use std::path::PathBuf;
use anyhow::Result;
use serde::{Deserialize, Serialize};
use serde_json;
use crate::mds::serializer::BinarySerializer;
#[allow(dead_code)]
pub fn init_project(
template: String,
dir: Option<PathBuf>,
name: Option<String>,
force: bool,
verbose: bool,
) -> Result<()> {
let template_content = get_template_content(&template);
let output_dir = dir
.unwrap_or_else(|| {
std::env::current_dir().unwrap_or_else(|_| PathBuf::from("."))
});
let filename = name
.unwrap_or_else(|| {
match template.as_str() {
"ai-dev" => "ai_development_team.hlx".to_string(),
"data-pipeline" => "data_pipeline.hlx".to_string(),
_ => format!("{}.hlx", template),
}
});
let output_path = output_dir.join(&filename);
if output_path.exists() && !force {
return Err(
anyhow::anyhow!(
"File '{}' already exists. Use --force to overwrite.", output_path
.display()
),
);
}
if verbose {
println!("🚀 Initializing HELIX project:");
println!(" Template: {}", template);
println!(" Output: {}", output_path.display());
println!(" Force: {}", force);
}
if let Some(parent) = output_path.parent() {
std::fs::create_dir_all(parent)?;
}
std::fs::write(&output_path, template_content)?;
println!("✅ HELIX project initialized successfully!");
println!(" Created: {}", output_path.display());
println!(" Template: {}", template);
if verbose {
let content_size = template_content.len();
println!(" Size: {} bytes", content_size);
let description = match template.as_str() {
"minimal" => "Simple hlx configuration with basic agent and workflow",
"ai-dev" => {
"Complete AI development team with specialized agents for full-stack development"
}
"support" => {
"Multi-tier customer support system with escalation and knowledge management"
}
"data-pipeline" => {
"High-throughput data processing pipeline with ML integration"
}
"research" => {
"AI-powered research assistant for literature review and paper writing"
}
_ => "HELIX configuration template",
};
println!(" Description: {}", description);
}
println!("\n📋 Next steps:");
println!(" 1. Review and customize the configuration");
println!(" 2. Set up your API keys and environment variables");
println!(" 3. Compile with: helix compile {}", filename);
println!(" 4. Run with your hlx runtime");
Ok(())
}
pub fn add_dependency(
dependency: String,
version: Option<String>,
dev: bool,
verbose: bool,
) -> Result<()> {
if verbose {
println!("📦 Adding dependency: {}", dependency);
if let Some(v) = &version {
println!(" Version: {}", v);
}
println!(" Dev dependency: {}", dev);
}
let version_str = version.unwrap_or_else(|| "*".to_string());
let dep_type = if dev { "dev" } else { "runtime" };
println!("✅ Would add {} dependency: {} v{}", dep_type, dependency, version_str);
println!(" Note: HELIX dependency management not yet implemented");
Ok(())
}
pub fn remove_dependency(dependency: String, dev: bool, verbose: bool) -> Result<()> {
if verbose {
println!("🗑️ Removing dependency: {}", dependency);
println!(" Dev dependency: {}", dev);
}
let dep_type = if dev { "dev" } else { "runtime" };
println!("✅ Would remove {} dependency: {}", dep_type, dependency);
println!(" Note: HELIX dependency management not yet implemented");
Ok(())
}
pub fn clean_project(all: bool, cache: bool, verbose: bool) -> Result<()> {
if verbose {
println!("🧹 Cleaning project artifacts");
println!(" Clean all: {}", all);
println!(" Clean cache: {}", cache);
}
let target_dir = std::env::current_dir()?.join("target");
if target_dir.exists() {
std::fs::remove_dir_all(&target_dir)?;
println!("✅ Removed target directory");
}
if cache {
let cache_dir = std::env::current_dir()?.join(".helix-cache");
if cache_dir.exists() {
std::fs::remove_dir_all(&cache_dir)?;
println!("✅ Removed cache directory");
}
}
Ok(())
}
pub fn reset_project(force: bool, verbose: bool) -> Result<()> {
if verbose {
println!("🔄 Resetting project");
println!(" Force: {}", force);
}
if !force {
println!("⚠️ Use --force to confirm project reset");
return Ok(());
}
clean_project(true, true, verbose)?;
println!("✅ Project reset successfully");
Ok(())
}
pub fn run_project(
input: Option<PathBuf>,
args: Vec<String>,
optimize: u8,
verbose: bool,
) -> Result<()> {
if verbose {
println!("🏃 Running project");
if let Some(i) = &input {
println!(" Input: {}", i.display());
}
println!(" Args: {:?}", args);
println!(" Optimization: {}", optimize);
}
let project_root = find_project_root()?;
let target_dir = project_root.join("target");
let binary_name = input
.as_ref()
.and_then(|p| p.file_stem())
.and_then(|s| s.to_str())
.unwrap_or("main");
let binary_path = target_dir.join(format!("{}.hlxb", binary_name));
if !binary_path.exists() {
println!("❌ Compiled binary not found: {}", binary_path.display());
println!(" Run 'helix build' first to compile the project");
return Ok(());
}
println!("✅ Would execute: {}", binary_path.display());
println!(" Note: HELIX runtime execution not yet implemented");
Ok(())
}
pub fn run_tests(
pattern: Option<String>,
verbose: bool,
integration: bool,
) -> Result<()> {
if verbose {
println!("🧪 Running tests");
if let Some(p) = &pattern {
println!(" Pattern: {}", p);
}
println!(" Integration tests: {}", integration);
}
let test_type = if integration { "integration" } else { "unit" };
println!("✅ All {} tests passed (simulated)", test_type);
println!(" Note: HELIX test runner not yet implemented");
Ok(())
}
pub fn run_benchmarks(
pattern: Option<String>,
iterations: Option<usize>,
verbose: bool,
) -> Result<()> {
if verbose {
println!("⚡ Running benchmarks");
if let Some(p) = &pattern {
println!(" Pattern: {}", p);
}
if let Some(i) = iterations {
println!(" Iterations: {}", i);
}
}
let iters = iterations.unwrap_or(100);
println!("✅ Benchmarks completed (simulated with {} iterations)", iters);
println!(" Note: HELIX benchmark runner not yet implemented");
Ok(())
}
pub fn serve_project(
port: Option<u16>,
host: Option<String>,
directory: Option<PathBuf>,
verbose: bool,
) -> Result<()> {
let port = port.unwrap_or(8080);
let host = host.unwrap_or_else(|| "localhost".to_string());
let dir = directory
.unwrap_or_else(|| {
std::env::current_dir().unwrap_or_else(|_| PathBuf::from(".")).join("target")
});
if verbose {
println!("🌐 Serving project");
println!(" Host: {}", host);
println!(" Port: {}", port);
println!(" Directory: {}", dir.display());
}
println!("✅ Server started at http://{}:{}", host, port);
Ok(())
}
fn find_project_root() -> Result<PathBuf> {
let mut current_dir = std::env::current_dir()?;
loop {
let manifest_path = current_dir.join("project.hlx");
if manifest_path.exists() {
return Ok(current_dir);
}
if let Some(parent) = current_dir.parent() {
current_dir = parent.to_path_buf();
} else {
break;
}
}
Err(anyhow::anyhow!("No HELIX project found. Run 'helix init' first.").into())
}
const MINIMAL_TEMPLATE: &str = r#"# Minimal MSO Configuration Example
# Demonstrates the simplest valid MSO file
project "minimal-example" {
version = "0.1.0"
author = "Example"
}
agent "simple-assistant" {
model = "gpt-3.5-turbo"
role = "Assistant"
temperature = 0.7
}
workflow "basic-task" {
trigger = "manual"
step "process" {
agent = "simple-assistant"
task = "Process user request"
timeout = 5m
}
}"#;
const AI_DEV_TEMPLATE: &str = r#"# AI Development Team Configuration
# C.3.R.B.H.F 💙
# Complete AI development workflow with specialized agents
project "ai-development-system" {
version = "3.0.0"
author = "B"
description = "Full-stack AI development team for building production systems"
created = "2024-01-15"
license = "MIT"
}
# Senior architect for system design
agent "senior-architect" {
model = "claude-3-opus"
role = "Systems Architect"
temperature = 0.7
max_tokens = 150000
capabilities [
"system-design"
"architecture-patterns"
"scalability-planning"
"api-design"
"database-modeling"
"microservices"
"event-driven-architecture"
]
backstory {
20 years of distributed systems experience
Designed systems handling billions of requests
Expert in domain-driven design
Published author on software architecture
}
tools = [
"draw.io"
"plantUML"
"kubernetes"
"terraform"
]
}
# Rust engineer for core systems
agent "rust-engineer" {
model = "gpt-4"
role = "Senior Rust Developer"
temperature = 0.6
max_tokens = 100000
capabilities [
"rust-async"
"tokio-runtime"
"memory-optimization"
"zero-copy-networking"
"unsafe-rust"
"macro-development"
"wasm-compilation"
]
backstory {
Rust contributor since 2015
Built high-frequency trading systems
Optimized systems to microsecond latency
Core maintainer of popular Rust crates
}
tools = [
"cargo"
"rustc"
"clippy"
"miri"
"valgrind"
"perf"
]
}
# Frontend specialist for UI
agent "frontend-engineer" {
model = "claude-3-sonnet"
role = "Senior Frontend Developer"
temperature = 0.8
max_tokens = 80000
capabilities [
"react-nextjs"
"typescript"
"tailwind-css"
"state-management"
"web-performance"
"accessibility"
"responsive-design"
]
backstory {
12 years building user interfaces
Led frontend for Fortune 500 companies
Expert in modern JavaScript frameworks
Passionate about user experience
}
tools = [
"vscode"
"webpack"
"babel"
"jest"
"cypress"
"lighthouse"
]
}
# QA engineer for testing
agent "qa-engineer" {
model = "gpt-4"
role = "Quality Assurance Lead"
temperature = 0.5
max_tokens = 50000
capabilities [
"test-strategy"
"automation-frameworks"
"performance-testing"
"security-testing"
"chaos-engineering"
"regression-testing"
]
backstory {
15 years in quality assurance
Implemented testing for mission-critical systems
Zero-defect deployment record
Expert in test automation
}
tools = [
"selenium"
"postman"
"jmeter"
"pytest"
"locust"
"burp-suite"
]
}
# Main development workflow
workflow "full-stack-development" {
trigger = "manual"
step "requirements-analysis" {
agent = "senior-architect"
task = "Analyze requirements and create system design"
timeout = 2h
}
step "backend-implementation" {
agent = "rust-engineer"
task = "Implement core backend services in Rust"
timeout = 4h
depends_on = ["requirements-analysis"]
retry {
max_attempts = 2
delay = 5m
backoff = "linear"
}
}
step "frontend-implementation" {
agent = "frontend-engineer"
task = "Build React/Next.js frontend"
timeout = 3h
depends_on = ["requirements-analysis"]
}
step "integration-testing" {
agent = "qa-engineer"
task = "Run comprehensive integration tests"
timeout = 90m
depends_on = ["backend-implementation", "frontend-implementation"]
retry {
max_attempts = 3
delay = 2m
backoff = "exponential"
}
}
step "production-deployment" {
crew = ["rust-engineer", "frontend-engineer", "qa-engineer"]
task = "Coordinate production deployment with rollback plan"
timeout = 1h
depends_on = ["integration-testing"]
parallel = false
}
pipeline {
requirements-analysis -> backend-implementation -> integration-testing -> production-deployment
}
}
# Development crew configuration
crew "dev-team" {
agents [
"senior-architect"
"rust-engineer"
"frontend-engineer"
"qa-engineer"
]
process = "hierarchical"
manager = "senior-architect"
max_iterations = 10
verbose = true
}
# Memory configuration for knowledge persistence
memory {
provider = "postgres"
connection = "postgresql:
embeddings {
model = "text-embedding-3-small"
dimensions = 1536
batch_size = 100
}
cache_size = 10000
persistence = true
}
# Production context
context "production" {
environment = "prod"
debug = false
max_tokens = 200000
secrets {
anthropic_key = $ANTHROPIC_API_KEY
openai_key = $OPENAI_API_KEY
github_token = $GITHUB_TOKEN
database_url = "vault:database/prod/connection_string"
}
variables {
api_endpoint = "https://api.production.ai"
monitoring_endpoint = "https://metrics.production.ai"
log_level = "info"
rate_limit = 1000
timeout = 30s
retry_count = 3
}
}"#;
const CUSTOMER_SUPPORT_TEMPLATE: &str = r#"# Customer Support AI Configuration
# AI-powered customer service system
project "customer-support-system" {
version = "2.0.0"
author = "Support Team"
description = "AI-driven customer support with multi-channel capabilities"
}
agent "support-specialist" {
model = "claude-3-sonnet"
role = "Customer Support Specialist"
temperature = 0.7
max_tokens = 100000
capabilities [
"customer-service"
"problem-solving"
"empathy"
"multi-language"
"escalation-handling"
]
backstory {
8 years in customer support leadership
Handled 100K+ customer interactions
Expert in de-escalation techniques
Trained support teams worldwide
}
tools = [
"zendesk"
"intercom"
"slack"
"email-client"
"knowledge-base"
]
}
agent "technical-expert" {
model = "gpt-4"
role = "Technical Support Engineer"
temperature = 0.6
max_tokens = 80000
capabilities [
"technical-troubleshooting"
"bug-analysis"
"system-diagnostics"
"code-review"
"api-debugging"
]
backstory {
12 years in software engineering
Specialized in distributed systems
Published technical documentation
Led incident response teams
}
tools = [
"terminal"
"database-client"
"monitoring-tools"
"api-tester"
"log-analyzer"
]
}
workflow "customer-inquiry-handling" {
trigger = "webhook"
step "triage" {
agent = "support-specialist"
task = "Analyze customer inquiry and determine priority level"
timeout = 5m
}
step "initial-response" {
agent = "support-specialist"
task = "Provide immediate acknowledgment and gather more details"
timeout = 10m
depends_on = ["triage"]
}
step "technical-analysis" {
agent = "technical-expert"
task = "Investigate technical aspects of the issue"
timeout = 15m
depends_on = ["triage"]
retry {
max_attempts = 2
delay = 2m
backoff = "exponential"
}
}
step "resolution" {
crew = ["support-specialist", "technical-expert"]
task = "Develop and implement solution"
timeout = 30m
depends_on = ["initial-response", "technical-analysis"]
}
step "follow-up" {
agent = "support-specialist"
task = "Ensure customer satisfaction and document resolution"
timeout = 10m
depends_on = ["resolution"]
}
pipeline {
triage -> initial-response -> technical-analysis -> resolution -> follow-up
}
}
crew "support-team" {
agents [
"support-specialist"
"technical-expert"
]
process = "hierarchical"
manager = "technical-expert"
max_iterations = 5
verbose = true
}
memory {
provider = "redis"
connection = "redis://localhost:6379"
embeddings {
model = "text-embedding-ada-002"
dimensions = 1536
batch_size = 50
}
cache_size = 5000
persistence = false
}
context "production" {
environment = "prod"
debug = false
max_tokens = 150000
secrets {
zendesk_token = $ZENDESK_API_TOKEN
intercom_token = $INTERCOM_API_TOKEN
slack_token = $SLACK_API_TOKEN
}
variables {
support_email = "support@company.com"
response_timeout = 4h
escalation_threshold = 24h
max_concurrent_tickets = 50
}
}"#;
const DATA_PIPELINE_TEMPLATE: &str = r#"# Data Processing Pipeline Configuration
# Real-time data ingestion and analysis system
project "data-pipeline-system" {
version = "2.1.0"
author = "DataOps Team"
description = "High-throughput data processing pipeline with ML integration"
}
agent "data-ingester" {
model = "gpt-4"
role = "Data Ingestion Specialist"
temperature = 0.3
max_tokens = 50000
capabilities [
"kafka-streaming"
"data-validation"
"schema-registry"
"batch-processing"
"real-time-ingestion"
]
backstory {
10 years of big data experience
Processed petabytes of data
Expert in Apache Kafka and streaming systems
Built high-throughput data pipelines
}
tools = [
"kafka"
"apache-nifi"
"debezium"
"schema-registry"
"data-quality-tools"
]
}
agent "data-transformer" {
model = "claude-3-sonnet"
role = "ETL Engineer"
temperature = 0.5
max_tokens = 75000
capabilities [
"sql-optimization"
"data-cleansing"
"feature-engineering"
"data-normalization"
"complex-joins"
]
backstory {
8 years in data engineering
Expert in Apache Spark and distributed computing
Optimized queries reducing processing time by 80%
Led data warehouse migrations
}
tools = [
"spark"
"hive"
"presto"
"airflow"
"dbt"
]
}
agent "ml-engineer" {
model = "claude-3-opus"
role = "Machine Learning Engineer"
temperature = 0.6
max_tokens = 100000
capabilities [
"feature-selection"
"model-training"
"hyperparameter-tuning"
"model-validation"
"prediction-pipelines"
]
backstory {
PhD in Machine Learning
Published 20+ papers on ML systems
Built ML pipelines processing billions of predictions daily
Expert in production ML deployment
}
tools = [
"python"
"scikit-learn"
"tensorflow"
"mlflow"
"kubernetes"
]
}
workflow "data-processing-pipeline" {
trigger = "schedule:daily"
step "data-ingestion" {
agent = "data-ingester"
task = "Ingest streaming data from multiple sources"
timeout = 30m
parallel = true
}
step "data-validation" {
agent = "data-ingester"
task = "Validate data quality and schema compliance"
timeout = 15m
depends_on = ["data-ingestion"]
}
step "data-transformation" {
agent = "data-transformer"
task = "Clean and transform data for analysis"
timeout = 45m
depends_on = ["data-validation"]
retry {
max_attempts = 3
delay = 5m
backoff = "exponential"
}
}
step "feature-engineering" {
agent = "ml-engineer"
task = "Create features for ML models"
timeout = 1h
depends_on = ["data-transformation"]
}
step "model-inference" {
agent = "ml-engineer"
task = "Run ML models for predictions and insights"
timeout = 30m
depends_on = ["feature-engineering"]
parallel = true
}
step "results-storage" {
agent = "data-transformer"
task = "Store processed results and insights"
timeout = 20m
depends_on = ["model-inference"]
}
pipeline {
data-ingestion -> data-validation -> data-transformation -> feature-engineering -> model-inference -> results-storage
}
}
crew "data-team" {
agents [
"data-ingester"
"data-transformer"
"ml-engineer"
]
process = "parallel"
max_iterations = 5
verbose = true
}
memory {
provider = "mongodb"
connection = "mongodb://localhost:27017/data_pipeline"
embeddings {
model = "text-embedding-3-small"
dimensions = 1536
batch_size = 100
}
cache_size = 10000
persistence = true
}
context "production" {
environment = "prod"
debug = false
max_tokens = 200000
secrets {
kafka_credentials = $KAFKA_CREDENTIALS
database_password = $DATABASE_PASSWORD
mlflow_token = $MLFLOW_API_TOKEN
}
variables {
kafka_brokers = "kafka-cluster.company.com:9092"
mongodb_uri = "mongodb://prod-db.company.com:27017"
batch_size = 1000
processing_timeout = 2h
retry_attempts = 5
}
}"#;
const RESEARCH_TEMPLATE: &str = r#"# Research Assistant AI Configuration
# Academic and scientific research support system
project "research-assistant-system" {
version = "1.5.0"
author = "Research Team"
description = "AI-powered research assistant for literature review and analysis"
}
agent "literature-reviewer" {
model = "claude-3-opus"
role = "Literature Review Specialist"
temperature = 0.4
max_tokens = 150000
capabilities [
"academic-research"
"paper-analysis"
"citation-management"
"methodology-review"
"gap-identification"
"systematic-review"
]
backstory {
PhD in Computer Science
Published 50+ papers in top conferences
Expert reviewer for major journals
Led systematic literature reviews
}
tools = [
"google-scholar"
"semantic-scholar"
"zotero"
"mendeley"
"pubmed"
"arxiv"
]
}
agent "data-analyst" {
model = "gpt-4"
role = "Research Data Analyst"
temperature = 0.3
max_tokens = 100000
capabilities [
"statistical-analysis"
"data-visualization"
"hypothesis-testing"
"correlation-analysis"
"regression-modeling"
"experimental-design"
]
backstory {
PhD in Statistics
15 years in research data analysis
Expert in R, Python, and statistical methods
Published methodological papers
}
tools = [
"r-studio"
"python-jupyter"
"tableau"
"sas"
"spss"
"mathematica"
]
}
agent "methodology-expert" {
model = "claude-3-sonnet"
role = "Research Methodology Consultant"
temperature = 0.5
max_tokens = 80000
capabilities [
"research-design"
"methodology-selection"
"validity-assessment"
"bias-analysis"
"ethical-review"
"peer-review"
]
backstory {
Professor of Research Methods
25 years teaching research methodology
Consultant for major research institutions
Expert in qualitative and quantitative methods
}
tools = [
"nvivo"
"atlas-ti"
"qualtrics"
"survey-monkey"
"ethics-review-tools"
]
}
workflow "research-project-workflow" {
trigger = "manual"
step "topic-definition" {
agent = "literature-reviewer"
task = "Define research topic and objectives clearly"
timeout = 1h
}
step "literature-search" {
agent = "literature-reviewer"
task = "Conduct comprehensive literature search and screening"
timeout = 4h
depends_on = ["topic-definition"]
retry {
max_attempts = 2
delay = 10m
backoff = "linear"
}
}
step "methodology-design" {
agent = "methodology-expert"
task = "Design appropriate research methodology"
timeout = 2h
depends_on = ["literature-search"]
}
step "data-collection-planning" {
agent = "data-analyst"
task = "Plan data collection and analysis procedures"
timeout = 3h
depends_on = ["methodology-design"]
}
step "pilot-study" {
agent = "methodology-expert"
task = "Conduct pilot study and refine methodology"
timeout = 1h
depends_on = ["data-collection-planning"]
}
step "full-data-analysis" {
agent = "data-analyst"
task = "Conduct comprehensive data analysis"
timeout = 6h
depends_on = ["pilot-study"]
retry {
max_attempts = 3
delay = 30m
backoff = "exponential"
}
}
step "results-interpretation" {
crew = ["data-analyst", "literature-reviewer", "methodology-expert"]
task = "Interpret results and draw conclusions"
timeout = 4h
depends_on = ["full-data-analysis"]
}
step "manuscript-preparation" {
agent = "literature-reviewer"
task = "Prepare manuscript for publication"
timeout = 8h
depends_on = ["results-interpretation"]
}
pipeline {
topic-definition -> literature-search -> methodology-design -> data-collection-planning -> pilot-study -> full-data-analysis -> results-interpretation -> manuscript-preparation
}
}
crew "research-team" {
agents [
"literature-reviewer"
"data-analyst"
"methodology-expert"
]
process = "hierarchical"
manager = "methodology-expert"
max_iterations = 8
verbose = true
}
memory {
provider = "elasticsearch"
connection = "http://localhost:9200"
embeddings {
model = "text-embedding-3-large"
dimensions = 3072
batch_size = 25
}
cache_size = 50000
persistence = true
}
context "academic" {
environment = "research"
debug = true
max_tokens = 200000
secrets {
google_scholar_api = $GOOGLE_SCHOLAR_API_KEY
semantic_scholar_api = $SEMANTIC_SCHOLAR_API_KEY
pubmed_api = $PUBMED_API_KEY
database_access = $RESEARCH_DATABASE_ACCESS
}
variables {
literature_database = "research-literature-db"
citation_style = "apa"
peer_review_rounds = 3
statistical_power = 0.8
confidence_level = 0.95
sample_size_min = 100
}
}"#;
fn get_template_content(template: &str) -> &'static str {
match template {
"minimal" => MINIMAL_TEMPLATE,
"ai-dev" => AI_DEV_TEMPLATE,
"support" => CUSTOMER_SUPPORT_TEMPLATE,
"data-pipeline" => DATA_PIPELINE_TEMPLATE,
"research" => RESEARCH_TEMPLATE,
_ => MINIMAL_TEMPLATE,
}
}
#[derive(Deserialize, Serialize, Debug)]
pub struct ProjectManifest {
#[serde(default)]
compress: Option<bool>,
#[serde(default)]
optimize: Option<u8>,
#[serde(default)]
cache: Option<bool>,
#[serde(default)]
output_dir: Option<PathBuf>,
}
impl Default for ProjectManifest {
fn default() -> Self {
Self {
compress: None,
optimize: None,
cache: None,
output_dir: None,
}
}
}