import { LLMOrbiter, OpenRouterProvider } from 'libllmorbiter';
import * as db from '../db/modelconversation.js';
import * as dbCompanySetting from '../db/companysetting.js';
import env from '../config/env.js';
import { emit as emitCompanyEvent } from '../api/sse.js';
import { readFileSync } from 'fs';
import { resolve, dirname } from 'path';
import { fileURLToPath } from 'url';
import { randomUUID } from 'crypto';
import { createDashbackRequestTool } from '../tools/dashback-request.js';
import { createAskUserTool } from '../tools/ask-user.js';
import { createJavascriptExecuteTool } from '../tools/javascript.js';
import { createResetVfsTool } from '../tools/reset-vfs.js';
import { resolveVfsRoot } from '../tools/vfs.js';
import { listSkillFiles } from '../tools/skill.js';
import { createApiSuggestionsOrIssuesTool } from '../tools/apisuggestionsorissues.js';
import { createFindFilesTool } from '../tools/find-files.js';
import { createReadFileTool } from '../tools/read-file.js';
import { createPatchTool } from '../tools/patch.js';
import { createRmTool } from '../tools/rm.js';
import { createSearchImagesTool } from '../tools/search-images.js';
import { createAnalyzeImageTool } from '../tools/analyze-image.js';
import { resolveOpenRouterKey, resolveDefaultModel, RETIRED_MODELS, hasConfiguredOpenRouterKey } from '../tools/llm-key.js';

const KEEP_ALIVE_MS = 20_000;

const ROUTES_DOC_PATH = resolve(dirname(fileURLToPath(import.meta.url)), '../../docs/api/routes.md');

/**
 * Builds the SKILL index block for the system prompt.
 *
 * Listing the skills inline means the model knows what guidance exists BEFORE it decides what
 * to do, so it can reach for `read_file` on the right skill instead of guessing a procedure
 * from scratch. Only the filename, title and line count are included — the body is read on
 * demand, which keeps the prompt small no matter how many skills exist.
 *
 * @param {string} vfsRoot
 * @returns {string}
 */
function buildSkillIndex(vfsRoot) {
    // A null root means the VFS could not be resolved (no company selected, or a store
    // error). Say so plainly instead of pretending the company has no skills — otherwise the
    // model would conclude no guidance exists and invent its own procedure.
    if (!vfsRoot) {
        return 'AVAILABLE SKILLS: unknown (the virtual filesystem could not be read this turn). Call find_files({ skills: true }) to check.';
    }

    let skills;
    try {
        skills = listSkillFiles(vfsRoot);
    } catch (err) {
        console.warn('conversation: failed to list skill files:', err.message);
        return 'AVAILABLE SKILLS: could not be read this turn. Call find_files({ skills: true }) to check.';
    }

    if (skills.length === 0) {
        return [
            'AVAILABLE SKILLS: none. This company has no SKILL files yet.',
        ].join('\n');
    }

    const width = Math.max(...skills.map((s) => s.path.length));
    return [
        `AVAILABLE SKILLS (${skills.length}): stored in the VFS root as flat <slug>.SKILL files.`,
        'Before starting a task that any of these cover, read the matching skill with read_file and follow it.',
        '',
        ...skills.map((s) => `  ${s.path.padEnd(width)}  ${String(s.lines).padStart(4)} lines  | ${s.title}`),
    ].join('\n');
}

/**
 * System prompt: base instructions plus the generated API routes reference
 * (docs/api/routes.md), read on each run so it stays current.
 *
 * @param {string} vfsRoot Resolved VFS root for this user's company, used to list SKILL files.
 * @returns {string}
 */
async function buildSystemPrompt(vfsRoot) {
    let routesDoc = '';
    try {
        routesDoc = readFileSync(ROUTES_DOC_PATH, 'utf8').trim();
    } catch (err) {
        console.error('conversation: failed to read routes doc:', err);
    }
    const today = new Date().toISOString().split('T')[0];
    return [
        'You are an intelligent assistant specialized in exploring, querying, and managing data from the Dashback REST API.',
        `Today's date: ${today}`,
        '',
        'YOUR TOOLS:',
        '1. dashback_request: Execute HTTP requests (GET, POST, PUT, PATCH, DELETE) against Dashback endpoints.',
        '2. ask_user: Ask clarifying questions or request decisions from the user if their intent is ambiguous or requires choices. When you call ask_user, execution pauses immediately and waits for the user to provide their response.',
        '3. javascript_execute: Run JavaScript snippets in a sandbox to compute, aggregate, transform, or analyze data. Assign module.exports to return a value. Snippets can call Dashback API routes using fetch() (e.g. await fetch("/article/2")). A sandboxed virtual filesystem (fs / vfs) is available to store and retrieve artifacts across runs.',
        '4. find_files: List what is stored in the virtual filesystem (VFS). Call find_files({ skills: true }) to list SKILL files only.',
        '5. read_file: Read a VFS file as text, windowed with seek/limit for large files.',
        '6. patch: Create, overwrite, or search-and-replace text in a VFS file. This is the only text write path — it also creates SKILL files.',
        '7. search_images: Search Google Images and store the candidates in the VFS as AVIF files.',
        '8. analyze_image: Have a vision model look at up to 9 images (VFS paths or "image/<fullHash>") and return its judgement. This is how you "see" — never claim to have inspected an image yourself.',
        '9. reset_vfs: Delete files, images, and artifacts stored in the virtual filesystem (VFS) to free up storage or clean up. SKILL files are exempt and survive.',
        '10. rm: Delete a single file from the VFS. This is how a SKILL file is deleted on purpose, since reset_vfs always spares skills.',
        '11. apisuggestionsorissues: Report missing API endpoints, bugs, or issues to be triaged. Automatically writes an entry to the company history table for review.',
        '',
        'VIRTUAL FILESYSTEM:',
        '- You have a persistent per-company VFS. Paths are relative to the VFS root and always use forward slashes, e.g. "labels/sheet1.png".',
        '- To READ text: find_files (list) then read_file (content). To WRITE text: patch. Binary artifacts (images, PDFs, buffers) go through javascript_execute.',
        '- Never invent a VFS path. Always confirm with find_files before reading or referencing a file.',
        '',
        'SKILL FILES:',
        '- A SKILL file is a reusable instruction document: a file named "<slug>.SKILL" at the VFS root whose FIRST LINE is its title and whose remaining lines are markdown.',
        '- SKILL file names may NOT contain "/" — skills are flat, at the root only. Use find_files({ skills: true }) to discover them; this listing is not affected by pagination or by how many artifacts exist.',
        '- Read a skill with read_file BEFORE starting a task it covers, and follow it. If you learn a better procedure during the task, offer to write it back as a new SKILL file.',
        '- SKILL files are exempt from reset_vfs: artifacts are disposable, skills are not. Never call reset_vfs expecting to clear skills, and never put throwaway output in a .SKILL file.',
        '- To delete a skill (or any single file) use rm — it is the only way to remove one. Deletion is final.',
        '',
        buildSkillIndex(vfsRoot),
        '',
        'IMAGES:',
        '- You cannot see images yourself. To evaluate one, call analyze_image and report what the vision model says.',
        '- Typical flow: search_images("query") → analyze_image({ paths: [...candidates], prompt: "…" }) → upload the winner with dashback_request.',
        '- The `image/` folder holds database images and is READ-ONLY. Generated images go anywhere else (e.g. "images/…").',
        '',
        'OPERATING GUIDELINES:',
        '- Refer to the DASHBACK API ROUTES documentation below to find appropriate endpoints, parameters, and HTTP methods.',
        '- SELLING PRICES: In article responses (GET /article/{sku} and returned article), the selling price field is named net (NOT price). The structure of entries in article.prices is { minmultiplier, net, taxRate, customergroupId }.',
        '- GROSS vs NET PRICES: Users almost always mean GROSS (brutto, incl. VAT) prices when they specify a price (e.g. "1,60 €"), but the API stores and accepts NET prices only. Convert: net = gross / (1 + taxRate/100). Send net with high precision (at least 4–6 decimal places) so rounding back gives the exact gross. Calculate this in javascript_execute.',
        '- DATA TYPES & WIRE FORMAT: Dashback strictly validates types (numeric values like weight, price, quantity, IDs must be numbers; flags must be booleans).',
        '- SHOWING GENERATED IMAGES: Markdown images ARE supported and render inline in the chat. After writing an image into the virtual filesystem (e.g. `fs.writeFileSync("artifacts/example.png", buf)`), reference the exact path you wrote with standard markdown `![title](artifacts/example.png)`, which renders inline. Make the alt text descriptive. Other file types (PDF, JSON, CSV) are NOT rendered inline — link to them instead and offer them as a download.',
        '- LABEL TEMPLATES (/labeltemplate): Label templates are NOT ZPL. content is Dashback\'s JSON label schema: { "objects": [ ... ] }. Do NOT write or suggest ZPL.',
        '- Format responses clearly using markdown tables, bullet lists, or bold key-value pairs where appropriate.',
        ...(routesDoc ? ['', 'DASHBACK API ROUTES:', routesDoc] : []),
    ].join('\n');
}

/**
 * Per-conversation manager. Owns the SSE subscribers of one conversation and
 * runs the LLM (via LLMOrbiter) for each posted user message.
 *
 * Persistence: the database holds the transcript (`modelmessage`) and a binary
 * `idle`/`busy` state. The finer SSE states (`thinking`, `working`, `done`)
 * and the streamed `delta` chunks are SSE-only.
 */
export function toLlmMessage(m) {
    let payload = {};
    if (typeof m.message === 'string') {
        try {
            payload = JSON.parse(m.message);
        } catch {
            payload = { content: m.message };
        }
    } else if (m.message && typeof m.message === 'object') {
        payload = m.message;
    }

    if (m.role === 'user') {
        return { role: 'user', content: payload.content ?? (typeof m.message === 'string' ? m.message : '') };
    }
    if (m.role === 'assistant') {
        const msg = {
            role: 'assistant',
            content: payload.content ?? null,
        };
        if (Array.isArray(payload.tool_calls) && payload.tool_calls.length > 0) {
            msg.tool_calls = payload.tool_calls;
        }
        if (Array.isArray(payload.reasoning_details) && payload.reasoning_details.length > 0) {
            msg.reasoning_details = payload.reasoning_details;
        }
        if (payload.reasoning) {
            msg.reasoning = payload.reasoning;
        } else if (payload.reasoning_content) {
            msg.reasoning = payload.reasoning_content;
        }
        return msg;
    }
    if (m.role === 'tool') {
        return {
            role: 'tool',
            tool_call_id: payload.tool_call_id ?? '',
            content: typeof payload.content === 'string' ? payload.content : JSON.stringify(payload.content ?? ''),
            ...(payload.name ? { name: payload.name } : {}),
        };
    }
    return { role: m.role, content: payload.content ?? m.message };
}

export class ConversationManager {
    /**
     * @param {number} conversationId
     * @param {{ companyId: number, googleAuthId: number }} meta
     */
    constructor(conversationId, { companyId, googleAuthId }) {
        this.conversationId = conversationId;
        this.companyId = companyId;
        this.googleAuthId = googleAuthId;
        /** @type {Set<{ res: import('express').Response, keepAlive: NodeJS.Timeout }>} */
        this.subscribers = new Set();
        /** @type {LLMOrbiter|null} */
        this.orbiter = null;
        this.destroyed = false;
        this.isProcessing = false;
    }

    /**
     * Broadcast an SSE event to all subscribers of this conversation.
     * @param {string} event
     * @param {object} data
     */
    broadcast(event, data) {
        if (this.destroyed) return;
        const payload = `event: ${event}\ndata: ${JSON.stringify(data)}\n\n`;
        for (const client of this.subscribers) {
            if (!client.res.writableEnded && !client.res.destroyed) {
                client.res.write(payload);
            }
        }
    }

    /**
     * Add an SSE subscriber for this conversation.
     * @param {import('express').Response} res
     * @param {import('express').Request} [req]
     * @returns {() => void} unsubscribe
     */
    subscribe(res, req) {
        req?.socket?.setTimeout(0);
        res.setTimeout?.(0);

        res.writeHead(200, {
            'Content-Type': 'text/event-stream',
            'Cache-Control': 'no-cache, no-transform',
            'Connection': 'keep-alive',
            'X-Accel-Buffering': 'no',
        });
        res.flushHeaders?.();
        res.write(': ping\n\n');

        const client = {
            res,
            keepAlive: setInterval(() => {
                if (res.writableEnded || res.destroyed) return;
                res.write(': heartbeat\n\n');
            }, KEEP_ALIVE_MS),
        };
        this.subscribers.add(client);

        const unsubscribe = () => {
            clearInterval(client.keepAlive);
            this.subscribers.delete(client);
        };

        res.on('close', unsubscribe);
        req?.on('close', unsubscribe);
        return unsubscribe;
    }

    /**
     * Handle an incoming user message.
     * 1. Persists the user message (DB refuses unless `idle`).
     * 2. Broadcasts it, marks the conversation `busy` (DB) and `thinking` (SSE).
     * 3. Starts the LLM run in the background; the HTTP request returns immediately.
     *
     * @param {number} googleAuthId
     * @param {{ message: string }} input
     * @returns {Promise<object>} The created user message
     * @throws {Error} 'Conversation is not idle' while a run is active.
     */
    async handleUserMessage(googleAuthId, { message }) {
        if (this.isProcessing) throw new Error('Conversation is not idle');
        this.isProcessing = true;

        let userMsg;
        try {
            const rawContent = typeof message === 'string' ? message : (message?.content || '');
            const payload = JSON.stringify({ content: rawContent });
            userMsg = await db.addMessage(googleAuthId, this.conversationId, { message: payload, role: 'user' });
            await db.setState(googleAuthId, this.conversationId, 'busy');
        } catch (err) {
            this.isProcessing = false;
            throw err;
        }

        this.broadcast('message', userMsg);
        this.broadcast('state', { state: 'thinking' });
        emitCompanyEvent('modelconversation', { companyId: this.companyId });

        this.runGeneration(googleAuthId).catch((err) => {
            console.error(`[ConversationManager ${this.conversationId}] unexpected generation error:`, err);
        });

        return userMsg;
    }

    /**
     * Handle an incoming tool response message (e.g. from the user answering ask_user).
     * 1. Persists the tool response message (DB refuses unless `idle`).
     * 2. Broadcasts it, marks the conversation `busy` (DB) and `working` (SSE).
     * 3. Starts the LLM run in the background; the HTTP request returns immediately.
     *
     * @param {number} googleAuthId
     * @param {{ tool_call_id: string, content?: any, answer?: any }} input
     * @returns {Promise<object>} The created tool message
     * @throws {Error} 'Conversation is not idle' while a run is active.
     */
    async handleToolMessage(googleAuthId, { tool_call_id, content, answer }) {
        if (!tool_call_id) throw new Error('tool_call_id is required');
        if (this.isProcessing) throw new Error('Conversation is not idle');
        this.isProcessing = true;

        let toolMsg;
        try {
            const rawContent = content !== undefined
                ? (typeof content === 'string' ? content : JSON.stringify(content))
                : JSON.stringify({ answer: answer ?? '' });
            const payload = JSON.stringify({ content: rawContent, tool_call_id });
            toolMsg = await db.addMessage(googleAuthId, this.conversationId, { message: payload, role: 'tool' });
            await db.setState(googleAuthId, this.conversationId, 'busy');
        } catch (err) {
            this.isProcessing = false;
            throw err;
        }

        this.broadcast('message', toolMsg);
        this.broadcast('state', { state: 'working' });
        emitCompanyEvent('modelconversation', { companyId: this.companyId });

        this.runGeneration(googleAuthId).catch((err) => {
            console.error(`[ConversationManager ${this.conversationId}] unexpected generation error:`, err);
        });

        return toolMsg;
    }


    /**
     * Run the LLM over the stored transcript, stream the answer over SSE,
     * persist reasoning, tool calls, and assistant messages, then return to `idle`.
     * @param {number} googleAuthId
     */
    async runGeneration(googleAuthId) {
        let usage = null;
        let error = null;

        try {
            const conversation = await db.get(googleAuthId, this.conversationId);
            if (conversation.provider !== 'openrouter') {
                throw new Error(`Unsupported provider: ${conversation.provider}`);
            }
            // Fall back to the configured default when the stored model is missing, blank, or
            // retired. If the stored model was defaulted to the free model and the company now has
            // a custom key configured, transition to the configured paid default model.
            const defaultModel = await resolveDefaultModel(googleAuthId);
            const hasCustomKey = await hasConfiguredOpenRouterKey(googleAuthId);
            const storedModel = typeof conversation.model === 'string' ? conversation.model.trim() : '';
            const model = (!storedModel || RETIRED_MODELS.has(storedModel) || (storedModel === env.openrouterFreeModel && hasCustomKey))
                ? defaultModel
                : storedModel;
            const apiKey = await resolveOpenRouterKey(googleAuthId);

            // Transcript comes from the database (includes user, assistant, tool, reasoning).
            const history = await db.listMessages(googleAuthId, this.conversationId);

            // Resolve the VFS root so the prompt can list the company's SKILL files. A failure
            // here must not abort the turn — the prompt simply falls back to "no skills".
            let vfsRoot = null;
            try {
                vfsRoot = await resolveVfsRoot(googleAuthId);
            } catch (err) {
                console.warn('conversation: could not resolve VFS root for skill index:', err.message);
            }

            const messages = [
                { role: 'system', content: await buildSystemPrompt(vfsRoot) },
                ...history.map(toLlmMessage),
            ];

            let working = false;
            let text = '';
            let currentReasoning = '';
            let savedToolCallsTurn = null;

            const markWorking = () => {
                if (working) return;
                working = true;
                this.broadcast('state', { state: 'working' });
            };

            this.orbiter = new LLMOrbiter(new OpenRouterProvider({ apiKey }), {
                onStatus: (status) => {
                    if (status === 'working') markWorking();
                },
                onReasoning: (reasoning) => {
                    markWorking();
                    currentReasoning = reasoning;
                    this.broadcast('reasoning', { text: reasoning });
                },
                onContent: (chunk) => {
                    markWorking();
                    text += chunk;
                    this.broadcast('delta', { delta: chunk, text });
                },
                onToolCall: async ({ name, args, id, callItem, tool_calls, reasoning_details, reasoning, content }) => {
                    markWorking();
                    try {
                        const effectiveToolCalls = tool_calls || (callItem ? [callItem] : [{
                            id: id || randomUUID(),
                            type: 'function',
                            function: {
                                name,
                                arguments: typeof args === 'string' ? args : JSON.stringify(args || {}),
                            },
                        }]);

                        if (savedToolCallsTurn !== effectiveToolCalls) {
                            savedToolCallsTurn = effectiveToolCalls;
                            const effectiveReasoningDetails = (Array.isArray(reasoning_details) && reasoning_details.length > 0)
                                ? reasoning_details
                                : undefined;
                            const effectiveReasoning = reasoning || currentReasoning.trim() || undefined;
                            currentReasoning = '';

                            const assistantPayload = {
                                content: (content || text).trim() || '',
                                tool_calls: effectiveToolCalls,
                                ...(effectiveReasoningDetails ? { reasoning_details: effectiveReasoningDetails } : {}),
                                ...(effectiveReasoning ? { reasoning: effectiveReasoning } : {}),
                            };

                            const callMsg = await db.addMessage(googleAuthId, this.conversationId, {
                                role: 'assistant',
                                message: JSON.stringify(assistantPayload),
                            });
                            text = '';
                            this.broadcast('message', callMsg);
                            emitCompanyEvent('modelconversation', { companyId: this.companyId });
                        }
                    } catch (err) {
                        console.error(`[ConversationManager ${this.conversationId}] failed to save tool call message:`, err);
                    }
                },
                onToolResult: async ({ name, result, id, callItem }) => {
                    try {
                        const resMsg = await db.addMessage(googleAuthId, this.conversationId, {
                            role: 'tool',
                            message: JSON.stringify({
                                content: typeof result === 'string' ? result : JSON.stringify(result),
                                tool_call_id: id || callItem?.id || '',
                            }),
                        });
                        this.broadcast('message', resMsg);
                        emitCompanyEvent('modelconversation', { companyId: this.companyId });
                    } catch (err) {
                        console.error(`[ConversationManager ${this.conversationId}] failed to save tool result message:`, err);
                    }
                },
                onToolError: async ({ name, error, id, callItem }) => {
                    try {
                        const errMsg = await db.addMessage(googleAuthId, this.conversationId, {
                            role: 'tool',
                            message: JSON.stringify({
                                content: `Tool error [${name}]: ${error}`,
                                tool_call_id: id || callItem?.id || '',
                            }),
                        });
                        this.broadcast('message', errMsg);
                        emitCompanyEvent('modelconversation', { companyId: this.companyId });
                    } catch (err) {
                        console.error(`[ConversationManager ${this.conversationId}] failed to save tool error message:`, err);
                    }
                },
                onUsage: (u) => {
                    // A call that consumed 0 input tokens AND produced 0 output means the
                    // provider returned nothing at all — not a real generation. LLMOrbiter
                    // treats "no content and no tool calls" as "keep looping", so several of
                    // these in a row burn iterations for nothing. Flag them loudly so the
                    // cause is visible instead of showing up as a silent weird loop.
                    if (u.input === 0 && u.output === 0) {
                        console.warn(
                            `[conversation ${this.conversationId}] EMPTY LLM RESPONSE ` +
                            `(iteration reported no usage at all). The provider returned no content ` +
                            `and no tool calls, so the orchestrator will retry. ` +
                            `If this repeats, the upstream model/API is failing — check the model id ` +
                            `and the key.`
                        );
                    }
                    console.log(
                        `[conversation ${this.conversationId}] usage: input=${u.input}, cached=${u.cached}, output=${u.output}` +
                        (u.cost != null ? `, cost=$${u.cost.toFixed(4)}` : '')
                    );
                },
                onLLMCall: (call) => {
                    console.log(
                        `[conversation ${this.conversationId}] LLM call #${call.iteration}: ` +
                        `model=${call.model}, messages=${call.messageCount}, flow=${call.messages}, ` +
                        `promptChars=${call.promptSize}`
                    );
                },
            });

            const result = await this.orbiter.run({
                messages,
                model,
                tools: [
                    createDashbackRequestTool({ googleAuthId }),
                    createAskUserTool(),
                    // VFS tools take no companyId on purpose — each resolves the user's
                    // currently selected company itself, so the on-disk VFS and the database
                    // can never end up pointed at different companies.
                    createJavascriptExecuteTool({ googleAuthId }),
                    createFindFilesTool({ googleAuthId }),
                    createReadFileTool({ googleAuthId }),
                    createPatchTool({ googleAuthId }),
                    createRmTool({ googleAuthId }),
                    createSearchImagesTool({ googleAuthId }),
                    createAnalyzeImageTool({ googleAuthId, model }),
                    createResetVfsTool({ googleAuthId }),
                    createApiSuggestionsOrIssuesTool({ googleAuthId, companyId: this.companyId }),
                ],
            });

            if (this.destroyed) return;

            // Log the shape of every run, including degenerate ones. When nothing comes back
            // there is nothing persisted and the UI just sits there — this line is the only
            // clue, so make it explicit about which field was empty.
            console.log(
                `[conversation ${this.conversationId}] run finished: ` +
                `iterations=${result?.iterations}, ` +
                `content=${result?.content ? `${result.content.length} chars` : 'EMPTY'}, ` +
                `toolCalls=${result?.toolResults ? Object.keys(result.toolResults).length : 0}, ` +
                `directReturn=${result?.directReturn === true}`
            );

            // Direct return (e.g. from ask_user) halts without adding an additional assistant message.
            if (result?.directReturn) {
                return;
            }

            const content = (result.content ?? '').trim();
            const finalReasoningDetails = (Array.isArray(result.reasoning_details) && result.reasoning_details.length > 0)
                ? result.reasoning_details
                : undefined;
            const finalReasoning = result.reasoning || currentReasoning.trim() || undefined;

            // Nothing came back at all: no content, no reasoning. Surface it to the user rather
            // than silently returning to `idle` with an empty turn, which reads as a hang.
            if (!content && !finalReasoningDetails && !finalReasoning) {
                console.error(
                    `[conversation ${this.conversationId}] generation produced no content after ` +
                    `${result?.iterations ?? 0} iteration(s). The model returned neither text nor tool calls.`
                );
                error = 'The model returned an empty response. Please try again.';
            }

            if (content || finalReasoningDetails || finalReasoning) {
                const assistantMsg = await db.addMessage(googleAuthId, this.conversationId, {
                    message: JSON.stringify({
                        content: content || '',
                        ...(finalReasoningDetails ? { reasoning_details: finalReasoningDetails } : {}),
                        ...(finalReasoning ? { reasoning: finalReasoning } : {}),
                    }),
                    role: 'assistant',
                });
                this.broadcast('message', assistantMsg);
            }

            usage = {
                input: result.usage.input,
                cached: result.usage.cached,
                output: result.usage.output,
                cost: result.usage.cost,
            };
        } catch (err) {
            if (this.destroyed) return;
            error = err.message;
            console.error(`[ConversationManager ${this.conversationId}] generation failed:`, err);
        } finally {
            this.orbiter = null;
            this.isProcessing = false;
        }

        try {
            await db.setState(googleAuthId, this.conversationId, 'idle');
        } catch (err) {
            console.error(`[ConversationManager ${this.conversationId}] failed to reset state:`, err);
        }
        emitCompanyEvent('modelconversation', { companyId: this.companyId });
        this.broadcast('state', error ? { state: 'done', error } : { state: 'done', usage });
    }

    destroy() {
        this.destroyed = true;
        this.orbiter?.abort('Conversation manager closed');
        for (const client of this.subscribers) {
            clearInterval(client.keepAlive);
            if (!client.res.writableEnded && !client.res.destroyed) {
                client.res.end();
            }
        }
        this.subscribers.clear();
    }
}

/** @type {Map<number, ConversationManager>} */
const activeManagers = new Map();

/**
 * Get or create an active ConversationManager for a conversation.
 * @param {number} conversationId
 * @param {{ companyId: number, googleAuthId: number }} meta
 * @returns {ConversationManager}
 */
export function getOrCreateManager(conversationId, { companyId, googleAuthId }) {
    let manager = activeManagers.get(conversationId);
    if (!manager) {
        manager = new ConversationManager(conversationId, { companyId, googleAuthId });
        activeManagers.set(conversationId, manager);
    } else {
        if (companyId) manager.companyId = companyId;
        if (googleAuthId) manager.googleAuthId = googleAuthId;
    }
    return manager;
}

/**
 * Get an active ConversationManager if one exists.
 * @param {number} conversationId
 * @returns {ConversationManager|undefined}
 */
export function getActiveManager(conversationId) {
    return activeManagers.get(conversationId);
}

/**
 * Close, destroy, and remove an active ConversationManager if one exists.
 * @param {number} conversationId
 */
export function removeManager(conversationId) {
    const manager = activeManagers.get(conversationId);
    if (manager) {
        manager.destroy();
        activeManagers.delete(conversationId);
    }
}

/**
 * Close and destroy all active conversation managers.
 */
export function closeAllManagers() {
    for (const [id, manager] of activeManagers.entries()) {
        manager.destroy();
        activeManagers.delete(id);
    }
}