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AI Dungeon Game

The StoryApi comprises of a single API generate_story which given a Game and a list of Action’s for context, will progress a story. This API will be implemented as a streaming API in Python/FastAPI and will additionally demonstrate how changes can be made to the generated code to be fit for purpose.

To create our API, we first need to install a couple of additional dependencies.

  • boto3 will be used to call Amazon Bedrock;
  • uvicorn will be used to start our API when used in conjunction with the Lambda Web Adapter (LWA).
  • copyfiles is an npm dependency that we will need to support cross-platform copying of files when updating our bundle task.

To install these dependencies, run the following commands:

Terminal window
pnpm nx run dungeon_adventure.story_api:add --args boto3 uvicorn
Terminal window
pnpm add -Dw copyfiles

Now let’s replace the contents of the following files in packages/story_api/story_api:

import json
from boto3 import client
from fastapi.responses import PlainTextResponse, StreamingResponse
from pydantic import BaseModel
from .init import app, lambda_handler
handler = lambda_handler
bedrock = client('bedrock-runtime')
class Action(BaseModel):
role: str
content: str
class StoryRequest(BaseModel):
genre: str
playerName: str
actions: list[Action]
async def bedrock_stream(request: StoryRequest):
messages = [
{"role": "user", "content": "Continue or create a new story..."}
]
for action in request.actions:
messages.append({"role": action.role, "content": action.content})
response = bedrock.invoke_model_with_response_stream(
modelId='anthropic.claude-3-sonnet-20240229-v1:0',
body=json.dumps({
"system":f"""
You are running an AI text adventure game in the {request.genre} genre.
Player: {request.playerName}. Return less than 200 characters of text.
""",
"messages": messages,
"max_tokens": 1000,
"temperature": 0.7,
"anthropic_version": "bedrock-2023-05-31"
})
)
stream = response.get('body')
if stream:
for event in stream:
chunk = event.get('chunk')
if chunk:
message = json.loads(chunk.get("bytes").decode())
if message['type'] == "content_block_delta":
yield message['delta']['text'] or ""
elif message['type'] == "message_stop":
yield "\n"
@app.post("/story/generate",
openapi_extra={'x-streaming': True},
response_class=PlainTextResponse)
def generate_story(request: StoryRequest) -> str:
return StreamingResponse(bedrock_stream(request), media_type="text/plain")

Analyzing the code above:

  • We use the x-streaming setting to indicate that this is a streaming API when we eventually generate our client SDK. This will allow us to consume this API in a streaming manner whilst maintaining type-safety!
  • Our API simply returns a stream of text as defined by both the media_type="text/plain" and the response_class=PlainTextResponse

The Infrastructure we set up previously assumes that all APIs have an API Gateway integrating with Lambda functions. For our story_api we actually don’t want to use API Gateway as this does not support streaming repsonses. Instead, we will use a Lambda Function URL configured with response streaming.

To support this, we are going to first update our CDK constructs as follows:

import { Duration, Stack, CfnOutput } from 'aws-cdk-lib';
import { IGrantable, Grant } from 'aws-cdk-lib/aws-iam';
import {
Runtime,
Code,
Tracing,
LayerVersion,
FunctionUrlAuthType,
InvokeMode,
Function,
} from 'aws-cdk-lib/aws-lambda';
import { Construct } from 'constructs';
import url from 'url';
import { RuntimeConfig } from '../../core/runtime-config.js';
export class StoryApi extends Construct {
public readonly handler: Function;
constructor(scope: Construct, id: string) {
super(scope, id);
this.handler = new Function(this, 'Handler', {
runtime: Runtime.PYTHON_3_12,
handler: 'run.sh',
code: Code.fromAsset(
url.fileURLToPath(
new URL(
'../../../../../../dist/packages/story_api/bundle',
import.meta.url,
),
),
),
timeout: Duration.seconds(30),
tracing: Tracing.ACTIVE,
environment: {
AWS_CONNECTION_REUSE_ENABLED: '1',
},
});
const stack = Stack.of(this);
this.handler.addLayers(
LayerVersion.fromLayerVersionArn(
this,
'LWALayer',
`arn:aws:lambda:${stack.region}:753240598075:layer:LambdaAdapterLayerX86:24`,
),
);
this.handler.addEnvironment('PORT', '8000');
this.handler.addEnvironment('AWS_LWA_INVOKE_MODE', 'response_stream');
this.handler.addEnvironment('AWS_LAMBDA_EXEC_WRAPPER', '/opt/bootstrap');
const functionUrl = this.handler.addFunctionUrl({
authType: FunctionUrlAuthType.AWS_IAM,
invokeMode: InvokeMode.RESPONSE_STREAM,
cors: {
allowedOrigins: ['*'],
allowedHeaders: [
'authorization',
'content-type',
'x-amz-content-sha256',
'x-amz-date',
'x-amz-security-token',
],
},
});
new CfnOutput(this, 'StoryApiUrl', { value: functionUrl.url });
// Register the API URL in runtime configuration for client discovery
RuntimeConfig.ensure(this).config.apis = {
...RuntimeConfig.ensure(this).config.apis!,
StoryApi: functionUrl.url,
};
}
public grantInvokeAccess(grantee: IGrantable) {
Grant.addToPrincipal({
grantee,
actions: ['lambda:InvokeFunctionUrl'],
resourceArns: [this.handler.functionArn],
conditions: {
StringEquals: {
'lambda:FunctionUrlAuthType': 'AWS_IAM',
},
},
});
}
}

Now we will update the story_api to support the Lambda Web Adapter deployment.

#!/bin/bash
PATH=$PATH:$LAMBDA_TASK_ROOT/bin \
PYTHONPATH=$PYTHONPATH:/opt/python:$LAMBDA_RUNTIME_DIR \
exec python -m uvicorn --port=$PORT story_api.main:app

First, lets build the codebase:

Terminal window
pnpm nx run-many --target build --all

Your application can now be deployed by running the following command:

Terminal window
pnpm nx run @dungeon-adventure/infra:deploy dungeon-adventure-infra-sandbox/*

This deployment will take around 2 minutes to complete.

Once the deployment completes, you should see some outputs similar to the following (some values have been redacted):

Terminal window
dungeon-adventure-infra-sandbox-Application
dungeon-adventure-infra-sandbox-Application: deploying... [2/2]
dungeon-adventure-infra-sandbox-Application
Deployment time: 354s
Outputs:
dungeon-adventure-infra-sandbox-Application.ElectroDbTableTableNameXXX = dungeon-adventure-infra-sandbox-Application-ElectroDbTableXXX-YYY
dungeon-adventure-infra-sandbox-Application.GameApiEndpointXXX = https://xxx.execute-api.region.amazonaws.com/prod/
dungeon-adventure-infra-sandbox-Application.GameUIDistributionDomainNameXXX = xxx.cloudfront.net
dungeon-adventure-infra-sandbox-Application.StoryApiStoryApiUrlXXX = https://xxx.lambda-url.ap-southeast-2.on.aws/
dungeon-adventure-infra-sandbox-Application.UserIdentityUserIdentityIdentityPoolIdXXX = region:xxx
dungeon-adventure-infra-sandbox-Application.UserIdentityUserIdentityUserPoolIdXXX = region_xxx

We can test our API by either:

  • Starting a local instance of the FastApi server and invoke the API’s using curl.
  • Calling the deployed API using sigv4 enabled curl directly

Start your local FastAPI server by running the following command:

Terminal window
pnpm nx run dungeon_adventure.story_api:serve

Once the FastAPI server is up and running, call it by running the following command:

Terminal window
curl -N -X POST http://127.0.0.1:8000/story/generate \
-d '{"genre":"superhero", "actions":[], "playerName":"UnnamedHero"}' \
-H "Content-Type: application/json"

If the command executes successfully, you should see a response being streamed similar to:

UnnamedHero stood tall, his cape billowing in the wind....

Congratulations. You have built and deployed your first API using FastAPI! 🎉🎉🎉