kandil_code 2.1.1

//! Design agent
//!
//! Specialized agent for creating software architecture and design documents

use crate::core::adapters::ai::KandilAI;
use crate::core::agents::base::{Agent, AgentState, ReActLoop};
use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DesignDocument {
    pub architecture: ArchitectureStyle,
    pub components: Vec<Component>,
    pub data_flow: Vec<DataFlow>,
    pub technology_stack: TechnologyStack,
    pub design_patterns: Vec<DesignPattern>,
    pub diagrams: Vec<Diagram>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ArchitectureStyle {
    CleanArchitecture,
    Hexagonal,
    Layered,
    Microservices,
    EventDriven,
    Other(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Component {
    pub name: String,
    pub description: String,
    pub responsibilities: Vec<String>,
    pub interfaces: Vec<Interface>,
    pub dependencies: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Interface {
    pub name: String,
    pub methods: Vec<Method>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Method {
    pub name: String,
    pub parameters: Vec<Parameter>,
    pub return_type: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Parameter {
    pub name: String,
    pub parameter_type: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataFlow {
    pub source: String,
    pub destination: String,
    pub data_type: String,
    pub description: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TechnologyStack {
    pub frontend: Vec<String>,
    pub backend: Vec<String>,
    pub database: Vec<String>,
    pub infrastructure: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DesignPattern {
    pub name: String,
    pub description: String,
    pub use_cases: Vec<String>,
    pub implementation_notes: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Diagram {
    pub title: String,
    pub description: String,
    pub content: String, // This could be Mermaid.js syntax or other diagram definition
    pub diagram_type: DiagramType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DiagramType {
    Component,
    Sequence,
    Class,
    Deployment,
    Flowchart,
    Other(String),
}

pub struct DesignAgent {
    ai: Arc<KandilAI>,
}

impl DesignAgent {
    pub fn new(ai: Arc<KandilAI>) -> Self {
        Self { ai }
    }

    pub async fn generate_design_document(&self, requirements_doc: &str) -> Result<DesignDocument> {
        let loop_engine = ReActLoop::new(5);
        let task = format!(
            "As a Software Architect, design a system based on these requirements: {}.\n\nCreate a comprehensive design document covering architecture, components, data flow, technology stack, design patterns, and diagrams.",
            requirements_doc
        );

        let result = loop_engine.run(self, &task).await?;

        // For now, we'll create a basic document structure from the AI response
        // In a real implementation, we would properly parse the structured response
        Ok(DesignDocument {
            architecture: ArchitectureStyle::CleanArchitecture,
            components: vec![],
            data_flow: vec![],
            technology_stack: TechnologyStack {
                frontend: vec![],
                backend: vec![],
                database: vec![],
                infrastructure: vec![],
            },
            design_patterns: vec![],
            diagrams: vec![],
        })
    }
}

#[async_trait]
impl Agent for DesignAgent {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "Given these design requirements: {}\n\nCurrent state: Step {}/{}\n\nPlan the next step to design the system architecture. Consider architectural patterns, technology selection, and component decomposition.",
            state.task, state.current_step + 1, state.max_steps
        );

        self.ai.chat(&prompt).await
    }

    async fn act(&self, plan: &str) -> Result<String> {
        // In a real implementation, this would involve:
        // - Creating architecture diagrams
        // - Selecting appropriate technologies
        // - Defining component interfaces
        // For simulation, we'll use the AI to generate design elements based on the plan

        let prompt = format!(
            "Execute this design plan: {}\n\nGenerate specific architectural elements, component definitions, or technology recommendations.",
            plan
        );

        self.ai.chat(&prompt).await
    }

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze the design result and determine next steps
        let prompt = format!(
            "Analyze this design result: {}\n\nHow does this contribute to the overall system architecture? What aspects need refinement?",
            result
        );

        self.ai.chat(&prompt).await
    }
}