feat: Add custom model download functionality and enhance model management

- Implemented `model-download-custom` command to download HuggingFace models.
- Added support for discovering manually placed models in the cache.
- Enhanced the model list view to display recommended and discovered models separately.
- Introduced JSON editor for direct configuration mode in API settings.
- Added validation and formatting features for JSON input.
- Updated translations for new API settings and common actions.
- Improved user interface for model management, including action buttons and tooltips.

ccw/src/core/routes/codexlens/config-handlers.ts

@@ -497,6 +497,46 @@ export async function handleCodexLensConfigRoutes(ctx: RouteContext): Promise<bo
     return true;
   }
 
+  // API: CodexLens Model Download Custom (download any HuggingFace model)
+  if (pathname === '/api/codexlens/models/download-custom' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      const { model_name, model_type } = body as { model_name?: unknown; model_type?: unknown };
+      const resolvedModelName = typeof model_name === 'string' && model_name.trim().length > 0 ? model_name.trim() : undefined;
+      const resolvedModelType = typeof model_type === 'string' ? model_type.trim() : 'embedding';
+
+      if (!resolvedModelName) {
+        return { success: false, error: 'model_name is required', status: 400 };
+      }
+
+      // Validate model name format
+      if (!resolvedModelName.includes('/')) {
+        return { success: false, error: 'Invalid model_name format. Expected: org/model-name', status: 400 };
+      }
+
+      try {
+        const result = await executeCodexLens([
+          'model-download-custom', resolvedModelName,
+          '--type', resolvedModelType,
+          '--json'
+        ], { timeout: 600000 }); // 10 min for download
+
+        if (result.success) {
+          try {
+            const parsed = extractJSON(result.output ?? '');
+            return { success: true, ...parsed };
+          } catch {
+            return { success: true, output: result.output };
+          }
+        } else {
+          return { success: false, error: result.error, status: 500 };
+        }
+      } catch (err: unknown) {
+        return { success: false, error: err instanceof Error ? err.message : String(err), status: 500 };
+      }
+    });
+    return true;
+  }
+
   // API: CodexLens Model Delete (delete embedding model by profile)
   if (pathname === '/api/codexlens/models/delete' && req.method === 'POST') {
     handlePostRequest(req, res, async (body) => {
@@ -526,6 +566,47 @@ export async function handleCodexLensConfigRoutes(ctx: RouteContext): Promise<bo
     return true;
   }
 
+  // API: CodexLens Model Delete by Path (delete discovered/manually placed model)
+  if (pathname === '/api/codexlens/models/delete-path' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      const { cache_path } = body as { cache_path?: unknown };
+      const resolvedPath = typeof cache_path === 'string' && cache_path.trim().length > 0 ? cache_path.trim() : undefined;
+
+      if (!resolvedPath) {
+        return { success: false, error: 'cache_path is required', status: 400 };
+      }
+
+      // Security: Validate that the path is within the HuggingFace cache directory
+      const { homedir } = await import('os');
+      const { join, resolve, normalize } = await import('path');
+      const { rm } = await import('fs/promises');
+
+      const hfCacheDir = process.env.HF_HOME || join(homedir(), '.cache', 'huggingface');
+      const normalizedCachePath = normalize(resolve(resolvedPath));
+      const normalizedHfCacheDir = normalize(resolve(hfCacheDir));
+
+      // Ensure the path is within the HuggingFace cache directory
+      if (!normalizedCachePath.startsWith(normalizedHfCacheDir)) {
+        return { success: false, error: 'Path must be within the HuggingFace cache directory', status: 400 };
+      }
+
+      // Ensure it's a models-- directory
+      const pathParts = normalizedCachePath.split(/[/\\]/);
+      const lastPart = pathParts[pathParts.length - 1];
+      if (!lastPart.startsWith('models--')) {
+        return { success: false, error: 'Path must be a model cache directory (models--*)', status: 400 };
+      }
+
+      try {
+        await rm(normalizedCachePath, { recursive: true, force: true });
+        return { success: true, message: 'Model deleted successfully', cache_path: normalizedCachePath };
+      } catch (err: unknown) {
+        return { success: false, error: err instanceof Error ? err.message : String(err), status: 500 };
+      }
+    });
+    return true;
+  }
+
   // API: CodexLens Model Info (get model info by profile)
   if (pathname === '/api/codexlens/models/info' && req.method === 'GET') {
     const profile = url.searchParams.get('profile');

ccw/src/templates/dashboard-css/31-api-settings.css

@@ -2457,4 +2457,315 @@ select.cli-input {
     width: 100%;
     justify-content: flex-end;
   }
+}
+
+/* ===========================
+   CLI Settings Form
+   =========================== */
+
+/* Tool Detail Header */
+.tool-detail-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 1rem 1.5rem;
+  border-bottom: 1px solid hsl(var(--border));
+  background: hsl(var(--muted) / 0.2);
+  flex-shrink: 0;
+}
+
+.tool-detail-header h3 {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin: 0;
+  font-size: 1.125rem;
+  font-weight: 600;
+  color: hsl(var(--foreground));
+}
+
+.tool-detail-header h3 svg,
+.tool-detail-header h3 i {
+  width: 1.25rem;
+  height: 1.25rem;
+  color: hsl(var(--primary));
+}
+
+/* Claude Config Form Container */
+.claude-config-form {
+  flex: 1;
+  overflow-y: auto;
+  padding: 1.5rem;
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+}
+
+/* Config Mode Toggle */
+.config-mode-toggle {
+  display: flex;
+  gap: 0.5rem;
+  margin-bottom: 0.5rem;
+  padding: 0.25rem;
+  background: hsl(var(--muted) / 0.3);
+  border-radius: 0.5rem;
+}
+
+.config-mode-btn {
+  flex: 1;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.5rem;
+  padding: 0.625rem 1rem;
+  font-size: 0.875rem;
+  font-weight: 500;
+  color: hsl(var(--muted-foreground));
+  background: transparent;
+  border: 1px solid transparent;
+  border-radius: 0.375rem;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+
+.config-mode-btn:hover {
+  color: hsl(var(--foreground));
+  background: hsl(var(--muted) / 0.5);
+}
+
+.config-mode-btn.active {
+  color: hsl(var(--primary));
+  background: hsl(var(--card));
+  border-color: hsl(var(--border));
+  box-shadow: 0 1px 3px hsl(var(--foreground) / 0.05);
+}
+
+.config-mode-btn svg,
+.config-mode-btn i {
+  width: 1rem;
+  height: 1rem;
+}
+
+/* Model Config Section */
+.model-config-section {
+  margin-top: 1rem;
+  padding: 1rem;
+  background: hsl(var(--muted) / 0.2);
+  border: 1px solid hsl(var(--border));
+  border-radius: 0.5rem;
+}
+
+.model-config-section h4 {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin: 0 0 1rem 0;
+  font-size: 0.875rem;
+  font-weight: 600;
+  color: hsl(var(--foreground));
+}
+
+.model-config-section h4 svg,
+.model-config-section h4 i {
+  width: 1rem;
+  height: 1rem;
+  color: hsl(var(--primary));
+}
+
+.model-config-grid {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 0.75rem;
+}
+
+.model-config-grid .form-group {
+  margin-bottom: 0;
+}
+
+.model-config-grid .form-group label {
+  font-size: 0.75rem;
+  font-family: 'SF Mono', 'Consolas', 'Liberation Mono', monospace;
+  color: hsl(var(--muted-foreground));
+}
+
+/* Form Actions - Sticky at bottom */
+.claude-config-form .form-actions {
+  position: sticky;
+  bottom: 0;
+  margin-top: auto;
+  padding: 1rem 0 0;
+  background: hsl(var(--card));
+  border-top: 1px solid hsl(var(--border));
+}
+
+/* Responsive adjustments */
+@media (max-width: 640px) {
+  .model-config-grid {
+    grid-template-columns: 1fr;
+  }
+
+  .config-mode-toggle {
+    flex-direction: column;
+  }
+}
+
+/* ===========================
+   JSON Editor Section
+   =========================== */
+
+.json-editor-section {
+  margin-top: 1rem;
+  border: 1px solid hsl(var(--border));
+  border-radius: 0.5rem;
+  overflow: hidden;
+  background: hsl(var(--card));
+}
+
+.json-editor-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 0.75rem 1rem;
+  background: hsl(var(--muted) / 0.3);
+  border-bottom: 1px solid hsl(var(--border));
+}
+
+.json-editor-header h4 {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin: 0;
+  font-size: 0.875rem;
+  font-weight: 600;
+  color: hsl(var(--foreground));
+}
+
+.json-editor-header h4 svg,
+.json-editor-header h4 i {
+  width: 1rem;
+  height: 1rem;
+  color: hsl(var(--primary));
+}
+
+.json-editor-actions {
+  display: flex;
+  gap: 0.25rem;
+}
+
+.json-editor-actions .btn-ghost {
+  padding: 0.375rem 0.625rem;
+  font-size: 0.75rem;
+  background: transparent;
+  border: none;
+  color: hsl(var(--muted-foreground));
+  cursor: pointer;
+  border-radius: 0.25rem;
+  display: inline-flex;
+  align-items: center;
+  gap: 0.25rem;
+  transition: all 0.15s;
+}
+
+.json-editor-actions .btn-ghost:hover {
+  background: hsl(var(--muted) / 0.5);
+  color: hsl(var(--foreground));
+}
+
+.json-editor-actions .btn-ghost svg,
+.json-editor-actions .btn-ghost i {
+  width: 0.875rem;
+  height: 0.875rem;
+}
+
+.json-editor-body {
+  display: flex;
+  position: relative;
+  min-height: 200px;
+  max-height: 350px;
+}
+
+.json-line-numbers {
+  width: 2.5rem;
+  padding: 0.75rem 0.5rem;
+  background: hsl(var(--muted) / 0.2);
+  border-right: 1px solid hsl(var(--border));
+  font-family: 'SF Mono', 'Consolas', 'Liberation Mono', monospace;
+  font-size: 0.75rem;
+  line-height: 1.5;
+  color: hsl(var(--muted-foreground));
+  text-align: right;
+  user-select: none;
+  overflow: hidden;
+  flex-shrink: 0;
+}
+
+.json-line-numbers span {
+  display: block;
+}
+
+.json-textarea {
+  flex: 1;
+  padding: 0.75rem;
+  border: none;
+  resize: none;
+  font-family: 'SF Mono', 'Consolas', 'Liberation Mono', monospace;
+  font-size: 0.8125rem;
+  line-height: 1.5;
+  color: hsl(var(--foreground));
+  background: hsl(var(--background));
+  outline: none;
+  overflow-y: auto;
+  tab-size: 2;
+}
+
+.json-textarea::placeholder {
+  color: hsl(var(--muted-foreground) / 0.5);
+}
+
+.json-textarea.invalid {
+  background: hsl(var(--destructive) / 0.05);
+  border-left: 2px solid hsl(var(--destructive));
+}
+
+.json-editor-footer {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 0.5rem 1rem;
+  background: hsl(var(--muted) / 0.2);
+  border-top: 1px solid hsl(var(--border));
+}
+
+.json-status {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.25rem;
+  font-size: 0.75rem;
+  font-weight: 500;
+}
+
+.json-status svg,
+.json-status i {
+  width: 0.875rem;
+  height: 0.875rem;
+}
+
+.json-status.valid {
+  color: hsl(142 76% 36%);
+}
+
+.json-status.invalid {
+  color: hsl(var(--destructive));
+}
+
+.json-hint {
+  font-size: 0.6875rem;
+  color: hsl(var(--muted-foreground));
+}
+
+/* Button styles for JSON editor */
+.btn-sm {
+  padding: 0.375rem 0.75rem;
+  font-size: 0.75rem;
+  border-radius: 0.375rem;
 }

ccw/src/templates/dashboard-js/i18n.js

@@ -28,6 +28,7 @@ const i18n = {
     'common.deleteFailed': 'Delete failed',
     'common.retry': 'Retry',
     'common.refresh': 'Refresh',
+    'common.format': 'Format',
     'common.back': 'Back',
     'common.search': 'Search...',
     'common.minutes': 'minutes',
@@ -1771,6 +1772,14 @@ const i18n = {
     'apiSettings.settingsFilePath': 'Settings File Path',
     'apiSettings.nameRequired': 'Name is required',
     'apiSettings.status': 'Status',
+    'apiSettings.providerBinding': 'Provider Binding',
+    'apiSettings.directConfig': 'Direct Configuration',
+    'apiSettings.modelConfig': 'Model Configuration',
+    'apiSettings.configJson': 'Configuration JSON',
+    'apiSettings.syncToJson': 'Sync to JSON',
+    'apiSettings.jsonEditorHint': 'Edit JSON directly to add advanced settings',
+    'apiSettings.jsonValid': 'Valid JSON',
+    'apiSettings.jsonInvalid': 'Invalid JSON',
 
     // Model Pools (High Availability)
     'apiSettings.modelPools': 'Model Pools',
@@ -2153,6 +2162,7 @@ const i18n = {
     'common.deleteFailed': '删除失败',
     'common.retry': '重试',
     'common.refresh': '刷新',
+    'common.format': '格式化',
     'common.back': '返回',
     'common.search': '搜索...',
     'common.minutes': '分钟',
@@ -3906,6 +3916,14 @@ const i18n = {
     'apiSettings.nameRequired': '名称为必填项',
     'apiSettings.tokenRequired': 'API 令牌为必填项',
     'apiSettings.status': '状态',
+    'apiSettings.providerBinding': '供应商绑定',
+    'apiSettings.directConfig': '直接配置',
+    'apiSettings.modelConfig': '模型配置',
+    'apiSettings.configJson': '配置 JSON',
+    'apiSettings.syncToJson': '同步到 JSON',
+    'apiSettings.jsonEditorHint': '直接编辑 JSON 添加高级配置',
+    'apiSettings.jsonValid': 'JSON 有效',
+    'apiSettings.jsonInvalid': 'JSON 无效',
 
     // Model Pools (High Availability)
     'apiSettings.modelPools': '高可用',

ccw/src/templates/dashboard-js/views/api-settings.js

@@ -4031,10 +4031,13 @@ function renderCliConfigModeContent(existingEndpoint) {
   if (cliConfigMode === 'provider') {
     renderProviderModeContent(container, settings);
   } else {
-    renderDirectModeContent(container, env);
+    renderDirectModeContent(container, env, settings);
   }
 
   if (window.lucide) lucide.createIcons();
+
+  // Initialize JSON editor after rendering
+  initCliJsonEditor(settings);
 }
 
 /**
@@ -4081,13 +4084,16 @@ function renderProviderModeContent(container, settings) {
     '<input type="text" id="cli-model-opus" class="form-control" placeholder="claude-3-opus-20240229" value="' + escapeHtml(env.ANTHROPIC_DEFAULT_OPUS_MODEL || '') + '" />' +
     '</div>' +
     '</div>' +
-    '</div>';
+    '</div>' +
+    // JSON Preview/Editor Section
+    buildJsonEditorSection(settings);
 }
 
 /**
  * Render Direct Configuration mode content
  */
-function renderDirectModeContent(container, env) {
+function renderDirectModeContent(container, env, settings) {
+  settings = settings || { env: env };
   container.innerHTML =
     '<div class="form-group">' +
     '<label for="cli-auth-token">ANTHROPIC_AUTH_TOKEN *</label>' +
@@ -4118,9 +4124,227 @@ function renderDirectModeContent(container, env) {
     '<input type="text" id="cli-model-opus" class="form-control" placeholder="claude-3-opus-20240229" value="' + escapeHtml(env.ANTHROPIC_DEFAULT_OPUS_MODEL || '') + '" />' +
     '</div>' +
     '</div>' +
+    '</div>' +
+    // JSON Preview/Editor Section
+    buildJsonEditorSection(settings);
+}
+
+/**
+ * Build JSON Editor Section HTML
+ */
+function buildJsonEditorSection(settings) {
+  return '<div class="json-editor-section">' +
+    '<div class="json-editor-header">' +
+    '<h4><i data-lucide="code-2"></i> ' + (t('apiSettings.configJson') || 'Configuration JSON') + '</h4>' +
+    '<div class="json-editor-actions">' +
+    '<button type="button" class="btn btn-sm btn-ghost" onclick="formatCliJson()" title="' + (t('common.format') || 'Format') + '">' +
+    '<i data-lucide="align-left"></i> ' + (t('common.format') || 'Format') +
+    '</button>' +
+    '<button type="button" class="btn btn-sm btn-ghost" onclick="syncFormToJson()" title="' + (t('apiSettings.syncToJson') || 'Sync to JSON') + '">' +
+    '<i data-lucide="refresh-cw"></i>' +
+    '</button>' +
+    '</div>' +
+    '</div>' +
+    '<div class="json-editor-body">' +
+    '<div class="json-line-numbers" id="cli-json-line-numbers"></div>' +
+    '<textarea id="cli-json-editor" class="json-textarea" spellcheck="false" placeholder="{\n  &quot;env&quot;: {},\n  &quot;model&quot;: &quot;&quot;\n}"></textarea>' +
+    '</div>' +
+    '<div class="json-editor-footer">' +
+    '<span class="json-status" id="cli-json-status"></span>' +
+    '<span class="json-hint">' + (t('apiSettings.jsonEditorHint') || 'Edit JSON directly to add advanced settings') + '</span>' +
+    '</div>' +
     '</div>';
 }
 
+/**
+ * Initialize JSON Editor with settings data
+ */
+function initCliJsonEditor(settings) {
+  var editor = document.getElementById('cli-json-editor');
+  if (!editor) return;
+
+  // Build initial JSON from settings (without sensitive fields for display)
+  var displaySettings = buildDisplaySettings(settings);
+  var jsonStr = JSON.stringify(displaySettings, null, 2);
+
+  editor.value = jsonStr;
+  updateJsonLineNumbers();
+  validateCliJson();
+
+  // Add event listeners
+  editor.addEventListener('input', function() {
+    updateJsonLineNumbers();
+    validateCliJson();
+  });
+
+  editor.addEventListener('scroll', function() {
+    var lineNumbers = document.getElementById('cli-json-line-numbers');
+    if (lineNumbers) {
+      lineNumbers.scrollTop = editor.scrollTop;
+    }
+  });
+
+  editor.addEventListener('keydown', function(e) {
+    // Handle Tab key for indentation
+    if (e.key === 'Tab') {
+      e.preventDefault();
+      var start = this.selectionStart;
+      var end = this.selectionEnd;
+      this.value = this.value.substring(0, start) + '  ' + this.value.substring(end);
+      this.selectionStart = this.selectionEnd = start + 2;
+      updateJsonLineNumbers();
+    }
+  });
+}
+
+/**
+ * Build display settings object (hide sensitive values)
+ */
+function buildDisplaySettings(settings) {
+  var result = {};
+
+  // Copy non-env fields
+  for (var key in settings) {
+    if (key !== 'env' && key !== 'configMode' && key !== 'providerId') {
+      result[key] = settings[key];
+    }
+  }
+
+  // Copy env with masked sensitive values
+  if (settings.env) {
+    result.env = {};
+    for (var envKey in settings.env) {
+      if (envKey === 'ANTHROPIC_AUTH_TOKEN') {
+        // Mask the token
+        var token = settings.env[envKey] || '';
+        result.env[envKey] = token ? (token.substring(0, 10) + '...') : '';
+      } else {
+        result.env[envKey] = settings.env[envKey];
+      }
+    }
+  }
+
+  return result;
+}
+
+/**
+ * Update JSON line numbers
+ */
+function updateJsonLineNumbers() {
+  var editor = document.getElementById('cli-json-editor');
+  var lineNumbers = document.getElementById('cli-json-line-numbers');
+  if (!editor || !lineNumbers) return;
+
+  var lines = editor.value.split('\n').length;
+  var html = '';
+  for (var i = 1; i <= lines; i++) {
+    html += '<span>' + i + '</span>';
+  }
+  lineNumbers.innerHTML = html;
+}
+
+/**
+ * Validate JSON in editor
+ */
+function validateCliJson() {
+  var editor = document.getElementById('cli-json-editor');
+  var status = document.getElementById('cli-json-status');
+  if (!editor || !status) return false;
+
+  try {
+    JSON.parse(editor.value);
+    status.innerHTML = '<i data-lucide="check-circle"></i> ' + (t('apiSettings.jsonValid') || 'Valid JSON');
+    status.className = 'json-status valid';
+    editor.classList.remove('invalid');
+    if (window.lucide) lucide.createIcons();
+    return true;
+  } catch (e) {
+    status.innerHTML = '<i data-lucide="alert-circle"></i> ' + (t('apiSettings.jsonInvalid') || 'Invalid JSON') + ': ' + e.message;
+    status.className = 'json-status invalid';
+    editor.classList.add('invalid');
+    if (window.lucide) lucide.createIcons();
+    return false;
+  }
+}
+
+/**
+ * Format JSON in editor
+ */
+function formatCliJson() {
+  var editor = document.getElementById('cli-json-editor');
+  if (!editor) return;
+
+  try {
+    var obj = JSON.parse(editor.value);
+    editor.value = JSON.stringify(obj, null, 2);
+    updateJsonLineNumbers();
+    validateCliJson();
+  } catch (e) {
+    showRefreshToast(t('apiSettings.jsonInvalid') || 'Invalid JSON', 'error');
+  }
+}
+window.formatCliJson = formatCliJson;
+
+/**
+ * Sync form fields to JSON editor
+ */
+function syncFormToJson() {
+  var editor = document.getElementById('cli-json-editor');
+  if (!editor) return;
+
+  // Get current JSON
+  var currentObj = {};
+  try {
+    currentObj = JSON.parse(editor.value);
+  } catch (e) {
+    currentObj = { env: {} };
+  }
+
+  // Update env from form fields
+  currentObj.env = currentObj.env || {};
+
+  // Model fields
+  var modelDefault = document.getElementById('cli-model-default');
+  var modelHaiku = document.getElementById('cli-model-haiku');
+  var modelSonnet = document.getElementById('cli-model-sonnet');
+  var modelOpus = document.getElementById('cli-model-opus');
+
+  if (modelDefault && modelDefault.value) currentObj.env.ANTHROPIC_MODEL = modelDefault.value;
+  if (modelHaiku && modelHaiku.value) currentObj.env.ANTHROPIC_DEFAULT_HAIKU_MODEL = modelHaiku.value;
+  if (modelSonnet && modelSonnet.value) currentObj.env.ANTHROPIC_DEFAULT_SONNET_MODEL = modelSonnet.value;
+  if (modelOpus && modelOpus.value) currentObj.env.ANTHROPIC_DEFAULT_OPUS_MODEL = modelOpus.value;
+
+  // Direct mode fields
+  if (cliConfigMode === 'direct') {
+    var authToken = document.getElementById('cli-auth-token');
+    var baseUrl = document.getElementById('cli-base-url');
+    if (authToken && authToken.value) currentObj.env.ANTHROPIC_AUTH_TOKEN = authToken.value;
+    if (baseUrl && baseUrl.value) currentObj.env.ANTHROPIC_BASE_URL = baseUrl.value;
+  }
+
+  // Ensure DISABLE_AUTOUPDATER
+  currentObj.env.DISABLE_AUTOUPDATER = '1';
+
+  editor.value = JSON.stringify(currentObj, null, 2);
+  updateJsonLineNumbers();
+  validateCliJson();
+}
+window.syncFormToJson = syncFormToJson;
+
+/**
+ * Get settings from JSON editor (merges with form data)
+ */
+function getSettingsFromJsonEditor() {
+  var editor = document.getElementById('cli-json-editor');
+  if (!editor) return null;
+
+  try {
+    return JSON.parse(editor.value);
+  } catch (e) {
+    return null;
+  }
+}
+
 /**
  * Submit CLI Settings Form (handles both Provider and Direct modes)
  */
@@ -4214,6 +4438,33 @@ async function submitCliSettingsForm() {
     data.settings.env.ANTHROPIC_DEFAULT_OPUS_MODEL = opusModel;
   }
 
+  // Merge additional settings from JSON editor
+  var jsonSettings = getSettingsFromJsonEditor();
+  if (jsonSettings) {
+    // Merge env variables (JSON editor values take precedence for non-core fields)
+    if (jsonSettings.env) {
+      for (var envKey in jsonSettings.env) {
+        // Skip core fields that are managed by form inputs
+        if (envKey === 'ANTHROPIC_AUTH_TOKEN' || envKey === 'ANTHROPIC_BASE_URL') {
+          // Only use JSON editor value if form field is empty
+          if (!data.settings.env[envKey] && jsonSettings.env[envKey] && !jsonSettings.env[envKey].endsWith('...')) {
+            data.settings.env[envKey] = jsonSettings.env[envKey];
+          }
+        } else if (!['ANTHROPIC_MODEL', 'ANTHROPIC_DEFAULT_HAIKU_MODEL', 'ANTHROPIC_DEFAULT_SONNET_MODEL', 'ANTHROPIC_DEFAULT_OPUS_MODEL'].includes(envKey)) {
+          // For non-model env vars, use JSON editor value
+          data.settings.env[envKey] = jsonSettings.env[envKey];
+        }
+      }
+    }
+
+    // Merge non-env settings from JSON editor
+    for (var settingKey in jsonSettings) {
+      if (settingKey !== 'env' && settingKey !== 'configMode' && settingKey !== 'providerId') {
+        data.settings[settingKey] = jsonSettings[settingKey];
+      }
+    }
+  }
+
   // Set ID if editing
   if (id) {
     data.id = id;

ccw/src/templates/dashboard-js/views/codexlens-manager.js

@@ -2540,7 +2540,15 @@ async function loadModelList() {
     // Show models for local backend
     if (embeddingBackend !== 'litellm') {
       var models = result.result.models;
-      models.forEach(function(model) {
+      var predefinedModels = models.filter(function(m) { return m.source !== 'discovered'; });
+      var discoveredModels = models.filter(function(m) { return m.source === 'discovered'; });
+      
+      // Split predefined models into recommended and others
+      var recommendedModels = predefinedModels.filter(function(m) { return m.recommended; });
+      var otherModels = predefinedModels.filter(function(m) { return !m.recommended; });
+
+      // Helper function to render model card
+      function renderModelCard(model) {
         var statusIcon = model.installed
           ? '<i data-lucide="check-circle" class="w-3.5 h-3.5 text-success"></i>'
           : '<i data-lucide="circle" class="w-3.5 h-3.5 text-muted"></i>';
@@ -2553,11 +2561,15 @@ async function loadModelList() {
           ? '<button class="text-xs text-destructive hover:underline" onclick="deleteModel(\'' + model.profile + '\')">Delete</button>'
           : '<button class="text-xs text-primary hover:underline" onclick="downloadModel(\'' + model.profile + '\')">Download</button>';
 
-        html +=
+        var recommendedBadge = model.recommended
-          '<div class="flex items-center justify-between p-2 bg-muted/30 rounded" id="model-' + model.profile + '">' +
+          ? '<span class="text-[10px] px-1 py-0.5 bg-success/20 text-success rounded">Rec</span>'
+          : '';
+
+        return '<div class="flex items-center justify-between p-2 bg-muted/30 rounded" id="model-' + model.profile + '">' +
             '<div class="flex items-center gap-2">' +
               statusIcon +
               '<span class="text-sm font-medium">' + model.profile + '</span>' +
+              recommendedBadge +
               '<button class="text-muted-foreground hover:text-foreground p-0.5" onclick="copyToClipboard(\'' + escapeHtml(model.model_name) + '\')" title="' + escapeHtml(model.model_name) + '">' +
                 '<i data-lucide="copy" class="w-3 h-3"></i>' +
               '</button>' +
@@ -2568,7 +2580,108 @@ async function loadModelList() {
               actionBtn +
             '</div>' +
           '</div>';
-      });
+      }
+
+      // Show recommended models (always visible)
+      if (recommendedModels.length > 0) {
+        html += '<div class="text-xs font-medium text-muted-foreground mb-1 mt-2 flex items-center gap-1">' +
+          '<i data-lucide="star" class="w-3 h-3"></i> Recommended Models (' + recommendedModels.length + ')</div>';
+        recommendedModels.forEach(function(model) {
+          html += renderModelCard(model);
+        });
+      }
+
+      // Show other models (collapsed by default)
+      if (otherModels.length > 0) {
+        html += '<div class="mt-3">' +
+          '<button onclick="toggleOtherModels()" class="text-xs font-medium text-muted-foreground mb-1 flex items-center gap-1 hover:text-foreground">' +
+            '<i data-lucide="chevron-right" class="w-3 h-3 transition-transform" id="otherModelsChevron"></i>' +
+            'Other Models (' + otherModels.length + ')' +
+          '</button>' +
+          '<div id="otherModelsContainer" class="hidden space-y-1">';
+        otherModels.forEach(function(model) {
+          html += renderModelCard(model);
+        });
+        html += '</div></div>';
+      }
+      
+      // Show discovered models (user manually placed)
+      if (discoveredModels.length > 0) {
+        html += '<div class="text-xs font-medium text-muted-foreground mb-1 mt-3 flex items-center gap-1">' +
+          '<i data-lucide="folder-search" class="w-3 h-3"></i> Discovered Models</div>';
+        discoveredModels.forEach(function(model) {
+          var sizeText = model.actual_size_mb ? model.actual_size_mb.toFixed(0) + ' MB' : 'Unknown';
+          var safeProfile = model.profile.replace(/[^a-zA-Z0-9-_]/g, '-');
+          
+          html +=
+            '<div class="flex items-center justify-between p-2 bg-amber-500/10 border border-amber-500/20 rounded" id="model-' + safeProfile + '">' +
+              '<div class="flex items-center gap-2">' +
+                '<i data-lucide="check-circle" class="w-3.5 h-3.5 text-amber-500"></i>' +
+                '<span class="text-sm font-medium">' + escapeHtml(model.model_name) + '</span>' +
+                '<span class="text-[10px] px-1 py-0.5 bg-amber-500/20 text-amber-600 rounded">Manual</span>' +
+                '<span class="text-xs text-muted-foreground">' + (model.dimensions || '?') + 'd</span>' +
+              '</div>' +
+              '<div class="flex items-center gap-3">' +
+                '<span class="text-xs text-muted-foreground">' + sizeText + '</span>' +
+                '<button class="text-xs text-destructive hover:underline" onclick="deleteDiscoveredModel(\'' + escapeHtml(model.cache_path) + '\')">Delete</button>' +
+              '</div>' +
+            '</div>';
+        });
+      }
+      
+      // Show manual install guide
+      var guide = result.result.manual_install_guide;
+      if (guide) {
+        html += '<div class="mt-4 p-3 bg-blue-500/5 border border-blue-500/20 rounded">' +
+          '<div class="flex items-center gap-1 text-xs font-medium text-blue-600 mb-2">' +
+            '<i data-lucide="download" class="w-3 h-3"></i> Manual Model Installation' +
+          '</div>' +
+          '<div class="text-xs text-muted-foreground space-y-1">';
+        if (guide.steps) {
+          guide.steps.forEach(function(step) {
+            html += '<div>' + escapeHtml(step) + '</div>';
+          });
+        }
+        if (guide.example) {
+          html += '<div class="mt-2 font-mono text-[10px] bg-muted/50 p-1.5 rounded overflow-x-auto">' +
+            '<code>' + escapeHtml(guide.example) + '</code>' +
+          '</div>';
+        }
+        // Show multi-platform paths
+        if (guide.paths) {
+          html += '<div class="mt-2 space-y-1">' +
+            '<div class="text-[10px] font-medium">Cache paths:</div>' +
+            '<div class="font-mono text-[10px] bg-muted/50 p-1.5 rounded space-y-0.5">';
+          if (guide.paths.windows) {
+            html += '<div><span class="text-muted-foreground">Windows:</span> ' + escapeHtml(guide.paths.windows) + '</div>';
+          }
+          if (guide.paths.linux) {
+            html += '<div><span class="text-muted-foreground">Linux:</span> ' + escapeHtml(guide.paths.linux) + '</div>';
+          }
+          if (guide.paths.macos) {
+            html += '<div><span class="text-muted-foreground">macOS:</span> ' + escapeHtml(guide.paths.macos) + '</div>';
+          }
+          html += '</div></div>';
+        }
+        html += '</div></div>';
+      }
+      
+      // Custom model download section
+      html += '<div class="mt-4 p-3 bg-green-500/5 border border-green-500/20 rounded">' +
+        '<div class="flex items-center gap-1 text-xs font-medium text-green-600 mb-2">' +
+          '<i data-lucide="plus-circle" class="w-3 h-3"></i> Download Custom Model' +
+        '</div>' +
+        '<div class="flex gap-2">' +
+          '<input type="text" id="customModelInput" placeholder="e.g., BAAI/bge-small-en-v1.5" ' +
+            'class="flex-1 text-xs px-2 py-1.5 border border-border rounded bg-background focus:border-primary focus:ring-1 focus:ring-primary outline-none" />' +
+          '<button onclick="downloadCustomModel()" class="text-xs px-3 py-1.5 bg-primary text-primary-foreground rounded hover:bg-primary/90">' +
+            'Download' +
+          '</button>' +
+        '</div>' +
+        '<div class="text-[10px] text-muted-foreground mt-2">' +
+          'Enter any HuggingFace model name compatible with FastEmbed' +
+        '</div>' +
+      '</div>';
     } else {
       // LiteLLM backend - show API info
       html +=
@@ -2588,6 +2701,19 @@ async function loadModelList() {
   }
 }
 
+/**
+ * Toggle visibility of other (non-recommended) models
+ */
+function toggleOtherModels() {
+  var container = document.getElementById('otherModelsContainer');
+  var chevron = document.getElementById('otherModelsChevron');
+  if (container && chevron) {
+    var isHidden = container.classList.contains('hidden');
+    container.classList.toggle('hidden');
+    chevron.style.transform = isHidden ? 'rotate(90deg)' : '';
+  }
+}
+
 /**
  * Download model (simplified version)
  */
@@ -2675,6 +2801,82 @@ async function deleteModel(profile) {
   }
 }
 
+/**
+ * Download a custom HuggingFace model by name
+ */
+async function downloadCustomModel() {
+  var input = document.getElementById('customModelInput');
+  if (!input) return;
+  
+  var modelName = input.value.trim();
+  if (!modelName) {
+    showRefreshToast('Please enter a model name', 'error');
+    return;
+  }
+  
+  if (!modelName.includes('/')) {
+    showRefreshToast('Invalid format. Use: org/model-name', 'error');
+    return;
+  }
+  
+  // Disable input and show loading
+  input.disabled = true;
+  var originalPlaceholder = input.placeholder;
+  input.placeholder = 'Downloading...';
+  input.value = '';
+  
+  try {
+    var response = await fetch('/api/codexlens/models/download-custom', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ model_name: modelName, model_type: 'embedding' })
+    });
+
+    var result = await response.json();
+
+    if (result.success) {
+      showRefreshToast('Custom model downloaded: ' + modelName, 'success');
+      loadModelList();
+    } else {
+      showRefreshToast('Download failed: ' + result.error, 'error');
+      input.disabled = false;
+      input.placeholder = originalPlaceholder;
+    }
+  } catch (err) {
+    showRefreshToast('Error: ' + err.message, 'error');
+    input.disabled = false;
+    input.placeholder = originalPlaceholder;
+  }
+}
+
+/**
+ * Delete a discovered (manually placed) model by its cache path
+ */
+async function deleteDiscoveredModel(cachePath) {
+  if (!confirm('Delete this manually placed model?\n\nPath: ' + cachePath)) {
+    return;
+  }
+
+  try {
+    var response = await fetch('/api/codexlens/models/delete-path', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ cache_path: cachePath })
+    });
+
+    var result = await response.json();
+
+    if (result.success) {
+      showRefreshToast('Model deleted successfully', 'success');
+      loadModelList();
+    } else {
+      showRefreshToast('Delete failed: ' + result.error, 'error');
+    }
+  } catch (err) {
+    showRefreshToast('Error: ' + err.message, 'error');
+  }
+}
+
 // ============================================================
 // RERANKER MODEL MANAGEMENT
 // ============================================================

codex-lens/src/codexlens/cli/commands.py

@@ -1995,6 +1995,55 @@ def model_delete(
         console.print(f"  Freed space: {data['deleted_size_mb']:.1f} MB")
 
 
+@app.command(name="model-download-custom")
+def model_download_custom(
+    model_name: str = typer.Argument(..., help="Full HuggingFace model name (e.g., BAAI/bge-small-en-v1.5)."),
+    model_type: str = typer.Option("embedding", "--type", help="Model type: embedding or reranker."),
+    json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
+) -> None:
+    """Download a custom HuggingFace model by name.
+
+    This allows downloading any fastembed-compatible model from HuggingFace.
+
+    Example:
+        codexlens model-download-custom BAAI/bge-small-en-v1.5
+        codexlens model-download-custom BAAI/bge-reranker-base --type reranker
+    """
+    try:
+        from codexlens.cli.model_manager import download_custom_model
+
+        if not json_mode:
+            console.print(f"[bold]Downloading custom model:[/bold] {model_name}")
+            console.print(f"[dim]Model type: {model_type}[/dim]")
+            console.print("[dim]This may take a few minutes depending on your internet connection...[/dim]\n")
+
+        progress_callback = None if json_mode else lambda msg: console.print(f"[cyan]{msg}[/cyan]")
+
+        result = download_custom_model(model_name, model_type=model_type, progress_callback=progress_callback)
+
+        if json_mode:
+            print_json(**result)
+        else:
+            if not result["success"]:
+                console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
+                raise typer.Exit(code=1)
+
+            data = result["result"]
+            console.print(f"[green]✓[/green] Custom model downloaded successfully!")
+            console.print(f"  Model: {data['model_name']}")
+            console.print(f"  Type: {data['model_type']}")
+            console.print(f"  Cache size: {data['cache_size_mb']:.1f} MB")
+            console.print(f"  Location: [dim]{data['cache_path']}[/dim]")
+
+    except ImportError:
+        if json_mode:
+            print_json(success=False, error="fastembed not installed. Install with: pip install codexlens[semantic]")
+        else:
+            console.print("[red]Error:[/red] fastembed not installed")
+            console.print("[yellow]Install with:[/yellow] pip install codexlens[semantic]")
+            raise typer.Exit(code=1)
+
+
 @app.command(name="model-info")
 def model_info(
     profile: str = typer.Argument(..., help="Model profile to get info (fast, code, multilingual, balanced)."),

codex-lens/src/codexlens/cli/model_manager.py

@@ -76,6 +76,31 @@ RERANKER_MODEL_PROFILES = {
         "use_case": "Fast reranking with good accuracy",
         "recommended": True,
     },
+    # Additional reranker models (commonly used)
+    "bge-reranker-v2-m3": {
+        "model_name": "BAAI/bge-reranker-v2-m3",
+        "cache_name": "BAAI/bge-reranker-v2-m3",
+        "size_mb": 560,
+        "description": "BGE v2 M3 reranker, multilingual",
+        "use_case": "Multilingual reranking, latest BGE version",
+        "recommended": True,
+    },
+    "bge-reranker-v2-gemma": {
+        "model_name": "BAAI/bge-reranker-v2-gemma",
+        "cache_name": "BAAI/bge-reranker-v2-gemma",
+        "size_mb": 2000,
+        "description": "BGE v2 Gemma reranker, best quality",
+        "use_case": "Maximum quality with Gemma backbone",
+        "recommended": False,
+    },
+    "cross-encoder-ms-marco": {
+        "model_name": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        "cache_name": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        "size_mb": 90,
+        "description": "Original cross-encoder MS MARCO",
+        "use_case": "Classic cross-encoder baseline",
+        "recommended": False,
+    },
 }
 
 
@@ -138,6 +163,106 @@ MODEL_PROFILES = {
         "use_case": "High-quality semantic search, balanced performance",
         "recommended": False,  # 1024d not recommended
     },
+    # Additional embedding models (commonly used)
+    "bge-large": {
+        "model_name": "BAAI/bge-large-en-v1.5",
+        "cache_name": "qdrant/bge-large-en-v1.5-onnx-q",
+        "dimensions": 1024,
+        "size_mb": 650,
+        "description": "BGE large model, highest quality",
+        "use_case": "Maximum quality semantic search",
+        "recommended": False,
+    },
+    "e5-small": {
+        "model_name": "intfloat/e5-small-v2",
+        "cache_name": "qdrant/e5-small-v2-onnx",
+        "dimensions": 384,
+        "size_mb": 80,
+        "description": "E5 small model, fast and lightweight",
+        "use_case": "Low latency applications",
+        "recommended": True,
+    },
+    "e5-base": {
+        "model_name": "intfloat/e5-base-v2",
+        "cache_name": "qdrant/e5-base-v2-onnx",
+        "dimensions": 768,
+        "size_mb": 220,
+        "description": "E5 base model, balanced",
+        "use_case": "General purpose semantic search",
+        "recommended": True,
+    },
+    "e5-large": {
+        "model_name": "intfloat/e5-large-v2",
+        "cache_name": "qdrant/e5-large-v2-onnx",
+        "dimensions": 1024,
+        "size_mb": 650,
+        "description": "E5 large model, high quality",
+        "use_case": "High quality semantic search",
+        "recommended": False,
+    },
+    "jina-base-en": {
+        "model_name": "jinaai/jina-embeddings-v2-base-en",
+        "cache_name": "jinaai/jina-embeddings-v2-base-en",
+        "dimensions": 768,
+        "size_mb": 150,
+        "description": "Jina base English model",
+        "use_case": "English text semantic search",
+        "recommended": True,
+    },
+    "jina-small-en": {
+        "model_name": "jinaai/jina-embeddings-v2-small-en",
+        "cache_name": "jinaai/jina-embeddings-v2-small-en",
+        "dimensions": 512,
+        "size_mb": 60,
+        "description": "Jina small English model, very fast",
+        "use_case": "Low latency English text search",
+        "recommended": True,
+    },
+    "snowflake-arctic": {
+        "model_name": "Snowflake/snowflake-arctic-embed-m",
+        "cache_name": "Snowflake/snowflake-arctic-embed-m",
+        "dimensions": 768,
+        "size_mb": 220,
+        "description": "Snowflake Arctic embedding model",
+        "use_case": "Enterprise semantic search, high quality",
+        "recommended": True,
+    },
+    "nomic-embed": {
+        "model_name": "nomic-ai/nomic-embed-text-v1.5",
+        "cache_name": "nomic-ai/nomic-embed-text-v1.5",
+        "dimensions": 768,
+        "size_mb": 280,
+        "description": "Nomic embedding model, open source",
+        "use_case": "Open source text embedding",
+        "recommended": True,
+    },
+    "gte-small": {
+        "model_name": "thenlper/gte-small",
+        "cache_name": "thenlper/gte-small",
+        "dimensions": 384,
+        "size_mb": 70,
+        "description": "GTE small model, fast",
+        "use_case": "Fast text embedding",
+        "recommended": True,
+    },
+    "gte-base": {
+        "model_name": "thenlper/gte-base",
+        "cache_name": "thenlper/gte-base",
+        "dimensions": 768,
+        "size_mb": 220,
+        "description": "GTE base model, balanced",
+        "use_case": "General purpose text embedding",
+        "recommended": True,
+    },
+    "gte-large": {
+        "model_name": "thenlper/gte-large",
+        "cache_name": "thenlper/gte-large",
+        "dimensions": 1024,
+        "size_mb": 650,
+        "description": "GTE large model, high quality",
+        "use_case": "High quality text embedding",
+        "recommended": False,
+    },
 }
 
 
@@ -179,6 +304,92 @@ def _get_model_cache_path(cache_dir: Path, info: Dict) -> Path:
     return cache_dir / sanitized_name
 
 
+def scan_discovered_models(model_type: str = "embedding") -> List[Dict]:
+    """Scan cache directory for manually placed models not in predefined profiles.
+    
+    This allows users to manually download models (e.g., via huggingface-cli or 
+    by copying the model directory) and have them recognized automatically.
+    
+    Args:
+        model_type: Type of models to scan for ("embedding" or "reranker")
+    
+    Returns:
+        List of discovered model info dictionaries
+    """
+    cache_dir = get_cache_dir()
+    if not cache_dir.exists():
+        return []
+    
+    # Get known model cache names based on type
+    if model_type == "reranker":
+        known_cache_names = {
+            f"models--{info.get('cache_name', info['model_name']).replace('/', '--')}"
+            for info in RERANKER_MODEL_PROFILES.values()
+        }
+    else:
+        known_cache_names = {
+            f"models--{info.get('cache_name', info['model_name']).replace('/', '--')}"
+            for info in MODEL_PROFILES.values()
+        }
+    
+    discovered = []
+    
+    # Scan for model directories in cache
+    for item in cache_dir.iterdir():
+        if not item.is_dir() or not item.name.startswith("models--"):
+            continue
+        
+        # Skip known predefined models
+        if item.name in known_cache_names:
+            continue
+        
+        # Parse model name from directory (models--org--model -> org/model)
+        parts = item.name[8:].split("--")  # Remove "models--" prefix
+        if len(parts) >= 2:
+            model_name = "/".join(parts)
+        else:
+            model_name = parts[0] if parts else item.name
+        
+        # Detect model type by checking for common patterns
+        is_reranker = any(keyword in model_name.lower() for keyword in [
+            "reranker", "cross-encoder", "ms-marco"
+        ])
+        is_embedding = any(keyword in model_name.lower() for keyword in [
+            "embed", "bge", "e5", "jina", "minilm", "gte", "nomic", "arctic"
+        ])
+        
+        # Filter based on requested type
+        if model_type == "reranker" and not is_reranker:
+            continue
+        if model_type == "embedding" and is_reranker:
+            continue
+        
+        # Calculate cache size
+        try:
+            total_size = sum(
+                f.stat().st_size
+                for f in item.rglob("*")
+                if f.is_file()
+            )
+            cache_size_mb = round(total_size / (1024 * 1024), 1)
+        except (OSError, PermissionError):
+            cache_size_mb = 0
+        
+        discovered.append({
+            "profile": f"discovered:{model_name.replace('/', '-')}",
+            "model_name": model_name,
+            "cache_name": model_name,
+            "cache_path": str(item),
+            "actual_size_mb": cache_size_mb,
+            "description": f"Manually discovered model",
+            "use_case": "User-provided model",
+            "installed": True,
+            "source": "discovered",  # Mark as discovered
+        })
+    
+    return discovered
+
+
 def list_models() -> Dict[str, any]:
     """List available model profiles and their installation status.
 
@@ -224,14 +435,45 @@ def list_models() -> Dict[str, any]:
             "description": info["description"],
             "use_case": info["use_case"],
             "installed": installed,
+            "source": "predefined",  # Mark as predefined
+            "recommended": info.get("recommended", True),
         })
 
+    # Add discovered models (manually placed by user)
+    discovered = scan_discovered_models(model_type="embedding")
+    for model in discovered:
+        # Try to estimate dimensions based on common model patterns
+        dimensions = 768  # Default
+        name_lower = model["model_name"].lower()
+        if "small" in name_lower or "mini" in name_lower:
+            dimensions = 384
+        elif "large" in name_lower:
+            dimensions = 1024
+        
+        model["dimensions"] = dimensions
+        model["estimated_size_mb"] = model.get("actual_size_mb", 0)
+        model["recommended"] = False  # User-provided models are not recommended by default
+        models.append(model)
+
     return {
         "success": True,
         "result": {
             "models": models,
             "cache_dir": str(cache_dir),
             "cache_exists": cache_exists,
+            "manual_install_guide": {
+                "steps": [
+                    "1. Download: huggingface-cli download <org>/<model>",
+                    "2. Or copy to cache directory (see paths below)",
+                    "3. Refresh to see discovered models"
+                ],
+                "example": "huggingface-cli download BAAI/bge-small-en-v1.5",
+                "paths": {
+                    "windows": "%USERPROFILE%\\.cache\\huggingface\\models--<org>--<model>",
+                    "linux": "~/.cache/huggingface/models--<org>--<model>",
+                    "macos": "~/.cache/huggingface/models--<org>--<model>",
+                },
+            },
         },
     }
 
@@ -313,6 +555,92 @@ def download_model(profile: str, progress_callback: Optional[callable] = None) -
         }
 
 
+def download_custom_model(model_name: str, model_type: str = "embedding", progress_callback: Optional[callable] = None) -> Dict[str, any]:
+    """Download a custom model by HuggingFace model name.
+    
+    This allows users to download any HuggingFace model that is compatible
+    with fastembed (TextEmbedding or TextCrossEncoder).
+    
+    Args:
+        model_name: Full HuggingFace model name (e.g., "BAAI/bge-small-en-v1.5")
+        model_type: Type of model ("embedding" or "reranker")
+        progress_callback: Optional callback function to report progress
+    
+    Returns:
+        Result dictionary with success status
+    """
+    if model_type == "embedding":
+        if not FASTEMBED_AVAILABLE:
+            return {
+                "success": False,
+                "error": "fastembed not installed. Install with: pip install codexlens[semantic]",
+            }
+    else:
+        if not RERANKER_AVAILABLE:
+            return {
+                "success": False,
+                "error": "fastembed reranker not available. Install with: pip install fastembed>=0.4.0",
+            }
+    
+    # Validate model name format (org/model-name)
+    if not model_name or "/" not in model_name:
+        return {
+            "success": False,
+            "error": "Invalid model name format. Expected: 'org/model-name' (e.g., 'BAAI/bge-small-en-v1.5')",
+        }
+    
+    try:
+        cache_dir = get_cache_dir()
+        
+        if progress_callback:
+            progress_callback(f"Downloading custom model {model_name}...")
+        
+        if model_type == "reranker":
+            # Download reranker model
+            reranker = TextCrossEncoder(model_name=model_name, cache_dir=str(cache_dir))
+            if progress_callback:
+                progress_callback(f"Initializing reranker {model_name}...")
+            list(reranker.rerank("test query", ["test document"]))
+        else:
+            # Download embedding model
+            embedder = TextEmbedding(model_name=model_name, cache_dir=str(cache_dir))
+            if progress_callback:
+                progress_callback(f"Initializing {model_name}...")
+            list(embedder.embed(["test"]))
+        
+        if progress_callback:
+            progress_callback(f"Custom model {model_name} downloaded successfully")
+        
+        # Get cache info
+        sanitized_name = f"models--{model_name.replace('/', '--')}"
+        model_cache_path = cache_dir / sanitized_name
+        
+        cache_size = 0
+        if model_cache_path.exists():
+            total_size = sum(
+                f.stat().st_size
+                for f in model_cache_path.rglob("*")
+                if f.is_file()
+            )
+            cache_size = round(total_size / (1024 * 1024), 1)
+        
+        return {
+            "success": True,
+            "result": {
+                "model_name": model_name,
+                "model_type": model_type,
+                "cache_size_mb": cache_size,
+                "cache_path": str(model_cache_path),
+            },
+        }
+    
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to download custom model: {str(e)}",
+        }
+
+
 def delete_model(profile: str) -> Dict[str, any]:
     """Delete a downloaded model from cache.
 
@@ -464,14 +792,35 @@ def list_reranker_models() -> Dict[str, any]:
             "use_case": info["use_case"],
             "installed": installed,
             "recommended": info.get("recommended", True),
+            "source": "predefined",  # Mark as predefined
         })
 
+    # Add discovered reranker models (manually placed by user)
+    discovered = scan_discovered_models(model_type="reranker")
+    for model in discovered:
+        model["estimated_size_mb"] = model.get("actual_size_mb", 0)
+        model["recommended"] = False  # User-provided models are not recommended by default
+        models.append(model)
+
     return {
         "success": True,
         "result": {
             "models": models,
             "cache_dir": str(cache_dir),
             "cache_exists": cache_exists,
+            "manual_install_guide": {
+                "steps": [
+                    "1. Download: huggingface-cli download <org>/<model>",
+                    "2. Or copy to cache directory (see paths below)",
+                    "3. Refresh to see discovered models",
+                ],
+                "example": "huggingface-cli download BAAI/bge-reranker-base",
+                "paths": {
+                    "windows": "%USERPROFILE%\\.cache\\huggingface\\models--<org>--<model>",
+                    "linux": "~/.cache/huggingface/models--<org>--<model>",
+                    "macos": "~/.cache/huggingface/models--<org>--<model>",
+                },
+            },
         },
     }