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add node gen ai release test
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‎.github/workflows/node-ec2-adot-genesis-test.yml‎

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## Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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## SPDX-License-Identifier: Apache-2.0
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name: Node EC2 Genesis Use Case
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on:
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workflow_dispatch: # be able to run the workflow on demand
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push:
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branches:
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- genesis-release-test
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workflow_call:
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inputs:
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caller-workflow-name:
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required: true
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type: string
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staging-wheel-name:
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required: false
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default: '@aws/aws-distro-opentelemetry-node-autoinstrumentation'
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type: string
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permissions:
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id-token: write
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contents: read
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env:
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E2E_TEST_AWS_REGION: 'us-west-2'
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E2E_TEST_ACCOUNT_ID: ${{ secrets.APPLICATION_SIGNALS_E2E_TEST_ACCOUNT_ID }}
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E2E_TEST_ROLE_NAME: ${{ secrets.APPLICATION_SIGNALS_E2E_TEST_ROLE_NAME }}
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ADOT_WHEEL_NAME: ${{ inputs.staging-wheel-name }}
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METRIC_NAMESPACE: genesis
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LOG_GROUP_NAME: test/genesis
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TEST_RESOURCES_FOLDER: ${GITHUB_WORKSPACE}
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SAMPLE_APP_ZIP: s3://aws-appsignals-sample-app-prod-us-east-1/node-sample-app.zip
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jobs:
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python-ec2-adot-genai:
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runs-on: ubuntu-latest
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timeout-minutes: 30
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steps:
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- uses: actions/checkout@v4
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with:
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repository: 'aws-observability/aws-application-signals-test-framework'
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ref: ${{ inputs.caller-workflow-name == 'main-build' && 'main' || github.ref }}
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fetch-depth: 0
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- name: Set Get ADOT Instrumentation command environment variable
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run: echo GET_ADOT_INSTRUMENTATION_COMMAND="aws s3 cp s3://adot-genai-js-test/aws-aws-distro-opentelemetry-node-autoinstrumentation-0.6.0-dev0.tar ./adot-instrumentation.tar --region us-east-1 && npm install ./adot-instrumentation.tar" >> $GITHUB_ENV
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# if [ "${{ github.event.repository.name }}" = "aws-otel-js-instrumentation" ]; then
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# echo GET_ADOT_INSTRUMENTATION_COMMAND="aws s3 cp s3://adot-autoinstrumentation-node-staging/${{ env.ADOT_INSTRUMENTATION_NAME }} ./${{ env.ADOT_INSTRUMENTATION_NAME }} --region us-east-1 && npm install ${{ env.ADOT_INSTRUMENTATION_NAME }}" >> $GITHUB_ENV
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# else
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# echo GET_ADOT_INSTRUMENTATION_COMMAND="npm install ${{ env.ADOT_INSTRUMENTATION_NAME }}" >> $GITHUB_ENV
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# fi
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- name: Initiate Gradlew Daemon
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uses: ./.github/workflows/actions/execute_and_retry
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continue-on-error: true
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with:
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command: "./gradlew :validator:build"
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cleanup: "./gradlew clean"
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max_retry: 3
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sleep_time: 60
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- name: Generate testing id
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run: echo TESTING_ID="${{ github.run_id }}-${{ github.run_number }}-${RANDOM}" >> $GITHUB_ENV
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- name: Generate XRay and W3C trace ID
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run: |
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ID_1="$(printf '%08x' $(date +%s))"
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ID_2="$(openssl rand -hex 12)"
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W3C_TRACE_ID="${ID_1}${ID_2}"
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XRAY_TRACE_ID="1-${ID_1}-${ID_2}"
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PARENT_ID="$(openssl rand -hex 8)"
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TRACE_ID_HEADER="Root=${XRAY_TRACE_ID};Parent=${PARENT_ID};Sampled=1"
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echo "XRAY_TRACE_ID=${XRAY_TRACE_ID}" >> $GITHUB_ENV
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echo "W3C_TRACE_ID=${W3C_TRACE_ID}" >> $GITHUB_ENV
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echo "TRACE_ID_HEADER=${TRACE_ID_HEADER}" >> $GITHUB_ENV
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echo "Generated XRay Trace ID: ${XRAY_TRACE_ID}"
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echo "Generated W3C Trace ID: ${W3C_TRACE_ID}"
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echo "Generated Trace ID Header: ${TRACE_ID_HEADER}"
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: arn:aws:iam::${{ env.E2E_TEST_ACCOUNT_ID }}:role/${{ env.E2E_TEST_ROLE_NAME }}
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aws-region: ${{ env.E2E_TEST_AWS_REGION }}
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- name: Set up terraform
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uses: ./.github/workflows/actions/execute_and_retry
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with:
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command: "wget -O- https://apt.releases.hashicorp.com/gpg | sudo gpg --dearmor -o /usr/share/keyrings/hashicorp-archive-keyring.gpg"
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post-command: 'echo "deb [signed-by=/usr/share/keyrings/hashicorp-archive-keyring.gpg] https://apt.releases.hashicorp.com $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/hashicorp.list
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&& sudo apt update && sudo apt install terraform'
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- name: Initiate Terraform
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uses: ./.github/workflows/actions/execute_and_retry
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with:
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command: "cd ${{ env.TEST_RESOURCES_FOLDER }}/terraform/python/ec2/adot-genai && terraform init && terraform validate"
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cleanup: "rm -rf .terraform && rm -rf .terraform.lock.hcl"
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max_retry: 6
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- name: Deploy service via terraform
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working-directory: terraform/python/ec2/adot-genai
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run: |
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terraform apply -auto-approve \
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-var="aws_region=${{ env.E2E_TEST_AWS_REGION }}" \
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-var="test_id=${{ env.TESTING_ID }}" \
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-var="service_zip_url=${{ env.SAMPLE_APP_ZIP }}" \
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-var="trace_id=${{ env.TRACE_ID_HEADER }}" \
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-var="get_adot_wheel_command=${{ env.GET_ADOT_WHEEL_COMMAND }}" \
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- name: Get deployment info
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working-directory: terraform/python/ec2/adot-genai
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run: |
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echo "INSTANCE_IP=$(terraform output langchain_service_public_ip)" >> $GITHUB_ENV
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echo "INSTANCE_ID=$(terraform output langchain_service_instance_id)" >> $GITHUB_ENV
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- name: Waiting 5 Minutes for Gen AI service to be ready and emit logs, traces, and metrics
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run: sleep 300
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- name: Validate generated logs
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run: ./gradlew validator:run --args='-c python/ec2/adot-genai/log-validation.yml
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--testing-id ${{ env.TESTING_ID }}
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--endpoint http://${{ env.INSTANCE_IP }}:8000
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--region ${{ env.E2E_TEST_AWS_REGION }}
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--metric-namespace ${{ env.METRIC_NAMESPACE }}
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--log-group ${{ env.LOG_GROUP_NAME }}
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--service-name langchain-traceloop-app
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--instance-id ${{ env.INSTANCE_ID }}
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--trace-id ${{ env.W3C_TRACE_ID }}'
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- name: Validate generated traces
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if: (success() || failure()) && !cancelled()
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run: ./gradlew validator:run --args='-c python/ec2/adot-genai/trace-validation.yml
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--testing-id ${{ env.TESTING_ID }}
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--endpoint http://${{ env.INSTANCE_IP }}:8000
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--region ${{ env.E2E_TEST_AWS_REGION }}
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--metric-namespace ${{ env.METRIC_NAMESPACE }}
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--service-name langchain-traceloop-app
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--instance-id ${{ env.INSTANCE_ID }}
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--trace-id ${{ env.XRAY_TRACE_ID }}'
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- name: Validate generated metrics
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if: (success() || failure()) && !cancelled()
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run: ./gradlew validator:run --args='-c python/ec2/adot-genai/metric-validation.yml
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--testing-id ${{ env.TESTING_ID }}
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--endpoint http://${{ env.INSTANCE_IP }}:8000
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--region ${{ env.E2E_TEST_AWS_REGION }}
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--metric-namespace ${{ env.METRIC_NAMESPACE }}
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--log-group ${{ env.LOG_GROUP_NAME }}
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--service-name langchain-traceloop-app
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--instance-id ${{ env.INSTANCE_ID }}'
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- name: Cleanup
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if: always()
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continue-on-error: true
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working-directory: terraform/python/ec2/adot-genai
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run: |
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terraform destroy -auto-approve \
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-var="aws_region=${{ env.E2E_TEST_AWS_REGION }}" \
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-var="test_id=${{ env.TESTING_ID }}" \
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-var="service_zip_url=${{ env.SAMPLE_APP_ZIP }}" \
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-var="trace_id=${{ env.TRACE_ID_HEADER }}" \
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-var="get_adot_wheel_command=${{ env.GET_ADOT_WHEEL_COMMAND }}"

‎.github/workflows/python-ec2-genesis-test.yml‎

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## Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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## SPDX-License-Identifier: Apache-2.0
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name: Python EC2 Gen AI Use Case
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name: Python EC2 Genesis Use Case
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on:
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workflow_dispatch: # be able to run the workflow on demand
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‎sample-apps/node/genai-service/customInstrumentation.js‎

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const { trace } = require('@opentelemetry/api');
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const { registerInstrumentations } = require('@opentelemetry/instrumentation');
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const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');
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const tracerProvider = trace.getTracerProvider();
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const {
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LangChainInstrumentation,
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} = require("@traceloop/instrumentation-langchain");
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const AgentsModule = require('langchain/agents');
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const ChainsModule = require('langchain/chains');
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const RunnableModule = require('@langchain/core/runnables')
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const ToolsModule = require('@langchain/core/tools')
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const VectorStoresModule = require('@langchain/core/vectorstores')
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const traceloop = require("@traceloop/node-server-sdk")
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traceloop.initialize({
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appName: "myTestApp",
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instrumentModules: {
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langchain: {
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runnablesModule: RunnableModule,
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toolsModule: ToolsModule,
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chainsModule: ChainsModule,
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agentsModule: AgentsModule,
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vectorStoreModule: VectorStoresModule,
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}
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}
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});

‎sample-apps/node/genai-service/index.js‎

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const express = require('express');
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const { BedrockChat } = require("@langchain/community/chat_models/bedrock");
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const { ChatPromptTemplate } = require("@langchain/core/prompts");
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const traceloop = require("@traceloop/node-server-sdk")
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const logger = require('pino')()
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const app = express();
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app.use(express.json());
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const PORT = parseInt(process.env.SAMPLE_APP_PORT || '8000', 10);
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const llm = new BedrockChat({
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model: "anthropic.claude-3-sonnet-20240229-v1:0",
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region: "us-east-1",
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credentials: {
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accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
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secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
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},
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temperature: 0.7,
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});
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const prompt = ChatPromptTemplate.fromMessages([
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[
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"system",
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"You are a helpful assistant. Provide a helpful response to the following user input.",
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],
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["human", "{input}"],
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]);
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const chain = prompt.pipe(llm);
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app.get('/health', (req, res) => {
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res.json({ status: 'healthy' });
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});
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app.post('/ai-chat', async (req, res) => {
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const { message } = req.body;
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if (!message) {
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return res.status(400).json({ error: 'Message is required' });
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}
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try {
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logger.info(`Question asked: ${message}`);
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const response = await traceloop.withWorkflow({ name: "sample_chat" }, () => {
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return traceloop.withTask({ name: "parent_task" }, () => {
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return chain.invoke({
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input_language: "English",
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output_language: "English",
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input: message,
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});
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});
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});
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res.json({ response: response.content });
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} catch (error) {
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logger.error(`Error processing request: ${error.message}`);
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res.status(500).json({ error: 'Internal server error' });
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}
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});
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app.listen(PORT, () => {
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logger.info(`GenAI service listening on port ${PORT}`);
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});

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