feat(ai): USER_NAME env + shared summary prompt + test-bin knobs
Introduces USER_NAME (default "Me") as the single source for the message sender label and the first-person persona across daily summaries, SMS context, insight generation, and chat. Eliminates the "Me:" transcript / "what I did" ambiguity that confused smaller models, and unhardcodes "Cameron" from prompt text + the knowledge-graph owner entity. Set USER_NAME=Cameron in .env to preserve the existing owner entity row (keyed on UNIQUE(name, entity_type)) — otherwise the next run creates a fresh owner entity and orphans the existing facts/photo-links. Also: - search_messages redirect: when the model calls it with date/contact but no query, return a hint pointing at get_sms_messages instead of a bare missing-parameter error (prevents same-turn retry loops) - sharpen search_messages vs get_sms_messages tool descriptions so content-vs-time-based intent is unambiguous - extract build_daily_summary_prompt (+ DAILY_SUMMARY_MESSAGE_LIMIT, DAILY_SUMMARY_SYSTEM_PROMPT) shared by daily_summary_job and test_daily_summary binary — prompt tweaks now land in both - EMBEDDING_MODEL const; fixes both insert sites that stored "mxbai-embed-large:335m" while generate_embeddings actually runs "nomic-embed-text:v1.5" - test_daily_summary: add --num-ctx / --temperature / --top-p / --top-k / --min-p flags wired into OllamaClient setters, and print the configured knobs at the top of each run - OllamaClient::generate now logs prompt/gen token counts and tok/s via log_chat_metrics (symmetric with chat_with_tools) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Cameron
@@ -1,7 +1,10 @@
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use anyhow::Result;
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use chrono::NaiveDate;
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use clap::Parser;
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use image_api::ai::{OllamaClient, SmsApiClient, strip_summary_boilerplate};
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use image_api::ai::{
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EMBEDDING_MODEL, OllamaClient, SmsApiClient, build_daily_summary_prompt,
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strip_summary_boilerplate, user_display_name,
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};
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use image_api::database::{DailySummaryDao, InsertDailySummary, SqliteDailySummaryDao};
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use std::env;
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use std::sync::{Arc, Mutex};
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@@ -25,6 +28,26 @@ struct Args {
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#[arg(short, long)]
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model: Option<String>,
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/// Context window size passed as Ollama `num_ctx`. Omit for server default.
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#[arg(long)]
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num_ctx: Option<i32>,
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/// Sampling temperature. Omit for server default.
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#[arg(long)]
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temperature: Option<f32>,
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/// Top-p (nucleus) sampling. Omit for server default.
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#[arg(long)]
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top_p: Option<f32>,
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/// Top-k sampling. Omit for server default.
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#[arg(long)]
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top_k: Option<i32>,
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/// Min-p sampling. Omit for server default.
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#[arg(long)]
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min_p: Option<f32>,
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/// Test mode: Generate but don't save to database (shows output only)
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#[arg(short = 't', long, default_value_t = false)]
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test_mode: bool,
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@@ -86,12 +109,28 @@ async fn main() -> Result<()> {
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.unwrap_or_else(|_| "nemotron-3-nano:30b".to_string())
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});
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let ollama = OllamaClient::new(
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let mut ollama = OllamaClient::new(
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ollama_primary_url,
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ollama_fallback_url.clone(),
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model_to_use.clone(),
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Some(model_to_use), // Use same model for fallback
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);
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if let Some(ctx) = args.num_ctx {
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ollama.set_num_ctx(Some(ctx));
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}
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if args.temperature.is_some()
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|| args.top_p.is_some()
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|| args.top_k.is_some()
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|| args.min_p.is_some()
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{
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ollama.set_sampling_params(args.temperature, args.top_p, args.top_k, args.min_p);
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}
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// Surface what's actually configured so comparison runs are auditable.
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println!(
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"num_ctx={:?} temperature={:?} top_p={:?} top_k={:?} min_p={:?}",
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args.num_ctx, args.temperature, args.top_p, args.top_k, args.min_p
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);
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let sms_api_url =
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env::var("SMS_API_URL").unwrap_or_else(|_| "http://localhost:8000".to_string());
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@@ -160,9 +199,10 @@ async fn main() -> Result<()> {
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println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
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if args.verbose {
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let user_name = user_display_name();
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println!("\nMessage preview:");
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for (i, msg) in messages.iter().take(3).enumerate() {
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let sender = if msg.is_sent { "Me" } else { &msg.contact };
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let sender: &str = if msg.is_sent { &user_name } else { &msg.contact };
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let preview = msg.body.chars().take(60).collect::<String>();
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println!(" {}. {}: {}...", i + 1, sender, preview);
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}
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@@ -172,64 +212,11 @@ async fn main() -> Result<()> {
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println!();
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}
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// Format messages for LLM
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let messages_text: String = messages
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.iter()
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.take(200)
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.map(|m| {
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if m.is_sent {
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format!("Me: {}", m.body)
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} else {
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format!("{}: {}", m.contact, m.body)
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}
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})
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.collect::<Vec<_>>()
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.join("\n");
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let prompt = format!(
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r#"Summarize this day's conversation between me and {}.
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CRITICAL FORMAT RULES:
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- Do NOT start with "Based on the conversation..." or "Here is a summary..." or similar preambles
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- Do NOT repeat the date at the beginning
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- Start DIRECTLY with the content - begin with a person's name or action
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- Write in past tense, as if recording what happened
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NARRATIVE (3-5 sentences):
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- What specific topics, activities, or events were discussed?
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- What places, people, or organizations were mentioned?
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- What plans were made or decisions discussed?
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- Clearly distinguish between what "I" did versus what {} did
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KEYWORDS (comma-separated):
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5-10 specific keywords that capture this conversation's unique content:
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- Proper nouns (people, places, brands)
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- Specific activities ("drum corps audition" not just "music")
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- Distinctive terms that make this day unique
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Date: {} ({})
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Messages:
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{}
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YOUR RESPONSE (follow this format EXACTLY):
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Summary: [Start directly with content, NO preamble]
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Keywords: [specific, unique terms]"#,
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args.contact,
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args.contact,
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date.format("%B %d, %Y"),
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weekday,
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messages_text
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);
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let (prompt, system_prompt) = build_daily_summary_prompt(&args.contact, date, messages);
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println!("Generating summary...");
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let summary = ollama
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.generate(
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&prompt,
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Some("You are a conversation summarizer. Create clear, factual summaries with precise subject attribution AND extract distinctive keywords. Focus on specific, unique terms that differentiate this conversation from others."),
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)
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.await?;
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let summary = ollama.generate(&prompt, Some(system_prompt)).await?;
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println!("\n📝 GENERATED SUMMARY:");
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println!("─────────────────────────────────────────");
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@@ -256,8 +243,7 @@ Keywords: [specific, unique terms]"#,
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message_count: messages.len() as i32,
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embedding,
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created_at: chrono::Utc::now().timestamp(),
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// model_version: "nomic-embed-text:v1.5".to_string(),
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model_version: "mxbai-embed-large:335m".to_string(),
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model_version: EMBEDDING_MODEL.to_string(),
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};
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let mut dao = summary_dao.lock().expect("Unable to lock DailySummaryDao");
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