deepwiki-rs 1.2.8

deepwiki-rs(also known as Litho) is a high-performance automatic generation engine for C4 architecture documentation, developed using Rust. It can intelligently analyze project structures, identify core components, parse dependency relationships, and leverage large language models (LLMs) to automatically generate professional architecture documentation.
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use anyhow::Result;

use crate::generator::context::GeneratorContext;
use crate::generator::research::agents::architecture_researcher::ArchitectureResearcher;
use crate::generator::research::agents::boundary_analyzer::BoundaryAnalyzer;
use crate::generator::research::agents::database_overview_analyzer::DatabaseOverviewAnalyzer;
use crate::generator::research::agents::domain_modules_detector::DomainModulesDetector;
use crate::generator::research::agents::key_modules_insight::KeyModulesInsight;
use crate::generator::research::agents::system_context_researcher::SystemContextResearcher;
use crate::generator::research::agents::workflow_researcher::WorkflowResearcher;
use crate::generator::step_forward_agent::StepForwardAgent;
use crate::generator::preprocess::memory::{MemoryScope, ScopedKeys};
use crate::types::code::{CodeInsight, CodePurpose};

/// Multi-agent research orchestrator
#[derive(Default)]
pub struct ResearchOrchestrator;

impl ResearchOrchestrator {
    /// Execute all agent analysis pipelines
    pub async fn execute_research_pipeline(&self, context: &GeneratorContext) -> Result<()> {
        println!("🚀 Starting Litho Studies Research investigation pipeline...");

        // First layer: Macro analysis (C1)
        self.execute_agent(&SystemContextResearcher, context)
            .await?;

        // Second layer: Meso analysis (C2)
        self.execute_agent(&DomainModulesDetector, context)
            .await?;
        self.execute_agent(&ArchitectureResearcher, context)
            .await?;
        self.execute_agent(&WorkflowResearcher, context)
            .await?;

        // Third layer: Micro analysis (C3-C4)
        self.execute_agent(&KeyModulesInsight, context)
            .await?;

        // Boundary interface analysis
        self.execute_agent(&BoundaryAnalyzer::default(), context)
            .await?;

        // Database overview analysis (only if database files exist)
        if self.has_database_files(context).await {
            self.execute_agent(&DatabaseOverviewAnalyzer::default(), context)
                .await?;
        }

        println!("✓ Litho Studies Research pipeline execution completed");

        Ok(())
    }

    /// Check if the project has database-related files
    async fn has_database_files(&self, context: &GeneratorContext) -> bool {
        if let Some(insights) = context
            .get_from_memory::<Vec<CodeInsight>>(MemoryScope::PREPROCESS, ScopedKeys::CODE_INSIGHTS)
            .await
        {
            insights.iter().any(|insight| {
                matches!(insight.code_dossier.code_purpose, CodePurpose::Database)
                    || insight.code_dossier.file_path.to_string_lossy().ends_with(".sql")
                    || insight.code_dossier.file_path.to_string_lossy().ends_with(".sqlproj")
            })
        } else {
            false
        }
    }

    /// Execute a single agent
    async fn execute_agent<T>(
        &self,
        agent: &T,
        context: &GeneratorContext,
    ) -> Result<()>
    where
        T: StepForwardAgent + Send + Sync,
    {
        // Use localized agent name if available
        let agent_name = if let Some(agent_enum) = agent.agent_type_enum() {
            agent_enum.display_name(&context.config.target_language)
        } else {
            agent.agent_type()
        };
        
        println!("🤖 Executing {} agent analysis...", agent_name);

        agent.execute(context).await?;
        println!("✓ {} analysis completed", agent_name);
        Ok(())
    }
}