AWS Bedrock Integration with Future AGI for LLM Tracing

pip install traceAI-bedrock
pip install boto3

npm install @traceai/bedrock @traceai/fi-core @opentelemetry/instrumentation @aws-sdk/client-bedrock-runtime

import os

os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-access-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"

process.env.AWS_ACCESS_KEY_ID = "your-aws-access-key-id";
process.env.AWS_SECRET_ACCESS_KEY = "your-aws-secret-access-key";
process.env.FI_API_KEY = "your-futureagi-api-key";
process.env.FI_SECRET_KEY = "your-futureagi-secret-key";

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="bedrock_project",
)

import { register, ProjectType } from "@traceai/fi-core";

const tracerProvider = register({
    projectType: ProjectType.OBSERVE,
    projectName: "bedrock_project",
});

from traceai_bedrock import BedrockInstrumentor

BedrockInstrumentor().instrument(tracer_provider=trace_provider)

import { BedrockInstrumentation } from "@traceai/bedrock";
import { registerInstrumentations } from "@opentelemetry/instrumentation";

const bedrockInstrumentation = new BedrockInstrumentation({});

registerInstrumentations({
    instrumentations: [bedrockInstrumentation],
    tracerProvider: tracerProvider,
});

import boto3

client = boto3.client(
    service_name="bedrock-runtime",
    region_name="your-region",
    aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
    aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
)

import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime";

const client = new BedrockRuntimeClient({
    region: "your-region",
});

def converse_with_claude():
    system_prompt = [{"text": "You are an expert at creating music playlists"}]
    messages = [
        {
            "role": "user",
            "content": [{"text": "Hello, how are you?"}, {"text": "What's your name?"}],
        }
    ]
    inference_config = {"maxTokens": 1024, "temperature": 0.0}

    try:
        response = client.converse(
            modelId="model_id",
            system=system_prompt,
            messages=messages,
            inferenceConfig=inference_config,
        )
        out = response["output"]["message"]
        messages.append(out)
        print(out)
    except Exception as e:
        print(f"Error: {str(e)}")

if __name__ == "__main__":
    converse_with_claude()

import { ConverseCommand } from "@aws-sdk/client-bedrock-runtime";

async function converseWithClaude() {
    const system = [{ text: "You are an expert at creating music playlists" }];
    const messages = [
        {
            role: "user",
            content: [{ text: "Hello, how are you?" }, { text: "What's your name?" }],
        },
    ];
    const inferenceConfig = { maxTokens: 1024, temperature: 0.0 };

    try {
        const response = await client.send(
            new ConverseCommand({
                modelId: "model_id",
                system,
                messages,
                inferenceConfig,
            })
        );
        const out = response.output?.message;
        if (out) {
            console.log(out);
        }
    } catch (e) {
        console.error("Error:", e);
    }
}

converseWithClaude();