Pipelines Config

Pipeline configuration API response types.

`AboutDeploymentMetadata`

Bases: BaseModel

Represents metadata about a deployment for the About tab.

Contains versioned metadata that appears in the About section of a deployment, including educational and descriptive content.

Attributes:

Name	Type	Description
`version`	`int`	The version of the About Deployment metadata schema
`video_url`	`str`	The URL of the instructional or demonstration video

`AgentDetailItemDefinition`

Bases: BaseModel

Represents a single configuration item for agent details.

Defines configurable parameters for agent behavior, including input types, values, and available options for form-based configuration.

Attributes:

Name	Type	Description
`item_type`	`str`	The type of input control (text, select, multiselect, etc.)
`name`	`str`	The parameter name for this configuration item
`value`	`str`	The current value of the parameter
`options`	`Optional[List[str]]`	Optional list of available options for select/multiselect types

`AgentDetailsEntry`

Bases: BaseModel

Represents a single agent details entry.

Agent details entries define configurable parameters and metadata for agent behavior, including input types, default values, and available options.

Attributes:

Name	Type	Description
`type`	`str`	The input type (e.g., text, select, multiselect, number)
`name`	`str`	The parameter name for this configuration entry
`value`	`Any`	The current value of this parameter
`options`	`Optional[List[str]]`	Available options for select or multiselect input types

`AgentTrigger`

Bases: BaseModel

Represents a trigger used to start a pipeline execution.

Agent triggers enable automatic pipeline execution based on external events, particularly email-based triggers for document processing and workflow automation.

Attributes:

Name	Type	Description
`email_id`	`str`	The email address that can trigger this agent
`allowed_type`	`str`	The type of content allowed to trigger execution
`allowed_values`	`str`	The specific values that are permitted for triggering
`pipeline_id`	`str`	The ID of the pipeline to execute when triggered
`data_source_id`	`Optional[str]`	Optional ID of the data source for input data
`store_connector_id`	`Optional[str]`	Optional ID of the storage connector for file handling
`email_action`	`str`	The action to perform when the email trigger activates
`forward_to`	`Optional[str]`	Optional email address to forward results to after execution

`Deployment`

Bases: BaseModel

Represents a deployment.

Deployments make pipelines available to end users with specific configurations, branding, and access controls. They define how users interact with pipelines in production environments.

Attributes:

Name	Type	Description
`pipeline_id`	`str`	The ID of the pipeline being deployed
`name`	`str`	The human-readable name of the deployment
`description`	`str`	A detailed description of the deployment's purpose and functionality
`deployment_user_prompts`	`Optional[List[DeploymentUserPrompt]]`	List of predefined prompts available to users
`deployment_prompt`	`Optional[str]`	Optional system prompt that configures the deployment behavior
`project_id`	`str`	The ID of the project containing this deployment
`is_recommended`	`bool`	Whether this deployment is featured/recommended to users
`tags`	`Optional[List[str]]`	Categorization tags for discovery and organization
`conversation_type`	`str`	The type of conversation interface (chat, form, etc.)
`about`	`Optional[AboutDeploymentMetadata]`	Optional metadata for the About/help section
`assignments`	`Optional[List[DeploymentAssignment]]`	Optional list of user/group assignments for access control

`DeploymentAssignment`

Bases: BaseModel

Represents a deployment assignment.

Deployment assignments link deployments to specific entities (users, groups, etc.) to control access and visibility of deployed pipelines.

Attributes:

Name	Type	Description
`deployment_id`	`str`	The ID of the deployment being assigned
`id`	`str`	The unique identifier of this assignment
`entity_id`	`str`	The ID of the entity (user, group) receiving the assignment
`entity_type`	`str`	The type of entity being assigned (user, group, etc.)
`created_at`	`Optional[datetime]`	Timestamp when the assignment was created
`updated_at`	`Optional[datetime]`	Timestamp when the assignment was last modified
`user_id`	`Optional[str]`	The ID of the user who created this assignment

`DeploymentUserPrompt`

Bases: BaseModel

A join entity that represents a user prompt associated with a deployment.

Links deployments with predefined user prompts to provide template interactions and guided user experiences.

Attributes:

Name	Type	Description
`deployment_id`	`str`	The ID of the deployment this prompt is associated with
`user_prompt_id`	`str`	The ID of the user prompt template

`ExportChunkingConfig`

Bases: BaseModel

Represents chunking configuration for data processing.

Defines how documents are split into chunks for vector storage and retrieval operations.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the chunking configuration
`chunk_size`	`int`	The size of each chunk in characters or tokens
`chunk_overlap`	`int`	The number of characters/tokens that overlap between chunks
`strategy_type`	`str`	The chunking strategy used (sentence, paragraph, etc.)

`ExportCredentialDataList`

Bases: BaseModel

Represents a key-value pair within credential data.

Used to store credential information as structured key-value pairs for authentication and authorization purposes.

Attributes:

Name	Type	Description
`key`	`str`	The key name for the credential data
`value`	`str`	The value associated with the key

`ExportCredentials`

Bases: BaseModel

Represents credential information for external service authentication.

Defines the complete credential configuration including type, source, and associated data required for authenticating with external services.

Attributes:

Name	Type	Description
`name`	`str`	The name of the credential set
`credential_type`	`str`	The type of credential (API key, OAuth, etc.)
`source_type`	`str`	The source where the credential originates
`credential_data_list`	`List[ExportCredentialDataList]`	List of key-value pairs containing credential data
`display_identifier_name`	`str`	The display name for the credential identifier
`administrative_scope`	`str`	The administrative scope of the credential
`origin`	`str`	The origin of the credential
`custom_credentials`	`Optional[dict]`	Optional custom credentials configuration
`id`	`str`	The unique identifier for the credential set

`ExportDataSource`

Bases: BaseModel

Represents a complete data source definition for export.

Contains all configuration and metadata for a data source, including chunking settings, database configuration, and associated files.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the data source
`name`	`Optional[str]`	Optional name of the data source
`execution_name`	`Optional[str]`	Optional execution name for programmatic access
`chunking_config`	`ExportChunkingConfig`	The chunking configuration for document processing
`data_source_type`	`str`	The type of data source (file, database, etc.)
`database_type`	`Optional[str]`	The database type for storage
`embedding_provider`	`Optional[str]`	The provider for text embeddings
`is_user_specific`	`bool`	Whether the data source is user-specific
`files`	`Optional[List[ExportDataSourceFile]]`	Optional list of files associated with the data source
`file_count`	`Optional[int]`	Optional count of files in the data source
`configuration_json`	`Optional[str]`	Optional JSON configuration string
`credentials`	`Optional[ExportCredentials]`	Optional credential information for access
`is_image_processing_enabled`	`bool`	Whether image processing is enabled
`description`	`Optional[str]`	Optional description of the data source
`vector_store`	`Optional[ExportVectorStore]`	Optional vector store configuration
`scan_document_for_images`	`bool`	Whether to scan documents for images
`image_processing_prompt`	`Optional[str]`	Optional prompt for image processing
`store_type`	`Optional[str]`	Optional store type
`table_document_processing_mode`	`Optional[str]`	Optional table document processing mode
`parser_configuration_json`	`Optional[str]`	Optional parser configuration JSON

`ExportDataSourceFile`

Bases: BaseModel

Represents a file within a data source for export.

Defines file information including location, path, and access tokens for files associated with data sources.

Attributes:

Name	Type	Description
`data_source_id`	`str`	The ID of the associated data source
`file_path`	`Optional[str]`	Optional path or location of the file
`input_token`	`Optional[str]`	Optional access token for the file

`ExportDependency`

Bases: BaseModel

Represents a dependency relationship between pipeline steps.

Defines the connection between a parent step's output handle and a child step's input handle, establishing data flow dependencies.

Attributes:

Name	Type	Description
`parent_id`	`str`	The UUID of the parent pipeline step
`parent_handle_id`	`str`	The UUID of the parent's output handle
`handle_id`	`str`	The UUID of the child's input handle

`ExportDeployment`

Bases: BaseModel

Represents a deployment configuration for export.

Contains all settings for deploying a pipeline to end users, including branding, user prompts, and access controls.

Attributes:

Name	Type	Description
`name`	`str`	The name of the deployment
`deployment_icon`	`Optional[str]`	Optional base64-encoded icon for the deployment
`deployment_description`	`str`	Description of the deployment's purpose
`user_prompts`	`Optional[List[ExportUserPrompt]]`	Optional list of predefined user prompts
`deployment_prompt`	`Optional[str]`	Optional hardcoded system prompt
`is_recommended`	`bool`	Whether this is a featured/recommended deployment
`tags`	`Optional[List[str]]`	Optional list of tags for categorization
`deployment_type`	`str`	The type of deployment
`conversation_type`	`str`	The type of conversation interface
`about_json`	`Optional[str]`	Optional JSON metadata about the deployment
`supported_input_modes`	`Optional[List[str]]`	Optional list of supported input modes
`browser_extension_config`	`Optional[ExportBrowserExtensionConfig]`	Optional browser extension configuration
`display_consumption_info`	`bool`	Whether to display consumption information

`ExportHandle`

Bases: BaseModel

Represents a connection handle for pipeline steps.

Handles are connection points that allow data flow between pipeline steps. They define input and output points with positioning and labeling information.

Attributes:

Name	Type	Description
`uuid`	`str`	The unique identifier of the handle
`type`	`str`	The type of handle (source or target)
`label`	`Optional[str]`	Optional display label for the handle
`tooltip`	`Optional[str]`	Optional tooltip text for the handle
`x`	`float`	The X-coordinate position of the handle
`y`	`float`	The Y-coordinate position of the handle

`ExportMemory`

Bases: BaseModel

Represents a memory definition for export.

Defines persistent memory storage for maintaining context and state across pipeline executions.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the memory
`name`	`str`	The name of the memory
`is_user_specific`	`bool`	Whether the memory is specific to individual users

`ExportMetadata`

Bases: BaseModel

Represents metadata for pipeline export operations.

Contains versioning, description, and export configuration information for pipeline import/export operations.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the export metadata
`export_version`	`Optional[str]`	Optional version timestamp for the export
`tagline`	`str`	A brief tagline describing the pipeline
`agent_description`	`str`	Detailed description of the agent
`industry`	`str`	The primary industry served
`tasks`	`str`	Description of tasks the pipeline performs
`credential_export_option`	`str`	How credentials are handled in export
`data_source_export_option`	`str`	How data sources are handled in export
`version_information`	`str`	Information about the pipeline version
`state`	`str`	The current state of the agent
`contributor_id`	`Optional[str]`	Optional ID of the contributor
`contributor_given_name`	`Optional[str]`	Optional given name of the contributor
`contributor_surname`	`Optional[str]`	Optional surname of the contributor
`video_link`	`Optional[str]`	Optional URL to instructional video
`readiness`	`str`	The readiness status of the pipeline
`department`	`str`	The department this pipeline is associated with
`agent_last_updated`	`str`	Timestamp of when the agent was last updated
`country`	`Optional[str]`	Optional country information

`ExportModel`

Bases: BaseModel

Represents a complete AI model definition for export.

Contains all configuration and metadata for an AI model, including deployment settings, pricing, and capability information.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the model
`display_name`	`str`	The human-readable name for display
`model_name`	`Optional[str]`	Optional internal model name
`prompt_id`	`Optional[str]`	Optional ID of the associated system prompt
`system_prompt_definition`	`Optional[ExportPrompt]`	Optional system prompt definition
`url`	`Optional[str]`	Optional API endpoint URL for the model
`input_type`	`str`	The type of input the model accepts
`provider`	`str`	The AI provider (OpenAI, Anthropic, etc.)
`credentials_definition`	`Optional[ExportCredentials]`	Optional credential requirements
`deployment_type`	`Optional[str]`	The deployment type (cloud, on-premises, etc.)
`source_type`	`str`	The source type of the model
`connection_string`	`Optional[str]`	Optional connection string for deployment
`container_name`	`Optional[str]`	Optional container name for deployment
`deployed_key`	`Optional[str]`	Optional deployment key
`deployed_url`	`Optional[str]`	Optional deployed URL
`state`	`Optional[str]`	Optional current state of the model
`uploaded_container_id`	`Optional[str]`	Optional ID of uploaded container
`library_model_id`	`Optional[str]`	Optional ID of the library model
`input_token_price`	`Optional[str]`	Optional pricing for input tokens
`output_token_price`	`Optional[str]`	Optional pricing for output tokens
`token_units`	`Optional[int]`	Optional token unit multiplier
`has_tool_support`	`Optional[bool]`	Whether the model supports tool calling
`allow_airia_credentials`	`Optional[bool]`	Whether to allow Airia-provided credentials
`allow_byok_credentials`	`Optional[bool]`	Whether to allow bring-your-own-key credentials
`author`	`Optional[str]`	Optional author information
`price_type`	`Optional[str]`	Optional pricing model type
`category`	`Optional[str]`	Optional category classification

`ExportPipeline`

Bases: BaseModel

Represents a complete pipeline definition for export.

Contains all metadata and configuration for a pipeline, including identification, description, industry classification, and step definitions.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the pipeline
`name`	`str`	The name of the pipeline
`execution_name`	`Optional[str]`	The name used for programmatic execution
`agent_icon`	`Optional[str]`	Optional icon reference
`agent_description`	`str`	Detailed description of the pipeline's purpose
`markdown_description`	`Optional[str]`	Markdown-formatted description
`tagline`	`Optional[str]`	Optional brief tagline for the pipeline
`video_link`	`Optional[str]`	Optional URL to instructional video
`industry`	`Optional[str]`	The primary industry served by this pipeline
`sub_industries`	`Optional[List[str]]`	Additional industry classifications
`tags`	`Optional[List[str]]`	Optional list of tags for categorization
`agent_details`	`Optional[Dict[str, List[AgentDetailItemDefinition]]]`	Dictionary of configurable agent parameters
`steps`	`List[ExportPipelineStep]`	List of pipeline steps that make up the workflow
`behaviours`	`Optional[List[str]]`	List of pipeline behaviours
`agent_unicode_icon`	`Optional[str]`	Optional unicode icon for the agent
`agent_icon_base64`	`Optional[str]`	Optional base64-encoded icon image
`alignment`	`str`	The alignment/layout of the pipeline

`ExportPipelineDefinitionResponse`

Bases: BaseModel

Represents the complete response for pipeline export operations.

The main response object containing all components of a pipeline export, including the pipeline definition, associated resources, and metadata.

Attributes:

Name	Type	Description
`available`	`bool`	Whether the pipeline is available for export
`metadata`	`ExportMetadata`	Export metadata and configuration information
`agent`	`ExportPipeline`	The main pipeline definition and configuration
`data_sources`	`Optional[List[ExportDataSource]]`	Optional list of data sources used by the pipeline
`prompts`	`Optional[List[ExportPrompt]]`	Optional list of prompts used by the pipeline
`tools`	`Optional[List[ExportTool]]`	Optional list of external tools used by the pipeline
`models`	`Optional[List[ExportModel]]`	Optional list of AI models used by the pipeline
`memories`	`Optional[List[ExportMemory]]`	Optional list of memory definitions used by the pipeline
`python_code_blocks`	`Optional[List[ExportPythonCodeBlock]]`	Optional list of Python code blocks used by the pipeline
`routers`	`Optional[List[ExportRouter]]`	Optional list of router configurations used by the pipeline
`approval_requests`	`Optional[List[ExportApprovalRequest]]`	Optional list of approval requests
`agent_cards`	`Optional[List[ExportAgentCard]]`	Optional list of agent cards
`deployment`	`Optional[ExportDeployment]`	Optional deployment configuration for the pipeline
`interfaces`	`Optional[ExportInterfaces]`	Optional list of interface configurations

`ExportPipelineStep`

Bases: BaseModel

Represents a complete pipeline step definition for export.

Contains all configuration and metadata for a single step in a pipeline, including positioning, connections, and step-specific parameters.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the step
`step_type`	`str`	The type of step (LLM, retrieval, etc.)
`position`	`ExportPosition`	The visual position of the step
`handles`	`List[ExportHandle]`	List of connection handles for the step
`dependencies_object`	`List[ExportDependency]`	List of input dependencies from other steps
`temperature`	`Optional[float]`	Optional temperature setting for LLM steps
`include_date_time_context`	`Optional[bool]`	Whether to include datetime context
`prompt_id`	`Optional[str]`	Optional ID of the associated prompt
`model_id`	`Optional[str]`	Optional ID of the associated model
`tool_ids`	`Optional[List[str]]`	Optional list of tool IDs used by this step
`tool_params_json`	`Optional[str]`	Optional JSON string with default parameters for tools
`data_source_id`	`Optional[str]`	Optional ID of the associated data source
`top_k`	`Optional[int]`	Optional number of top results to retrieve
`relevance_threshold`	`Optional[int]`	Optional relevance threshold for retrieval
`neighboring_chunks_count`	`Optional[int]`	Optional number of neighboring chunks
`hybrid_search_alpha`	`Optional[float]`	Optional alpha parameter for hybrid search
`database_type`	`Optional[str]`	Optional database type for data operations
`memory_id`	`Optional[str]`	Optional ID of the associated memory
`python_code_block_id`	`Optional[str]`	Optional ID of the associated Python code
`background_color`	`Optional[str]`	Optional background color for the step
`content`	`Optional[str]`	Optional content or configuration data
`step_title`	`str`	The human-readable title of the step
`width`	`Optional[int]`	The width of the step in the UI
`height`	`Optional[int]`	The height of the step in the UI
`ai_operation_step_include_chat_history`	`Optional[bool]`	Optional flag for including chat history in AI operations
`input_variables_json`	`Optional[str]`	Optional JSON string with input variables configuration

`ExportPosition`

Bases: BaseModel

Represents the position coordinates for UI elements.

Defines X and Y coordinates for positioning elements within the visual pipeline editor interface.

Attributes:

Name	Type	Description
`x`	`str`	The X-coordinate position
`y`	`str`	The Y-coordinate position

`ExportPrompt`

Bases: BaseModel

Represents a complete prompt definition for export.

Contains all information for a prompt including name, version information, and the sequence of messages that make up the prompt.

Attributes:

Name	Type	Description
`name`	`str`	The name of the prompt
`version_change_description`	`str`	Description of changes in this version
`prompt_message`	`Optional[str]`	Optional consolidated prompt message text
`is_agent_specific`	`Optional[bool]`	Whether the prompt is specific to an agent
`prompt_message_list`	`Optional[List[ExportPromptMessageList]]`	List of messages that make up the prompt
`id`	`str`	The unique identifier of the prompt

`ExportPromptMessageList`

Bases: BaseModel

Represents a single message within a prompt sequence.

Defines individual messages that make up a prompt, including content and ordering information.

Attributes:

Name	Type	Description
`text`	`str`	The content of the message
`order`	`int`	The order of this message in the prompt sequence

`ExportPythonCodeBlock`

Bases: BaseModel

Represents a Python code block for export.

Defines executable Python code that can be used within pipeline steps for custom processing logic.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the code block
`code`	`str`	The Python code content

`ExportRouter`

Bases: BaseModel

Represents a complete router definition for export.

Contains all configuration for intelligent routing of pipeline execution based on content analysis and decision logic.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the router
`model_id`	`Optional[str]`	Optional ID of the model used for routing decisions
`router_config_json`	`Optional[str]`	Optional JSON string of router configuration
`router_config`	`Optional[Dict[str, ExportRouterConfig]]`	Dictionary of routing configurations
`is_multi_route`	`Optional[bool]`	Optional flag indicating if this is a multi-route router
`include_chat_history`	`bool`	Whether to include chat history in routing decisions

`ExportRouterConfig`

Bases: BaseModel

Represents router configuration for conditional execution.

Defines routing rules and conditions for directing pipeline execution flow based on content analysis.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the router configuration
`prompt`	`str`	The prompt used for routing decisions
`is_default`	`Optional[bool]`	Whether this is the default routing option

`ExportTool`

Bases: BaseModel

Represents a complete external tool definition for export.

Contains all configuration and metadata for an external tool, including API endpoints, authentication, and parameter definitions.

Attributes:

Name	Type	Description
`tool_type`	`str`	The type of tool (native, external, etc.)
`name`	`str`	The name of the tool
`standardized_name`	`str`	The standardized name for the tool
`tool_description`	`str`	Description of the tool's functionality
`purpose`	`str`	The purpose or use case for the tool
`api_endpoint`	`str`	The API endpoint URL for the tool
`credentials_definition`	`Optional[ExportCredentials]`	Optional credential requirements
`headers_definition`	`Optional[List[ExportToolHeaders]]`	Optional HTTP headers for API calls
`body`	`Optional[str]`	Optional request body template
`parameters_definition`	`Optional[List[ExportToolParameters]]`	Optional parameter definitions
`method_type`	`str`	The HTTP method type (GET, POST, etc.)
`route_through_acc`	`bool`	Whether to route through the Access Control Center
`acc_group`	`Optional[str]`	The access control group
`use_user_credentials`	`bool`	Whether to use user-specific credentials
`use_user_credentials_type`	`Optional[str]`	The type of user credentials to use
`provider`	`str`	The provider of the tool
`body_type`	`str`	The type of the request body
`number_of_pages`	`Optional[int]`	Optional number of pages to retrieve
`should_retrieve_full_page_content`	`Optional[bool]`	Optional flag for full page content retrieval
`schema_definition`	`Optional[str]`	Optional schema definition for the tool
`request_timeout`	`Optional[int]`	The timeout for requests in seconds
`should_reroute`	`Optional[bool]`	Whether requests should be rerouted
`credentials_source_type`	`Optional[str]`	The source type of the credentials
`id`	`str`	The unique identifier of the tool

`ExportToolHeaders`

Bases: BaseModel

Represents HTTP headers for tool API calls.

Defines key-value pairs for HTTP headers used when making API calls to external tools.

Attributes:

Name	Type	Description
`key`	`str`	The header name
`value`	`str`	The header value

`ExportToolParameters`

Bases: BaseModel

Represents a parameter definition for external tools.

Defines input parameters for external tools including type, description, default values, and validation options.

Attributes:

Name	Type	Description
`name`	`str`	The parameter name
`parameter_type`	`str`	The data type of the parameter
`parameter_description`	`str`	Description of the parameter's purpose
`default`	`str`	The default value for the parameter
`valid_options`	`Optional[List[str]]`	Optional list of valid values for the parameter
`array_item_type`	`Optional[str]`	Optional type of items if this is an array parameter
`parameters`	`Optional[List[ExportToolParameters]]`	Optional nested parameters for complex types
`requirement`	`str`	Whether the parameter is required or optional
`id`	`Optional[str]`	The unique identifier of the parameter

`ExportUserPrompt`

Bases: BaseModel

Represents a user prompt template for export.

Defines predefined prompts that users can select from when interacting with deployed pipelines.

Attributes:

Name	Type	Description
`name`	`str`	The name of the user prompt
`message`	`str`	The prompt message content
`prompt_description`	`str`	Description of the prompt's purpose

`GetPipelinesConfigResponse`

Bases: BaseModel

Response model for the get_pipelines_config endpoint.

Attributes:

Name	Type	Description
`items`	`List[PipelineConfigItem]`	List of pipeline configuration items
`total_count`	`int`	Total count of pipeline configurations

`Pipeline`

Bases: BaseModel

Represents a processing pipeline.

Pipelines are the core workflow definition in the Airia platform, containing a series of connected steps that process data and execute AI tasks. Each pipeline can have multiple versions and can be deployed for end-user access.

Attributes:

Name	Type	Description
`id`	`str`	The unique identifier of the pipeline
`tenant_id`	`Optional[str]`	The tenant ID this pipeline belongs to
`project_id`	`Optional[str]`	The project ID this pipeline belongs to
`created_at`	`Optional[datetime]`	Timestamp when the pipeline was created
`updated_at`	`Optional[datetime]`	Timestamp when the pipeline was last modified
`user_id`	`Optional[str]`	The ID of the user who created this pipeline
`active_version_id`	`Optional[str]`	The ID of the currently active version for execution
`name`	`Optional[str]`	The human-readable name of the pipeline
`execution_name`	`Optional[str]`	The name used when executing the pipeline programmatically
`description`	`Optional[str]`	A detailed description of the pipeline's purpose and functionality
`video_link`	`Optional[str]`	Optional URL to a video demonstration or tutorial
`agent_icon_id`	`Optional[str]`	The ID of the icon used to represent this pipeline
`versions`	`Optional[List[PipelineVersion]]`	All available versions of this pipeline
`execution_stats`	`Optional[PipelineExecutionStats]`	Historical execution performance statistics
`industry`	`Optional[str]`	The primary industry or domain this pipeline serves
`sub_industries`	`Optional[List[str]]`	Additional industry classifications and tags
`agent_details`	`Optional[Dict[str, List[AgentDetailsEntry]]]`	Configurable parameters and metadata for agent behavior
`agent_details_tags`	`Optional[List[str]]`	Tags for categorizing agent capabilities
`active_version`	`Optional[PipelineVersion]`	The complete active version object (if loaded)
`backup_pipeline_id`	`Optional[str]`	ID of a fallback pipeline in case of failure
`deployment`	`Optional[Deployment]`	The deployment configuration for end-user access
`library_agent_id`	`Optional[str]`	ID of the library agent this pipeline is based on
`library_imported_hash`	`Optional[str]`	Hash of the imported library agent for version tracking
`library_imported_version`	`Optional[str]`	Version of the imported library agent
`is_deleted`	`Optional[bool]`	Whether this pipeline is marked for deletion
`agent_trigger`	`Optional[AgentTrigger]`	Optional trigger configuration for automatic execution
`api_key_id`	`Optional[str]`	ID of the API key used for external service authentication
`is_seeded`	`Optional[bool]`	Whether this pipeline comes from the platform's seed data
`behaviours`	`Optional[List[str]]`	List of capabilities and behaviors this agent can perform

`PipelineConfigItem`

Bases: BaseModel

Represents a single pipeline configuration item.

Attributes:

Name	Type	Description
`deployment_id`	`Optional[str]`	Deployment identifier (optional)
`deployment_name`	`str`	Name of the deployment
`deployment_description`	`str`	Description of the deployment
`user_keys`	`Optional[Any]`	User keys (optional)
`group_keys`	`Optional[Any]`	Group keys (optional)
`agent_icon`	`Optional[Any]`	Agent icon (optional)
`external`	`bool`	Whether this is an external pipeline
`active_version_id`	`str`	ID of the active version
`name`	`str`	Name of the pipeline
`execution_name`	`str`	Execution name for the pipeline
`description`	`str`	Description of the pipeline
`video_link`	`str`	Link to video documentation
`agent_icon_id`	`Optional[str]`	Agent icon identifier (optional)
`agent_unicode_icon`	`str`	Unicode icon for the agent
`versions`	`List[PipelineVersion]`	List of pipeline versions
`execution_stats`	`Optional[PipelineExecutionStats]`	Execution statistics
`industry`	`str`	Industry category
`sub_industries`	`Optional[Any]`	Sub-industry categories (optional)
`agent_details`	`Optional[Any]`	Agent details (optional)
`agent_details_tags`	`Optional[Any]`	Agent details tags (optional)
`active_version`	`PipelineVersion`	The active version details
`backup_pipeline_id`	`Optional[str]`	Backup pipeline ID (optional)
`deployment`	`Optional[Deployment]`	Deployment configuration (optional)
`library_agent_id`	`Optional[str]`	Library agent ID (optional)
`library_imported_hash`	`str`	Hash of imported library
`library_imported_version`	`str`	Version of imported library
`is_deleted`	`Optional[bool]`	Whether this pipeline is deleted (optional)
`agent_trigger`	`Optional[AgentTrigger]`	Agent trigger configuration (optional)
`api_key_id`	`Optional[str]`	API key identifier (optional)
`is_seeded`	`bool`	Whether this pipeline is seeded
`behaviours`	`Optional[Any]`	Behaviours configuration (optional)
`department_id`	`Optional[str]`	Department identifier (optional)
`department`	`Optional[Any]`	Department information (optional)
`id`	`str`	Unique identifier for the pipeline
`tenant_id`	`str`	Tenant identifier
`project_id`	`str`	Project identifier
`created_at`	`datetime`	Creation timestamp
`updated_at`	`datetime`	Last update timestamp
`user_id`	`Optional[str]`	User identifier who created the pipeline

`PipelineConfigResponse`

Bases: Pipeline

Represents a complete pipeline configuration response.

Extends the base Pipeline model with additional deployment and access control information. This is the primary response model for pipeline configuration endpoints that need full context including deployment details and user permissions.

Attributes:

Name	Type	Description
`deployment_id`	`Optional[str]`	The ID of the associated deployment (if deployed)
`deployment_name`	`Optional[str]`	The name of the associated deployment
`deployment_description`	`Optional[str]`	The description of the associated deployment
`user_keys`	`Optional[Dict[str, str]]`	Dictionary mapping user roles to user IDs for access control
`group_keys`	`Optional[Dict[str, str]]`	Dictionary mapping group roles to group IDs for access control
`agent_icon`	`Optional[str]`	Base64 encoded agent icon image data
`external`	`Optional[bool]`	Whether this is an external agent imported via Agent-to-Agent (A2A)

`PipelineExecutionStats`

Bases: BaseModel

Represents the execution statistics for a pipeline.

Tracks the historical performance and reliability metrics of pipeline executions to provide insights into pipeline health and success rates.

Attributes:

Name	Type	Description
`success_count`	`int`	The total number of successful executions
`failure_count`	`int`	The total number of failed executions

`PipelineStep`

Bases: BaseModel

Represents a step within a pipeline.

Pipeline steps are individual processing units that perform specific tasks within a pipeline workflow. Each step has a type, position, and connection points for data flow.

Attributes:

Name	Type	Description
`position`	`Optional[PipelineStepPosition]`	The graphical position of this step within the pipeline editor
`position_id`	`Optional[str]`	The identifier of the position for layout management
`handles`	`Optional[List[PipelineStepHandle]]`	The connection points (handles) of this step for data flow
`dependencies_object`	`Optional[List[PipelineStepDependency]]`	The input dependencies of this step from other steps
`step_type`	`str`	The type of processing this step performs
`pipeline_version_id`	`str`	The Pipeline Version Id this step belongs to
`step_title`	`str`	The human-readable title/name of the step

`PipelineStepDependency`

Bases: BaseModel

Represents a dependency between pipeline steps.

Dependencies define the flow of data between pipeline steps by connecting output handles of parent steps to input handles of child steps.

Attributes:

Name	Type	Description
`pipeline_step_id`	`str`	The PipelineStepId this dependency belongs to
`parent_id`	`str`	The UUID of the parent pipeline step that provides input
`parent_handle_id`	`str`	The UUID of the parent handle (source output)
`handle_id`	`str`	The UUID of this handle (target input)

`PipelineStepHandle`

Bases: BaseModel

Represents a handle for a pipeline step.

Handles are connection points on pipeline steps that allow data flow between steps. They can be either source handles (output) or target handles (input).

Attributes:

Name	Type	Description
`pipeline_step_id`	`str`	The PipelineStepId this handle belongs to
`uuid`	`str`	The UUID of the handle
`type`	`str`	The type of the handle (source or target)
`label`	`Optional[str]`	The label of the handle for display purposes
`tooltip`	`Optional[str]`	The tooltip text shown when hovering over the handle
`x`	`Optional[float]`	The X-coordinate of the handle within the pipeline step
`y`	`Optional[float]`	The Y-coordinate of the handle within the pipeline step

`PipelineStepPosition`

Bases: BaseModel

Represents the graphical position of a pipeline step within a visual representation of a pipeline.

Used for positioning pipeline steps in the visual pipeline editor interface.

Attributes:

Name	Type	Description
`x`	`float`	The X-coordinate of the pipeline step in a 2D space
`y`	`float`	The Y-coordinate of the pipeline step in a 2D space

`PipelineVersion`

Bases: BaseModel

Represents a specific version of a pipeline.

Pipeline versions allow for iteration and deployment management of pipelines. Each version contains a complete definition of the pipeline structure and steps.

Attributes:

Name	Type	Description
`pipeline_id`	`str`	The identifier of the parent pipeline this version belongs to
`major_version`	`int`	The major version number for significant changes
`minor_version`	`int`	The minor version number for incremental changes
`version_number`	`str`	The complete version number in string format (e.g., "1.2")
`is_draft_version`	`bool`	Whether this is a draft version or a released production version
`is_latest`	`bool`	Whether this version is the latest/current version of the pipeline
`steps`	`Optional[List[PipelineStep]]`	The list of processing steps that make up this pipeline version
`alignment`	`str`	The UI alignment setting for pipeline visualization