Cloudflare AI Gatewayを利用して、Amazon Bedrockを使ったAPIの利用状況の可視化、分析、ロギングできるか試してみた

import { Hono } from 'hono'; import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime'; type Bindings = {	AWS_ACCESS_KEY_ID: string;	AWS_SECRET_ACCESS_KEY: string;	AWS_REGION: string; }; const bedrock = new Hono<{ Bindings: Bindings }>(); bedrock.get('/:modelId', async (c) => {	const modelId = c.req.param('modelId');	let prompt = c.req.query('p');	let payload = {};	if (modelId.startsWith('anthropic.claude-3')) {	payload = {	anthropic_version: 'bedrock-2023-05-31',	max_tokens: 1000,	messages: [	{	role: 'user',	content: [{ type: 'text', text: prompt }],	},	],	};	} else if (modelId.startsWith('anthropic')) {	// promptを変更	prompt = `Human: ${prompt}\n\nAssistant:`;	payload = {	prompt,	max_tokens_to_sample: 500,	temperature: 0.5,	stop_sequences: ['\n\nHuman:'],	};	} else {	const textGenerationConfig = {	maxTokenCount: 4096,	stopSequences: [],	temperature: 0,	topP: 1,	};	payload = {	inputText: prompt,	textGenerationConfig,	};	}	const { AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION } = c.env;	const client = new BedrockRuntimeClient({	region: AWS_REGION,	credentials: {	accessKeyId: AWS_ACCESS_KEY_ID,	secretAccessKey: AWS_SECRET_ACCESS_KEY,	},	});	const command = new InvokeModelCommand({	body: JSON.stringify(payload),	contentType: 'application/json',	accept: 'application/json',	modelId,	});	const response = await client.send(command);	const decodedResponseBody = new TextDecoder().decode(response.body);	const responseBody = JSON.parse(decodedResponseBody);	const res = responseBody;	return c.json({ modelId, prompt, res }); }); export { bedrock }; 

import { Hono } from 'hono'; import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime'; type Bindings = {	AWS_ACCESS_KEY_ID: string;	AWS_SECRET_ACCESS_KEY: string;	AWS_REGION: string;	AI_GATEWAY_HOST: string;	AI_GATEWAY_PATH: string; }; const bedrock = new Hono<{ Bindings: Bindings }>(); bedrock.get('/:modelId', async (c) => {	const modelId = c.req.param('modelId');	let prompt = c.req.query('p');	let payload = {};	if (modelId.startsWith('anthropic.claude-3')) {	payload = {	anthropic_version: 'bedrock-2023-05-31',	max_tokens: 1000,	messages: [	{	role: 'user',	content: [{ type: 'text', text: prompt }],	},	],	};	} else if (modelId.startsWith('anthropic')) {	// promptを変更	prompt = `Human: ${prompt}\n\nAssistant:`;	payload = {	prompt,	max_tokens_to_sample: 500,	temperature: 0.5,	stop_sequences: ['\n\nHuman:'],	};	} else {	const textGenerationConfig = {	maxTokenCount: 4096,	stopSequences: [],	temperature: 0,	topP: 1,	};	payload = {	inputText: prompt,	textGenerationConfig,	};	}	const { AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, AI_GATEWAY_HOST, AI_GATEWAY_PATH } = c.env;	const client = new BedrockRuntimeClient({	region: AWS_REGION,	credentials: {	accessKeyId: AWS_ACCESS_KEY_ID,	secretAccessKey: AWS_SECRET_ACCESS_KEY,	},	});	client.middlewareStack.add(	(next, context) => async (args: any) => {	args.request.headers['host'] = AI_GATEWAY_HOST;	args.request.hostname = AI_GATEWAY_HOST;	args.request.path = `${AI_GATEWAY_PATH}/aws-bedrock/bedrock-runtime/${AWS_REGION}${args.request.path}`;	const result = await next(args);	return result;	},	{	step: 'finalizeRequest',	}	);	const command = new InvokeModelCommand({	body: JSON.stringify(payload),	contentType: 'application/json',	accept: 'application/json',	modelId,	});	const response = await client.send(command);	const decodedResponseBody = new TextDecoder().decode(response.body);	const responseBody = JSON.parse(decodedResponseBody);	const res = responseBody;	return c.json({ modelId, prompt, res }); }); export { bedrock }; 

 const client = new BedrockRuntimeClient({ endpoint: "https://gateway.ai.cloudflare.com/v1/xxxx/nishimura-test-ai-app", region: "ap-northeast-1", }); 

... client.config.endpoint = "https://gateway.ai.cloudflare.com/v1/xxxx/nishimura-test-ai-app" const response = await client.send(command); ...