Vertex AI Project Context - Clojure Integration Setup

Last Updated: 2025-10-27 Status: Python/ADK environment validated, ready for Clojure integration GCP Project: saas3-476116

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🎯 Overview

This skill consolidates complete context for the existing Vertex AI project to facilitate integration with Clojure. The goal is to leverage the already-configured GCP infrastructure and lessons learned from Python ADK to create agents using Clojure.

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🔐 Credentials and GCP Configuration

Google Cloud Project

Property	Value
Project ID	saas3-476116
Primary Region	us-central1
Service Account	vertex-ai-app@saas3-476116.iam.gserviceaccount.com
JSON Key	~/.gcp-credentials/vertex-ai-app-key.json
Key Permissions	chmod 600 (always verify)

Enabled APIs

# Critical APIs already enabled
- aiplatform.googleapis.com         # Vertex AI API
- generativelanguage.googleapis.com # Gemini API
- cloudbuild.googleapis.com         # Cloud Build
- run.googleapis.com                # Cloud Run

# Verify APIs
gcloud services list --enabled --project=saas3-476116

Essential Environment Variables

File: ~/.env (chmod 600, DO NOT commit)

# Google Cloud Configuration
export GOOGLE_CLOUD_PROJECT="saas3-476116"
export GOOGLE_APPLICATION_CREDENTIALS="$HOME/.gcp-credentials/vertex-ai-app-key.json"
export GOOGLE_CLOUD_LOCATION="us-central1"

# Vertex AI Endpoint
export VERTEX_AI_ENDPOINT="us-central1-aiplatform.googleapis.com"

# For Vertex AI with Google Generative AI SDK
export GOOGLE_GENAI_USE_VERTEXAI="TRUE"

# ADK Configuration
export ADK_MODEL="gemini-1.5-flash"
export ADK_TEMPERATURE="0.7"
export ADK_MAX_TOKENS="2048"

⚠️ CRITICAL: Protect file:

chmod 600 ~/.env

Clojure Configuration

Read environment variables in Clojure:

(ns lab.config.google-cloud
  "Google Cloud configuration from environment"
  (:require [clojure.java.io :as io]))

(defn get-env
  "Reads environment variable with fallback"
  [key default]
  (or (System/getenv key) default))

(def gcp-config
  {:project-id (get-env "GOOGLE_CLOUD_PROJECT" "saas3-476116")
   :location (get-env "GOOGLE_CLOUD_LOCATION" "us-central1")
   :credentials (get-env "GOOGLE_APPLICATION_CREDENTIALS"
                        (str (System/getProperty "user.home")
                             "/.gcp-credentials/vertex-ai-app-key.json"))
   :endpoint (get-env "VERTEX_AI_ENDPOINT"
                     "us-central1-aiplatform.googleapis.com")})

(defn validate-credentials
  "Validates that credentials file exists and is readable"
  []
  (let [creds-file (io/file (:credentials gcp-config))]
    (when-not (.exists creds-file)
      (throw (ex-info "Credentials file not found"
                      {:path (:credentials gcp-config)})))
    (when-not (.canRead creds-file)
      (throw (ex-info "Credentials file not readable"
                      {:path (:credentials gcp-config)})))
    true))

;; Usage
(comment
  (validate-credentials)
  ;; => true or throws ex-info

  (:project-id gcp-config)
  ;; => "saas3-476116"
  )

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📊 IAM and Security

Service Account Roles

Current Role: roles/aiplatform.user (least privilege)

# Verify current roles
gcloud projects get-iam-policy saas3-476116 \
  --flatten="bindings[].members" \
  --filter="bindings.members:serviceAccount:vertex-ai-app*"

# Expected output:
# - roles/aiplatform.user (use Vertex AI)

Key Rotation (Mandatory every 90 days)

# 1. List existing keys
gcloud iam service-accounts keys list \
  --iam-account=vertex-ai-app@saas3-476116.iam.gserviceaccount.com \
  --format="table(name,validAfterTime,validBeforeTime)"

# 2. Create new key
gcloud iam service-accounts keys create \
  ~/.gcp-credentials/vertex-ai-app-key-new.json \
  --iam-account=vertex-ai-app@saas3-476116.iam.gserviceaccount.com

# 3. Protect
chmod 600 ~/.gcp-credentials/vertex-ai-app-key-new.json

# 4. Test
GOOGLE_APPLICATION_CREDENTIALS=~/.gcp-credentials/vertex-ai-app-key-new.json \
  gcloud auth application-default print-access-token

# 5. Update ~/.env and delete old key
gcloud iam service-accounts keys delete OLD_KEY_ID \
  --iam-account=vertex-ai-app@saas3-476116.iam.gserviceaccount.com

Security Audit Checklist (Monthly)

• Rotate keys (every 90 days)
• Review IAM permissions
• Verify enabled APIs (disable unused)
• Analyze costs
• Verify .gitignore (no keys committed)
• Check service account activity logs
• Review Vertex AI quotas

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💰 Observed Costs and Estimates

Real Costs (Healthcare Pipeline - 20 posts/month)

Component	Cost per Execution	Monthly (20 posts)
S.1.1 (Type B - LGPD Extraction)	$0.045	$0.90
S.1.2 (Type A - Claims ID)	$0.021	$0.42
S.2-1.2 (Type C - References)	$0.067	$1.34
S.3-2 (Type B - SEO)	$0.078	$1.56
S.4 (Type D - Consolidation)	$0.18	$3.60
Total per post	$0.391	$7.82

With optimizations (cache + parallel):

• Cost per post: $0.162 (-58.6%)
• Monthly: $3.24 (-58.6%)

Model Pricing (us-central1)

Model	Input (1M tokens)	Output (1M tokens)	Best For
gemini-1.5-flash	$0.075	$0.30	Type A/B (70% of tasks)
gemini-1.5-pro	$1.25	$5.00	Type C/D (complex reasoning)
gemini-2.5-flash	$0.10	$0.40	Balanced (future)

Multi-model strategy savings: 41% vs Claude-only

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🛠️ Clojure + Vertex AI Setup

deps.edn Configuration

{:paths ["src" "resources"]
 :deps {org.clojure/clojure {:mvn/version "1.11.1"}

        ;; Google Cloud Vertex AI (Java SDK)
        com.google.cloud/google-cloud-aiplatform {:mvn/version "3.36.0"}
        com.google.cloud/google-cloud-vertexai {:mvn/version "0.2.0"}

        ;; Google Auth
        com.google.auth/google-auth-library-oauth2-http {:mvn/version "1.19.0"}

        ;; JSON handling
        cheshire/cheshire {:mvn/version "5.11.0"}

        ;; HTTP client
        clj-http/clj-http {:mvn/version "3.12.3"}

        ;; Database (if using Type B/D)
        com.github.seancorfield/next.jdbc {:mvn/version "1.3.909"}
        org.postgresql/postgresql {:mvn/version "42.6.0"}

        ;; Python interop (optional, for libpython-clj)
        clj-python/libpython-clj {:mvn/version "2.025"}

        ;; Logging
        org.clojure/tools.logging {:mvn/version "1.2.4"}
        ch.qos.logback/logback-classic {:mvn/version "1.4.11"}}

 :aliases
 {:dev {:extra-paths ["dev"]
        :extra-deps {nrepl/nrepl {:mvn/version "1.0.0"}
                     cider/cider-nrepl {:mvn/version "0.30.0"}}}

  :test {:extra-paths ["test"]
         :extra-deps {org.clojure/test.check {:mvn/version "1.1.1"}}}}}

Basic Vertex AI Agent (Type A)

(ns lab.agents.basic
  "Basic Type A agent example"
  (:require [lab.config.google-cloud :as gc]
            [cheshire.core :as json])
  (:import [com.google.cloud.vertexai VertexAI]
           [com.google.cloud.vertexai.generativeai GenerativeModel]
           [com.google.cloud.vertexai.api GenerationConfig]))

(defn create-vertex-ai-instance
  "Creates Vertex AI instance with credentials."
  []
  (let [{:keys [project-id location credentials]} gc/gcp-config]
    (gc/validate-credentials)
    (VertexAI. project-id location)))

(defn create-generative-model
  "Creates a generative model."
  ([vertex-ai] (create-generative-model vertex-ai "gemini-1.5-flash"))
  ([vertex-ai model-name]
   (let [generation-config (-> (GenerationConfig/newBuilder)
                               (.setTemperature 0.7)
                               (.setMaxOutputTokens 2048)
                               (.setTopP 0.95)
                               (.build))
         model (.getGenerativeModel vertex-ai model-name)]
     (.withGenerationConfig model generation-config))))

(defn generate-content
  "Generates content from prompt."
  [model prompt]
  (let [response (.generateContent model prompt)]
    {:text (-> response .getText)
     :metadata {:model (.getModelName model)
                :finish-reason (-> response .getCandidates first .getFinishReason str)}}))

;; REPL Usage
(comment
  ;; 1. Create Vertex AI instance
  (def vertex-ai (create-vertex-ai-instance))

  ;; 2. Create model
  (def model (create-generative-model vertex-ai "gemini-1.5-flash"))

  ;; 3. Generate content
  (def result
    (generate-content
      model
      "Explain functional programming in Clojure in 3 sentences."))

  (:text result)
  ;; => "Functional programming in Clojure emphasizes..."

  ;; 4. Clean up
  (.close vertex-ai)
  )

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🔒 Security Best Practices

1. Credentials Management

DO:

• ✅ Store credentials in ~/.gcp-credentials/ (outside repo)
• ✅ Use chmod 600 on all credential files
• ✅ Set GOOGLE_APPLICATION_CREDENTIALS environment variable
• ✅ Rotate keys every 90 days
• ✅ Use service accounts (not user accounts)

DON'T:

• ❌ Commit credentials to git
• ❌ Share credentials via chat/email
• ❌ Use keys older than 90 days
• ❌ Give excessive permissions (roles/owner)

2. .gitignore (Mandatory)

# GCP Credentials
.gcp-credentials/
*-key.json
*.json.enc

# Environment files
.env
.env.local
.env.*.local

# Secrets
secrets/
credentials/

3. Least Privilege IAM

Minimum roles needed:

• roles/aiplatform.user - Use Vertex AI
• roles/logging.viewer - View logs (optional)

Avoid:

• roles/owner - Too broad
• roles/editor - Too broad
• roles/aiplatform.admin - Unless deploying

4. LGPD Compliance (Brazil)

When processing personal data:

(ns lab.compliance.lgpd
  "LGPD compliance utilities")

(def sensitive-data-patterns
  "Patterns for detecting sensitive personal data"
  {:cpf #"\d{3}\.\d{3}\.\d{3}-\d{2}"
   :rg #"\d{1,2}\.\d{3}\.\d{3}-\d{1,2}"
   :email #"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
   :phone #"\(?[0-9]{2}\)?\s?[0-9]{4,5}-?[0-9]{4}"
   :health-data #"(?i)(diabetes|hipertens[ãa]o|depress[ãa]o|ansiedade)"})

(defn detect-sensitive-data
  "Detects sensitive personal data in text."
  [text]
  (reduce-kv
    (fn [acc data-type pattern]
      (if (re-find pattern text)
        (conj acc data-type)
        acc))
    []
    sensitive-data-patterns))

(defn sanitize-for-logging
  "Removes sensitive data before logging."
  [text]
  (reduce-kv
    (fn [sanitized data-type pattern]
      (clojure.string/replace sanitized pattern "[REDACTED]"))
    text
    sensitive-data-patterns))

;; Usage
(comment
  (detect-sensitive-data "Cliente: João Silva, CPF: 123.456.789-00")
  ;; => [:cpf]

  (sanitize-for-logging "Cliente: João Silva, CPF: 123.456.789-00")
  ;; => "Cliente: João Silva, CPF: [REDACTED]"
  )

📘 Full compliance guide: See cva-healthcare-compliance skill.

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🧪 Testing and Validation

Verify GCP Setup

# 1. Verify gcloud auth
gcloud auth application-default print-access-token

# 2. Verify project
gcloud config get-value project
# Expected: saas3-476116

# 3. Verify APIs
gcloud services list --enabled --project=saas3-476116 | grep aiplatform

# 4. Test Vertex AI access
gcloud ai models list --region=us-central1 --project=saas3-476116

Clojure Integration Test

(ns lab.test.integration
  "Integration tests for Vertex AI"
  (:require [clojure.test :refer :all]
            [lab.config.google-cloud :as gc]
            [lab.agents.basic :as basic]))

(deftest test-vertex-ai-connection
  (testing "Can create Vertex AI instance"
    (let [vertex-ai (basic/create-vertex-ai-instance)]
      (is (some? vertex-ai))
      (.close vertex-ai))))

(deftest test-model-generation
  (testing "Can generate content"
    (let [vertex-ai (basic/create-vertex-ai-instance)
          model (basic/create-generative-model vertex-ai)
          result (basic/generate-content model "Say 'test'")]
      (is (string? (:text result)))
      (is (> (count (:text result)) 0))
      (.close vertex-ai))))

Run tests:

clojure -X:test

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🚨 Troubleshooting

"Credentials not found"

Problem: GOOGLE_APPLICATION_CREDENTIALS not set or file doesn't exist

Solution:

# Verify file exists
ls -la ~/.gcp-credentials/vertex-ai-app-key.json

# Verify permissions
chmod 600 ~/.gcp-credentials/vertex-ai-app-key.json

# Set environment variable
export GOOGLE_APPLICATION_CREDENTIALS="$HOME/.gcp-credentials/vertex-ai-app-key.json"

# Test
gcloud auth application-default print-access-token

"API not enabled"

Problem: Vertex AI API not enabled for project

Solution:

gcloud services enable aiplatform.googleapis.com --project=saas3-476116

"Permission denied"

Problem: Service account lacks necessary roles

Solution:

gcloud projects add-iam-policy-binding saas3-476116 \
  --member="serviceAccount:vertex-ai-app@saas3-476116.iam.gserviceaccount.com" \
  --role="roles/aiplatform.user"

"Quota exceeded"

Problem: Exceeded Vertex AI quotas

Solution:

# Check quotas
gcloud compute project-info describe --project=saas3-476116

# Request quota increase (if needed)
# https://console.cloud.google.com/iam-admin/quotas?project=saas3-476116

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🔗 Related Skills

• cva-setup-clojure - Clojure project structure
• cva-setup-interop - libpython-clj setup
• cva-healthcare-compliance - LGPD compliance ⭐
• cva-concepts-adk - Google ADK architecture
• cva-quickref-adk - ADK quick reference

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📘 Additional Documentation

• GCP Console
• Vertex AI Documentation
• IAM Best Practices
• Security Checklist (skill-specific doc)
• Cost Optimization (skill-specific doc)

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This setup is validated for production healthcare systems with proven ROI. Follow security best practices rigorously.