deepwiki-rs 1.2.6

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::research::memory::MemoryScope;
use crate::generator::research::types::{AgentType, DomainModulesReport};
use crate::generator::{
    context::GeneratorContext,
    step_forward_agent::{
        AgentDataConfig, DataSource, FormatterConfig, LLMCallMode, PromptTemplate, StepForwardAgent,
    },
};

/// Domain Division and Top-Level Abstract Module Researcher - Identifies high-level system domain architecture and abstract modules, as well as their internal relationships.
#[derive(Default)]
pub struct DomainModulesDetector;

impl StepForwardAgent for DomainModulesDetector {
    type Output = DomainModulesReport;

    fn agent_type(&self) -> String {
        AgentType::DomainModulesDetector.to_string()
    }

    fn agent_type_enum(&self) -> Option<AgentType> {
        Some(AgentType::DomainModulesDetector)
    }

    fn memory_scope_key(&self) -> String {
        MemoryScope::STUDIES_RESEARCH.to_string()
    }

    fn data_config(&self) -> AgentDataConfig {
        AgentDataConfig {
            required_sources: vec![
                DataSource::ResearchResult(AgentType::SystemContextResearcher.to_string()),
                DataSource::DEPENDENCY_ANALYSIS,
                DataSource::CODE_INSIGHTS,
            ],
            optional_sources: vec![DataSource::PROJECT_STRUCTURE],
        }
    }

    fn prompt_template(&self) -> PromptTemplate {
        PromptTemplate {
            system_prompt: r#"You are a professional software architecture analyst, specializing in identifying domain architecture and modules in projects based on the provided information and research materials"#
                .to_string(),

            opening_instruction: "Based on the following research materials, conduct a high-level architecture analysis:".to_string(),

            closing_instruction: r#"
## Analysis Requirements:
- Use a top-down analysis approach, domains first then modules
- Domain division should reflect functional value, not technical implementation
- Maintain a reasonable level of abstraction, avoid excessive detail
- Focus on core business logic and key dependency relationships"#
                .to_string(),

            llm_call_mode: LLMCallMode::Extract,
            formatter_config: FormatterConfig {
                only_directories_when_files_more_than: Some(500),
                ..FormatterConfig::default()
            },
        }
    }

    /// Post-processing - Store analysis results to memory
    fn post_process(
        &self,
        result: &DomainModulesReport,
        _context: &GeneratorContext,
    ) -> Result<()> {
        // Simplified storage logic
        println!("✅ Domain architecture analysis completed:");
        println!("   - Identified domain modules: {}", result.domain_modules.len());

        let total_sub_modules: usize = result
            .domain_modules
            .iter()
            .map(|d| d.sub_modules.len())
            .sum();
        println!("   - Total sub-modules: {}", total_sub_modules);
        println!("   - Domain relations: {}", result.domain_relations.len());
        println!("   - Business flows: {}", result.business_flows.len());
        println!("   - Confidence score: {:.1}/10", result.confidence_score);

        Ok(())
    }
}