' +
@@ -2901,12 +2962,14 @@ async function updateSemanticStatusBadge() {
* @param {string} embeddingModel - Model profile: 'code', 'fast'
* @param {string} embeddingBackend - Backend: 'fastembed' (local) or 'litellm' (API)
* @param {number} maxWorkers - Max concurrent API calls for embedding generation (default: 1)
+ * @param {boolean} incremental - Incremental mode: true=skip unchanged, false=full rebuild (default: false)
*/
-async function initCodexLensIndex(indexType, embeddingModel, embeddingBackend, maxWorkers) {
+async function initCodexLensIndex(indexType, embeddingModel, embeddingBackend, maxWorkers, incremental) {
indexType = indexType || 'vector';
embeddingModel = embeddingModel || 'code';
embeddingBackend = embeddingBackend || 'fastembed';
maxWorkers = maxWorkers || 1;
+ incremental = incremental !== undefined ? incremental : false; // Default: full rebuild
// For vector/full index with local backend, check if semantic dependencies are available
// LiteLLM backend uses remote embeddings and does not require fastembed/ONNX deps.
@@ -3011,7 +3074,7 @@ async function initCodexLensIndex(indexType, embeddingModel, embeddingBackend, m
var apiIndexType = (indexType === 'full') ? 'vector' : indexType;
// Start indexing with specified type and model
- startCodexLensIndexing(apiIndexType, embeddingModel, embeddingBackend, maxWorkers);
+ startCodexLensIndexing(apiIndexType, embeddingModel, embeddingBackend, maxWorkers, incremental);
}
/**
@@ -3020,12 +3083,14 @@ async function initCodexLensIndex(indexType, embeddingModel, embeddingBackend, m
* @param {string} embeddingModel - Model profile: 'code', 'fast'
* @param {string} embeddingBackend - Backend: 'fastembed' (local) or 'litellm' (API)
* @param {number} maxWorkers - Max concurrent API calls for embedding generation (default: 1)
+ * @param {boolean} incremental - Incremental mode (default: false for full rebuild)
*/
-async function startCodexLensIndexing(indexType, embeddingModel, embeddingBackend, maxWorkers) {
+async function startCodexLensIndexing(indexType, embeddingModel, embeddingBackend, maxWorkers, incremental) {
indexType = indexType || 'vector';
embeddingModel = embeddingModel || 'code';
embeddingBackend = embeddingBackend || 'fastembed';
maxWorkers = maxWorkers || 1;
+ incremental = incremental !== undefined ? incremental : false; // Default: full rebuild
var statusText = document.getElementById('codexlensIndexStatus');
var progressBar = document.getElementById('codexlensIndexProgressBar');
var percentText = document.getElementById('codexlensIndexPercent');
@@ -3057,11 +3122,11 @@ async function startCodexLensIndexing(indexType, embeddingModel, embeddingBacken
}
try {
- console.log('[CodexLens] Starting index for:', projectPath, 'type:', indexType, 'model:', embeddingModel, 'backend:', embeddingBackend, 'maxWorkers:', maxWorkers);
+ console.log('[CodexLens] Starting index for:', projectPath, 'type:', indexType, 'model:', embeddingModel, 'backend:', embeddingBackend, 'maxWorkers:', maxWorkers, 'incremental:', incremental);
var response = await fetch('/api/codexlens/init', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
- body: JSON.stringify({ path: projectPath, indexType: indexType, embeddingModel: embeddingModel, embeddingBackend: embeddingBackend, maxWorkers: maxWorkers })
+ body: JSON.stringify({ path: projectPath, indexType: indexType, embeddingModel: embeddingModel, embeddingBackend: embeddingBackend, maxWorkers: maxWorkers, incremental: incremental })
});
var result = await response.json();
@@ -4165,6 +4230,121 @@ function initCodexLensIndexFromPage(indexType) {
}
}
+// ============================================================
+// INDEX OPERATIONS - 4 Button Functions
+// ============================================================
+
+/**
+ * Run FTS full index (rebuild full-text search index)
+ * Creates FTS index without embeddings
+ */
+window.runFtsFullIndex = async function runFtsFullIndex() {
+ showRefreshToast(t('codexlens.startingFtsFullIndex') || 'Starting FTS full index...', 'info');
+ // FTS only, no embeddings, full rebuild (incremental=false)
+ initCodexLensIndex('normal', null, 'fastembed', 1, false);
+}
+
+/**
+ * Run FTS incremental update
+ * Updates FTS index for changed files only
+ */
+window.runFtsIncrementalUpdate = async function runFtsIncrementalUpdate() {
+ var projectPath = window.CCW_PROJECT_ROOT || '.';
+ showRefreshToast(t('codexlens.startingFtsIncremental') || 'Starting FTS incremental update...', 'info');
+
+ try {
+ // Use index update endpoint for FTS incremental
+ var response = await fetch('/api/codexlens/init', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify({
+ path: projectPath,
+ indexType: 'normal', // FTS only
+ incremental: true
+ })
+ });
+ var result = await response.json();
+
+ if (result.success) {
+ showRefreshToast(t('codexlens.ftsIncrementalComplete') || 'FTS incremental update completed', 'success');
+ renderCodexLensManager();
+ } else {
+ showRefreshToast((t('codexlens.ftsIncrementalFailed') || 'FTS incremental failed') + ': ' + (result.error || 'Unknown error'), 'error');
+ }
+ } catch (err) {
+ showRefreshToast((t('common.error') || 'Error') + ': ' + err.message, 'error');
+ }
+}
+
+/**
+ * Run Vector full index (generate all embeddings)
+ * Generates embeddings for all files
+ */
+window.runVectorFullIndex = async function runVectorFullIndex() {
+ showRefreshToast(t('codexlens.startingVectorFullIndex') || 'Starting Vector full index...', 'info');
+
+ try {
+ // Fetch env settings to get the configured embedding model
+ var envResponse = await fetch('/api/codexlens/env');
+ var envData = await envResponse.json();
+ var embeddingModel = envData.CODEXLENS_EMBEDDING_MODEL || envData.LITELLM_EMBEDDING_MODEL || 'code';
+
+ // Use litellm backend with env-configured model, full rebuild (incremental=false)
+ initCodexLensIndex('vector', embeddingModel, 'litellm', 4, false);
+ } catch (err) {
+ // Fallback to default model if env fetch fails
+ initCodexLensIndex('vector', 'code', 'litellm', 4, false);
+ }
+}
+
+/**
+ * Run Vector incremental update
+ * Generates embeddings for new/changed files only
+ */
+window.runVectorIncrementalUpdate = async function runVectorIncrementalUpdate() {
+ var projectPath = window.CCW_PROJECT_ROOT || '.';
+ showRefreshToast(t('codexlens.startingVectorIncremental') || 'Starting Vector incremental update...', 'info');
+
+ try {
+ // Fetch env settings to get the configured embedding model
+ var envResponse = await fetch('/api/codexlens/env');
+ var envData = await envResponse.json();
+ var embeddingModel = envData.CODEXLENS_EMBEDDING_MODEL || envData.LITELLM_EMBEDDING_MODEL || null;
+
+ // Use embeddings endpoint for vector incremental
+ var requestBody = {
+ path: projectPath,
+ incremental: true, // Only new/changed files
+ backend: 'litellm',
+ maxWorkers: 4
+ };
+
+ // Add model if configured in env
+ if (embeddingModel) {
+ requestBody.model = embeddingModel;
+ }
+
+ var response = await fetch('/api/codexlens/embeddings/generate', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify(requestBody)
+ });
+ var result = await response.json();
+
+ if (result.success) {
+ var stats = result.result || {};
+ var msg = (t('codexlens.vectorIncrementalComplete') || 'Vector incremental completed') +
+ (stats.chunks_created ? ': ' + stats.chunks_created + ' chunks' : '');
+ showRefreshToast(msg, 'success');
+ renderCodexLensManager();
+ } else {
+ showRefreshToast((t('codexlens.vectorIncrementalFailed') || 'Vector incremental failed') + ': ' + (result.error || 'Unknown error'), 'error');
+ }
+ } catch (err) {
+ showRefreshToast((t('common.error') || 'Error') + ': ' + err.message, 'error');
+ }
+}
+
/**
* Run incremental update on the current workspace index
*/
diff --git a/ccw/src/templates/dashboard-js/views/core-memory.js b/ccw/src/templates/dashboard-js/views/core-memory.js
index 99fced53..af1db942 100644
--- a/ccw/src/templates/dashboard-js/views/core-memory.js
+++ b/ccw/src/templates/dashboard-js/views/core-memory.js
@@ -228,15 +228,31 @@ function renderMemoryCard(memory) {
const updatedDate = memory.updated_at ? new Date(memory.updated_at).toLocaleString() : createdDate;
const isArchived = memory.archived || false;
- const metadata = memory.metadata || {};
+ // Parse metadata - it may be double-encoded JSON string from the backend
+ let metadata = {};
+ if (memory.metadata) {
+ try {
+ let parsed = typeof memory.metadata === 'string' ? JSON.parse(memory.metadata) : memory.metadata;
+ // Handle double-encoded JSON (string within string)
+ if (typeof parsed === 'string') {
+ parsed = JSON.parse(parsed);
+ }
+ metadata = parsed;
+ console.log('[DEBUG] Memory', memory.id, 'metadata parsed:', metadata, 'favorite:', metadata.favorite);
+ } catch (e) {
+ console.warn('Failed to parse memory metadata:', e);
+ }
+ }
const tags = metadata.tags || [];
const priority = metadata.priority || 'medium';
+ const isFavorite = metadata.favorite === true;
+ console.log('[DEBUG] Memory', memory.id, 'isFavorite:', isFavorite);
return `