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