Deployment Guide

This document provides detailed instructions for deploying the Congress.gov API Chatbot application to various environments, with a focus on Tanzu Platform for Cloud Foundry.

Prerequisites
Deployment to Tanzu Platform for Cloud Foundry
Deployment to Other Environments
- Docker Deployment
- Kubernetes Deployment
Continuous Integration and Deployment
Troubleshooting Deployment Issues

Prerequisites

Before deploying the application, ensure you have the following:

Access to a Tanzu Platform for Cloud Foundry environment
Cloud Foundry CLI installed and configured
Access to the GenAI tile and LLM service instances
Congress.gov API key (get your API key at https://api.congress.gov/sign-up/)
Go 1.24+ installed (for building the application)

Deployment to Tanzu Platform for Cloud Foundry

Preparing for Deployment

Build the application:

go build -o congress-chatbot cmd/server/main.go

Ensure the manifest.yml file is properly configured:

applications:
- name: congress-chatbot
  memory: 256M
  instances: 1
  buildpacks:
    - go_buildpack
  env:
    GOPACKAGENAME: github.com/cf-toolsuite/tanzu-genai-showcase/go-fiber-langchaingo
    GO_INSTALL_PACKAGE_SPEC: github.com/cf-toolsuite/tanzu-genai-showcase/go-fiber-langchaingo/cmd/server
    ENV: production
  health-check-type: http
  health-check-http-endpoint: /health

Verify that your .env.example file contains all the necessary environment variables:

CONGRESS_API_KEY=your_congress_api_key
GENAI_API_KEY=your_GENAI_API_KEY
GENAI_API_BASE_URL=your_GENAI_API_BASE_URL
LLM=gpt-4o-mini

Deploying the Application

cf login -a <API_ENDPOINT> -u <USERNAME> -p <PASSWORD> -o <ORG> -s <SPACE>

Push the application without starting it:

cf push --no-start

Set the Congress.gov API key:

cf set-env congress-chatbot CONGRESS_API_KEY <YOUR_CONGRESS_API_KEY>

Binding to the GenAI LLM Service

List available plans for the GenAI service offering:

cf marketplace -e genai

Create an LLM service instance (if not already created):

cf create-service genai <PLAN_NAME> congress-llm

Replace <PLAN_NAME> with the name of an available plan of the GenAI tile service offering.

Bind the service to your application:

cf bind-service congress-chatbot congress-llm

Start your application to apply the binding:

cf start congress-chatbot

Configuring Environment Variables

If you need to configure additional environment variables:

cf set-env congress-chatbot ENV production
cf set-env congress-chatbot PORT 8080
cf restage congress-chatbot

Scaling the Application

To scale the application horizontally (add more instances):

cf scale congress-chatbot -i 3

To scale the application vertically (adjust memory or disk):

cf scale congress-chatbot -m 512M -k 512M

Monitoring the Application

Check the application status:

cf app congress-chatbot

View application logs:

cf logs congress-chatbot --recent

Stream application logs:

cf logs congress-chatbot

Deployment to Other Environments

Docker Deployment

Create a Dockerfile in the project root:

FROM golang:1.24-alpine AS builder

WORKDIR /app
COPY . .
RUN go mod download
RUN go build -o congress-chatbot cmd/server/main.go

FROM alpine:latest

WORKDIR /app
COPY --from=builder /app/congress-chatbot .
COPY --from=builder /app/public ./public

ENV PORT=8080
EXPOSE 8080

CMD ["./congress-chatbot"]

Build the Docker image:

docker build -t congress-chatbot .

Run the Docker container:

docker run -p 8080:8080 \
  -e CONGRESS_API_KEY=<YOUR_CONGRESS_API_KEY> \
  -e GENAI_API_KEY=<YOUR_GENAI_API_KEY> \
  -e GENAI_API_BASE_URL=<YOUR_GENAI_API_BASE_URL> \
  -e LLM=gpt-4o-mini \
  congress-chatbot

Kubernetes Deployment

Create a Kubernetes deployment manifest (deployment.yaml):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: congress-chatbot
spec:
  replicas: 3
  selector:
    matchLabels:
      app: congress-chatbot
  template:
    metadata:
      labels:
        app: congress-chatbot
    spec:
      containers:
      - name: congress-chatbot
        image: congress-chatbot:latest
        ports:
        - containerPort: 8080
        env:
        - name: CONGRESS_API_KEY
          valueFrom:
            secretKeyRef:
              name: congress-chatbot-secrets
              key: congress-api-key
        - name: GENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: congress-chatbot-secrets
              key: genai-api-key
        - name: GENAI_API_BASE_URL
          valueFrom:
            secretKeyRef:
              name: congress-chatbot-secrets
              key: genai-api-base-url
        - name: LLM
          value: "gpt-4o-mini"

Create a Kubernetes service manifest (service.yaml):

apiVersion: v1
kind: Service
metadata:
  name: congress-chatbot
spec:
  selector:
    app: congress-chatbot
  ports:
  - port: 80
    targetPort: 8080
  type: LoadBalancer

Create a Kubernetes secret for the API keys:

kubectl create secret generic congress-chatbot-secrets \
  --from-literal=congress-api-key=<YOUR_CONGRESS_API_KEY> \
  --from-literal=genai-api-key=<YOUR_GENAI_API_KEY> \
  --from-literal=genai-api-base-url=<YOUR_GENAI_API_BASE_URL>

Apply the Kubernetes manifests:

kubectl apply -f deployment.yaml
kubectl apply -f service.yaml

Continuous Integration and Deployment

The project includes CI/CD configurations for various platforms:

Jenkins

The ci/jenkins/Jenkinsfile contains a pipeline configuration for Jenkins:

pipeline {
    agent any

    stages {
        stage('Build') {
            steps {
                sh 'go build -o congress-chatbot cmd/server/main.go'
            }
        }

        stage('Test') {
            steps {
                sh 'go test ./...'
            }
        }

        stage('Deploy') {
            steps {
                sh 'cf login -a $CF_API -u $CF_USERNAME -p $CF_PASSWORD -o $CF_ORG -s $CF_SPACE'
                sh 'cf push --no-start'
                sh 'cf set-env congress-chatbot CONGRESS_API_KEY $CONGRESS_API_KEY'
                sh 'cf bind-service congress-chatbot congress-llm'
                sh 'cf start congress-chatbot'
            }
        }
    }
}

GitLab CI

The ci/gitlab/.gitlab-ci.yml contains a pipeline configuration for GitLab CI:

stages:
  - build
  - test
  - deploy

build:
  stage: build
  image: golang:1.24
  script:
    - go build -o congress-chatbot cmd/server/main.go
  artifacts:
    paths:
      - congress-chatbot

test:
  stage: test
  image: golang:1.24
  script:
    - go test ./...

deploy:
  stage: deploy
  image: governmentpaas/cf-cli
  script:
    - cf login -a $CF_API -u $CF_USERNAME -p $CF_PASSWORD -o $CF_ORG -s $CF_SPACE
    - cf push --no-start
    - cf set-env congress-chatbot CONGRESS_API_KEY $CONGRESS_API_KEY
    - cf bind-service congress-chatbot congress-llm
    - cf start congress-chatbot
  only:
    - main

Bitbucket Pipelines

The ci/bitbucket/bitbucket-pipelines.yml contains a pipeline configuration for Bitbucket Pipelines:

image: golang:1.24

pipelines:
  default:
    - step:
        name: Build
        script:
          - go build -o congress-chatbot cmd/server/main.go
        artifacts:
          - congress-chatbot
    - step:
        name: Test
        script:
          - go test ./...
  branches:
    main:
      - step:
          name: Deploy
          image: governmentpaas/cf-cli
          script:
            - cf login -a $CF_API -u $CF_USERNAME -p $CF_PASSWORD -o $CF_ORG -s $CF_SPACE
            - cf push --no-start
            - cf set-env congress-chatbot CONGRESS_API_KEY $CONGRESS_API_KEY
            - cf bind-service congress-chatbot congress-llm
            - cf start congress-chatbot

Troubleshooting Deployment Issues

Common Issues and Solutions

Application Fails to Start

Symptoms:

The application fails to start after deployment
cf start command fails

Possible Causes and Solutions:

Missing environment variables
- Check if all required environment variables are set:
```
cf env congress-chatbot
```
- Set any missing environment variables:
```
cf set-env congress-chatbot CONGRESS_API_KEY <YOUR_CONGRESS_API_KEY>
```

Service binding issues

Check if the service is properly bound:

cf services

Rebind the service if necessary:

cf unbind-service congress-chatbot congress-llm
cf bind-service congress-chatbot congress-llm

Build issues
- Check the application logs for build errors:
```
cf logs congress-chatbot --recent
```
- Ensure the Go version is compatible:
```
cf set-env congress-chatbot GOVERSION go1.24
```

Application Crashes After Starting

Symptoms:

The application starts but crashes shortly after
Health check fails

Possible Causes and Solutions:

Memory issues
- Increase the memory allocation:
```
cf scale congress-chatbot -m 512M
```
Configuration issues
- Check the application logs for configuration errors:
```
cf logs congress-chatbot --recent
```
- Verify the service credentials are correctly parsed:
```
cf env congress-chatbot
```
API connectivity issues
- Ensure the application can connect to the Congress.gov API and the GenAI LLM service
- Check for network or firewall issues

Performance Issues

Symptoms:

The application is slow to respond
Timeouts occur during API calls

Possible Causes and Solutions:

Insufficient resources
- Scale the application vertically:
```
cf scale congress-chatbot -m 512M
```
- Scale the application horizontally:
```
cf scale congress-chatbot -i 3
```
External API latency
- Check the application logs for slow API calls:
```
cf logs congress-chatbot --recent
```
- Adjust the timeout settings in the application code
Caching issues
- Verify that the caching mechanism is working correctly
- Adjust the cache TTL if necessary

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Deployment Guide

Table of Contents

Prerequisites

Deployment to Tanzu Platform for Cloud Foundry

Preparing for Deployment

Deploying the Application

Binding to the GenAI LLM Service

Configuring Environment Variables

Scaling the Application

Monitoring the Application

Deployment to Other Environments

Docker Deployment

Kubernetes Deployment

Continuous Integration and Deployment

Jenkins

GitLab CI

Bitbucket Pipelines

Troubleshooting Deployment Issues

Common Issues and Solutions

Application Fails to Start

Application Crashes After Starting

Performance Issues

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Deployment Guide

Table of Contents

Prerequisites

Deployment to Tanzu Platform for Cloud Foundry

Preparing for Deployment

Deploying the Application

Binding to the GenAI LLM Service

Configuring Environment Variables

Scaling the Application

Monitoring the Application

Deployment to Other Environments

Docker Deployment

Kubernetes Deployment

Continuous Integration and Deployment

Jenkins

GitLab CI

Bitbucket Pipelines

Troubleshooting Deployment Issues

Common Issues and Solutions

Application Fails to Start

Application Crashes After Starting

Performance Issues