Managing AI Workflow States with AWS AppSync Subscriptions

Question

Pulumi · Accepted Answer

To manage AI workflow states with AWS AppSync subscriptions, you will create a GraphQL API using AWS AppSync. The real-time nature of subscriptions in GraphQL allows clients to receive updates from the server whenever specific events are triggered. This is particularly useful for AI workflows where you want to keep track of the state of various tasks, and get notified when there is a change.

To illustrate how to achieve this, we’ll set up an AWS Appsync GraphQL API, define a schema that includes a subscription, create a data source, and then create the necessary resolvers. In this context, we'll use the `aws.appsync.GraphQLApi`, `aws.appsync.DataSource`, and `aws.appsync.Type` resources from the `pulumi_aws` package.

Here is a high-level overview of the steps:
1. Define the GraphQL API.
2. Attach a schema to the API.
3. Define a data source. This data source is what the GraphQL resolvers will use to fetch data. For simplicity, we'll use a Lambda function as the data source, but it could be any supported data source like DynamoDB, Amazon Elasticsearch, etc.
4. Create resolvers attaching them to fields in your GraphQL schema. Resolvers are responsible for fetching the data for a particular field in the schema.

Below is the Pulumi program written in Python that builds upon these resources:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Create the GraphQL API
api = aws.appsync.GraphQLApi("myApi",
    authentication_type="API_KEY",  # API_KEY is one of the simplest authentication types
    name="myApi",
    schema= """schema {
            query: Query
            mutation: Mutation
            subscription: Subscription
        }

type Query { ... }
        type Mutation { ... }
        
        # Define subscription for state change
        type Subscription {
            workflowStateUpdated: WorkflowState!
        }
        
        type WorkflowState { ... }
        """,
)

# Step 2: Create a data source, using AWS Lambda as an example:
example_lambda = aws.lambda_.Function("exampleLambda",
    code=pulumi.FileArchive("./lambda.zip"),
    role=lambda_role.arn,
    handler="index.handler",
    runtime="python3.8",
)

data_source = aws.appsync.DataSource("myDataSource",
    type="AWS_LAMBDA",
    api_id=api.id,
    lambda_config={
        "function_arn": example_lambda.arn,
    },
    service_role_arn=appsync_service_role.arn
)

# Note: Additional setup such as defining lambda_role and appsync_service_role is necessary
# This is common setup that involves creating an IAM role that the Lambda function will assume
# and a service role that will allow AppSync to invoke the Lambda function.
# For brevity, the complete setup for these roles is not shown here.

# Step 3: Create resolvers for query, mutation, and subscription
query_resolver = aws.appsync.Resolver("queryResolver",
    api_id=api.id,
    data_source=data_source.name,
    field="getWorkflowState",
    type="Query",
    request_template="""{
        "version": "2017-02-28",
        "operation": "Invoke",
        "payload": {
            "field": "getWorkflowState",
            "arguments": $util.toJson($context.arguments)
        }
    }""",
    response_template="$util.toJson($context.result)"
)

mutation_resolver = aws.appsync.Resolver("mutationResolver",
    api_id=api.id,
    data_source=data_source.name,
    field="updateWorkflowState",
    type="Mutation",
    request_template="""{
        "version": "2017-02-28",
        "operation": "Invoke",
        "payload": {
            "field": "updateWorkflowState",
            "arguments": $util.toJson($context.arguments)
        }
    }""",
    response_template="$util.toJson($context.result)"
)

# For the subscriptions we don’t need a resolver since
# it will be using the results from mutation to get real-time updates

# The below export could be useful to get the API URL and key for accessing the GraphQL endpoint.
pulumi.export('graphql_api_url', api.uris['GRAPHQL'])
pulumi.export('api_key', api.api_key)
```

In the above program, we've set up a basic GraphQL API with a schema that includes stubs for queries, mutations, and subscriptions. The resolvers are linked to a Lambda data source. When a mutation is performed (which updates data), anyone subscribed to that data via a subscription will receive the new state. This setup is ideal for tracking states in an AI workflow.

Replace the `...` in types `Query`, `Mutation`, `Subscription`, and `WorkflowState` with actual fields and types you expect in your workflow. The `request_template` and `response_template` for the resolvers include Apache Velocity Template Language (VTL) snippets that define the payload format and operations to be performed when invoking the data source.

Keep in mind this is a basic layout, and you may have more complex data structures or additional authentication and authorization layers depending on your use case. The placeholders must be replaced with actual implementation details for the resolvers and data sources according to your business logic.

Before running the program, ensure that you have configured the Pulumi AWS provider and have the necessary permissions to create these resources. If this is your first time using Pulumi or AWS AppSync, it would be useful to read through the [Pulumi AWS documentation](https://www.pulumi.com/docs/reference/pkg/aws/appsync/graphqlapi/) and the [AWS AppSync documentation](https://docs.aws.amazon.com/appsync/latest/devguide/welcome.html) to get familiar with concepts, properties, and typical configuration.