feat: add reranker models to ProviderCredential and improve FastEmbedReranker scoring

- Added `rerankerModels` property to the `ProviderCredential` interface in `litellm-api-config.ts` to support additional reranker configurations.
- Introduced a numerically stable sigmoid function in `FastEmbedReranker` for score normalization.
- Updated the scoring logic in `FastEmbedReranker` to use raw float scores from the encoder and normalize them using the new sigmoid function.
- Adjusted the result mapping to maintain original document order while applying normalization.

ccw/src/core/routes/litellm-api-routes.ts

@@ -789,6 +789,46 @@ export async function handleLiteLLMApiRoutes(ctx: RouteContext): Promise<boolean
     return true;
   }
 
+  // GET /api/litellm-api/reranker-pool - Get available reranker models from all providers
+  if (pathname === '/api/litellm-api/reranker-pool' && req.method === 'GET') {
+    try {
+      // Get list of all available reranker models from all providers
+      const config = loadLiteLLMApiConfig(initialPath);
+      const availableModels: Array<{ modelId: string; modelName: string; providers: string[] }> = [];
+      const modelMap = new Map<string, { modelId: string; modelName: string; providers: string[] }>();
+
+      for (const provider of config.providers) {
+        if (!provider.enabled || !provider.rerankerModels) continue;
+
+        for (const model of provider.rerankerModels) {
+          if (!model.enabled) continue;
+
+          const key = model.id;
+          if (modelMap.has(key)) {
+            modelMap.get(key)!.providers.push(provider.name);
+          } else {
+            modelMap.set(key, {
+              modelId: model.id,
+              modelName: model.name,
+              providers: [provider.name],
+            });
+          }
+        }
+      }
+
+      availableModels.push(...Array.from(modelMap.values()));
+
+      res.writeHead(200, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({
+        availableModels,
+      }));
+    } catch (err) {
+      res.writeHead(500, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({ error: (err as Error).message }));
+    }
+    return true;
+  }
+
   // GET /api/litellm-api/embedding-pool/discover/:model - Preview auto-discovery results
   const discoverMatch = pathname.match(/^\/api\/litellm-api\/embedding-pool\/discover\/([^/]+)$/);
   if (discoverMatch && req.method === 'GET') {

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

@@ -1465,6 +1465,10 @@ const i18n = {
     'apiSettings.noProvidersFound': 'No providers found',
     'apiSettings.llmModels': 'LLM Models',
     'apiSettings.embeddingModels': 'Embedding Models',
+    'apiSettings.rerankerModels': 'Reranker Models',
+    'apiSettings.addRerankerModel': 'Add Reranker Model',
+    'apiSettings.rerankerTopK': 'Default Top K',
+    'apiSettings.rerankerTopKHint': 'Number of top results to return (default: 10)',
     'apiSettings.manageModels': 'Manage',
     'apiSettings.addModel': 'Add Model',
     'apiSettings.multiKeySettings': 'Multi-Key Settings',
@@ -3416,6 +3420,10 @@ const i18n = {
     'apiSettings.noProvidersFound': '未找到供应商',
     'apiSettings.llmModels': '大语言模型',
     'apiSettings.embeddingModels': '向量模型',
+    'apiSettings.rerankerModels': '重排模型',
+    'apiSettings.addRerankerModel': '添加重排模型',
+    'apiSettings.rerankerTopK': '默认 Top K',
+    'apiSettings.rerankerTopKHint': '返回的最高排名结果数量（默认：10）',
     'apiSettings.manageModels': '管理',
     'apiSettings.addModel': '添加模型',
     'apiSettings.multiKeySettings': '多密钥设置',

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

@@ -1221,6 +1221,9 @@ function renderProviderDetail(providerId) {
     '<button class="model-tab' + (activeModelTab === 'embedding' ? ' active' : '') + '" onclick="switchModelTab(\'embedding\')">' +
     t('apiSettings.embeddingModels') +
     '</button>' +
+    '<button class="model-tab' + (activeModelTab === 'reranker' ? ' active' : '') + '" onclick="switchModelTab(\'reranker\')">' +
+    t('apiSettings.rerankerModels') +
+    '</button>' +
     '</div>' +
     '<div class="model-section-actions">' +
     '<button class="btn btn-secondary" onclick="showManageModelsModal(\'' + providerId + '\')">' +
@@ -1275,6 +1278,8 @@ function renderModelTree(provider) {
 
   var models = activeModelTab === 'llm'
     ? (provider.llmModels || [])
+    : activeModelTab === 'reranker'
+    ? (provider.rerankerModels || [])
     : (provider.embeddingModels || []);
 
   if (models.length === 0) {
@@ -1537,10 +1542,11 @@ function showAddModelModal(providerId, modelType) {
   if (!provider) return;
 
   const isLlm = modelType === 'llm';
-  const title = isLlm ? t('apiSettings.addLlmModel') : t('apiSettings.addEmbeddingModel');
+  const isReranker = modelType === 'reranker';
+  const title = isLlm ? t('apiSettings.addLlmModel') : isReranker ? t('apiSettings.addRerankerModel') : t('apiSettings.addEmbeddingModel');
 
   // Get model presets based on provider type
-  const presets = isLlm ? getLlmPresetsForType(provider.type) : getEmbeddingPresetsForType(provider.type);
+  const presets = isLlm ? getLlmPresetsForType(provider.type) : isReranker ? getRerankerPresetsForType(provider.type) : getEmbeddingPresetsForType(provider.type);
 
   // Group presets by series
   const groupedPresets = groupPresetsBySeries(presets);
@@ -1562,9 +1568,8 @@ function showAddModelModal(providerId, modelType) {
     Object.keys(groupedPresets).map(function(series) {
       return '<optgroup label="' + series + '">' +
         groupedPresets[series].map(function(m) {
-          return '<option value="' + m.id + '">' + m.name + ' ' +
+          var info = isLlm ? '(' + (m.contextWindow/1000) + 'K)' : isReranker ? '' : '(' + m.dimensions + 'D)';
-            (isLlm ? '(' + (m.contextWindow/1000) + 'K)' : '(' + m.dimensions + 'D)') +
+          return '<option value="' + m.id + '">' + m.name + ' ' + info + '</option>';
-            '</option>';
         }).join('') +
         '</optgroup>';
     }).join('') +
@@ -1607,6 +1612,12 @@ function showAddModelModal(providerId, modelType) {
       '<input type="checkbox" id="cap-vision" /> ' + t('apiSettings.vision') +
       '</label>' +
       '</div>'
+    : isReranker ?
+      '<div class="form-group">' +
+      '<label>' + t('apiSettings.rerankerTopK') + '</label>' +
+      '<input type="number" id="model-top-k" class="cli-input" value="10" min="1" max="100" />' +
+      '<span class="field-hint">' + t('apiSettings.rerankerTopKHint') + '</span>' +
+      '</div>'
     :
       '<div class="form-group">' +
       '<label>' + t('apiSettings.embeddingDimensions') + ' *</label>' +
@@ -1691,6 +1702,37 @@ function getEmbeddingPresetsForType(providerType) {
   return presets[providerType] || presets.custom;
 }
 
+/**
+ * Get reranker model presets based on provider type
+ */
+function getRerankerPresetsForType(providerType) {
+  const presets = {
+    openai: [
+      { id: 'BAAI/bge-reranker-v2-m3', name: 'BGE Reranker v2 M3', series: 'BGE Reranker', topK: 10 },
+      { id: 'BAAI/bge-reranker-large', name: 'BGE Reranker Large', series: 'BGE Reranker', topK: 10 },
+      { id: 'BAAI/bge-reranker-base', name: 'BGE Reranker Base', series: 'BGE Reranker', topK: 10 }
+    ],
+    cohere: [
+      { id: 'rerank-english-v3.0', name: 'Rerank English v3.0', series: 'Cohere Rerank', topK: 10 },
+      { id: 'rerank-multilingual-v3.0', name: 'Rerank Multilingual v3.0', series: 'Cohere Rerank', topK: 10 },
+      { id: 'rerank-english-v2.0', name: 'Rerank English v2.0', series: 'Cohere Rerank', topK: 10 }
+    ],
+    voyage: [
+      { id: 'rerank-2', name: 'Rerank 2', series: 'Voyage Rerank', topK: 10 },
+      { id: 'rerank-2-lite', name: 'Rerank 2 Lite', series: 'Voyage Rerank', topK: 10 },
+      { id: 'rerank-1', name: 'Rerank 1', series: 'Voyage Rerank', topK: 10 }
+    ],
+    jina: [
+      { id: 'jina-reranker-v2-base-multilingual', name: 'Jina Reranker v2 Multilingual', series: 'Jina Reranker', topK: 10 },
+      { id: 'jina-reranker-v1-base-en', name: 'Jina Reranker v1 English', series: 'Jina Reranker', topK: 10 }
+    ],
+    custom: [
+      { id: 'custom-reranker', name: 'Custom Reranker', series: 'Custom', topK: 10 }
+    ]
+  };
+  return presets[providerType] || presets.custom;
+}
+
 /**
  * Group presets by series
  */
@@ -1721,7 +1763,8 @@ function fillModelFromPreset(presetId, modelType) {
   if (!provider) return;
 
   const isLlm = modelType === 'llm';
-  const presets = isLlm ? getLlmPresetsForType(provider.type) : getEmbeddingPresetsForType(provider.type);
+  const isReranker = modelType === 'reranker';
+  const presets = isLlm ? getLlmPresetsForType(provider.type) : isReranker ? getRerankerPresetsForType(provider.type) : getEmbeddingPresetsForType(provider.type);
   const preset = presets.find(function(p) { return p.id === presetId; });
 
   if (preset) {
@@ -1732,7 +1775,11 @@ function fillModelFromPreset(presetId, modelType) {
     if (isLlm && preset.contextWindow) {
       document.getElementById('model-context-window').value = preset.contextWindow;
     }
-    if (!isLlm && preset.dimensions) {
+    if (isReranker && preset.topK) {
+      var topKEl = document.getElementById('model-top-k');
+      if (topKEl) topKEl.value = preset.topK;
+    }
+    if (!isLlm && !isReranker && preset.dimensions) {
       document.getElementById('model-dimensions').value = preset.dimensions;
       if (preset.maxTokens) {
         document.getElementById('model-max-tokens').value = preset.maxTokens;
@@ -1748,6 +1795,7 @@ function saveNewModel(event, providerId, modelType) {
   event.preventDefault();
 
   const isLlm = modelType === 'llm';
+  const isReranker = modelType === 'reranker';
   const now = new Date().toISOString();
 
   const newModel = {
@@ -1769,6 +1817,11 @@ function saveNewModel(event, providerId, modelType) {
       functionCalling: document.getElementById('cap-function-calling').checked,
       vision: document.getElementById('cap-vision').checked
     };
+  } else if (isReranker) {
+    var topKEl = document.getElementById('model-top-k');
+    newModel.capabilities = {
+      topK: topKEl ? parseInt(topKEl.value) || 10 : 10
+    };
   } else {
     newModel.capabilities = {
       embeddingDimension: parseInt(document.getElementById('model-dimensions').value) || 1536,
@@ -1780,7 +1833,7 @@ function saveNewModel(event, providerId, modelType) {
   fetch('/api/litellm-api/providers/' + providerId)
     .then(function(res) { return res.json(); })
     .then(function(provider) {
-      const modelsKey = isLlm ? 'llmModels' : 'embeddingModels';
+      const modelsKey = isLlm ? 'llmModels' : isReranker ? 'rerankerModels' : 'embeddingModels';
       const models = provider[modelsKey] || [];
 
       // Check for duplicate ID
@@ -1824,7 +1877,8 @@ function showModelSettingsModal(providerId, modelId, modelType) {
   if (!provider) return;
 
   var isLlm = modelType === 'llm';
-  var models = isLlm ? (provider.llmModels || []) : (provider.embeddingModels || []);
+  var isReranker = modelType === 'reranker';
+  var models = isLlm ? (provider.llmModels || []) : isReranker ? (provider.rerankerModels || []) : (provider.embeddingModels || []);
   var model = models.find(function(m) { return m.id === modelId; });
   if (!model) return;
 
@@ -1834,7 +1888,7 @@ function showModelSettingsModal(providerId, modelId, modelType) {
   // Calculate endpoint preview URL
   var providerBase = provider.apiBase || getDefaultApiBase(provider.type);
   var modelBaseUrl = endpointSettings.baseUrl || providerBase;
-  var endpointPath = isLlm ? '/chat/completions' : '/embeddings';
+  var endpointPath = isLlm ? '/chat/completions' : isReranker ? '/rerank' : '/embeddings';
   var endpointPreview = modelBaseUrl + endpointPath;
 
   var modalHtml = '<div class="modal-overlay" id="model-settings-modal">' +
@@ -1848,7 +1902,7 @@ function showModelSettingsModal(providerId, modelId, modelType) {
 
     // Endpoint Preview Section (combined view + settings)
     '<div class="form-section endpoint-preview-section">' +
-    '<h4><i data-lucide="' + (isLlm ? 'message-square' : 'box') + '"></i> ' + t('apiSettings.endpointPreview') + '</h4>' +
+    '<h4><i data-lucide="' + (isLlm ? 'message-square' : isReranker ? 'sort-asc' : 'box') + '"></i> ' + t('apiSettings.endpointPreview') + '</h4>' +
     '<div class="endpoint-preview-box">' +
     '<code id="model-endpoint-preview">' + escapeHtml(endpointPreview) + '</code>' +
     '<button type="button" class="btn-icon-sm" onclick="copyModelEndpoint()" title="' + t('common.copy') + '">' +
@@ -1857,7 +1911,7 @@ function showModelSettingsModal(providerId, modelId, modelType) {
     '</div>' +
     '<div class="form-group">' +
     '<label>' + t('apiSettings.modelBaseUrlOverride') + ' <span class="text-muted">(' + t('common.optional') + ')</span></label>' +
-    '<input type="text" id="model-settings-baseurl" class="cli-input" value="' + escapeHtml(endpointSettings.baseUrl || '') + '" placeholder="' + escapeHtml(providerBase) + '" oninput="updateModelEndpointPreview(\'' + (isLlm ? 'chat/completions' : 'embeddings') + '\', \'' + escapeHtml(providerBase) + '\')">' +
+    '<input type="text" id="model-settings-baseurl" class="cli-input" value="' + escapeHtml(endpointSettings.baseUrl || '') + '" placeholder="' + escapeHtml(providerBase) + '" oninput="updateModelEndpointPreview(\'' + (isLlm ? 'chat/completions' : isReranker ? 'rerank' : 'embeddings') + '\', \'' + escapeHtml(providerBase) + '\')">' +
     '<small class="form-hint">' + t('apiSettings.modelBaseUrlHint') + '</small>' +
     '</div>' +
     '</div>' +
@@ -1968,7 +2022,8 @@ function saveModelSettings(event, providerId, modelId, modelType) {
   event.preventDefault();
 
   var isLlm = modelType === 'llm';
-  var modelsKey = isLlm ? 'llmModels' : 'embeddingModels';
+  var isReranker = modelType === 'reranker';
+  var modelsKey = isLlm ? 'llmModels' : isReranker ? 'rerankerModels' : 'embeddingModels';
 
   fetch('/api/litellm-api/providers/' + providerId)
     .then(function(res) { return res.json(); })
@@ -1994,6 +2049,11 @@ function saveModelSettings(event, providerId, modelId, modelType) {
           functionCalling: document.getElementById('model-settings-function-calling').checked,
           vision: document.getElementById('model-settings-vision').checked
         };
+      } else if (isReranker) {
+        var topKEl = document.getElementById('model-settings-top-k');
+        models[modelIndex].capabilities = {
+          topK: topKEl ? parseInt(topKEl.value) || 10 : 10
+        };
       } else {
         models[modelIndex].capabilities = {
           embeddingDimension: parseInt(document.getElementById('model-settings-dimensions').value) || 1536,
@@ -2042,7 +2102,8 @@ function deleteModel(providerId, modelId, modelType) {
   if (!confirm(t('common.confirmDelete'))) return;
 
   var isLlm = modelType === 'llm';
-  var modelsKey = isLlm ? 'llmModels' : 'embeddingModels';
+  var isReranker = modelType === 'reranker';
+  var modelsKey = isLlm ? 'llmModels' : isReranker ? 'rerankerModels' : 'embeddingModels';
 
   fetch('/api/litellm-api/providers/' + providerId)
     .then(function(res) { return res.json(); })

ccw/src/types/litellm-api-config.ts

@@ -226,6 +226,9 @@ export interface ProviderCredential {
   /** Embedding models configured for this provider */
   embeddingModels?: ModelDefinition[];
 
+  /** Reranker models configured for this provider */
+  rerankerModels?: ModelDefinition[];
+
   /** Creation timestamp (ISO 8601) */
   createdAt: string;
 

codex-lens/src/codexlens/semantic/reranker/fastembed_reranker.py

@@ -125,6 +125,16 @@ class FastEmbedReranker(BaseReranker):
 
             logger.debug("FastEmbed reranker model loaded successfully")
 
+    @staticmethod
+    def _sigmoid(x: float) -> float:
+        """Numerically stable sigmoid function."""
+        if x < -709:
+            return 0.0
+        if x > 709:
+            return 1.0
+        import math
+        return 1.0 / (1.0 + math.exp(-x))
+
     def score_pairs(
         self,
         pairs: Sequence[tuple[str, str]],
@@ -165,8 +175,8 @@ class FastEmbedReranker(BaseReranker):
             indices = [idx for idx, _ in indexed_docs]
 
             try:
-                # TextCrossEncoder.rerank returns list of RerankResult with score attribute
+                # TextCrossEncoder.rerank returns raw float scores in same order as input
-                results = list(
+                raw_scores = list(
                     self._encoder.rerank(
                         query=query,
                         documents=docs,
@@ -174,22 +184,12 @@ class FastEmbedReranker(BaseReranker):
                     )
                 )
 
-                # Map scores back to original positions
+                # Map scores back to original positions and normalize with sigmoid
-                # Results are returned in descending score order, but we need original order
+                for i, raw_score in enumerate(raw_scores):
-                for result in results:
+                    if i < len(indices):
-                    # Each result has 'index' (position in input docs) and 'score'
+                        original_idx = indices[i]
-                    doc_idx = result.index if hasattr(result, "index") else 0
+                        # Normalize score to [0, 1] using stable sigmoid
-                    score = result.score if hasattr(result, "score") else 0.0
+                        scores[original_idx] = self._sigmoid(float(raw_score))
-
-                    if doc_idx < len(indices):
-                        original_idx = indices[doc_idx]
-                        # Normalize score to [0, 1] using sigmoid if needed
-                        # FastEmbed typically returns scores in [0, 1] already
-                        if score < 0 or score > 1:
-                            import math
-
-                            score = 1.0 / (1.0 + math.exp(-score))
-                        scores[original_idx] = float(score)
 
             except Exception as e:
                 logger.warning("FastEmbed rerank failed for query: %s", str(e)[:100])
@@ -227,7 +227,8 @@ class FastEmbedReranker(BaseReranker):
             return []
 
         try:
-            results = list(
+            # TextCrossEncoder.rerank returns raw float scores in same order as input
+            raw_scores = list(
                 self._encoder.rerank(
                     query=query,
                     documents=list(documents),
@@ -235,13 +236,13 @@ class FastEmbedReranker(BaseReranker):
                 )
             )
 
-            # Convert to our format: (score, document, original_index)
+            # Convert to our format: (normalized_score, document, original_index)
             ranked = []
-            for result in results:
+            for idx, raw_score in enumerate(raw_scores):
-                idx = result.index if hasattr(result, "index") else 0
+                if idx < len(documents):
-                score = result.score if hasattr(result, "score") else 0.0
+                    # Normalize score to [0, 1] using stable sigmoid
-                doc = documents[idx] if idx < len(documents) else ""
+                    normalized = self._sigmoid(float(raw_score))
-                ranked.append((float(score), doc, idx))
+                    ranked.append((normalized, documents[idx], idx))
 
             # Sort by score descending
             ranked.sort(key=lambda x: x[0], reverse=True)