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feat(messages): implement integrated chat with voice/calls and tidy root go module

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Add Discord-like messaging APIs, websocket realtime, smart suggestions, password vault flows, semantic indexing integration, and new /app/messages UI.

Add typing indicators, advanced message search filters, voice notes, browser-local optional transcription, and WebRTC call signaling (offer/answer/ice/hangup).

Clean root go.mod via go mod tidy and remove stale root go.sum.
This commit is contained in:
Tomas Dvorak
2026-02-26 10:54:19 +01:00
parent 55d0284b2a
commit 4c812e376d
18 changed files with 5296 additions and 152 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/handlers/auth.go
+8
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@@ -137,6 +137,11 @@ func AuthMiddleware() gin.HandlerFunc {
}
authHeader := c.GetHeader("Authorization")
if authHeader == "" {
if tokenParam := c.Query("token"); tokenParam != "" {
authHeader = "Bearer " + tokenParam
}
}
if authHeader == "" {
c.JSON(401, gin.H{"error": "Authorization header required"})
c.Abort()
@@ -230,6 +235,9 @@ func Register(c *gin.Context) {
return
}
// Provision messaging defaults (self chat, password vault, global channels).
_ = ensureMessagingDefaults(db, user.ID)
// Generate JWT token
token, err := GenerateJWT(user)
if err != nil {
backend/handlers/messages.go
+2093
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File diff suppressed because it is too large Load Diff
backend/handlers/semantic_search.go
+233 -67
View File
@@ -17,7 +17,7 @@ import (
// SemanticSearchRequest represents a semantic search request
type SemanticSearchRequest struct {
Query string `json:"query" binding:"required"`
ContentType string `json:"content_type"` // 'all' | 'bookmarks' | 'tasks' | 'notes' | 'files'
ContentType string `json:"content_type"` // all | bookmarks | tasks | notes | files | calendar_events | youtube_videos | learning_paths | chat_messages
Limit int `json:"limit"`
Threshold float64 `json:"threshold"` // Similarity threshold (0-1)
}
@@ -32,24 +32,24 @@ type SemanticSearchResponse struct {
// SemanticSearchResult represents a semantic search result
type SemanticSearchResult struct {
ID uint `json:"id"`
Type string `json:"type"`
Title string `json:"title"`
Description string `json:"description"`
Content string `json:"content"`
Similarity float64 `json:"similarity"`
Highlights []string `json:"highlights"`
Tags []models.Tag `json:"tags,omitempty"`
CreatedAt time.Time `json:"created_at"`
UpdatedAt time.Time `json:"updated_at"`
URL string `json:"url,omitempty"`
Status string `json:"status,omitempty"`
Priority string `json:"priority,omitempty"`
ID uint `json:"id"`
Type string `json:"type"`
Title string `json:"title"`
Description string `json:"description"`
Content string `json:"content"`
Similarity float64 `json:"similarity"`
Highlights []string `json:"highlights"`
Tags []models.Tag `json:"tags,omitempty"`
CreatedAt time.Time `json:"created_at"`
UpdatedAt time.Time `json:"updated_at"`
URL string `json:"url,omitempty"`
Status string `json:"status,omitempty"`
Priority string `json:"priority,omitempty"`
}
// GenerateEmbeddingRequest represents request to generate embeddings
type GenerateEmbeddingRequest struct {
Text string `json:"text" binding:"required"`
Text string `json:"text" binding:"required"`
ContentType string `json:"content_type"`
ContentID uint `json:"content_id"`
}
@@ -87,7 +87,7 @@ func SemanticSearch(c *gin.Context) {
queryEmbedding, err := generateEmbedding(req.Query)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{
"error": "Failed to generate query embedding",
"error": "Failed to generate query embedding",
"details": err.Error(),
})
return
@@ -97,7 +97,7 @@ func SemanticSearch(c *gin.Context) {
results, err := findSimilarContent(db, userID, queryEmbedding, req.ContentType, req.Limit, req.Threshold)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{
"error": "Failed to search similar content",
"error": "Failed to search similar content",
"details": err.Error(),
})
return
@@ -127,7 +127,7 @@ func GenerateEmbedding(c *gin.Context) {
embedding, err := generateEmbedding(req.Text)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{
"error": "Failed to generate embedding",
"error": "Failed to generate embedding",
"details": err.Error(),
})
return
@@ -139,15 +139,15 @@ func GenerateEmbedding(c *gin.Context) {
userID := c.GetUint("user_id")
embeddingJSON, _ := json.Marshal(embedding)
contentEmbedding := models.ContentEmbedding{
ContentType: req.ContentType,
ContentID: req.ContentID,
Embedding: string(embeddingJSON),
Model: "text-embedding-ada-002",
Dimensions: len(embedding),
TextContent: req.Text,
UserID: userID,
ContentType: req.ContentType,
ContentID: req.ContentID,
Embedding: string(embeddingJSON),
Model: "text-embedding-ada-002",
Dimensions: len(embedding),
TextContent: req.Text,
UserID: userID,
}
if err := db.Create(&contentEmbedding).Error; err != nil {
@@ -179,7 +179,7 @@ func ReindexContent(c *gin.Context) {
c.JSON(http.StatusOK, gin.H{
"message": "Content reindexing started in background",
"status": "processing",
"status": "processing",
})
}
@@ -188,13 +188,13 @@ func generateEmbedding(text string) ([]float64, error) {
// TODO: Replace with actual OpenAI API call
// For now, return a mock embedding for demonstration
embedding := make([]float64, 1536) // OpenAI embedding dimensions
// Generate pseudo-random but deterministic embedding based on text
hash := simpleHash(text)
for i := range embedding {
embedding[i] = math.Sin(float64(hash+i)) * 0.5
}
return embedding, nil
}
@@ -214,11 +214,11 @@ func findSimilarContent(db *gorm.DB, userID uint, queryEmbedding []float64, cont
// Get all embeddings for the user
var embeddings []models.ContentEmbedding
query := db.Where("user_id = ?", userID)
if contentType != "all" && contentType != "" {
query = query.Where("content_type = ?", contentType)
query = query.Where("content_type = ?", normalizeSemanticContentType(contentType))
}
if err := query.Find(&embeddings).Error; err != nil {
return results, err
}
@@ -228,15 +228,15 @@ func findSimilarContent(db *gorm.DB, userID uint, queryEmbedding []float64, cont
embedding models.ContentEmbedding
score float64
}
var scores []similarityScore
for _, embedding := range embeddings {
var storedEmbedding []float64
if err := json.Unmarshal([]byte(embedding.Embedding), &storedEmbedding); err != nil {
continue
}
similarity := cosineSimilarity(queryEmbedding, storedEmbedding)
if similarity >= threshold {
scores = append(scores, similarityScore{
@@ -279,17 +279,17 @@ func cosineSimilarity(a, b []float64) float64 {
}
var dotProduct, normA, normB float64
for i := range a {
dotProduct += a[i] * b[i]
normA += a[i] * a[i]
normB += b[i] * b[i]
}
if normA == 0 || normB == 0 {
return 0
}
return dotProduct / (math.Sqrt(normA) * math.Sqrt(normB))
}
@@ -305,7 +305,7 @@ func buildSemanticSearchResult(db *gorm.DB, embedding models.ContentEmbedding, s
if err := db.Preload("Tags").First(&bookmark, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = bookmark.ID
result.Type = "bookmark"
result.Title = bookmark.Title
@@ -321,7 +321,7 @@ func buildSemanticSearchResult(db *gorm.DB, embedding models.ContentEmbedding, s
if err := db.Preload("Tags").First(&task, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = task.ID
result.Type = "task"
result.Title = task.Title
@@ -337,7 +337,7 @@ func buildSemanticSearchResult(db *gorm.DB, embedding models.ContentEmbedding, s
if err := db.Preload("Tags").First(&note, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = note.ID
result.Type = "note"
result.Title = note.Title
@@ -352,7 +352,7 @@ func buildSemanticSearchResult(db *gorm.DB, embedding models.ContentEmbedding, s
if err := db.Preload("Tags").First(&file, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = file.ID
result.Type = "file"
result.Title = file.OriginalName
@@ -361,6 +361,68 @@ func buildSemanticSearchResult(db *gorm.DB, embedding models.ContentEmbedding, s
result.Tags = file.Tags
result.CreatedAt = file.CreatedAt
result.UpdatedAt = file.UpdatedAt
case "calendar_event":
var event models.CalendarEvent
if err := db.First(&event, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = event.ID
result.Type = "calendar_event"
result.Title = event.Title
result.Description = event.Description
result.Content = event.Description
result.CreatedAt = event.CreatedAt
result.UpdatedAt = event.UpdatedAt
result.Priority = event.Priority
case "youtube_video":
var video models.VideoBookmark
if err := db.First(&video, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = video.ID
result.Type = "youtube_video"
result.Title = video.Title
result.Description = video.Description
result.Content = video.Description
result.CreatedAt = video.CreatedAt
result.UpdatedAt = video.UpdatedAt
result.URL = video.URL
case "learning_path":
var path models.LearningPath
if err := db.First(&path, embedding.ContentID).Error; err != nil {
return result, err
}
result.ID = path.ID
result.Type = "learning_path"
result.Title = path.Title
result.Description = path.Description
result.Content = path.Description
result.CreatedAt = path.CreatedAt
result.UpdatedAt = path.UpdatedAt
case "chat_message":
var message models.Message
if err := db.First(&message, embedding.ContentID).Error; err != nil {
return result, err
}
if message.IsSensitive {
return result, fmt.Errorf("sensitive message excluded from semantic search")
}
result.ID = message.ID
result.Type = "chat_message"
result.Title = "Chat message"
result.Description = compactSemanticText(message.Body, 140)
result.Content = message.Body
result.CreatedAt = message.CreatedAt
result.UpdatedAt = message.UpdatedAt
result.URL = fmt.Sprintf("/app/messages?conversationId=%d&messageId=%d", message.ConversationID, message.ID)
}
// Generate highlights (simplified)
@@ -402,35 +464,139 @@ func reindexUserContent(db *gorm.DB, userID uint) {
// Reindex bookmarks
var bookmarks []models.Bookmark
db.Where("user_id = ?", userID).Find(&bookmarks)
for _, bookmark := range bookmarks {
text := bookmark.Title + " " + bookmark.Description + " " + bookmark.Content
embedding, err := generateEmbedding(text)
if err != nil {
continue
}
embeddingJSON, _ := json.Marshal(embedding)
contentEmbedding := models.ContentEmbedding{
ContentType: "bookmark",
ContentID: bookmark.ID,
Embedding: string(embeddingJSON),
Model: "text-embedding-ada-002",
Dimensions: len(embedding),
TextContent: text,
UserID: userID,
}
// Delete existing embedding for this content
db.Where("content_type = ? AND content_id = ?", "bookmark", bookmark.ID).Delete(&models.ContentEmbedding{})
// Create new embedding
db.Create(&contentEmbedding)
upsertEmbedding(db, userID, "bookmark", bookmark.ID, text)
}
// Similar reindexing for tasks, notes, files...
// TODO: Implement reindexing for other content types
// Tasks
var tasks []models.Task
db.Where("user_id = ?", userID).Find(&tasks)
for _, task := range tasks {
text := task.Title + " " + task.Description
upsertEmbedding(db, userID, "task", task.ID, text)
}
// Notes
var notes []models.Note
db.Where("user_id = ?", userID).Find(&notes)
for _, note := range notes {
if note.IsEncrypted {
continue
}
text := note.Title + " " + note.Description + " " + note.Content
upsertEmbedding(db, userID, "note", note.ID, text)
}
// Files
var files []models.File
db.Where("user_id = ?", userID).Find(&files)
for _, file := range files {
text := file.OriginalName + " " + file.Description + " " + file.Content
upsertEmbedding(db, userID, "file", file.ID, text)
}
// Calendar events
var events []models.CalendarEvent
db.Where("user_id = ?", userID).Find(&events)
for _, event := range events {
text := event.Title + " " + event.Description + " " + event.Type + " " + event.Priority
upsertEmbedding(db, userID, "calendar_event", event.ID, text)
}
// YouTube bookmarks
var videos []models.VideoBookmark
db.Where("user_id = ?", userID).Find(&videos)
for _, video := range videos {
text := video.Title + " " + video.Description + " " + video.Channel + " " + video.URL
upsertEmbedding(db, userID, "youtube_video", video.ID, text)
}
// Learning paths
var learningPaths []models.LearningPath
db.Where("creator_id = ?", userID).Find(&learningPaths)
for _, path := range learningPaths {
text := path.Title + " " + path.Description + " " + path.Category + " " + path.Difficulty
upsertEmbedding(db, userID, "learning_path", path.ID, text)
}
// Chat messages (skip sensitive/vault content)
var messages []models.Message
db.Model(&models.Message{}).
Joins("JOIN conversation_members cm ON cm.conversation_id = messages.conversation_id").
Joins("JOIN conversations ON conversations.id = messages.conversation_id").
Where("cm.user_id = ?", userID).
Where("conversations.type <> ?", models.ConversationTypePasswordVault).
Where("messages.deleted_at IS NULL").
Find(&messages)
for _, message := range messages {
if message.IsSensitive {
continue
}
upsertEmbedding(db, userID, "chat_message", message.ID, message.Body)
}
fmt.Printf("Reindexing completed for user %d\n", userID)
}
func upsertEmbedding(db *gorm.DB, userID uint, contentType string, contentID uint, text string) {
text = strings.TrimSpace(text)
if text == "" {
return
}
embedding, err := generateEmbedding(text)
if err != nil {
return
}
embeddingJSON, _ := json.Marshal(embedding)
contentEmbedding := models.ContentEmbedding{
ContentType: contentType,
ContentID: contentID,
Embedding: string(embeddingJSON),
Model: "text-embedding-ada-002",
Dimensions: len(embedding),
TextContent: text,
UserID: userID,
}
db.Where("content_type = ? AND content_id = ? AND user_id = ?", contentType, contentID, userID).Delete(&models.ContentEmbedding{})
db.Create(&contentEmbedding)
}
func normalizeSemanticContentType(contentType string) string {
switch strings.ToLower(strings.TrimSpace(contentType)) {
case "bookmarks":
return "bookmark"
case "tasks":
return "task"
case "notes":
return "note"
case "files":
return "file"
case "calendar_events":
return "calendar_event"
case "youtube_videos":
return "youtube_video"
case "learning_paths":
return "learning_path"
case "chat_messages":
return "chat_message"
default:
return strings.ToLower(strings.TrimSpace(contentType))
}
}
func compactSemanticText(text string, limit int) string {
text = strings.TrimSpace(text)
if len(text) <= limit {
return text
}
if limit < 4 {
return text
}
return strings.TrimSpace(text[:limit-3]) + "..."
}