation links and citations in the response to verify that the generated advice aligns with your organization’s official standards.
- Improve assistant accuracy
- Rate the utility of responses by selecting the Thumbs up or Thumbs down icons. This feedback helps tune the model for your organization’s specific requirements.
To maintain security standards, do not include sensitive personal information, plain-text credentials, or confidential business data in your queries.
1.6.3. Monitor AI responses and context management
Developer Lightspeed for RHDH provides features to track the AI reasoning process and maintain the context of your development tasks.
- Thinking cards
- An expandable thinking card appears while the AI processes a query. A pulse animation indicates the reasoning phase. You can expand the card to view detailed reasoning or collapse it to minimize screen clutter.
- Tool call transparency
- An expandable card displays details for Model Context Protocol (MCP) tool calls, which allows you to monitor background processes.
- Context-aware citations
- Retrieval-Augmented Generation (RAG) citations appear only when the AI uses internal documentation. This makes sure that general knowledge responses remain concise.
- Context preservation during model changes
- When you select a different AI model, Developer Lightspeed for RHDH starts a new conversation. This makes sure that your previous chats remain available in your history.
- Structural readability
- The interface formats headings and bullet points automatically to make sure responses are scannable.
1.6.4. Managing chats
Manage your chat history in RHDH to organize your workspace, resume previous tasks, or find past solutions.
Prerequisites
- You have configured the Developer Lightspeed for RHDH plugin in Red Hat Developer Hub.
- You must be logged in to the portal.
Procedure
- Click the Lightspeed floating action button (FAB) at the bottom right of the screen to open the chat overlay.
Optional: Configure the interface display:
- Click the Chatbot options icon (⋮) to view chat history or start a new chat.
Click the Display menu icon and select any of the following views:
Overlay: A floating window appears over the current page content.
Dock to window: A panel attaches to the right side of the screen. Activating this mode automatically closes the Quickstart panel if it is already open.
Fullscreen: A dedicated page opens for intensive chat sessions. You can bookmark this URL for direct access.
- Optional: Toggle Enable pinned chats/Disable pinned chats to enable or hide the pinned chats. This is enabled by default.
Start a chat or load a previous session:
- Enter a prompt: Type a query in the Enter a prompt for Lightspeed chat field and press Enter.
- Use a sample: Click a prompt tile, such as Deploy with Tekton.
Attach a file: Click Attach to upload a
.yaml,.json, or.txtfile.- Click the file name to open the preview model.
View or edit the content of the file:
- Use voice: Click the Use microphone icon.
- Resume a chat: Select a title from the Recent list.
Organize your chat history:
- Start a new topic: Click New chat to reset the assistant’s context.
- Search history: Enter a keyword in the Search field.
Rename a session: Click Options next to a chat title, select Rename, and enter a new name.
- Pin a chat:: Click Options next to a chat title and select Pin. The chat moves to the Pinned group.
Sort chats:: Click Sort control and choose a sorting criteria, such as Date (Newest first).
Delete a chat: Click Options next to a chat title and select Delete.
- Hide the chat history section and reduce visual noise: Select the Close icon (x) next to New chat.
- Restore access to your pinned chat: Select the Chat history menu icon.
- Optional: To hide the interface, if you are in the Overlay or Dock to window mode, click the Close Lightspeed icon (X) to hide the window. If you are in Fullscreen mode, revert to the other modes and click the Close Lightspeed icon (X). The system preserves your active query and history.
- Optional: In Fullscreen mode, bookmark the URL in your browser to save a direct link to the chat interface.
Verification
- The main window displays the active chat or selected history.
- The chat history list reflects renamed, pinned, or deleted entries.
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)
- Gemini (available through Vertex AI)
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.