npcrs 0.1.0

use crate::error::{NpcError, Result};
use crate::r#gen::response_types::*;

use genai::chat::{
    ChatMessage, ChatRequest, ChatResponse as GenaiChatResponse, ContentPart,
    MessageContent as GenaiContent, Tool as GenaiTool, ToolCall as GenaiToolCall,
    ToolResponse as GenaiToolResponse,
};
use genai::Client as GenaiClient;

use std::sync::OnceLock;

static GENAI_CLIENT: OnceLock<GenaiClient> = OnceLock::new();

fn get_client() -> &'static GenaiClient {
    GENAI_CLIENT.get_or_init(GenaiClient::default)
}

pub async fn get_genai_response(
    _provider: &str,
    model: &str,
    messages: &[Message],
    tools: Option<&[ToolDef]>,
    _api_url_override: Option<&str>,
) -> Result<LlmResponse> {
    let client = get_client();

    let mut req = ChatRequest::new(Vec::new());

    for msg in messages {
        let content_str = msg.content.as_deref().unwrap_or("");

        match msg.role.as_str() {
            "system" => {
                req = req.with_system(content_str);
            }
            "user" => {
                req = req.append_message(ChatMessage::user(content_str));
            }
            "assistant" => {
                if let Some(ref tcs) = msg.tool_calls {
                    let genai_tcs: Vec<GenaiToolCall> = tcs
                        .iter()
                        .map(|tc| GenaiToolCall {
                            call_id: tc.id.clone(),
                            fn_name: tc.function.name.clone(),
                            fn_arguments: serde_json::from_str(&tc.function.arguments)
                                .unwrap_or(serde_json::Value::Object(serde_json::Map::new())),
                            thought_signatures: None,
                        })
                        .collect();
                    req = req.append_message(ChatMessage::assistant(
                        GenaiContent::from_tool_calls(genai_tcs),
                    ));
                } else {
                    req = req.append_message(ChatMessage::assistant(content_str));
                }
            }
            "tool" => {
                let call_id = msg.tool_call_id.as_deref().unwrap_or("");
                let tool_resp = GenaiToolResponse::new(call_id, content_str);
                req = req.append_message(ChatMessage::from(tool_resp));
            }
            _ => {
                req = req.append_message(ChatMessage::user(content_str));
            }
        }
    }

    if let Some(tool_defs) = tools {
        let genai_tools: Vec<GenaiTool> = tool_defs
            .iter()
            .map(|td| {
                let mut t = GenaiTool::new(&td.function.name);
                if let Some(ref desc) = td.function.description {
                    t = t.with_description(desc);
                }
                t = t.with_schema(td.function.parameters.clone());
                t
            })
            .collect();
        req = req.with_tools(genai_tools);
    }

    let genai_resp = client
        .exec_chat(model, req, None)
        .await
        .map_err(|e| NpcError::LlmRequest(format!("{}", e)))?;

    convert_genai_response(genai_resp, model)
}

fn convert_genai_response(resp: GenaiChatResponse, model: &str) -> Result<LlmResponse> {
    let mut content_text: Option<String> = None;
    let mut tool_calls: Option<Vec<ToolCall>> = None;

    let genai_content = &resp.content;

    let tcs = genai_content.tool_calls();
    if !tcs.is_empty() {
        tool_calls = Some(
            tcs.iter()
                .map(|tc| ToolCall {
                    id: tc.call_id.clone(),
                    r#type: "function".to_string(),
                    function: ToolCallFunction {
                        name: tc.fn_name.clone(),
                        arguments: serde_json::to_string(&tc.fn_arguments)
                            .unwrap_or_else(|_| "{}".to_string()),
                    },
                })
                .collect(),
        );
    }

    let text: Option<String> = genai_content.joined_texts();
    if let Some(ref t) = text {
        if !t.is_empty() {
            content_text = text;
        }
    }

    let usage = {
        let u = &resp.usage;
        Some(Usage {
            prompt_tokens: u.prompt_tokens.unwrap_or(0) as u64,
            completion_tokens: u.completion_tokens.unwrap_or(0) as u64,
            total_tokens: u.total_tokens.unwrap_or(0) as u64,
        })
    };

    Ok(LlmResponse {
        message: Message {
            role: "assistant".to_string(),
            content: content_text,
            tool_calls,
            tool_call_id: None,
            name: None,
        },
        usage,
        model: model.to_string(),
        finish_reason: None,
        cost_usd: None,
    })
}