#![allow(clippy::unwrap_used)]
//! tidalDB quickstart: schema, items, signals, ranking.
//!
//! Demonstrates the full ingestion-to-ranking loop:
//! 1. Define a schema with `view` and `like` signals
//! 2. Open an ephemeral database
//! 3. Write 20 items with metadata and 128D random embeddings
//! 4. Record engagement: view 5 items, like 3 of those
//! 5. Retrieve ranked results using the `trending` profile
//! 6. Print ranked items with scores
//!
//! # Running
//!
//! ```bash
//! cargo run -p tidaldb --example quickstart
//! ```

use std::{collections::HashMap, time::Duration};

use rand::Rng;
use tidaldb::{
    TidalDb,
    schema::{DecaySpec, EntityId, EntityKind, SchemaBuilder, Timestamp, Window},
};

/// Generate a random unit-normalized embedding of the given dimensionality.
fn random_unit_vector(dim: usize, rng: &mut impl Rng) -> Vec<f32> {
    let v: Vec<f32> = (0..dim).map(|_| rng.random::<f32>() - 0.5).collect();
    let norm = v.iter().map(|x| x * x).sum::<f32>().sqrt();
    if norm < f32::EPSILON {
        // Degenerate case: return a unit vector along the first axis.
        let mut unit = vec![0.0_f32; dim];
        unit[0] = 1.0;
        return unit;
    }
    v.iter().map(|x| x / norm).collect()
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize tracing so spans emitted by tidalDB are visible.
    tracing_subscriber::fmt()
        .with_env_filter("tidaldb=info")
        .init();

    // ── 1. Define the schema ────────────────────────────────────────────

    let mut schema = SchemaBuilder::new();

    // View signal: 7-day half-life, 1h + 24h windows, velocity enabled.
    let _ = schema
        .signal(
            "view",
            EntityKind::Item,
            DecaySpec::Exponential {
                half_life: Duration::from_secs(7 * 24 * 3600),
            },
        )
        .windows(&[Window::OneHour, Window::TwentyFourHours, Window::AllTime])
        .velocity(true)
        .add();

    // Like signal: 30-day half-life, AllTime window.
    let _ = schema
        .signal(
            "like",
            EntityKind::Item,
            DecaySpec::Exponential {
                half_life: Duration::from_secs(30 * 24 * 3600),
            },
        )
        .windows(&[Window::AllTime])
        .velocity(false)
        .add();

    // Share signal: needed by the trending profile's boost definitions.
    let _ = schema
        .signal(
            "share",
            EntityKind::Item,
            DecaySpec::Exponential {
                half_life: Duration::from_secs(3 * 24 * 3600),
            },
        )
        .windows(&[Window::TwentyFourHours, Window::AllTime])
        .velocity(true)
        .add();

    let schema = schema.build()?;

    // ── 2. Open an ephemeral database ───────────────────────────────────

    let db = TidalDb::builder().ephemeral().with_schema(schema).open()?;

    db.health_check()?;

    println!("tidalDB opened (ephemeral, build: {})", tidaldb::BUILD_HASH);
    println!();

    // ── 3. Write 20 items with metadata and embeddings ──────────────────

    let mut rng = rand::rng();
    let categories = ["music", "tech", "cooking", "sports", "art"];
    let dim = 128;

    for i in 1..=20 {
        let mut metadata = HashMap::new();
        metadata.insert("title".to_string(), format!("Item {i}: Great Content"));
        metadata.insert(
            "category".to_string(),
            categories[i % categories.len()].to_string(),
        );
        metadata.insert("format".to_string(), "video".to_string());
        metadata.insert("duration".to_string(), format!("{}", 60 + i * 30));
        metadata.insert(
            "created_at".to_string(),
            Timestamp::now().as_nanos().to_string(),
        );

        db.write_item_with_metadata(EntityId::new(i as u64), &metadata)?;

        let embedding = random_unit_vector(dim, &mut rng);
        db.write_item_embedding(EntityId::new(i as u64), &embedding)?;
    }

    println!(
        "Wrote {} items with metadata and {dim}D embeddings.",
        db.item_count()
    );

    // ── 4. Record engagement signals ────────────────────────────────────
    //
    // Different items receive different engagement counts. The `hot` profile
    // ranks by cumulative view count with age decay — items with more views
    // score higher, producing clearly differentiated results.
    //
    // Note: the `trending` profile ranks by *velocity* (events/second over a
    // rolling window), which requires signals arriving over real elapsed time
    // to populate hour-level buckets. For a self-contained demo, `hot` is the
    // right choice.

    let now = Timestamp::now();

    // (item_id, signal_type, count) — different counts drive different scores.
    let signals: &[(u64, &str, u32)] = &[
        (7, "view", 8),
        (18, "view", 6),
        (3, "view", 5),
        (12, "view", 4),
        (1, "view", 3),
        (4, "view", 2),
        (8, "view", 1),
        (3, "like", 4),
        (7, "like", 3),
        (18, "like", 2),
        (7, "share", 2),
        (3, "share", 1),
    ];

    let mut view_count = 0u32;
    let mut like_count = 0u32;
    let mut share_count = 0u32;

    for &(item_id, signal_type, count) in signals {
        for _ in 0..count {
            db.signal(signal_type, EntityId::new(item_id), 1.0, now)?;
        }
        match signal_type {
            "view" => view_count += count,
            "like" => like_count += count,
            "share" => share_count += count,
            _ => {}
        }
    }

    println!("Recorded {view_count} views, {like_count} likes, {share_count} shares.");

    // Verify signal state is live.
    let score = db.read_decay_score(EntityId::new(3), "view", 0)?;
    println!("Item 3 view decay score: {:.4}", score.unwrap_or(0.0));
    println!();

    // ── 5. Retrieve ranked results ──────────────────────────────────────

    // The `hot` builtin profile ranks by cumulative view count with age decay
    // (Reddit/HN-style). Items with more views score higher; the age factor
    // penalises older content. All items are treated as 24 hours old here
    // since metadata-based age lookup is wired in M3+.
    let query = tidaldb::query::retrieve::Retrieve::builder()
        .profile("hot")
        .limit(10)
        .build()?;

    let results = db.retrieve(&query)?;

    println!(
        "RETRIEVE profile=hot: {} results from {} candidates",
        results.items.len(),
        results.total_candidates
    );
    println!("{:<6} {:<12} {:<8} Signals", "Rank", "Entity ID", "Score");
    println!("{}", "-".repeat(50));

    for item in &results.items {
        let signal_summary: String = item
            .signals
            .iter()
            .map(|s| format!("{}={:.3}", s.name, s.value))
            .collect::<Vec<_>>()
            .join(", ");

        println!(
            "{:<6} {:<12} {:<8.4} {}",
            item.rank,
            item.entity_id.as_u64(),
            item.score,
            if signal_summary.is_empty() {
                "-".to_string()
            } else {
                signal_summary
            }
        );
    }

    println!();

    // ── 6. Clean up ─────────────────────────────────────────────────────

    db.close()?;
    println!("tidalDB closed. Quickstart complete.");

    Ok(())
}