Lightspeed Core Stack

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📋 Schemas for successful responses models

A2AStateConfiguration

A2A protocol persistent state configuration.

Configures how A2A task state and context-to-conversation mappings are stored. For multi-worker deployments, use SQLite or PostgreSQL to ensure state is shared across all workers.

If no configuration is provided, in-memory storage is used (default). This is suitable for single-worker deployments but state will be lost on restarts and not shared across workers.

Attributes: sqlite: SQLite database configuration for A2A state storage. postgres: PostgreSQL database configuration for A2A state storage.

Field	Type	Description
sqlite		SQLite database configuration for A2A state storage.
postgres		PostgreSQL database configuration for A2A state storage.

APIKeyTokenConfiguration

API Key Token configuration.

Field	Type	Description
api_key	string

AccessRule

Rule defining what actions a role can perform.

Field	Type	Description
role	string	Name of the role
actions	array	Allowed actions for this role

Action

Available actions in the system.

Note: this is not a real model, just an enumeration of all action names.

AllowedToolsFilter

Filter configuration for restricting which MCP tools can be used.

:param tool_names: (Optional) List of specific tool names that are allowed

Field	Type	Description
tool_names	array

ApprovalFilter

Granular approval control for specific MCP tools.

Attributes: always: Tool names that always require human approval before execution. never: Tool names that never require approval (pre-approved).

Field	Type	Description
always	array	List of tool names that always require human approval
never	array	List of tool names that never require approval

ApprovalsConfiguration

Configuration for human-in-the-loop approvals.

Attributes: approval_timeout_seconds: How long approval requests remain pending before expiring. approval_retention_days: How long to retain decided approvals for audit purposes before cleanup.

Field	Type	Description
approval_timeout_seconds	integer	Seconds before pending approval requests expire
approval_retention_days	integer	Days to retain decided approvals before cleanup

AuthenticationConfiguration

Authentication configuration.

Field	Type	Description
module	string
skip_tls_verification	boolean
skip_for_health_probes	boolean	Skip authorization for readiness and liveness probes
skip_for_metrics	boolean	Skip authorization for the /metrics endpoint
k8s_cluster_api	string
k8s_ca_cert_path	string
jwk_config
api_key_config
rh_identity_config
trusted_proxy_config

AuthorizationConfiguration

Authorization configuration.

Field	Type	Description
access_rules	array	Rules for role-based access control

AuthorizedResponse

Model representing a response to an authorization request.

Attributes: user_id: The ID of the logged in user. username: The name of the logged in user. skip_userid_check: Whether to skip the user ID check.

Field	Type	Description
user_id	string	User ID, for example UUID
username	string	User name
skip_userid_check	boolean	Whether to skip the user ID check

AzureEntraIdConfiguration

Microsoft Entra ID authentication attributes for Azure.

Field	Type	Description
tenant_id	string
client_id	string
client_secret	string
scope	string	Azure Cognitive Services scope for token requests. Override only if using a different Azure service.

ByokRag

BYOK (Bring Your Own Knowledge) RAG configuration.

Field	Type	Description
rag_id	string	Unique RAG ID
rag_type	string	Type of RAG database (e.g. ‘inline::faiss’, ‘remote::pgvector’).
embedding_model	string	Embedding model identification
embedding_dimension	integer	Dimensionality of embedding vectors.
vector_db_id	string	Vector database identification.
db_path	string	Path to RAG database. Required for inline::faiss.
score_multiplier	number	Multiplier applied to relevance scores from this vector store. Used to weight results when querying multiple knowledge sources. Values > 1 boost this store’s results; values < 1 reduce them.
host	string	PostgreSQL host for remote::pgvector. Defaults to ${env.POSTGRES_HOST} when rag_type is remote::pgvector.
port	string	PostgreSQL port for remote::pgvector. Defaults to ${env.POSTGRES_PORT} when rag_type is remote::pgvector.
db	string	PostgreSQL database name for remote::pgvector. Defaults to ${env.POSTGRES_DATABASE} when rag_type is remote::pgvector.
user	string	PostgreSQL user for remote::pgvector. Defaults to ${env.POSTGRES_USER} when rag_type is remote::pgvector.
password	string	PostgreSQL password for remote::pgvector. Defaults to ${env.POSTGRES_PASSWORD} when rag_type is remote::pgvector.

CORSConfiguration

CORS configuration.

CORS or ‘Cross-Origin Resource Sharing’ refers to the situations when a frontend running in a browser has JavaScript code that communicates with a backend, and the backend is in a different ‘origin’ than the frontend.

Useful resources:

• CORS in FastAPI
• Wikipedia article
• What is CORS?

Field	Type	Description
allow_origins	array	A list of origins allowed for cross-origin requests. An origin is the combination of protocol (http, https), domain (myapp.com, localhost, localhost.tiangolo.com), and port (80, 443, 8080). Use [‘*’] to allow all origins.
allow_credentials	boolean	Indicate that cookies should be supported for cross-origin requests
allow_methods	array	A list of HTTP methods that should be allowed for cross-origin requests. You can use [‘*’] to allow all standard methods.
allow_headers	array	A list of HTTP request headers that should be supported for cross-origin requests. You can use [‘*’] to allow all headers. The Accept, Accept-Language, Content-Language and Content-Type headers are always allowed for simple CORS requests.

CompactionConfiguration

Configuration for conversation history compaction.

Compaction summarizes older conversation turns when their estimated token count approaches the context window limit, keeping the conversation usable instead of failing with HTTP 413. The configuration here controls when compaction triggers and how much recent context is preserved verbatim.

Attributes: enabled: Master switch. When False, compaction never triggers and other fields are inert. threshold_ratio: Trigger compaction when estimated input tokens exceed this fraction of the model’s context window (clamped to 0.0..1.0). token_floor: Minimum estimated token count before compaction can trigger, regardless of threshold_ratio. Prevents triggering on very small context windows. buffer_turns: Initial number of recent turns to keep verbatim. The runtime applies a degrading guard — if these turns exceed the available budget, it reduces buffer_turns by one repeatedly until the budget fits, down to zero. buffer_max_ratio: Hard cap on the fraction of the context window the buffer zone may occupy, regardless of buffer_turns.

Field	Type	Description
enabled	boolean	When true, older conversation turns are summarized when estimated tokens approach the context window limit.
threshold_ratio	number	Trigger compaction when estimated tokens exceed this fraction of the model’s context window (0.0-1.0).
token_floor	integer	Minimum token count before compaction can trigger. Prevents triggering on very small context windows.
buffer_turns	integer	Number of recent turns to keep verbatim.
buffer_max_ratio	number	Maximum fraction of context window the buffer zone can occupy, regardless of buffer_turns.

Configuration

Global service configuration.

Field	Type	Description
name	string	Name of the service. That value will be used in REST API endpoints.
service		This section contains Lightspeed Core Stack service configuration.
llama_stack		This section contains Llama Stack configuration. Lightspeed Core Stack service can call Llama Stack in library mode or in server mode.
user_data_collection		This section contains configuration for subsystem that collects user data(transcription history and feedbacks).
database		Configuration for database to store conversation IDs and other runtime data
mcp_servers	array	MCP (Model Context Protocol) servers provide tools and capabilities to the AI agents. These are configured in this section. Only MCP servers defined in the lightspeed-stack.yaml configuration are available to the agents. Tools configured in the llama-stack run.yaml are not accessible to lightspeed-core agents.
authentication		Authentication configuration
authorization		Lightspeed Core Stack implements a modular authentication and authorization system with multiple authentication methods. Authorization is configurable through role-based access control. Authentication is handled through selectable modules configured via the module field in the authentication configuration.
customization		It is possible to customize Lightspeed Core Stack via this section. System prompt can be customized and also different parts of the service can be replaced by custom Python modules.
inference		One LLM provider and one its model might be selected as default ones. When no provider+model pair is specified in REST API calls (query endpoints), the default provider and model are used.
conversation_cache
compaction		Controls when conversation history is summarized to keep the model’s input below the context window limit. Disabled by default — when disabled, requests that exceed the window continue to surface as HTTP 413.
approvals		Settings for human-in-the-loop approval of MCP tool invocations
byok_rag	array	BYOK RAG configuration. This configuration can be used to reconfigure Llama Stack through its run.yaml configuration file
a2a_state		Configuration for A2A protocol persistent state storage.
quota_handlers		Quota handlers configuration
azure_entra_id
rlsapi_v1		Configuration for the rlsapi v1 /infer endpoint used by the RHEL Lightspeed Command Line Assistant (CLA).
splunk		Splunk HEC configuration for sending telemetry events.
deployment_environment	string	Deployment environment name (e.g., ‘development’, ‘staging’, ‘production’). Used in telemetry events.
rag		Configuration for all RAG strategies (inline and tool-based).
okp		OKP provider settings. Only used when ‘okp’ is listed in rag.inline or rag.tool.
reranker		Configuration for neural reranking of RAG chunks using cross-encoder.
skills		Agent skills configuration. Specifies paths to skill directories.

ConfigurationResponse

Success response model for the config endpoint.

Attributes: configuration: Parsed application configuration returned to the client.

Field	Type	Description
configuration

ConversationData

Model representing conversation data returned by cache list operations.

Attributes: conversation_id: The conversation ID topic_summary: The topic summary for the conversation (can be None) last_message_timestamp: The timestamp of the last message in the conversation

Field	Type	Description
conversation_id	string
topic_summary	string
last_message_timestamp	number

ConversationDeleteResponse

Response for deleting a conversation.

Field	Type	Description
deleted	boolean	Whether the deletion was successful.
conversation_id	string	Conversation identifier that was passed to delete.

ConversationDetails

Model representing the details of a user conversation.

Attributes: conversation_id: The conversation ID (UUID). created_at: When the conversation was created. last_message_at: When the last message was sent. message_count: Number of user messages in the conversation. last_used_model: The last model used for the conversation. last_used_provider: The provider of the last used model. topic_summary: The topic summary for the conversation.

Example: python conversation = ConversationDetails( conversation_id="123e4567-e89b-12d3-a456-426614174000", created_at="2024-01-01T00:00:00Z", last_message_at="2024-01-01T00:05:00Z", message_count=5, last_used_model="gemini/gemini-2.0-flash", last_used_provider="gemini", topic_summary="Openshift Microservices Deployment Strategies", )

Field	Type	Description
conversation_id	string	Conversation ID (UUID)
created_at	string	When the conversation was created
last_message_at	string	When the last message was sent
message_count	integer	Number of user messages in the conversation
last_used_model	string	Identification of the last model used for the conversation
last_used_provider	string	Identification of the last provider used for the conversation
topic_summary	string	Topic summary for the conversation

ConversationHistoryConfiguration

Conversation history configuration.

Field	Type	Description
type	string	Type of database where the conversation history is to be stored.
memory		In-memory cache configuration
sqlite		SQLite database configuration
postgres		PostgreSQL database configuration

ConversationResponse

Model representing a response for retrieving a conversation.

Attributes: conversation_id: The conversation ID (UUID). chat_history: The chat history as a list of conversation turns.

Field	Type	Description
conversation_id	string	Conversation ID (UUID)
chat_history	array	The simplified chat history as a list of conversation turns

ConversationTurn

Model representing a single conversation turn.

Attributes: messages: List of messages in this turn. tool_calls: List of tool calls made in this turn. tool_results: List of tool results from this turn. provider: Provider identifier used for this turn. model: Model identifier used for this turn. started_at: ISO 8601 timestamp when the turn started. completed_at: ISO 8601 timestamp when the turn completed.

Field	Type	Description
messages	array	List of messages in this turn
tool_calls	array	List of tool calls made in this turn
tool_results	array	List of tool results from this turn
provider	string	Provider identifier used for this turn
model	string	Model identifier used for this turn
started_at	string	ISO 8601 timestamp when the turn started
completed_at	string	ISO 8601 timestamp when the turn completed

ConversationUpdateResponse

Model representing a response for updating a conversation topic summary.

Attributes: conversation_id: The conversation ID (UUID) that was updated. success: Whether the update was successful. message: A message about the update result.

Field	Type	Description
conversation_id	string	The conversation ID (UUID) that was updated
success	boolean	Whether the update was successful
message	string	A message about the update result

ConversationsListResponse

Model representing a response for listing conversations of a user.

Attributes: conversations: List of conversation details associated with the user.

Field	Type	Description
conversations	array

ConversationsListResponseV2

Model representing a response for listing conversations of a user.

Attributes: conversations: List of conversation data associated with the user.

Field	Type	Description
conversations	array

CustomProfile

Custom profile customization for prompts and validation.

Field	Type	Description
path	string	Path to Python modules containing custom profile.
prompts	object	Dictionary containing map of system prompts

Customization

Service customization.

Field	Type	Description
profile_path	string
disable_query_system_prompt	boolean
disable_shield_ids_override	boolean
system_prompt_path	string
system_prompt	string
agent_card_path	string
agent_card_config	object
custom_profile

DatabaseConfiguration

Database configuration.

Field	Type	Description
sqlite		SQLite database configuration
postgres		PostgreSQL database configuration

FeedbackResponse

Model representing a response to a feedback request.

Attributes: response: The response of the feedback request.

Field	Type	Description
response	string	The response of the feedback request.

FeedbackStatusUpdateResponse

Model representing a response to a feedback status update request.

Attributes: status: The previous and current status of the service and who updated it.

Field	Type	Description
status	object

FileResponse

Response model containing a file object.

Attributes: id: File ID. filename: File name. bytes: File size in bytes. created_at: Unix timestamp when created. purpose: File purpose. object: Object type (always “file”).

Field	Type	Description
id	string	File ID
filename	string	File name
bytes	integer	File size in bytes
created_at	integer	Unix timestamp when created
purpose	string	File purpose
object	string	Object type

InMemoryCacheConfig

In-memory cache configuration.

Field	Type	Description
max_entries	integer	Maximum number of entries stored in the in-memory cache

InferenceConfiguration

Inference configuration.

Field	Type	Description
default_model	string	Identification of default model used when no other model is specified.
default_provider	string	Identification of default provider used when no other model is specified.
context_windows	object	Map of fully-qualified model identifier (e.g., “openai/gpt-4o-mini”) to context window size in tokens. Used by the conversation compaction trigger to decide when older turns must be summarized before the input exceeds the window. Models absent from this map have no registered window — callers fall back to their own default or skip the token-based trigger.

InfoResponse

Model representing a response to an info request.

Attributes: name: Service name. service_version: Service version. llama_stack_version: Llama Stack version.

Field	Type	Description
name	string	Service name
service_version	string	Service version
llama_stack_version	string	Llama Stack version

JsonPathOperator

Supported operators for JSONPath evaluation.

Note: this is not a real model, just an enumeration of all supported JSONPath operators.

JwkConfiguration

JWK (JSON Web Key) configuration.

A JSON Web Key (JWK) is a JavaScript Object Notation (JSON) data structure that represents a cryptographic key.

Useful resources:

• JSON Web Key
• RFC 7517

Field	Type	Description
url	string	HTTPS URL of the JWK (JSON Web Key) set used to validate JWTs.
jwt_configuration		JWT (JSON Web Token) configuration

JwtConfiguration

JWT (JSON Web Token) configuration.

JSON Web Token (JWT) is a compact, URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is used as the payload of a JSON Web Signature (JWS) structure or as the plaintext of a JSON Web Encryption (JWE) structure, enabling the claims to be digitally signed or integrity protected with a Message Authentication Code (MAC) and/or encrypted.

Useful resources:

• JSON Web Token
• RFC 7519
• JSON Web Tokens

Field	Type	Description
user_id_claim	string	JWT claim name that uniquely identifies the user (subject ID).
username_claim	string	JWT claim name that provides the human-readable username.
role_rules	array	Rules for extracting roles from JWT claims

JwtRoleRule

Rule for extracting roles from JWT claims.

Field	Type	Description
jsonpath	string	JSONPath expression to evaluate against the JWT payload
operator		JSON path comparison operator
negate	boolean	If set to true, the meaning of the rule is negated
value		Value to compare against
roles	array	Roles to be assigned if the rule matches

LivenessResponse

Model representing a response to a liveness request.

Attributes: alive: If app is alive.

Field	Type	Description
alive	boolean	Flag indicating that the app is alive

LlamaStackConfiguration

Llama stack configuration.

Llama Stack is a comprehensive system that provides a uniform set of tools for building, scaling, and deploying generative AI applications, enabling developers to create, integrate, and orchestrate multiple AI services and capabilities into an adaptable setup.

Useful resources:

• Llama Stack
• Python Llama Stack client
• Build AI Applications with Llama Stack

Field	Type	Description
url	string	URL to Llama Stack service; used when library mode is disabled. Must be a valid HTTP or HTTPS URL.
api_key	string	API key to access Llama Stack service
use_as_library_client	boolean	When set to true Llama Stack will be used in library mode, not in server mode (default)
library_client_config_path	string	Path to configuration file used when Llama Stack is run in library mode
timeout	integer	Timeout in seconds for requests to Llama Stack service. Default is 180 seconds (3 minutes) to accommodate long-running RAG queries.
max_retries	integer	Maximum number of connection attempts before giving up. Used on startup to connect to Llama Stack and retrieve its version. Connection attempts are retried with a fixed delay to handle the case where Llama Stack is still starting up (e.g., when running as a sidecar in the same pod).
retry_delay	integer	Delay in seconds between retry attempts. Used on startup to connect to Llama Stack and retrieve its version. Connection attempts are retried with a fixed delay to handle the case where Llama Stack is still starting up (e.g., when running as a sidecar in the same pod).
allow_degraded_mode	boolean	If enabled, Lightspeed Core can be started even when Llama Stack is not accessible (valid for server mode only)

MCPClientAuthOptionsResponse

Response containing MCP servers that accept client-provided authorization.

Attributes: servers: MCP servers that declare client authentication headers.

Field	Type	Description
servers	array	List of MCP servers that accept client-provided authorization

MCPListToolsTool

Tool definition returned by MCP list tools operation.

:param input_schema: JSON schema defining the tool’s input parameters :param name: Name of the tool :param description: (Optional) Description of what the tool does

Field	Type	Description
input_schema	object
name	string
description	string

MCPServerAuthInfo

Information about MCP server client authentication options.

Field	Type	Description
name	string	MCP server name
client_auth_headers	array	List of authentication header names for client-provided tokens

MCPServerDeleteResponse

Response indicating the outcome of an MCP server delete operation.

Attributes: name: Name of the MCP server targeted for deletion. deleted: Whether the server was successfully deleted (True) or not found (False). response: Description of the result, e.g. “MCP server deleted successfully”.

Field	Type	Description
deleted	boolean	Whether the deletion was successful.
name	string	MCP server name that was passed to delete.

MCPServerInfo

Information about a registered MCP server.

Attributes: name: Unique name of the MCP server. url: URL of the MCP server endpoint. provider_id: MCP provider identification. source: Whether the server was registered statically (config) or dynamically (api).

Field	Type	Description
name	string	MCP server name
url	string	MCP server URL
provider_id	string	MCP provider identification
source	string	How the server was registered: ‘config’ (static) or ‘api’ (dynamic)

MCPServerListResponse

Response listing all registered MCP servers.

Attributes: servers: All registered MCP servers (static and dynamic).

Field	Type	Description
servers	array	List of all registered MCP servers (static and dynamic)

MCPServerRegistrationResponse

Response for a successful MCP server registration.

Attributes: name: Registered MCP server name. url: Registered MCP server URL. provider_id: MCP provider identification. message: Status message.

Field	Type	Description
name	string	Registered MCP server name
url	string	Registered MCP server URL
provider_id	string	MCP provider identification
message	string	Status message

Message

Model representing a message in a conversation turn.

Attributes: content: The message content. type: The type of message. referenced_documents: Optional list of documents referenced in an assistant response.

Field	Type	Description
content	string	The message content
type	string	The type of message
referenced_documents	array	List of documents referenced in the response (assistant messages only)

ModelContextProtocolServer

Model context protocol server configuration.

MCP (Model Context Protocol) servers provide tools and capabilities to the AI agents. These are configured by this structure. Only MCP servers defined in the lightspeed-stack.yaml configuration are available to the agents. Tools configured in the llama-stack run.yaml are not accessible to lightspeed-core agents.

Useful resources:

• Model Context Protocol
• MCP FAQs
• Wikipedia article

Field	Type	Description
name	string	MCP server name that must be unique
provider_id	string	MCP provider identification
url	string	URL of the MCP server
authorization_headers	object	Headers to send to the MCP server. The map contains the header name and the path to a file containing the header value (secret). There are 3 special cases: 1. Usage of the kubernetes token in the header. To specify this use a string ‘kubernetes’ instead of the file path. 2. Usage of the client-provided token in the header. To specify this use a string ‘client’ instead of the file path. 3. Usage of the oauth token in the header. To specify this use a string ‘oauth’ instead of the file path.
headers	array	List of HTTP header names to automatically forward from the incoming request to this MCP server. Headers listed here are extracted from the original client request and included when calling the MCP server. This is useful when infrastructure components (e.g. API gateways) inject headers that MCP servers need, such as x-rh-identity in HCC. Header matching is case-insensitive. These headers are additive with authorization_headers and MCP-HEADERS.
require_approval		When to require human approval for tool invocations. ‘always’ requires approval for all tools, ‘never’ auto-approves, or use ApprovalFilter for granular control.
timeout	integer	Timeout in seconds for requests to the MCP server. If not specified, the default timeout from Llama Stack will be used. Note: This field is reserved for future use when Llama Stack adds timeout support.

ModelsResponse

Model representing a response to models request.

Field	Type	Description
models	array	List of models available

OkpConfiguration

OKP (Offline Knowledge Portal) provider configuration.

Controls provider-specific behaviour for the OKP vector store. Only relevant when "okp" is listed in rag.inline or rag.tool.

Field	Type	Description
rhokp_url	string	Base URL for the OKP server (http or https). Set to ${env.RH_SERVER_OKP} in YAML to use the environment variable. When unset, the default from constants is used.
offline	boolean	When True, use parent_id for OKP chunk source URLs. When False, use reference_url for chunk source URLs.
chunk_filter_query	string	Additional OKP filter query applied to every OKP search request. Use Solr boolean syntax, e.g. ‘product:ansible AND product:openshift’.

OpenAIResponseAnnotationCitation

URL citation annotation for referencing external web resources.

:param type: Annotation type identifier, always “url_citation” :param end_index: End position of the citation span in the content :param start_index: Start position of the citation span in the content :param title: Title of the referenced web resource :param url: URL of the referenced web resource

Field	Type	Description
type	string
end_index	integer
start_index	integer
title	string
url	string

OpenAIResponseAnnotationContainerFileCitation

Field	Type	Description
type	string
container_id	string
end_index	integer
file_id	string
filename	string
start_index	integer

OpenAIResponseAnnotationFileCitation

File citation annotation for referencing specific files in response content.

:param type: Annotation type identifier, always “file_citation” :param file_id: Unique identifier of the referenced file :param filename: Name of the referenced file :param index: Position index of the citation within the content

Field	Type	Description
type	string
file_id	string
filename	string
index	integer

OpenAIResponseAnnotationFilePath

Field	Type	Description
type	string
file_id	string
index	integer

OpenAIResponseContentPartRefusal

Refusal content within a streamed response part.

:param type: Content part type identifier, always “refusal” :param refusal: Refusal text supplied by the model

Field	Type	Description
type	string
refusal	string

OpenAIResponseError

Error details for failed OpenAI response requests.

:param code: Error code identifying the type of failure :param message: Human-readable error message describing the failure

Field	Type	Description
code	string
message	string

OpenAIResponseInputMessageContentFile

File content for input messages in OpenAI response format.

:param type: The type of the input item. Always input_file. :param file_data: The data of the file to be sent to the model. :param file_id: (Optional) The ID of the file to be sent to the model. :param file_url: The URL of the file to be sent to the model. :param filename: The name of the file to be sent to the model.

Field	Type	Description
type	string
file_data	string
file_id	string
file_url	string
filename	string

OpenAIResponseInputMessageContentImage

Image content for input messages in OpenAI response format.

:param detail: Level of detail for image processing, can be “low”, “high”, or “auto” :param type: Content type identifier, always “input_image” :param file_id: (Optional) The ID of the file to be sent to the model. :param image_url: (Optional) URL of the image content

Field	Type	Description
detail
type	string
file_id	string
image_url	string

OpenAIResponseInputMessageContentText

Text content for input messages in OpenAI response format.

:param text: The text content of the input message :param type: Content type identifier, always “input_text”

Field	Type	Description
text	string
type	string

OpenAIResponseInputToolChoiceAllowedTools

Constrains the tools available to the model to a pre-defined set.

:param mode: Constrains the tools available to the model to a pre-defined set :param tools: A list of tool definitions that the model should be allowed to call :param type: Tool choice type identifier, always “allowed_tools”

Field	Type	Description
mode	string
tools	array
type	string

OpenAIResponseInputToolChoiceCustomTool

Forces the model to call a custom tool.

:param type: Tool choice type identifier, always “custom” :param name: The name of the custom tool to call.

Field	Type	Description
type	string
name	string

OpenAIResponseInputToolChoiceFileSearch

Indicates that the model should use file search to generate a response.

:param type: Tool choice type identifier, always “file_search”

Field	Type	Description
type	string

OpenAIResponseInputToolChoiceFunctionTool

Forces the model to call a specific function.

:param name: The name of the function to call :param type: Tool choice type identifier, always “function”

Field	Type	Description
name	string
type	string

OpenAIResponseInputToolChoiceMCPTool

Forces the model to call a specific tool on a remote MCP server

:param server_label: The label of the MCP server to use. :param type: Tool choice type identifier, always “mcp” :param name: (Optional) The name of the tool to call on the server.

Field	Type	Description
server_label	string
type	string
name	string

OpenAIResponseInputToolChoiceMode

OpenAIResponseInputToolChoiceWebSearch

Indicates that the model should use web search to generate a response

:param type: Web search tool type variant to use

Field	Type	Description
type

OpenAIResponseInputToolFileSearch

File search tool configuration for OpenAI response inputs.

:param type: Tool type identifier, always “file_search” :param vector_store_ids: List of vector store identifiers to search within :param filters: (Optional) Additional filters to apply to the search :param max_num_results: (Optional) Maximum number of search results to return (1-50) :param ranking_options: (Optional) Options for ranking and scoring search results

Field	Type	Description
type	string
vector_store_ids	array
filters	object
max_num_results	integer
ranking_options

OpenAIResponseInputToolFunction

Function tool configuration for OpenAI response inputs.

:param type: Tool type identifier, always “function” :param name: Name of the function that can be called :param description: (Optional) Description of what the function does :param parameters: (Optional) JSON schema defining the function’s parameters :param strict: (Optional) Whether to enforce strict parameter validation

Field	Type	Description
type	string
name	string
description	string
parameters	object
strict	boolean

OpenAIResponseInputToolWebSearch

Web search tool configuration for OpenAI response inputs.

:param type: Web search tool type variant to use :param search_context_size: (Optional) Size of search context, must be “low”, “medium”, or “high”

Field	Type	Description
type
search_context_size	string

OpenAIResponseMCPApprovalRequest

A request for human approval of a tool invocation.

Field	Type	Description
arguments	string
id	string
name	string
server_label	string
type	string

OpenAIResponseMessage

Corresponds to the various Message types in the Responses API. They are all under one type because the Responses API gives them all the same “type” value, and there is no way to tell them apart in certain scenarios.

Field	Type	Description
content
role
type	string
id	string
status	string

OpenAIResponseOutputMessageContentOutputText

Field	Type	Description
text	string
type	string
annotations	array
logprobs	array

OpenAIResponseOutputMessageFileSearchToolCall

File search tool call output message for OpenAI responses.

:param id: Unique identifier for this tool call :param queries: List of search queries executed :param status: Current status of the file search operation :param type: Tool call type identifier, always “file_search_call” :param results: (Optional) Search results returned by the file search operation

Field	Type	Description
id	string
queries	array
status	string
type	string
results	array

OpenAIResponseOutputMessageFileSearchToolCallResults

Search results returned by the file search operation.

:param attributes: (Optional) Key-value attributes associated with the file :param file_id: Unique identifier of the file containing the result :param filename: Name of the file containing the result :param score: Relevance score for this search result (between 0 and 1) :param text: Text content of the search result

Field	Type	Description
attributes	object
file_id	string
filename	string
score	number
text	string

OpenAIResponseOutputMessageFunctionToolCall

Function tool call output message for OpenAI responses.

:param call_id: Unique identifier for the function call :param name: Name of the function being called :param arguments: JSON string containing the function arguments :param type: Tool call type identifier, always “function_call” :param id: (Optional) Additional identifier for the tool call :param status: (Optional) Current status of the function call execution

Field	Type	Description
call_id	string
name	string
arguments	string
type	string
id	string
status	string

OpenAIResponseOutputMessageMCPCall

Model Context Protocol (MCP) call output message for OpenAI responses.

:param id: Unique identifier for this MCP call :param type: Tool call type identifier, always “mcp_call” :param arguments: JSON string containing the MCP call arguments :param name: Name of the MCP method being called :param server_label: Label identifying the MCP server handling the call :param error: (Optional) Error message if the MCP call failed :param output: (Optional) Output result from the successful MCP call

Field	Type	Description
id	string
type	string
arguments	string
name	string
server_label	string
error	string
output	string

OpenAIResponseOutputMessageMCPListTools

MCP list tools output message containing available tools from an MCP server.

:param id: Unique identifier for this MCP list tools operation :param type: Tool call type identifier, always “mcp_list_tools” :param server_label: Label identifying the MCP server providing the tools :param tools: List of available tools provided by the MCP server

Field	Type	Description
id	string
type	string
server_label	string
tools	array

OpenAIResponseOutputMessageWebSearchToolCall

Web search tool call output message for OpenAI responses.

:param id: Unique identifier for this tool call :param status: Current status of the web search operation :param type: Tool call type identifier, always “web_search_call”

Field	Type	Description
id	string
status	string
type	string

OpenAIResponsePrompt

OpenAI compatible Prompt object that is used in OpenAI responses.

:param id: Unique identifier of the prompt template :param variables: Dictionary of variable names to OpenAIResponseInputMessageContent structure for template substitution. The substitution values can either be strings, or other Response input types like images or files. :param version: Version number of the prompt to use (defaults to latest if not specified)

Field	Type	Description
id	string
variables	object
version	string

OpenAIResponseReasoning

Configuration for reasoning effort in OpenAI responses.

Controls how much reasoning the model performs before generating a response.

:param effort: The effort level for reasoning. “low” favors speed and economical token usage, “high” favors more complete reasoning, “medium” is a balance between the two.

Field	Type	Description
effort	string

OpenAIResponseText

Text response configuration for OpenAI responses.

:param format: (Optional) Text format configuration specifying output format requirements

Field	Type	Description
format

OpenAIResponseTextFormat

Configuration for Responses API text format.

:param type: Must be “text”, “json_schema”, or “json_object” to identify the format type :param name: The name of the response format. Only used for json_schema. :param schema: The JSON schema the response should conform to. In a Python SDK, this is often a pydantic model. Only used for json_schema. :param description: (Optional) A description of the response format. Only used for json_schema. :param strict: (Optional) Whether to strictly enforce the JSON schema. If true, the response must match the schema exactly. Only used for json_schema.

Field	Type	Description
type
name	string
schema	object
description	string
strict	boolean

OpenAIResponseToolMCP

Model Context Protocol (MCP) tool configuration for OpenAI response object.

:param type: Tool type identifier, always “mcp” :param server_label: Label to identify this MCP server :param allowed_tools: (Optional) Restriction on which tools can be used from this server

Field	Type	Description
type	string
server_label	string
allowed_tools

OpenAIResponseUsage

Usage information for OpenAI response.

:param input_tokens: Number of tokens in the input :param output_tokens: Number of tokens in the output :param total_tokens: Total tokens used (input + output) :param input_tokens_details: Detailed breakdown of input token usage :param output_tokens_details: Detailed breakdown of output token usage

Field	Type	Description
input_tokens	integer
output_tokens	integer
total_tokens	integer
input_tokens_details
output_tokens_details

OpenAIResponseUsageInputTokensDetails

Token details for input tokens in OpenAI response usage.

:param cached_tokens: Number of tokens retrieved from cache

Field	Type	Description
cached_tokens	integer

OpenAIResponseUsageOutputTokensDetails

Token details for output tokens in OpenAI response usage.

:param reasoning_tokens: Number of tokens used for reasoning (o1/o3 models)

Field	Type	Description
reasoning_tokens	integer

OpenAITokenLogProb

The log probability for a token from an OpenAI-compatible chat completion response.

Field	Type	Description
token	string	The token.
bytes	array	The bytes for the token.
logprob	number	The log probability of the token.
top_logprobs	array	The top log probabilities for the token.

OpenAITopLogProb

The top log probability for a token from an OpenAI-compatible chat completion response.

Field	Type	Description
token	string	The token.
bytes	array	The bytes for the token.
logprob	number	The log probability of the token.

PostgreSQLDatabaseConfiguration

PostgreSQL database configuration.

PostgreSQL database is used by Lightspeed Core Stack service for storing information about conversation IDs. It can also be leveraged to store conversation history and information about quota usage.

Useful resources:

• Psycopg: connection classes
• PostgreSQL connection strings
• How to Use PostgreSQL in Python

Field	Type	Description
host	string	Database server host or socket directory
port	integer	Database server port
db	string	Database name to connect to
user	string	Database user name used to authenticate
password	string	Password used to authenticate
namespace	string	Database namespace
ssl_mode	string	SSL mode
gss_encmode	string	This option determines whether or with what priority a secure GSS TCP/IP connection will be negotiated with the server.
ca_cert_path	string	Path to CA certificate

PromptDeleteResponse

Result of deleting a stored prompt (always HTTP 200, like conversations v2).

Attributes: prompt_id: Prompt identifier that was passed to delete. deleted: Whether the prompt was deleted successfully response: Human readable response

Field	Type	Description
deleted	boolean	Whether the deletion was successful.
prompt_id	string	Prompt identifier that was passed to delete.

PromptResourceResponse

A stored prompt template as returned by Llama Stack.

Attributes: prompt_id: Prompt identifier from Llama Stack. version: Version number for this prompt. is_default: Whether this version is the default. prompt: Prompt text with placeholders. variables: Variable names used in the template.

Field	Type	Description
prompt_id	string	Prompt identifier from Llama Stack
version	integer	Version number for this prompt
is_default	boolean	Whether this version is the default
prompt	string	Prompt text with placeholders
variables	array	Variable names used in the template

PromptsListResponse

List of stored prompt templates returned by Llama Stack.

Attributes: data: Prompt entries as returned by the Llama Stack list API.

Field	Type	Description
data	array	Prompt entries (as returned by Llama Stack list)

ProviderHealthStatus

Model representing the health status of a provider.

Attributes: provider_id: The ID of the provider. status: The health status (‘ok’, ‘unhealthy’, ‘not_implemented’). message: Optional message about the health status.

Field	Type	Description
provider_id	string	The ID of the provider
status	string	The health status
message	string	Optional message about the health status

ProviderResponse

Model representing a response to get specific provider request.

Field	Type	Description
api	string	The API this provider implements
config	object	Provider configuration parameters
health	object	Current health status of the provider
provider_id	string	Unique provider identifier
provider_type	string	Provider implementation type

ProvidersListResponse

Model representing a response to providers request.

Field	Type	Description
providers	object	List of available API types and their corresponding providers

QueryResponse

Model representing LLM response to a query.

Attributes: conversation_id: The optional conversation ID (UUID). response: The response. rag_chunks: Deprecated. List of RAG chunks used to generate the response. This information is now available in tool_results under file_search_call type. referenced_documents: The URLs and titles for the documents used to generate the response. tool_calls: List of tool calls made during response generation. tool_results: List of tool results. truncated: Whether conversation history was truncated. input_tokens: Number of tokens sent to LLM. output_tokens: Number of tokens received from LLM. available_quotas: Quota available as measured by all configured quota limiters.

Field	Type	Description
conversation_id	string	The optional conversation ID (UUID)
response	string	Response from LLM
rag_chunks	array	Deprecated: List of RAG chunks used to generate the response.
referenced_documents	array	List of documents referenced in generating the response
truncated	boolean	Deprecated: whether conversation history was truncated
input_tokens	integer	Number of tokens sent to LLM
output_tokens	integer	Number of tokens received from LLM
available_quotas	object	Quota available as measured by all configured quota limiters
tool_calls	array	List of tool calls made during response generation
tool_results	array	List of tool results

QuotaHandlersConfiguration

Quota limiter configuration.

It is possible to limit quota usage per user or per service or services (that typically run in one cluster). Each limit is configured as a separate quota limiter. It can be of type user_limiter or cluster_limiter (which is name that makes sense in OpenShift deployment).

Field	Type	Description
sqlite		SQLite database configuration
postgres		PostgreSQL database configuration
limiters	array	Quota limiters configuration
scheduler		Quota scheduler configuration
enable_token_history	boolean	Enables storing information about token usage history

QuotaLimiterConfiguration

Configuration for one quota limiter.

There are three configuration options for each limiter:

1. period is specified in a human-readable form, see https://www.postgresql.org/docs/current/datatype-datetime.html#DATATYPE-INTERVAL-INPUT for all possible options. When the end of the period is reached, the quota is reset or increased.
2. initial_quota is the value set at the beginning of the period.
3. quota_increase is the value (if specified) used to increase the quota when the period is reached.

There are two basic use cases:

1. When the quota needs to be reset to a specific value periodically (for example on a weekly or monthly basis), set initial_quota to the required value.
2. When the quota needs to be increased by a specific value periodically (for example on a daily basis), set quota_increase.

Field	Type	Description
type	string	Quota limiter type, either user_limiter or cluster_limiter
name	string	Human readable quota limiter name
initial_quota	integer	Quota set at beginning of the period
quota_increase	integer	Delta value used to increase quota when period is reached
period	string	Period specified in human readable form

QuotaSchedulerConfiguration

Quota scheduler configuration.

Field	Type	Description
period	integer	Quota scheduler period specified in seconds
database_reconnection_count	integer	Database reconnection count on startup. When database for quota is not available on startup, the service tries to reconnect N times with specified delay.
database_reconnection_delay	integer	Database reconnection delay specified in seconds. When database for quota is not available on startup, the service tries to reconnect N times with specified delay.

RAGChunk

Model representing a RAG chunk used in the response.

Field	Type	Description
content	string	The content of the chunk
source	string	Index name identifying the knowledge source from configuration
score	number	Relevance score
attributes	object	Document metadata from the RAG provider (e.g., url, title, author)

RAGInfoResponse

Model representing a response with information about RAG DB.

Field	Type	Description
id	string	Vector DB unique ID
name	string	Human readable vector DB name
created_at	integer	When the vector store was created, represented as Unix time
last_active_at	integer	When the vector store was last active, represented as Unix time
usage_bytes	integer	Storage byte(s) used by this vector DB
expires_at	integer	When the vector store expires, represented as Unix time
object	string	Object type
status	string	Vector DB status

RAGListResponse

Model representing a response to list RAGs request.

Field	Type	Description
rags	array	List of RAG identifiers

RHIdentityConfiguration

Red Hat Identity authentication configuration.

Field	Type	Description
required_entitlements	array	List of all required entitlements.
max_header_size	integer	Maximum allowed size in bytes for the base64-encoded x-rh-identity header. Headers exceeding this size are rejected before decoding.

RagConfiguration

RAG strategy configuration.

Controls which RAG sources are used for inline and tool-based retrieval.

Each strategy lists RAG IDs to include. The special ID "okp" defined in constants, activates the OKP provider; all other IDs refer to entries in byok_rag.

Backward compatibility: - inline defaults to [] (no inline RAG). - tool defaults to [] (no tool RAG).

If no RAG strategy is defined (inline and tool are empty), the RAG tool will register all stores available to llama-stack.

Field	Type	Description
inline	array	RAG IDs whose sources are injected as context before the LLM call. Use ‘okp’ to enable OKP inline RAG. Empty by default (no inline RAG).
tool	array	RAG IDs made available to the LLM as a file_search tool. Use ‘okp’ to include the OKP vector store. When omitted, all registered BYOK vector stores are used (backward compatibility).

ReadinessResponse

Model representing response to a readiness request.

Attributes: ready: If service is ready. reason: The reason for the readiness. providers: List of unhealthy providers in case of readiness failure.

Field	Type	Description
ready	boolean	Flag indicating if service is ready
reason	string	The reason for the readiness
providers	array	List of unhealthy providers in case of readiness failure.

ReferencedDocument

Model representing a document referenced in generating a response.

Attributes: doc_url: Url to the referenced doc. doc_title: Title of the referenced doc. document_id: Document ID for preserving identity during deduplication.

Field	Type	Description
doc_url	string	URL of the referenced document
doc_title	string	Title of the referenced document
source	string	Index name identifying the knowledge source from configuration
document_id	string	Document ID for preserving identity during deduplication

RerankerConfiguration

Reranker configuration for RAG chunk reranking.

Field	Type	Description
enabled	boolean	When True, reranking applied to RAG chunks. When False, reranking is disabled and original scoring used.
model	string	Cross-encoder model name for reranking RAG chunks. Defaults to ‘cross-encoder/ms-marco-MiniLM-L6-v2’ from sentence-transformers.

ResponsesResponse

Model representing a response from the Responses API following LCORE specification.

Attributes: created_at: Unix timestamp when the response was created. completed_at: Unix timestamp when the response was completed, if applicable. error: Error details if the response failed or was blocked. id: Unique identifier for this response. model: Model identifier in “provider/model” format used for generation. object: Object type identifier, always “response”. output: List of structured output items containing messages, tool calls, and other content. This is the primary response content. parallel_tool_calls: Whether the model can make multiple tool calls in parallel. previous_response_id: Identifier of the previous response in a multi-turn conversation. prompt: The input prompt object that was sent to the model. status: Current status of the response (e.g., “completed”, “blocked”, “in_progress”). temperature: Temperature parameter used for generation (controls randomness). text: Text response configuration object used for OpenAI responses. top_p: Top-p sampling parameter used for generation. tools: List of tools available to the model during generation. tool_choice: Tool selection strategy used (e.g., “auto”, “required”, “none”). truncation: Strategy used for handling content that exceeds context limits. usage: Token usage statistics including input_tokens, output_tokens, and total_tokens. instructions: System instructions or guidelines provided to the model. max_tool_calls: Maximum number of tool calls allowed in a single response. reasoning: Reasoning configuration (effort level) used for the response. max_output_tokens: Upper bound for tokens generated in the response. safety_identifier: Safety/guardrail identifier applied to the request. metadata: Additional metadata dictionary with custom key-value pairs. store: Whether the response was stored. conversation: Conversation ID linking this response to a conversation thread (LCORE-specific). available_quotas: Remaining token quotas for the user (LCORE-specific). output_text: Aggregated text output from all output_text items in the output array.

Field	Type	Description
created_at	integer
completed_at	integer
error
id	string
model	string
object	string
output	array
parallel_tool_calls	boolean
previous_response_id	string
prompt
status	string
temperature	number
text
top_p	number
tools	array
tool_choice
truncation	string
usage
instructions	string
max_tool_calls	integer
reasoning
max_output_tokens	integer
safety_identifier	string
metadata	object
store	boolean
conversation	string
available_quotas	object
output_text	string

RlsapiV1Configuration

Configuration for the rlsapi v1 /infer endpoint.

Settings specific to the RHEL Lightspeed Command Line Assistant (CLA) stateless inference endpoint. Kept separate from shared configuration sections so that CLA-specific options do not affect other endpoints.

Field	Type	Description
allow_verbose_infer	boolean	Allow /v1/infer to return extended metadata (tool_calls, rag_chunks, token_usage) when the client sends “include_metadata”: true. Should NOT be enabled in production. If production use is needed, consider RBAC-based access control via an Action.RLSAPI_V1_INFER authorization rule.
quota_subject	string	Identity field used as the quota subject for /v1/infer. When set, token quota enforcement is enabled for this endpoint. Requires quota_handlers to be configured. “org_id” and “system_id” require rh-identity authentication; falls back to user_id when rh-identity data is unavailable.

RlsapiV1InferData

Response data for rlsapi v1 /infer endpoint.

Attributes: text: The generated response text. request_id: Unique identifier for the request. tool_calls: MCP tool calls made during inference (verbose mode only). tool_results: Results from MCP tool calls (verbose mode only). rag_chunks: RAG chunks retrieved from documentation (verbose mode only). referenced_documents: Source documents referenced (verbose mode only). input_tokens: Number of input tokens consumed (verbose mode only). output_tokens: Number of output tokens generated (verbose mode only).

Field	Type	Description
text	string	Generated response text
request_id	string	Unique request identifier
tool_calls	array	Tool calls made during inference (requires include_metadata=true)
tool_results	array	Results from tool calls (requires include_metadata=true)
rag_chunks	array	Retrieved RAG documentation chunks (requires include_metadata=true)
referenced_documents	array	Source documents referenced in answer (requires include_metadata=true)
input_tokens	integer	Number of input tokens consumed (requires include_metadata=true)
output_tokens	integer	Number of output tokens generated (requires include_metadata=true)

RlsapiV1InferResponse

RHEL Lightspeed rlsapi v1 /infer response.

Attributes: data: Response data containing text and request_id.

Field	Type	Description
data		Response data containing text and request_id

SQLiteDatabaseConfiguration

SQLite database configuration.

Field	Type	Description
db_path	string	Path to file where SQLite database is stored

SearchRankingOptions

Options for ranking and filtering search results.

This class configures how search results are ranked and filtered. You can use algorithm-based rerankers (weighted, RRF) or neural rerankers. Defaults from VectorStoresConfig are used when parameters are not provided.

Examples: # Weighted ranker with custom alpha SearchRankingOptions(ranker=”weighted”, alpha=0.7)

# RRF ranker with custom impact factor
SearchRankingOptions(ranker="rrf", impact_factor=50.0)

# Use config defaults (just specify ranker type)
SearchRankingOptions(ranker="weighted")  # Uses alpha from VectorStoresConfig

# Score threshold filtering
SearchRankingOptions(ranker="weighted", score_threshold=0.5)

:param ranker: (Optional) Name of the ranking algorithm to use. Supported values: - “weighted”: Weighted combination of vector and keyword scores - “rrf”: Reciprocal Rank Fusion algorithm - “neural”: Neural reranking model (requires model parameter, Part II) Note: For OpenAI API compatibility, any string value is accepted, but only the above values are supported. :param score_threshold: (Optional) Minimum relevance score threshold for results. Default: 0.0 :param alpha: (Optional) Weight factor for weighted ranker (0-1). - 0.0 = keyword only - 0.5 = equal weight (default) - 1.0 = vector only Only used when ranker=”weighted” and weights is not provided. Falls back to VectorStoresConfig.chunk_retrieval_params.weighted_search_alpha if not provided. :param impact_factor: (Optional) Impact factor (k) for RRF algorithm. Lower values emphasize higher-ranked results. Default: 60.0 (optimal from research). Only used when ranker=”rrf”. Falls back to VectorStoresConfig.chunk_retrieval_params.rrf_impact_factor if not provided. :param weights: (Optional) Dictionary of weights for combining different signal types. Keys can be “vector”, “keyword”, “neural”. Values should sum to 1.0. Used when combining algorithm-based reranking with neural reranking (Part II). Example: {“vector”: 0.3, “keyword”: 0.3, “neural”: 0.4} :param model: (Optional) Model identifier for neural reranker (e.g., “vllm/Qwen3-Reranker-0.6B”). Required when ranker=”neural” or when weights contains “neural” (Part II).

Field	Type	Description
ranker	string
score_threshold	number
alpha	number	Weight factor for weighted ranker
impact_factor	number	Impact factor for RRF algorithm
weights	object	Weights for combining vector, keyword, and neural scores. Keys: ‘vector’, ‘keyword’, ‘neural’
model	string	Model identifier for neural reranker

ServiceConfiguration

Service configuration.

Lightspeed Core Stack is a REST API service that accepts requests on a specified hostname and port. It is also possible to enable authentication and specify the number of Uvicorn workers. When more workers are specified, the service can handle requests concurrently.

Field	Type	Description
host	string	Service hostname
port	integer	Service port
base_url	string	Externally reachable base URL for the service; needed for A2A support.
auth_enabled	boolean	Enables the authentication subsystem
workers	integer	Number of Uvicorn worker processes to start
color_log	boolean	Enables colorized logging
access_log	boolean	Enables logging of all access information
tls_config		Transport Layer Security configuration for HTTPS support
root_path	string	ASGI root path for serving behind a reverse proxy on a subpath
cors		Cross-Origin Resource Sharing configuration for cross-domain requests

ShieldsResponse

Model representing a response to shields request.

Field	Type	Description
shields	array	List of shields available

SkillsConfiguration

Agent skills configuration.

Specifies paths to skill directories. Skill metadata (name, description) is read from SKILL.md frontmatter at startup.

Each path can point to either:

• A directory containing a SKILL.md file (single skill)
• A directory containing subdirectories with SKILL.md files (multiple skills)

Paths are validated at startup to ensure they exist and contain valid SKILL.md files.

Field	Type	Description
paths	array	Paths to skill directories or directories containing skill subdirectories.

SplunkConfiguration

Splunk HEC (HTTP Event Collector) configuration.

Splunk HEC allows sending events directly to Splunk over HTTP/HTTPS. This configuration is used to send telemetry events for inference requests to the corporate Splunk deployment.

Useful resources:

• Splunk HEC Docs
• About HEC

Field	Type	Description
enabled	boolean	Enable or disable Splunk HEC integration.
url	string	Splunk HEC endpoint URL.
token_path	string	Path to file containing the Splunk HEC authentication token.
index	string	Target Splunk index for events.
source	string	Event source identifier.
timeout	integer	HTTP timeout in seconds for HEC requests.
verify_ssl	boolean	Whether to verify SSL certificates for HEC endpoint.

StatusResponse

Model representing a response to a status request.

Attributes: functionality: The functionality of the service. status: The status of the service.

Field	Type	Description
functionality	string	The functionality of the service
status	object	The status of the service

StreamingInterruptResponse

Model representing a response to a streaming interrupt request.

Attributes: request_id: The streaming request ID targeted by the interrupt call. interrupted: Whether an in-progress stream was interrupted. message: Human-readable interruption status message.

Field	Type	Description
request_id	string	The streaming request ID targeted by the interrupt call
interrupted	boolean	Whether an in-progress stream was interrupted
message	string	Human-readable interruption status message

StreamingQueryResponse

Documentation-only model for streaming query responses using Server-Sent Events (SSE).

TLSConfiguration

TLS configuration.

Transport Layer Security (TLS) is a cryptographic protocol designed to provide communications security over a computer network, such as the Internet. The protocol is widely used in applications such as email, instant messaging, and voice over IP, but its use in securing HTTPS remains the most publicly visible.

Useful resources:

• FastAPI HTTPS Deployment
• Transport Layer Security Overview
• What is TLS

Field	Type	Description
tls_certificate_path	string	SSL/TLS certificate file path for HTTPS support.
tls_key_path	string	SSL/TLS private key file path for HTTPS support.
tls_key_password	string	Path to file containing the password to decrypt the SSL/TLS private key.

ToolCallSummary

Model representing a tool call made during response generation (for tool_calls list).

Field	Type	Description
id	string	ID of the tool call
name	string	Name of the tool called
args	object	Arguments passed to the tool
type	string	Type indicator for tool call

ToolResultSummary

Model representing a result from a tool call (for tool_results list).

Field	Type	Description
id	string	ID of the tool call/result, matches the corresponding tool call ‘id’
status	string	Status of the tool execution (e.g., ‘success’)
content	string	Content/result returned from the tool
type	string	Type indicator for tool result
round	integer	Round number or step of tool execution

ToolsResponse

Model representing a response to tools request.

Field	Type	Description
tools	array	List of tools available from all configured MCP servers and built-in toolgroups

TrustedProxyConfiguration

Configuration for trusted-proxy auth module.

Field	Type	Description
user_header	string	HTTP header containing the forwarded user identity.
allowed_service_accounts	array	Optional allowlist of Kubernetes ServiceAccount identities permitted to act as trusted proxies. When set to null/omitted, any ServiceAccount with a valid token is accepted. When set to a non-empty list, only the listed ServiceAccounts are allowed. An empty list behaves the same as null (no restriction).

TrustedProxyServiceAccount

A Kubernetes ServiceAccount identity for trusted-proxy allowlist.

Field	Type	Description
namespace	string	Kubernetes namespace of the ServiceAccount.
name	string	Name of the Kubernetes ServiceAccount.

UserDataCollection

User data collection configuration.

Field	Type	Description
feedback_enabled	boolean	When set to true the user feedback is stored and later sent for analysis.
feedback_storage	string	Path to directory where feedback will be saved for further processing.
transcripts_enabled	boolean	When set to true the conversation history is stored and later sent for analysis.
transcripts_storage	string	Path to directory where conversation history will be saved for further processing.

VectorStoreDeleteResponse

Result of deleting a vector store (always HTTP 200).

Field	Type	Description
deleted	boolean	Whether the deletion was successful.
vector_store_id	string	Vector store identifier that was passed to delete.

VectorStoreFileDeleteResponse

Result of deleting a file from a vector store (always HTTP 200).

Field	Type	Description
deleted	boolean	Whether the deletion was successful.
file_id	string	File identifier that was passed to delete.

VectorStoreFileResponse

Response model containing a vector store file object.

Attributes: id: Vector store file ID. vector_store_id: ID of the vector store. status: File processing status. attributes: Optional metadata key-value pairs. last_error: Optional error message if processing failed. object: Object type (always “vector_store.file”).

Field	Type	Description
id	string	Vector store file ID
vector_store_id	string	ID of the vector store
status	string	File processing status
attributes	object	Set of up to 16 key-value pairs for storing additional information. Keys: strings (max 64 chars). Values: strings (max 512 chars), booleans, or numbers.
last_error	string	Error message if processing failed
object	string	Object type

VectorStoreFilesListResponse

Response model containing a list of vector store files.

Attributes: data: List of vector store file objects. object: Object type (always “list”).

Field	Type	Description
data	array	List of vector store files
object	string	Object type

VectorStoreResponse

Response model containing a single vector store.

Attributes: id: Vector store ID. name: Vector store name. created_at: Unix timestamp when created. last_active_at: Unix timestamp of last activity. expires_at: Optional Unix timestamp when it expires. status: Vector store status. usage_bytes: Storage usage in bytes. metadata: Optional metadata dictionary for storing session information.

Field	Type	Description
id	string	Vector store ID
name	string	Vector store name
created_at	integer	Unix timestamp when created
last_active_at	integer	Unix timestamp of last activity
expires_at	integer	Unix timestamp when it expires
status	string	Vector store status
usage_bytes	integer	Storage usage in bytes
metadata	object	Metadata dictionary for storing session information

VectorStoresListResponse

Response model containing a list of vector stores.

Attributes: data: List of vector store objects. object: Object type (always “list”).

Field	Type	Description
data	array	List of vector stores
object	string	Object type