chore: initial commit

crates/server/src/services/embedding_service.rs

@@ -0,0 +1,110 @@
+use crate::db;
+use anyhow::Result;
+use rusqlite::Connection;
+use std::sync::Arc;
+use tokio::sync::{mpsc, Mutex};
+use tracing::{error, info, warn};
+
+/// Service responsible for generating embeddings for messages and words
+pub struct EmbeddingService {
+    us_db: Arc<Mutex<Connection>>,
+    rx: mpsc::Receiver<lib::Message>,
+    model_name: String,
+}
+
+impl EmbeddingService {
+    pub fn new(
+        us_db: Arc<Mutex<Connection>>,
+        rx: mpsc::Receiver<lib::Message>,
+        model_name: String,
+    ) -> Self {
+        Self {
+            us_db,
+            rx,
+            model_name,
+        }
+    }
+
+    pub async fn run(mut self) -> Result<()> {
+        info!("Starting embedding service with model: {}", self.model_name);
+
+        // TODO: Load the embedding model here
+        // For now, we'll create a placeholder implementation
+        info!("Loading embedding model...");
+        // let model = load_embedding_model(&self.model_name)?;
+        info!("Embedding model loaded (placeholder)");
+
+        while let Some(msg) = self.rx.recv().await {
+            if let Err(e) = self.process_message(&msg).await {
+                error!("Error processing message {}: {}", msg.rowid, e);
+            }
+        }
+
+        Ok(())
+    }
+
+    async fn process_message(&self, msg: &lib::Message) -> Result<()> {
+        // Get message ID from our database
+        let us_db = self.us_db.lock().await;
+        let message_id = match db::get_message_id_by_chat_rowid(&us_db, msg.rowid)? {
+            Some(id) => id,
+            None => {
+                warn!("Message {} not found in database, skipping", msg.rowid);
+                return Ok(());
+            }
+        };
+
+        // Check if embedding already exists
+        if db::get_message_embedding(&us_db, message_id)?.is_some() {
+            return Ok(());
+        }
+
+        // Skip if message has no text
+        let text = match &msg.text {
+            Some(t) if !t.is_empty() => t,
+            _ => return Ok(()),
+        };
+
+        drop(us_db);
+
+        // Generate embedding for the full message
+        // TODO: Replace with actual model inference
+        let message_embedding = self.generate_embedding(text)?;
+
+        // Store message embedding
+        let us_db = self.us_db.lock().await;
+        db::insert_message_embedding(&us_db, message_id, &message_embedding, &self.model_name)?;
+
+        // Tokenize and generate word embeddings
+        let words = self.tokenize(text);
+        for word in words {
+            // Check if word embedding exists
+            if db::get_word_embedding(&us_db, &word)?.is_none() {
+                // Generate embedding for word
+                let word_embedding = self.generate_embedding(&word)?;
+                db::insert_word_embedding(&us_db, &word, &word_embedding, &self.model_name)?;
+            }
+        }
+
+        drop(us_db);
+        info!("Generated embeddings for message {}", msg.rowid);
+
+        Ok(())
+    }
+
+    fn generate_embedding(&self, text: &str) -> Result<Vec<f32>> {
+        // TODO: Replace with actual model inference using Candle
+        // For now, return a placeholder embedding of dimension 1024
+        let embedding = vec![0.0f32; 1024];
+        Ok(embedding)
+    }
+
+    fn tokenize(&self, text: &str) -> Vec<String> {
+        // Simple word tokenization (split on whitespace and punctuation)
+        // TODO: Replace with proper tokenizer
+        text.split(|c: char| c.is_whitespace() || c.is_ascii_punctuation())
+            .filter(|s| !s.is_empty())
+            .map(|s| s.to_lowercase())
+            .collect()
+    }
+}