Workflow steps
Workflow steps form basic “building blocks” for process automation. Most steps directly correspond to OpenSearch or plugin API operations, such as CRUD operations on machine learning (ML) connectors, models, and agents. Some steps simplify the configuration by reusing the body expected by these APIs across multiple steps. For example, once you configure a tool, you can use it with multiple agents.
Workflow step fields
Workflow steps are actively being developed to expand automation capabilities. Workflow step (graph node) configuration includes the following fields.
| Field | Data type | Required/Optional | Description |
|---|---|---|---|
id | String | Required | A user-provided ID for the step. The ID must be unique within a given workflow and is useful for identifying resources created by the step. For example, a register_agent step may return an agent_id that has been registered. Using this ID, you can determine which step produced which resource. |
type | String | Required | The type of action to take, such as deploy_model, which corresponds to the API for which the step is used. Multiple steps may share the same type but must each have their own unique ID. For a list of supported types, see Workflow step types. |
previous_node_inputs | Object | Optional | A key-value map specifying user inputs that are produced by a previous step in the workflow. For each key-value pair, the key is the previous step’s id and the value is an API body field name (such as model_id) that will be produced as an output of a previous step in the workflow. For example, register_remote_model (key) may produce a model_id (value) that is required for a subsequent deploy_model step. A graph edge is automatically added to the workflow connecting the previous step’s key as the source and the current node as the destination. In some cases, you can include additional inputs in this field. |
user_inputs | Object | Optional | A key-value map of inputs supported by the corresponding API for this specific step. Some inputs are required for an API, while others are optional. Required inputs may be specified here, if known, or in the previous_node_inputs field. The Get Workflow Steps API identifies required inputs and step outputs. Substitutions are supported in string values, lists of strings, and maps with string values. The pattern ${{previous_step_id.output_key}} will be replaced by the value in the previous step’s output with the given key. For example, if a parameter map in the user inputs includes a key embedding_model_id with a value ${{deploy_embedding_model.model_id}}, then the model_id output of the deploy_embedding_model step will be substituted here. This performs a similar function to the previous_node_input map but is not validated and does not automatically infer edges. In some cases, you can include additional inputs in this field. |
Workflow step types
The following table lists the workflow step types. The user_inputs fields for these steps correspond directly to the linked APIs.
| Step type | Corresponding API | Description |
|---|---|---|
noop | No API | A no-operation (no-op) step that does nothing, which is useful for synchronizing parallel steps. If the user_inputs field contains a delay key, this step will wait for the specified amount of time. |
create_connector | Create Connector | Creates a connector to a model hosted on a third-party platform. |
delete_connector | Delete Connector | Deletes a connector to a model hosted on a third-party platform. |
register_model_group | Register Model Group | Registers a model group. The model group will be deleted automatically once no model is present in the group. |
register_remote_model | Register Model (remote) | Registers a model hosted on a third-party platform. If the user_inputs field contains a deploy key that is set to true, the model is also deployed. |
register_local_pretrained_model | Register Model (pretrained) | Registers an OpenSearch-provided pretrained text embedding model that is hosted on your OpenSearch cluster. If the user_inputs field contains a deploy key that is set to true, also deploys the model. |
register_local_sparse_encoding_model | Register Model (sparse) | Registers an OpenSearch-provided pretrained sparse encoding model that is hosted on your OpenSearch cluster. If the user_inputs field contains a deploy key that is set to true, also deploys the model. |
register_local_custom_model | Register Model (custom) | Registers a custom model that is hosted on your OpenSearch cluster. If the user_inputs field contains a deploy key that is set to true, also deploys the model. |
delete_model | Delete Model | Unregisters and deletes a model. |
deploy_model | Deploy Model | Deploys a registered model into memory. |
undeploy_model | Undeploy Model | Undeploys a deployed model from memory. |
register_agent | Register Agent API | Registers an agent as part of the ML Commons Agent Framework. |
delete_agent | Delete Agent API | Deletes an agent. |
create_tool | No API | A special-case non-API step encapsulating the specification of a tool for an agent in the ML Commons Agent Framework. These will be listed as previous_node_inputs for the appropriate register agent step, with the value set to tools. |
create_index | Create Index | Creates a new OpenSearch index. The inputs include index_name, which should be the name of the index to be created, and configurations, which contains the payload body of a regular REST request for creating an index. |
create_ingest_pipeline | Create Ingest Pipeline | Creates or updates an ingest pipeline. The inputs include pipeline_id, which should be the ID of the pipeline, and configurations, which contains the payload body of a regular REST request for creating an ingest pipeline. |
create_search_pipeline | Create Search Pipeline | Creates or updates a search pipeline. The inputs include pipeline_id, which should be the ID of the pipeline, and configurations, which contains the payload body of a regular REST request for creating a search pipeline. |
reindex | Reindex | The reindex document API operation lets you copy all or a subset of your data from a source index into a destination index. The input includes source_index, destination_index, and the following optional parameters from the document reindex API: refresh, requests_per_second, require_alias, slices, and max_docs. For more information, see Reindexing considerations. |
Reindexing considerations
Reindexing can be a resource-intensive operation, and if not managed properly, it can potentially destabilize your cluster.
When using a reindex step, follow these best practices to ensure a smooth reindexing process and prevent cluster instability:
-
Cluster scaling: Before initiating a reindexing operation, ensure that your OpenSearch cluster is properly scaled to handle the additional workload. Increase the number of nodes and adjust resource allocation (CPU, memory, and disk) as needed to accommodate the reindexing process without impacting other operations.
-
Request rate control: Use the
requests_per_secondparameter to control the rate at which the reindexing requests are sent to the cluster. This helps to regulate the load on the cluster and prevent resource exhaustion. Start with a lower value and gradually increase it based on your cluster’s capacity and performance. -
Slicing and parallelization: The
slicesparameter allows you to divide the reindexing process into smaller, parallel tasks. This can help distribute the workload across multiple nodes and improve overall performance. However, be cautious when increasing the number of slices because adding slices can increase resource consumption. -
Monitoring and adjustments: Closely monitor your cluster performance metrics (such as CPU, memory, disk usage, and thread pools) during the reindexing process. If you notice any signs of resource contention or performance degradation, adjust the reindexing parameters accordingly or consider pausing the operation until the cluster stabilizes.
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Prioritization and scheduling: If possible, schedule reindexing operations during off-peak hours or periods of lower cluster utilization to minimize the impact on other operations and user traffic.
By following these best practices and carefully managing the reindexing process, you can ensure that your OpenSearch cluster remains stable and performant while efficiently copying data between indexes.
Additional fields
You can include the following additional fields in the user_inputs field if the field is supported by the indicated step type.
| Field | Data type | Step type | Description |
|---|---|---|---|
node_timeout | Time units | All | A user-provided timeout for this step. For example, 20s for a 20-second timeout. |
deploy | Boolean | Register model | If set to true, also deploys the model. |
tools_order | List | Register agent | Specifies the ordering of tools. For example, specify ["foo_tool", "bar_tool"] to sequence those tools in that order. |
delay | Time units | No-op | Waits for the specified amount of time. For example, 250ms specifies to wait for 250 milliseconds before continuing the workflow. |
You can include the following additional fields in the previous_node_inputs field when indicated.
| Field | Data type | Description |
|---|---|---|
model_id | String | The model_id is used as an input for several steps. As a special case for the register_agent step type, if an llm.model_id field is not present in the user_inputs and not present in previous_node_inputs, then the model_id field from the previous node may be used as a backup for the model ID. The model_id will also be included in the parameters input of the create_tool step for the MLModelTool. |
agent_id | String | The agent_id is used as an input for several steps. The agent_id will also be included in the parameters input of the create_tool step for the AgentTool. |
connector_id | String | The connector_id is used as an input for several steps. The connector_id will also be included in the parameters input of the create_tool step for the ConnectorTool. |
Example workflow steps
For example workflow step implementations, see the Workflow tutorial.