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Cleanup unused message embedding code

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Fixup some warnings
This commit is contained in:
Cameron
2026-01-14 13:31:15 -05:00
parent e2d6cd7258
commit af35a996a3
17 changed files with 161 additions and 942 deletions
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16
src/ai/daily_summary_job.rs
View File

@@ -72,11 +72,12 @@ pub fn strip_summary_boilerplate(summary: &str) -> String {
// Remove any remaining leading markdown bold markers
if text.starts_with("**")
&& let Some(end) = text[2..].find("**") {
// Keep the content between ** but remove the markers
let bold_content = &text[2..2 + end];
text = format!("{}{}", bold_content, &text[4 + end..]);
}
&& let Some(end) = text[2..].find("**")
{
// Keep the content between ** but remove the markers
let bold_content = &text[2..2 + end];
text = format!("{}{}", bold_content, &text[4 + end..]);
}
text.trim().to_string()
}
@@ -141,10 +142,7 @@ pub async fn generate_daily_summaries(
if let Some(dt) = msg_dt {
let date = dt.date_naive();
if date >= start && date <= end {
messages_by_date
.entry(date)
.or_default()
.push(msg);
messages_by_date.entry(date).or_default().push(msg);
}
}
}

212
src/ai/embedding_job.rs
View File

@@ -1,212 +0,0 @@
use anyhow::Result;
use chrono::Utc;
use std::sync::{Arc, Mutex};
use tokio::time::{Duration, sleep};
use crate::ai::{OllamaClient, SmsApiClient};
use crate::database::{EmbeddingDao, InsertMessageEmbedding};
/// Background job to embed messages for a specific contact
/// This function is idempotent - it checks if embeddings already exist before processing
///
/// # Arguments
/// * `contact` - The contact name to embed messages for (e.g., "Amanda")
/// * `ollama` - Ollama client for generating embeddings
/// * `sms_client` - SMS API client for fetching messages
/// * `embedding_dao` - DAO for storing embeddings in the database
///
/// # Returns
/// Ok(()) on success, Err on failure
pub async fn embed_contact_messages(
contact: &str,
ollama: &OllamaClient,
sms_client: &SmsApiClient,
embedding_dao: Arc<Mutex<Box<dyn EmbeddingDao>>>,
) -> Result<()> {
log::info!("Starting message embedding job for contact: {}", contact);
let otel_context = opentelemetry::Context::new();
// Check existing embeddings count
let existing_count = {
let mut dao = embedding_dao.lock().expect("Unable to lock EmbeddingDao");
dao.get_message_count(&otel_context, contact).unwrap_or(0)
};
if existing_count > 0 {
log::info!(
"Contact '{}' already has {} embeddings, will check for new messages to embed",
contact,
existing_count
);
}
log::info!("Fetching all messages for contact: {}", contact);
// Fetch all messages for the contact
let messages = sms_client.fetch_all_messages_for_contact(contact).await?;
let total_messages = messages.len();
log::info!(
"Fetched {} messages for contact '{}'",
total_messages,
contact
);
if total_messages == 0 {
log::warn!(
"No messages found for contact '{}', nothing to embed",
contact
);
return Ok(());
}
// Filter out messages that already have embeddings and short/generic messages
log::info!("Filtering out messages that already have embeddings and short messages...");
let min_message_length = 30; // Skip short messages like "Thanks!" or "Yeah, it was :)"
let messages_to_embed: Vec<&crate::ai::SmsMessage> = {
let mut dao = embedding_dao.lock().expect("Unable to lock EmbeddingDao");
messages
.iter()
.filter(|msg| {
// Filter out short messages
if msg.body.len() < min_message_length {
return false;
}
// Filter out already embedded messages
!dao.message_exists(&otel_context, contact, &msg.body, msg.timestamp)
.unwrap_or(false)
})
.collect()
};
let skipped = total_messages - messages_to_embed.len();
let to_embed = messages_to_embed.len();
log::info!(
"Found {} messages to embed ({} already embedded)",
to_embed,
skipped
);
if to_embed == 0 {
log::info!("All messages already embedded for contact '{}'", contact);
return Ok(());
}
// Process messages in batches
let batch_size = 128; // Embed 128 messages per API call
let mut successful = 0;
let mut failed = 0;
for (batch_idx, batch) in messages_to_embed.chunks(batch_size).enumerate() {
let batch_start = batch_idx * batch_size;
let batch_end = batch_start + batch.len();
log::info!(
"Processing batch {}/{}: messages {}-{} ({:.1}% complete)",
batch_idx + 1,
to_embed.div_ceil(batch_size),
batch_start + 1,
batch_end,
(batch_end as f64 / to_embed as f64) * 100.0
);
match embed_message_batch(batch, contact, ollama, embedding_dao.clone()).await {
Ok(count) => {
successful += count;
log::debug!("Successfully embedded {} messages in batch", count);
}
Err(e) => {
failed += batch.len();
log::error!("Failed to embed batch: {:?}", e);
// Continue processing despite failures
}
}
// Small delay between batches to avoid overwhelming Ollama
if batch_end < to_embed {
sleep(Duration::from_millis(500)).await;
}
}
log::info!(
"Message embedding job complete for '{}': {}/{} new embeddings created ({} already embedded, {} failed)",
contact,
successful,
total_messages,
skipped,
failed
);
if failed > 0 {
log::warn!(
"{} messages failed to embed for contact '{}'",
failed,
contact
);
}
Ok(())
}
/// Embed a batch of messages using a single API call
/// Returns the number of successfully embedded messages
async fn embed_message_batch(
messages: &[&crate::ai::SmsMessage],
contact: &str,
ollama: &OllamaClient,
embedding_dao: Arc<Mutex<Box<dyn EmbeddingDao>>>,
) -> Result<usize> {
if messages.is_empty() {
return Ok(0);
}
// Collect message bodies for batch embedding
let bodies: Vec<&str> = messages.iter().map(|m| m.body.as_str()).collect();
// Generate embeddings for all messages in one API call
let embeddings = ollama.generate_embeddings(&bodies).await?;
if embeddings.len() != messages.len() {
return Err(anyhow::anyhow!(
"Embedding count mismatch: got {} embeddings for {} messages",
embeddings.len(),
messages.len()
));
}
// Build batch of insert records
let otel_context = opentelemetry::Context::new();
let created_at = Utc::now().timestamp();
let mut inserts = Vec::with_capacity(messages.len());
for (message, embedding) in messages.iter().zip(embeddings.iter()) {
// Validate embedding dimensions
if embedding.len() != 768 {
log::warn!(
"Invalid embedding dimensions: {} (expected 768), skipping",
embedding.len()
);
continue;
}
inserts.push(InsertMessageEmbedding {
contact: contact.to_string(),
body: message.body.clone(),
timestamp: message.timestamp,
is_sent: message.is_sent,
embedding: embedding.clone(),
created_at,
model_version: "nomic-embed-text:v1.5".to_string(),
});
}
// Store all embeddings in a single transaction
let mut dao = embedding_dao.lock().expect("Unable to lock EmbeddingDao");
let stored_count = dao
.store_message_embeddings_batch(&otel_context, inserts)
.map_err(|e| anyhow::anyhow!("Failed to store embeddings batch: {:?}", e))?;
Ok(stored_count)
}

32
src/ai/insight_generator.rs
View File

@@ -86,9 +86,10 @@ impl InsightGenerator {
// If path has at least 2 components (directory + file), extract first directory
if components.len() >= 2
&& let Some(component) = components.first()
&& let Some(os_str) = component.as_os_str().to_str() {
return Some(os_str.to_string());
}
&& let Some(os_str) = component.as_os_str().to_str()
{
return Some(os_str.to_string());
}
None
}
@@ -190,20 +191,19 @@ impl InsightGenerator {
.filter(|msg| {
// Extract date from formatted daily summary "[2024-08-15] Contact ..."
if let Some(bracket_end) = msg.find(']')
&& let Some(date_str) = msg.get(1..bracket_end) {
// Parse just the date (daily summaries don't have time)
if let Ok(msg_date) =
chrono::NaiveDate::parse_from_str(date_str, "%Y-%m-%d")
{
let msg_timestamp = msg_date
.and_hms_opt(12, 0, 0)
.unwrap()
.and_utc()
.timestamp();
let time_diff = (photo_timestamp - msg_timestamp).abs();
return time_diff > exclusion_window;
}
&& let Some(date_str) = msg.get(1..bracket_end)
{
// Parse just the date (daily summaries don't have time)
if let Ok(msg_date) = chrono::NaiveDate::parse_from_str(date_str, "%Y-%m-%d") {
let msg_timestamp = msg_date
.and_hms_opt(12, 0, 0)
.unwrap()
.and_utc()
.timestamp();
let time_diff = (photo_timestamp - msg_timestamp).abs();
return time_diff > exclusion_window;
}
}
false
})
.take(limit)

1
src/ai/mod.rs
View File

@@ -1,5 +1,4 @@
pub mod daily_summary_job;
pub mod embedding_job;
pub mod handlers;
pub mod insight_generator;
pub mod ollama;

99
src/ai/ollama.rs
View File

@@ -79,10 +79,11 @@ impl OllamaClient {
{
let cache = MODEL_LIST_CACHE.lock().unwrap();
if let Some(entry) = cache.get(url)
&& !entry.is_expired() {
log::debug!("Returning cached model list for {}", url);
return Ok(entry.data.clone());
}
&& !entry.is_expired()
{
log::debug!("Returning cached model list for {}", url);
return Ok(entry.data.clone());
}
}
log::debug!("Fetching fresh model list from {}", url);
@@ -188,10 +189,11 @@ impl OllamaClient {
{
let cache = MODEL_CAPABILITIES_CACHE.lock().unwrap();
if let Some(entry) = cache.get(url)
&& !entry.is_expired() {
log::debug!("Returning cached model capabilities for {}", url);
return Ok(entry.data.clone());
}
&& !entry.is_expired()
{
log::debug!("Returning cached model capabilities for {}", url);
return Ok(entry.data.clone());
}
}
log::debug!("Fetching fresh model capabilities from {}", url);
@@ -420,8 +422,8 @@ Return ONLY the title, nothing else."#,
)
}
} else if let Some(contact_name) = contact {
format!(
r#"Create a short title (maximum 8 words) about this moment:
format!(
r#"Create a short title (maximum 8 words) about this moment:
Date: {}
Location: {}
@@ -431,15 +433,15 @@ Return ONLY the title, nothing else."#,
Use specific details from the context above. The photo is from a folder for {}, so they are likely related to this moment. If no specific details are available, use a simple descriptive title.
Return ONLY the title, nothing else."#,
date.format("%B %d, %Y"),
location_str,
contact_name,
sms_str,
contact_name
)
} else {
format!(
r#"Create a short title (maximum 8 words) about this moment:
date.format("%B %d, %Y"),
location_str,
contact_name,
sms_str,
contact_name
)
} else {
format!(
r#"Create a short title (maximum 8 words) about this moment:
Date: {}
Location: {}
@@ -448,11 +450,11 @@ Return ONLY the title, nothing else."#,
Use specific details from the context above. If no specific details are available, use a simple descriptive title.
Return ONLY the title, nothing else."#,
date.format("%B %d, %Y"),
location_str,
sms_str
)
};
date.format("%B %d, %Y"),
location_str,
sms_str
)
};
let system = custom_system.unwrap_or("You are my long term memory assistant. Use only the information provided. Do not invent details.");
@@ -509,8 +511,8 @@ Analyze the image and use specific details from both the visual content and the
)
}
} else if let Some(contact_name) = contact {
format!(
r#"Write a 1-3 paragraph description of this moment based on the available information:
format!(
r#"Write a 1-3 paragraph description of this moment based on the available information:
Date: {}
Location: {}
@@ -518,27 +520,27 @@ Analyze the image and use specific details from both the visual content and the
Messages: {}
Use only the specific details provided above. The photo is from a folder for {}, so they are likely related to this moment. Mention people's names (especially {}), places, or activities if they appear in the context. Write in first person as Cameron with the tone of a journal entry. If limited information is available, keep it simple and factual. If the location is unknown omit it"#,
date.format("%B %d, %Y"),
location_str,
contact_name,
sms_str,
contact_name,
contact_name
)
} else {
format!(
r#"Write a 1-3 paragraph description of this moment based on the available information:
date.format("%B %d, %Y"),
location_str,
contact_name,
sms_str,
contact_name,
contact_name
)
} else {
format!(
r#"Write a 1-3 paragraph description of this moment based on the available information:
Date: {}
Location: {}
Messages: {}
Use only the specific details provided above. Mention people's names, places, or activities if they appear in the context. Write in first person as Cameron with the tone of a journal entry. If limited information is available, keep it simple and factual. If the location is unknown omit it"#,
date.format("%B %d, %Y"),
location_str,
sms_str
)
};
date.format("%B %d, %Y"),
location_str,
sms_str
)
};
let system = custom_system.unwrap_or("You are a memory refreshing assistant who is able to provide insights through analyzing past conversations. Use only the information provided. Do not invent details.");
@@ -642,15 +644,6 @@ Analyze the image and use specific details from both the visual content and the
Ok(embeddings)
}
/// Internal helper to try generating an embedding from a specific server
async fn try_generate_embedding(&self, url: &str, model: &str, text: &str) -> Result<Vec<f32>> {
let embeddings = self.try_generate_embeddings(url, model, &[text]).await?;
embeddings
.into_iter()
.next()
.ok_or_else(|| anyhow::anyhow!("No embedding returned from Ollama"))
}
/// Internal helper to try generating embeddings for multiple texts from a specific server
async fn try_generate_embeddings(
&self,
@@ -730,12 +723,6 @@ pub struct ModelCapabilities {
pub has_vision: bool,
}
#[derive(Serialize)]
struct OllamaEmbedRequest {
model: String,
input: String,
}
#[derive(Serialize)]
struct OllamaBatchEmbedRequest {
model: String,