"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.globalConfigSchematics = exports.defaultPluginSettings = void 0;
const sdk_1 = require("@lmstudio/sdk");
const os_1 = __importDefault(require("os"));
const path_1 = __importDefault(require("path"));
exports.defaultPluginSettings = {
    PREVIEW_IN_CHAT: true,
    HTTP_SERVER_PORT: 54760,
    embeddingBaseUrl: "http://127.0.0.1:1234/v1",
    embeddingApiKey: "",
    qwen3VlModelPath: "qwen/qwen3-vl-8b",
    visionPrompt: "",
    embedPngMetadata: true,
    serverMaxTokens: 768,
    serverTemperature: 0.7,
    qwen3VlOdPrompt: [
        "Detect objects in the image with strict hierarchical prioritization.",
        "",
        "PRIORITY 1 (CRITICAL - MUST DETECT FIRST):",
        '- You MUST detect "human face" (highest priority if a person is present)',
        '- You MUST detect "person" (if no face is clearly visible or if the person is the main subject)',
        "",
        "PRIORITY 2 (MAIN SUBJECT / HERO ELEMENT):",
        "- The most visually prominent object or subject that is NOT part of the background.",
        "- Use specific, concrete labels (e.g., 'red car', 'fluffy owl toy').",
        "- Avoid generic terms like 'object' or 'thing'.",
        "",
        "PRIORITY 3 (CONTEXTUAL BACKGROUND ELEMENTS):",
        "- Only detect background elements if they are significant to the scene composition OR if the main subject is interacting with them.",
        "- Do not detect minor or redundant background details.",
        "",
        "PRIORITY 4 (FOCUSSED MAIN SUBJECT / HERO ELEMENT):",
        "- All visible body parts (hands, feet, arms, legs).",
        "- Elements of the face, as far as clearly detectable and focussed on close-ups: nose, mouth, left and right eyes, eyebrows and ears",
        "- anatomical details, as far as recognizable as \\\"focussed\\\" or \\\"prominent\\\" (e.g., 'iris', 'pupil', 'eyelid')",
        "",
        "RULES:",
        "- Maximum 16 objects total.",
        "- Each bounding box must be unique and non-redundant.",
        "- For clothing, name the specific garment (e.g., 'tank top', 'jeans').",
        "- For body parts, qualify by position (e.g., 'left hand').",
        "- NEVER prioritize background elements over the main subject or human face.",
        "- NEVER prioritize anatomical details over general concepts unless they are solely focussed (e.g. only detect 'eyes' unless 'human face' is the dominant part of the image)",
        "- If the main subject is a person, focus on the person and their immediate interactions. Ignore background elements unless they are directly involved in the interaction.",
        "- If NO person or face is visible, ALWAYS detect Priority 2 and Priority 3 subjects regardless.",
    ].join("\n"),
    detectMaxTokens: 2048,
    detectTemperature: 0.3,
    includeGenerationMetadata: true,
    // VISION_API="llama-server" strand only (see LlamaServerRouterManager.ts) -- the plugin path has
    // no preset concept, so this always defaults to the LM Studio-bundled llama-server binary.
    llamaServerBinary: path_1.default.join(os_1.default.homedir(), ".lmstudio", "extensions", "backends", "llama.cpp-mac-arm64-apple-metal-advsimd-2.48.0", "llama-server"),
    // Port our own standalone router listens on -- entirely independent of embeddingBaseUrl (that's
    // the REAL Vision API's own fixed address; this is only where WE spawn our managed process).
    // 8099 is free of any other known default in this ecosystem (LM Studio: 1234, Unsloth Studio: 8888).
    llamaServerPort: 8099,
    llamaServerCtxSize: 8192,
    // analyse-image only ever requests 1 model through the router at a time -- kept adjustable for reuse elsewhere.
    llamaServerModelsMax: 1,
    llamaServerIdleTtlMinutes: 10,
};
exports.globalConfigSchematics = (0, sdk_1.createConfigSchematics)()
    .field("PREVIEW_IN_CHAT", "boolean", {
    displayName: "Previews in Chat",
    subtitle: "When enabled, tool responses include inline image previews. Recommended for local models without vision capability.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.PREVIEW_IN_CHAT)
    .field("HTTP_SERVER_PORT", "numeric", {
    displayName: "Local HTTP Server Port",
    subtitle: "Port for serving generated images over localhost (default: 54760).",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.HTTP_SERVER_PORT)
    .field("embeddingBaseUrl", "string", {
    displayName: "Vision API Base URL",
    subtitle: "OpenAI-compatible /v1 URL. Vision tools use the same server root and call LM Studio's internal /api/v1 vision endpoints. Separate from the agent API.",
    placeholder: "http://127.0.0.1:1234/v1",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embeddingBaseUrl)
    .field("embeddingApiKey", "string", {
    displayName: "Vision API Key",
    subtitle: "Optional key for the Qwen3-VL vision backend. Separate from the agent API key.",
    isProtected: true,
    placeholder: "sk-...",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embeddingApiKey)
    .field("qwen3VlModelPath", "string", {
    displayName: "Qwen3-VL Model",
    subtitle: "LM Studio model key for the Qwen3-VL Vision API backend, for example qwen/qwen3-vl-8b. This is not a filesystem path.",
    placeholder: "qwen/qwen3-vl-8b",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.qwen3VlModelPath)
    .field("visionPrompt", "string", {
    displayName: "Vision Prompt",
    subtitle: "Default prompt sent to the vision model when the agent does not supply one. Leave empty to disable automatic visual description.",
    placeholder: "Analyze this image based strictly on what is directly visible. Do not infer, assume, or complete information that is not present.",
    isParagraph: true,
}, exports.defaultPluginSettings.visionPrompt)
    .field("embedPngMetadata", "boolean", {
    displayName: "Embed Metadata in PNGs",
    subtitle: "Write analysis provenance, detected objects, and bounding boxes into saved PNGs as Draw Things-compatible XMP metadata.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embedPngMetadata)
    .field("includeGenerationMetadata", "boolean", {
    displayName: "Include Generation Metadata",
    subtitle: "When enabled, Draw Things generation parameters (prompt, model, sampler, seed, ...) embedded in PNG files are appended to each analysis result.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.includeGenerationMetadata)
    .field("serverMaxTokens", "numeric", {
    displayName: "Vision API: Max Tokens",
    subtitle: "Maximum response length in tokens (1-4096). Default: 768.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.serverMaxTokens)
    .field("serverTemperature", "numeric", {
    displayName: "Vision API: Temperature",
    subtitle: "Sampling temperature (0.0-2.0). Default: 0.7.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.serverTemperature)
    .field("qwen3VlOdPrompt", "string", {
    displayName: "Qwen3-VL: Object Detection Prompt",
    subtitle: "Instruction sent to Qwen3-VL for default object detection. Leave empty to use the built-in default.",
    placeholder: "",
    isParagraph: true,
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.qwen3VlOdPrompt)
    .field("detectMaxTokens", "numeric", {
    displayName: "Vision API Detect: Max Tokens",
    subtitle: "Maximum response length in tokens for object detection (1-4096). Default: 2048.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.detectMaxTokens)
    .field("detectTemperature", "numeric", {
    displayName: "Vision API Detect: Temperature",
    subtitle: "Sampling temperature for object detection (0.0-2.0). Default: 0.3.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.detectTemperature)
    .field("llamaServerBinary", "string", {
    displayName: "llama-server Strand: Binary Path",
    subtitle: "Absolute path to the llama-server binary used only by VISION_API=llama-server, which spawns and manages its own standalone router-mode llama-server process, independent of LM Studio's own model loading.",
    placeholder: exports.defaultPluginSettings.llamaServerBinary,
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerBinary)
    .field("llamaServerPort", "numeric", {
    displayName: "llama-server Strand: Port",
    subtitle: "Local port the standalone router-mode llama-server this plugin spawns listens on. Independent of the Vision API Base URL above (that one is the real Vision API's own fixed address). Default: 8099.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerPort)
    .field("llamaServerCtxSize", "numeric", {
    displayName: "llama-server Strand: Context Size",
    subtitle: "Context size (--ctx-size) applied to every model the standalone router-mode llama-server auto-loads. Without a cap, router mode defaults to a model's full native context, which can use excessive VRAM. Default: 8192.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerCtxSize)
    .field("llamaServerModelsMax", "numeric", {
    displayName: "llama-server Strand: Max Simultaneous Models",
    subtitle: "--models-max passed to the standalone router-mode llama-server -- maximum number of models it keeps loaded simultaneously. analyse-image only ever requests 1 model, so 1 is the default here. Default: 1.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerModelsMax)
    .field("llamaServerIdleTtlMinutes", "numeric", {
    displayName: "llama-server Strand: Idle Shutdown (minutes)",
    subtitle: "Minutes of inactivity before the standalone router-mode llama-server process this plugin spawned is stopped again. 0 disables idle shutdown. Default: 10.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerIdleTtlMinutes)
    .build();

"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.globalConfigSchematics = exports.defaultPluginSettings = void 0;
const sdk_1 = require("@lmstudio/sdk");
const os_1 = __importDefault(require("os"));
const path_1 = __importDefault(require("path"));
exports.defaultPluginSettings = {
    PREVIEW_IN_CHAT: true,
    HTTP_SERVER_PORT: 54760,
    embeddingBaseUrl: "http://127.0.0.1:1234/v1",
    embeddingApiKey: "",
    qwen3VlModelPath: "qwen/qwen3-vl-8b",
    visionPrompt: "",
    embedPngMetadata: true,
    serverMaxTokens: 768,
    serverTemperature: 0.7,
    qwen3VlOdPrompt: [
        "Detect objects in the image with strict hierarchical prioritization.",
        "",
        "PRIORITY 1 (CRITICAL - MUST DETECT FIRST):",
        '- You MUST detect "human face" (highest priority if a person is present)',
        '- You MUST detect "person" (if no face is clearly visible or if the person is the main subject)',
        "",
        "PRIORITY 2 (MAIN SUBJECT / HERO ELEMENT):",
        "- The most visually prominent object or subject that is NOT part of the background.",
        "- Use specific, concrete labels (e.g., 'red car', 'fluffy owl toy').",
        "- Avoid generic terms like 'object' or 'thing'.",
        "",
        "PRIORITY 3 (CONTEXTUAL BACKGROUND ELEMENTS):",
        "- Only detect background elements if they are significant to the scene composition OR if the main subject is interacting with them.",
        "- Do not detect minor or redundant background details.",
        "",
        "PRIORITY 4 (FOCUSSED MAIN SUBJECT / HERO ELEMENT):",
        "- All visible body parts (hands, feet, arms, legs).",
        "- Elements of the face, as far as clearly detectable and focussed on close-ups: nose, mouth, left and right eyes, eyebrows and ears",
        "- anatomical details, as far as recognizable as \\\"focussed\\\" or \\\"prominent\\\" (e.g., 'iris', 'pupil', 'eyelid')",
        "",
        "RULES:",
        "- Maximum 16 objects total.",
        "- Each bounding box must be unique and non-redundant.",
        "- For clothing, name the specific garment (e.g., 'tank top', 'jeans').",
        "- For body parts, qualify by position (e.g., 'left hand').",
        "- NEVER prioritize background elements over the main subject or human face.",
        "- NEVER prioritize anatomical details over general concepts unless they are solely focussed (e.g. only detect 'eyes' unless 'human face' is the dominant part of the image)",
        "- If the main subject is a person, focus on the person and their immediate interactions. Ignore background elements unless they are directly involved in the interaction.",
        "- If NO person or face is visible, ALWAYS detect Priority 2 and Priority 3 subjects regardless.",
    ].join("\n"),
    detectMaxTokens: 2048,
    detectTemperature: 0.3,
    includeGenerationMetadata: true,
    // VISION_API="llama-server" strand only (see LlamaServerRouterManager.ts) -- the plugin path has
    // no preset concept, so this always defaults to the LM Studio-bundled llama-server binary.
    llamaServerBinary: path_1.default.join(os_1.default.homedir(), ".lmstudio", "extensions", "backends", "llama.cpp-mac-arm64-apple-metal-advsimd-2.48.0", "llama-server"),
    // Port our own standalone router listens on -- entirely independent of embeddingBaseUrl (that's
    // the REAL Vision API's own fixed address; this is only where WE spawn our managed process).
    // 8099 is free of any other known default in this ecosystem (LM Studio: 1234, Unsloth Studio: 8888).
    llamaServerPort: 8099,
    llamaServerCtxSize: 8192,
    // analyse-image only ever requests 1 model through the router at a time -- kept adjustable for reuse elsewhere.
    llamaServerModelsMax: 1,
    llamaServerIdleTtlMinutes: 10,
};
exports.globalConfigSchematics = (0, sdk_1.createConfigSchematics)()
    .field("PREVIEW_IN_CHAT", "boolean", {
    displayName: "Previews in Chat",
    subtitle: "When enabled, tool responses include inline image previews. Recommended for local models without vision capability.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.PREVIEW_IN_CHAT)
    .field("HTTP_SERVER_PORT", "numeric", {
    displayName: "Local HTTP Server Port",
    subtitle: "Port for serving generated images over localhost (default: 54760).",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.HTTP_SERVER_PORT)
    .field("embeddingBaseUrl", "string", {
    displayName: "Vision API Base URL",
    subtitle: "OpenAI-compatible /v1 URL. Vision tools use the same server root and call LM Studio's internal /api/v1 vision endpoints. Separate from the agent API.",
    placeholder: "http://127.0.0.1:1234/v1",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embeddingBaseUrl)
    .field("embeddingApiKey", "string", {
    displayName: "Vision API Key",
    subtitle: "Optional key for the Qwen3-VL vision backend. Separate from the agent API key.",
    isProtected: true,
    placeholder: "sk-...",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embeddingApiKey)
    .field("qwen3VlModelPath", "string", {
    displayName: "Qwen3-VL Model",
    subtitle: "LM Studio model key for the Qwen3-VL Vision API backend, for example qwen/qwen3-vl-8b. This is not a filesystem path.",
    placeholder: "qwen/qwen3-vl-8b",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.qwen3VlModelPath)
    .field("visionPrompt", "string", {
    displayName: "Vision Prompt",
    subtitle: "Default prompt sent to the vision model when the agent does not supply one. Leave empty to disable automatic visual description.",
    placeholder: "Analyze this image based strictly on what is directly visible. Do not infer, assume, or complete information that is not present.",
    isParagraph: true,
}, exports.defaultPluginSettings.visionPrompt)
    .field("embedPngMetadata", "boolean", {
    displayName: "Embed Metadata in PNGs",
    subtitle: "Write analysis provenance, detected objects, and bounding boxes into saved PNGs as Draw Things-compatible XMP metadata.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.embedPngMetadata)
    .field("includeGenerationMetadata", "boolean", {
    displayName: "Include Generation Metadata",
    subtitle: "When enabled, Draw Things generation parameters (prompt, model, sampler, seed, ...) embedded in PNG files are appended to each analysis result.",
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.includeGenerationMetadata)
    .field("serverMaxTokens", "numeric", {
    displayName: "Vision API: Max Tokens",
    subtitle: "Maximum response length in tokens (1-4096). Default: 768.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.serverMaxTokens)
    .field("serverTemperature", "numeric", {
    displayName: "Vision API: Temperature",
    subtitle: "Sampling temperature (0.0-2.0). Default: 0.7.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.serverTemperature)
    .field("qwen3VlOdPrompt", "string", {
    displayName: "Qwen3-VL: Object Detection Prompt",
    subtitle: "Instruction sent to Qwen3-VL for default object detection. Leave empty to use the built-in default.",
    placeholder: "",
    isParagraph: true,
    engineDoesNotSupport: false,
}, exports.defaultPluginSettings.qwen3VlOdPrompt)
    .field("detectMaxTokens", "numeric", {
    displayName: "Vision API Detect: Max Tokens",
    subtitle: "Maximum response length in tokens for object detection (1-4096). Default: 2048.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.detectMaxTokens)
    .field("detectTemperature", "numeric", {
    displayName: "Vision API Detect: Temperature",
    subtitle: "Sampling temperature for object detection (0.0-2.0). Default: 0.3.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.detectTemperature)
    .field("llamaServerBinary", "string", {
    displayName: "llama-server Strand: Binary Path",
    subtitle: "Absolute path to the llama-server binary used only by VISION_API=llama-server, which spawns and manages its own standalone router-mode llama-server process, independent of LM Studio's own model loading.",
    placeholder: exports.defaultPluginSettings.llamaServerBinary,
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerBinary)
    .field("llamaServerPort", "numeric", {
    displayName: "llama-server Strand: Port",
    subtitle: "Local port the standalone router-mode llama-server this plugin spawns listens on. Independent of the Vision API Base URL above (that one is the real Vision API's own fixed address). Default: 8099.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerPort)
    .field("llamaServerCtxSize", "numeric", {
    displayName: "llama-server Strand: Context Size",
    subtitle: "Context size (--ctx-size) applied to every model the standalone router-mode llama-server auto-loads. Without a cap, router mode defaults to a model's full native context, which can use excessive VRAM. Default: 8192.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerCtxSize)
    .field("llamaServerModelsMax", "numeric", {
    displayName: "llama-server Strand: Max Simultaneous Models",
    subtitle: "--models-max passed to the standalone router-mode llama-server -- maximum number of models it keeps loaded simultaneously. analyse-image only ever requests 1 model, so 1 is the default here. Default: 1.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerModelsMax)
    .field("llamaServerIdleTtlMinutes", "numeric", {
    displayName: "llama-server Strand: Idle Shutdown (minutes)",
    subtitle: "Minutes of inactivity before the standalone router-mode llama-server process this plugin spawned is stopped again. 0 disables idle shutdown. Default: 10.",
    engineDoesNotSupport: true,
}, exports.defaultPluginSettings.llamaServerIdleTtlMinutes)
    .build();