zclaw_openfang/desktop/src-tauri/src/kernel_commands/orchestration.rs

//! Skill orchestration Tauri commands
//!
//! Exposes the multi-skill orchestration engine to the frontend,
//! enabling parallel/serial/conditional execution of skill graphs.

use std::collections::HashMap;
use serde::{Deserialize, Serialize};
use serde_json::Value;
use tauri::State;

use super::KernelState;

// ============================================================================
// Request/Response Types
// ============================================================================

/// Orchestration execution request
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct OrchestrationRequest {
    /// Skill IDs to orchestrate (for auto-compose)
    pub skill_ids: Vec<String>,
    /// Optional pre-defined graph (overrides auto-compose)
    pub graph: Option<OrchestrationGraphInput>,
    /// Input data for the orchestration
    pub inputs: HashMap<String, Value>,
    /// Agent context
    pub agent_id: String,
    pub session_id: String,
}

/// Graph input for pre-defined orchestration
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct OrchestrationGraphInput {
    pub id: String,
    pub name: String,
    #[serde(default)]
    pub description: String,
    pub nodes: Vec<NodeInput>,
    #[serde(default)]
    pub edges: Vec<EdgeInput>,
    #[serde(default)]
    pub on_error: Option<String>,
    #[serde(default)]
    pub timeout_secs: Option<u64>,
}

/// Node input for orchestration graph
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct NodeInput {
    pub id: String,
    pub skill_id: String,
    #[serde(default)]
    pub description: String,
    #[serde(default)]
    pub input_mappings: HashMap<String, String>,
    #[serde(default)]
    pub when: Option<String>,
    #[serde(default)]
    pub skip_on_error: Option<bool>,
    #[serde(default)]
    pub timeout_secs: Option<u64>,
}

/// Edge input for orchestration graph
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct EdgeInput {
    pub from_node: String,
    pub to_node: String,
    #[serde(default)]
    pub field_mapping: HashMap<String, String>,
    #[serde(default)]
    pub condition: Option<String>,
}

/// Orchestration execution result for frontend
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct OrchestrationResponse {
    pub success: bool,
    pub output: Value,
    pub node_count: usize,
    pub completed_nodes: usize,
    pub failed_nodes: usize,
    pub duration_ms: u64,
    pub error: Option<String>,
    pub node_results: HashMap<String, NodeResultResponse>,
}

/// Node execution result for frontend
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct NodeResultResponse {
    pub success: bool,
    pub output: Value,
    pub error: Option<String>,
    pub duration_ms: u64,
    pub retries: u32,
    pub skipped: bool,
}

/// Validation response for frontend
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct ValidationResponse {
    pub valid: bool,
    pub errors: Vec<ValidationErrorInfo>,
}

/// Validation error info
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct ValidationErrorInfo {
    pub code: String,
    pub message: String,
    pub location: Option<String>,
}

// ============================================================================
// Type Conversions
// ============================================================================

impl TryFrom<OrchestrationGraphInput> for zclaw_skills::orchestration::SkillGraph {
    type Error = String;

    fn try_from(input: OrchestrationGraphInput) -> Result<Self, Self::Error> {
        let nodes: Vec<zclaw_skills::orchestration::SkillNode> = input.nodes.into_iter().map(|n| {
            zclaw_skills::orchestration::SkillNode {
                id: n.id,
                skill_id: zclaw_types::SkillId::new(&n.skill_id),
                description: n.description,
                input_mappings: n.input_mappings,
                retry: None,
                timeout_secs: n.timeout_secs,
                when: n.when,
                skip_on_error: n.skip_on_error.unwrap_or(false),
            }
        }).collect();

        let edges: Vec<zclaw_skills::orchestration::SkillEdge> = input.edges.into_iter().map(|e| {
            zclaw_skills::orchestration::SkillEdge {
                from_node: e.from_node,
                to_node: e.to_node,
                field_mapping: e.field_mapping,
                condition: e.condition,
            }
        }).collect();

        let on_error = match input.on_error.as_deref() {
            Some("continue") => zclaw_skills::orchestration::ErrorStrategy::Continue,
            Some("retry") => zclaw_skills::orchestration::ErrorStrategy::Retry,
            _ => zclaw_skills::orchestration::ErrorStrategy::Stop,
        };

        Ok(zclaw_skills::orchestration::SkillGraph {
            id: input.id,
            name: input.name,
            description: input.description,
            nodes,
            edges,
            input_schema: None,
            output_mapping: HashMap::new(),
            on_error,
            timeout_secs: input.timeout_secs.unwrap_or(300),
        })
    }
}

impl From<zclaw_skills::orchestration::OrchestrationResult> for OrchestrationResponse {
    fn from(result: zclaw_skills::orchestration::OrchestrationResult) -> Self {
        let completed = result.node_results.values().filter(|r| r.success).count();
        let failed = result.node_results.values().filter(|r| !r.success).count();

        let node_results = result.node_results.into_iter().map(|(id, nr)| {
            (id, NodeResultResponse {
                success: nr.success,
                output: nr.output,
                error: nr.error,
                duration_ms: nr.duration_ms,
                retries: nr.retries,
                skipped: nr.skipped,
            })
        }).collect();

        Self {
            success: result.success,
            output: result.output,
            node_count: completed + failed,
            completed_nodes: completed,
            failed_nodes: failed,
            duration_ms: result.duration_ms,
            error: result.error,
            node_results,
        }
    }
}

// ============================================================================
// Tauri Commands
// ============================================================================

/// @reserved — no frontend UI yet
/// Execute a skill orchestration
/// @reserved — orchestration engine internal, no direct frontend caller
///
/// Either auto-composes a graph from skill_ids, or uses a pre-defined graph.
/// Executes with true parallel execution within each dependency level.
#[tauri::command]
pub async fn orchestration_execute(
    state: State<'_, KernelState>,
    request: OrchestrationRequest,
) -> Result<OrchestrationResponse, String> {
    let kernel_lock = state.lock().await;
    let kernel = kernel_lock.as_ref()
        .ok_or_else(|| "Kernel not initialized".to_string())?;

    // Build or auto-compose graph
    let graph = if let Some(graph_input) = request.graph {
        graph_input.try_into()?
    } else if request.skill_ids.is_empty() {
        return Err("Must provide either skill_ids or graph".to_string());
    } else {
        let skill_ids: Vec<zclaw_types::SkillId> = request.skill_ids.iter()
            .map(|s| zclaw_types::SkillId::new(s))
            .collect();

        kernel.auto_compose_skills(&skill_ids).await
            .map_err(|e| format!("Auto-compose failed: {}", e))?
    };

    // Build skill context
    let context = zclaw_skills::SkillContext {
        agent_id: request.agent_id,
        session_id: request.session_id,
        working_dir: None,
        env: HashMap::new(),
        timeout_secs: graph.timeout_secs,
        network_allowed: true,
        file_access_allowed: true,
        llm: None,
        tool_definitions: Vec::new(),
    };

    // Execute orchestration
    let result = kernel.execute_orchestration(&graph, request.inputs, &context).await
        .map_err(|e| format!("Orchestration failed: {}", e))?;

    Ok(OrchestrationResponse::from(result))
}

/// @reserved — no frontend UI yet
/// Validate an orchestration graph without executing it
#[tauri::command]
pub async fn orchestration_validate(
    state: State<'_, KernelState>,
    graph: OrchestrationGraphInput,
) -> Result<ValidationResponse, String> {
    let kernel_lock = state.lock().await;
    let kernel = kernel_lock.as_ref()
        .ok_or_else(|| "Kernel not initialized".to_string())?;

    let skill_graph: zclaw_skills::orchestration::SkillGraph = graph.try_into()?;

    let errors = kernel.validate_orchestration(&skill_graph).await;

    let valid = errors.is_empty();
    let error_infos = errors.into_iter().map(|e| ValidationErrorInfo {
        code: e.code,
        message: e.message,
        location: e.location,
    }).collect();

    Ok(ValidationResponse { valid, errors: error_infos })
}