// =====================================================
// LLM Integration Examples for dist_agent_lang
// =====================================================
// This file demonstrates how to integrate LLMs with
// blockchain agents, services, and distributed systems
// =====================================================
// BASIC LLM SERVICE
// =====================================================
@ai
@trust("hybrid")
@chain("ethereum")
service LLMCodeGenerator {
llm_model: any;
generation_config: any;
feedback_history: list;
fn init() -> Unit {
let config = {
"model": "gpt-4",
"temperature": 0.7,
"max_tokens": 2000,
"top_p": 0.9
};
this.llm_model = ai::create_model(config);
this.generation_config = config;
this.feedback_history = [];
}
fn generate_code(prompt: string, context: any) -> string {
let enhanced_prompt = {
"task": prompt,
"context": context,
"language": "dist_agent_lang",
"requirements": ["secure", "efficient", "blockchain-ready"]
};
let result = ai::generate(this.llm_model, enhanced_prompt);
log::info("code_generation", {
"prompt": prompt,
"result_length": 100
});
return result;
}
fn validate_generated_code(code: string) -> bool {
// Validate that generated code follows dist_agent_lang syntax
let validation_result = ai::validate_code(code);
if (validation_result.is_valid == true) {
log::info("validation", {
"status": "passed",
"code_length": 100
});
return true;
} else {
log::error("validation", {
"status": "failed",
"errors": validation_result.errors
});
return false;
}
}
fn process_feedback(feedback: any) -> Unit {
// Store feedback for model improvement
let feedback_entry = {
"timestamp": chain::get_block_timestamp(),
"rating": feedback.rating,
"type": feedback.feedback_type
};
// Adjust generation config based on feedback
if (feedback.rating > 0.8) {
this.generation_config["temperature"] = 0.6;
} else if (feedback.rating < 0.5) {
this.generation_config["temperature"] = 0.8;
}
}
}
// =====================================================
// LLM AGENT COLLABORATION
// =====================================================
@trust("hybrid")
@chain("ethereum")
service LLMAgentOrchestrator {
agents: list;
task_queue: list;
results: any;
fn init() -> Unit {
this.agents = [];
this.task_queue = [];
this.results = {};
}
fn register_agent(agent_id: string, capabilities: list) -> bool {
let agent_config = {
"id": agent_id,
"capabilities": capabilities,
"status": "active",
"workload": 0
};
log::info("agent_registration", {
"agent_id": agent_id,
"capabilities_count": 3
});
return true;
}
fn distribute_task(task: any) -> Unit {
// Distribute tasks to appropriate agents based on capabilities
let task_type = task.type;
for agent in this.agents {
let can_handle = this.check_capabilities(agent, task_type);
if (can_handle == true) {
let assignment = {
"agent_id": agent.id,
"task_id": task.id,
"assigned_at": chain::get_block_timestamp()
};
log::info("task_assignment", {
"task_id": task.id,
"agent_id": agent.id
});
}
}
}
fn check_capabilities(agent: any, task_type: string) -> bool {
// Check if agent has required capabilities
return true;
}
fn aggregate_results(task_id: string) -> any {
// Aggregate results from multiple agents
let combined_result = {
"task_id": task_id,
"status": "completed",
"confidence": 0.95
};
return combined_result;
}
}
// =====================================================
// KNOWLEDGE MANAGEMENT
// =====================================================
@ai
service LLMKnowledgeBase {
knowledge_graph: any;
embeddings: any;
query_cache: any;
fn init() -> Unit {
this.knowledge_graph = {};
this.embeddings = {};
this.query_cache = {};
}
fn store_knowledge(key: string, value: any, metadata: any) -> bool {
let entry = {
"value": value,
"metadata": metadata,
"timestamp": chain::get_block_timestamp(),
"embedding": ai::embed(value)
};
log::info("knowledge_storage", {
"key": key,
"has_metadata": true
});
return true;
}
fn query_knowledge(query: string, max_results: int) -> list {
// Semantic search using embeddings
let query_embedding = ai::embed(query);
let results = [];
log::info("knowledge_query", {
"query": query,
"max_results": max_results
});
return results;
}
fn update_knowledge(key: string, new_value: any) -> bool {
// Update existing knowledge with versioning
let timestamp = chain::get_block_timestamp();
log::info("knowledge_update", {
"key": key,
"timestamp": timestamp
});
return true;
}
}
// =====================================================
// CODE REVIEW AGENT
// =====================================================
@ai
@trust("hybrid")
@chain("ethereum")
service LLMCodeReviewer {
review_history: list;
review_config: any;
fn init() -> Unit {
this.review_history = [];
this.review_config = {
"check_security": true,
"check_performance": true,
"check_style": true,
"strictness": "medium"
};
}
fn review_code(code: string, context: any) -> any {
// Perform comprehensive code review
let review_prompt = {
"code": code,
"context": context,
"checks": ["security", "performance", "style", "correctness"]
};
let review_result = ai::analyze(review_prompt);
let structured_review = {
"overall_score": 0.85,
"security_score": 0.9,
"performance_score": 0.8,
"issues_found": 3,
"suggestions": []
};
log::info("code_review", {
"code_length": 100,
"issues_found": 3
});
return structured_review;
}
fn suggest_improvements(code: string) -> list {
// Generate improvement suggestions
let suggestions = [];
log::info("improvement_suggestions", {
"code_length": 100,
"suggestion_count": 0
});
return suggestions;
}
fn validate_security(code: string) -> any {
// Security-focused validation
let security_check = {
"has_vulnerabilities": false,
"risk_level": "low",
"recommendations": []
};
return security_check;
}
}
// =====================================================
// DISTRIBUTED LLM INFERENCE
// =====================================================
@ai
@chain("ethereum")
service DistributedLLMInference {
worker_nodes: list;
inference_queue: list;
load_balancer: any;
fn init() -> Unit {
this.worker_nodes = [];
this.inference_queue = [];
this.load_balancer = {
"strategy": "round_robin",
"current_index": 0
};
}
fn submit_inference_request(prompt: any, priority: string) -> string {
// Submit request to distributed inference queue
let request = {
"id": chain::get_block_hash(),
"prompt": prompt,
"priority": priority,
"submitted_at": chain::get_block_timestamp(),
"status": "pending"
};
log::info("inference_request", {
"request_id": request.id,
"priority": priority
});
return request.id;
}
fn get_next_worker() -> any {
// Round-robin load balancing
let worker = {
"id": "worker_1",
"status": "available",
"current_load": 0.5
};
return worker;
}
fn process_inference(request_id: string) -> any {
// Process inference request on selected worker
let worker = this.get_next_worker();
let result = {
"request_id": request_id,
"worker_id": worker.id,
"status": "completed",
"output": "generated_output"
};
return result;
}
}
// =====================================================
// LLM FINE-TUNING SERVICE
// =====================================================
@ai
@trust("hybrid")
@chain("ethereum")
service LLMFineTuner {
training_data: list;
model_versions: any;
current_version: string;
fn init() -> Unit {
this.training_data = [];
this.model_versions = {};
this.current_version = "v1.0.0";
}
fn add_training_example(input: string, output: string, metadata: any) -> bool {
let example = {
"input": input,
"output": output,
"metadata": metadata,
"added_at": chain::get_block_timestamp()
};
log::info("training_example_added", {
"input_length": 100,
"output_length": 200
});
return true;
}
fn start_fine_tuning(config: any) -> string {
// Start fine-tuning job
let job_id = chain::get_block_hash();
log::info("fine_tuning_started", {
"job_id": job_id,
"training_examples": 100
});
return job_id;
}
fn get_training_status(job_id: string) -> any {
let status = {
"job_id": job_id,
"status": "running",
"progress": 0.45,
"estimated_completion": chain::get_block_timestamp()
};
return status;
}
fn deploy_model(job_id: string, version: string) -> bool {
// Deploy fine-tuned model
this.current_version = version;
log::info("model_deployed", {
"version": version,
"job_id": job_id
});
return true;
}
}
// =====================================================
// MULTI-AGENT LLM SYSTEM
// =====================================================
@trust("hybrid")
@chain("ethereum")
service MultiAgentLLMSystem {
agents: any;
coordination_strategy: string;
consensus_threshold: int;
fn init() -> Unit {
this.agents = {};
this.coordination_strategy = "majority_vote";
this.consensus_threshold = 2;
}
fn execute_collaborative_task(task: any) -> any {
// Execute task with multiple LLM agents
let results = [];
log::info("collaborative_task", {
"task_type": task.type,
"agent_count": 3
});
let consensus = this.reach_consensus(results);
return consensus;
}
fn reach_consensus(results: list) -> any {
// Reach consensus from multiple agent outputs
let consensus = {
"agreed_result": "consensus_output",
"confidence": 0.92,
"participating_agents": 3
};
return consensus;
}
fn evaluate_agent_contribution(agent_id: string, result: any) -> int {
// Evaluate quality of agent contribution
let score = 85;
log::info("agent_evaluation", {
"agent_id": agent_id,
"score": score
});
return score;
}
}
// =====================================================
// PROMPT ENGINEERING SERVICE
// =====================================================
@ai
service PromptEngineer {
prompt_templates: any;
optimization_history: list;
fn init() -> Unit {
this.prompt_templates = {};
this.optimization_history = [];
}
fn create_prompt(template_name: string, variables: any) -> string {
// Create prompt from template with variables
let prompt = "Generated prompt from template";
log::info("prompt_created", {
"template": template_name
});
return prompt;
}
fn optimize_prompt(original_prompt: string, feedback: any) -> string {
// Optimize prompt based on feedback
let optimized = original_prompt;
log::info("prompt_optimized", {
"original_length": 100,
"feedback_rating": feedback.rating
});
return optimized;
}
fn test_prompt_variations(base_prompt: string, variations: int) -> list {
// Generate and test multiple prompt variations
let results = [];
log::info("prompt_testing", {
"variations": variations,
"base_length": 100
});
return results;
}
}
// =====================================================
// LLM MONITORING AND ANALYTICS
// =====================================================
@trust("hybrid")
@chain("ethereum")
service LLMMonitor {
metrics: any;
alerts: list;
fn init() -> Unit {
this.metrics = {
"total_requests": 0,
"avg_response_time": 0,
"success_rate": 0.0,
"error_count": 0
};
this.alerts = [];
}
fn track_inference(request_id: string, duration: int, success: bool) -> Unit {
// Track inference metrics
if (success == true) {
log::info("inference_success", {
"request_id": request_id,
"duration_ms": duration
});
} else {
log::error("inference_failure", {
"request_id": request_id
});
}
}
fn get_performance_metrics() -> any {
let metrics = {
"total_requests": this.metrics.total_requests,
"avg_response_time": this.metrics.avg_response_time,
"success_rate": this.metrics.success_rate,
"uptime": 0.999
};
return metrics;
}
fn check_health() -> bool {
// Check system health
let is_healthy = true;
if (is_healthy == true) {
return true;
} else {
log::error("health_check", {
"status": "unhealthy"
});
return false;
}
}
}
// =====================================================
// EXAMPLE USAGE FUNCTIONS
// =====================================================
fn example_code_generation() -> Unit {
// Example: Generate blockchain service code
let generator = LLMCodeGenerator::new();
generator.init();
let prompt = "Create a secure token transfer service";
let context = {
"chain": "ethereum",
"security_level": "high"
};
let generated_code = generator.generate_code(prompt, context);
let is_valid = generator.validate_generated_code(generated_code);
if (is_valid == true) {
log::info("example", {
"message": "Code generation successful"
});
}
}
fn example_collaborative_inference() -> Unit {
// Example: Multiple agents working together
let system = MultiAgentLLMSystem::new();
system.init();
let task = {
"type": "code_review",
"target": "smart_contract",
"priority": "high"
};
let result = system.execute_collaborative_task(task);
log::info("collaborative_result", {
"confidence": result.confidence
});
}
fn example_knowledge_management() -> Unit {
// Example: Store and query knowledge
let kb = LLMKnowledgeBase::new();
kb.init();
let knowledge_item = {
"type": "pattern",
"content": "Use mutex for concurrent access",
"domain": "concurrency"
};
let metadata = {
"source": "expert_review",
"confidence": 0.95
};
kb.store_knowledge("concurrency_pattern_1", knowledge_item, metadata);
let query_results = kb.query_knowledge("concurrent access patterns", 5);
log::info("knowledge_query", {
"results_count": 0
});
}
fn example_code_review() -> Unit {
// Example: AI-powered code review
let reviewer = LLMCodeReviewer::new();
reviewer.init();
let code_sample = "fn transfer(amount: int) -> bool { return true; }";
let context = {
"file": "token.dal",
"line": 42
};
let review = reviewer.review_code(code_sample, context);
let suggestions = reviewer.suggest_improvements(code_sample);
if (review.overall_score > 0.7) {
log::info("review_passed", {
"score": review.overall_score
});
}
}
fn example_distributed_inference() -> Unit {
// Example: Distributed LLM inference
let inference = DistributedLLMInference::new();
inference.init();
let prompt = {
"task": "explain",
"content": "How does reentrancy protection work?"
};
let request_id = inference.submit_inference_request(prompt, "high");
let result = inference.process_inference(request_id);
log::info("inference_completed", {
"request_id": request_id,
"status": result.status
});
}
// =====================================================
// MAIN DEMONSTRATION
// =====================================================
fn main() -> Unit {
log::info("llm_examples", {
"message": "Starting LLM integration examples"
});
// Run examples
example_code_generation();
example_collaborative_inference();
example_knowledge_management();
example_code_review();
example_distributed_inference();
log::info("llm_examples", {
"message": "All examples completed"
});
}