// AI-foreslåtte edges — sender innhold til LLM, foreslår mentions og topics.
//
// Jobbtype: "suggest_edges"
// Payload: { "node_id": "<uuid>" }
//
// Flyten:
// 1. Hent nodens innhold fra PG
// 2. Hent eksisterende topic-noder for gjenbruk
// 3. Send til LiteLLM (OpenAI-kompatibelt API via AI Gateway)
// 4. Parse LLM-svar (JSON med topics og mentions)
// 5. Opprett nye topic-noder for ukjente topics
// 6. Opprett mentions-edges fra innholdsnode til topic/entity-noder
//
// Ref: docs/infra/ai_gateway.md, docs/concepts/kunnskapsgrafen.md

use serde::{Deserialize, Serialize};
use sqlx::PgPool;
use uuid::Uuid;

use crate::jobs::JobRow;
use crate::stdb::StdbClient;

/// Eksisterende topic-node fra PG.
#[derive(sqlx::FromRow)]
struct TopicRow {
    id: Uuid,
    title: String,
}

/// Kilde-node fra PG.
#[derive(sqlx::FromRow)]
struct SourceNode {
    title: Option<String>,
    content: Option<String>,
    created_by: Option<Uuid>,
}

/// LLM-respons: foreslåtte topics og mentions.
#[derive(Deserialize, Debug)]
struct AiSuggestion {
    /// Emnene innholdet handler om (nye eller eksisterende).
    #[serde(default)]
    topics: Vec<String>,
    /// Entiteter nevnt i innholdet (personer, organisasjoner, steder).
    #[serde(default)]
    mentions: Vec<MentionSuggestion>,
}

#[derive(Deserialize, Debug)]
struct MentionSuggestion {
    /// Navn på entiteten.
    name: String,
    /// Type: person, organisasjon, sted, konsept.
    #[serde(default = "default_entity_type")]
    entity_type: String,
}

fn default_entity_type() -> String {
    "person".to_string()
}

/// OpenAI-kompatibel chat completion request.
#[derive(Serialize)]
struct ChatRequest {
    model: String,
    messages: Vec<ChatMessage>,
    temperature: f32,
    response_format: ResponseFormat,
}

#[derive(Serialize)]
struct ResponseFormat {
    r#type: String,
}

#[derive(Serialize)]
struct ChatMessage {
    role: String,
    content: String,
}

/// OpenAI-kompatibel chat completion response.
#[derive(Deserialize)]
struct ChatResponse {
    choices: Vec<Choice>,
}

#[derive(Deserialize)]
struct Choice {
    message: MessageContent,
}

#[derive(Deserialize)]
struct MessageContent {
    content: Option<String>,
}

const SYSTEM_PROMPT: &str = r#"Du er en innholdsanalysator for en norsk redaksjonsplattform. Analyser teksten og ekstraher:

1. **topics**: Emner/temaer teksten handler om. Bruk korte, presise norske termer (f.eks. "skolepolitikk", "klimaendringer", "statsbudsjettet"). Maks 5 topics.

2. **mentions**: Navngitte entiteter (personer, organisasjoner, steder) som er eksplisitt nevnt. Inkluder entity_type ("person", "organisasjon", "sted", "konsept").

Returner KUN et JSON-objekt med denne strukturen:
{
  "topics": ["emne1", "emne2"],
  "mentions": [{"name": "Navn", "entity_type": "person"}]
}

Regler:
- Returner tom liste hvis teksten ikke har meningsfullt innhold (hilsener, korte svar, etc.)
- Bruk eksisterende topics fra listen nedenfor der det passer, i stedet for å lage nye varianter
- Ikke inkluder generiske termer som "samtale" eller "diskusjon"
- Navngi entiteter med full, autoritativ form (f.eks. "Jonas Gahr Støre", ikke "Støre")"#;

/// Håndterer suggest_edges-jobb.
pub async fn handle_suggest_edges(
    job: &JobRow,
    db: &PgPool,
    stdb: &StdbClient,
) -> Result<serde_json::Value, String> {
    let node_id: Uuid = job
        .payload
        .get("node_id")
        .and_then(|v| v.as_str())
        .and_then(|s| s.parse().ok())
        .ok_or("Mangler gyldig node_id i payload")?;

    tracing::info!(node_id = %node_id, "Starter AI edge-forslag");

    // 1. Hent kildenode
    let source = sqlx::query_as::<_, SourceNode>(
        "SELECT title, content, created_by FROM nodes WHERE id = $1",
    )
    .bind(node_id)
    .fetch_optional(db)
    .await
    .map_err(|e| format!("PG-feil ved henting av node: {e}"))?
    .ok_or_else(|| format!("Node {node_id} finnes ikke"))?;

    let title = source.title.unwrap_or_default();
    let content = source.content.unwrap_or_default();

    // Ikke analyser tomme noder eller veldig korte meldinger
    let text = format!("{title}\n{content}").trim().to_string();
    if text.len() < 20 {
        tracing::info!(node_id = %node_id, len = text.len(), "For kort innhold, hopper over AI-analyse");
        return Ok(serde_json::json!({
            "status": "skipped",
            "reason": "content_too_short"
        }));
    }

    // 2. Hent eksisterende topic-noder for kontekst
    let existing_topics = sqlx::query_as::<_, TopicRow>(
        "SELECT id, title FROM nodes WHERE node_kind = 'topic' ORDER BY created_at DESC LIMIT 100",
    )
    .fetch_all(db)
    .await
    .map_err(|e| format!("PG-feil ved henting av topics: {e}"))?;

    let topic_list: Vec<&str> = existing_topics.iter().map(|t| t.title.as_str()).collect();

    // 3. Bygg prompt og kall LiteLLM
    let user_content = if topic_list.is_empty() {
        format!("Analyser følgende tekst:\n\n{text}")
    } else {
        format!(
            "Eksisterende topics: {}\n\nAnalyser følgende tekst:\n\n{text}",
            topic_list.join(", ")
        )
    };

    let suggestion = call_llm(&user_content).await?;

    tracing::info!(
        node_id = %node_id,
        topics = ?suggestion.topics,
        mentions = suggestion.mentions.len(),
        "LLM-forslag mottatt"
    );

    // 4. Opprett topic-noder og mentions-edges
    let created_by = source.created_by.unwrap_or(node_id);
    let mut created_topics = 0u32;
    let mut created_edges = 0u32;

    // Prosesser topics
    for topic_name in &suggestion.topics {
        let topic_name = topic_name.trim();
        if topic_name.is_empty() {
            continue;
        }

        // Finn eksisterende topic med case-insensitivt match
        let existing = existing_topics
            .iter()
            .find(|t| t.title.to_lowercase() == topic_name.to_lowercase());

        let topic_id = if let Some(t) = existing {
            t.id
        } else {
            // Opprett ny topic-node
            let new_id = Uuid::now_v7();
            create_topic_node(db, stdb, new_id, topic_name, created_by).await?;
            created_topics += 1;
            new_id
        };

        // Opprett mentions-edge: innholdsnode → topic
        if create_mentions_edge(db, stdb, node_id, topic_id, created_by).await? {
            created_edges += 1;
        }
    }

    // Prosesser mentions (entiteter)
    for mention in &suggestion.mentions {
        let name = mention.name.trim();
        if name.is_empty() {
            continue;
        }

        // Søk etter eksisterende entitet med samme tittel
        let existing_entity = sqlx::query_scalar::<_, Uuid>(
            "SELECT id FROM nodes WHERE node_kind = 'topic' AND LOWER(title) = LOWER($1) LIMIT 1",
        )
        .bind(name)
        .fetch_optional(db)
        .await
        .map_err(|e| format!("PG-feil ved entitet-søk: {e}"))?;

        let entity_id = if let Some(id) = existing_entity {
            id
        } else {
            // Opprett ny entitet som topic-node med entity_type i metadata
            let new_id = Uuid::now_v7();
            create_entity_node(db, stdb, new_id, name, &mention.entity_type, created_by).await?;
            created_topics += 1;
            new_id
        };

        // Opprett mentions-edge: innholdsnode → entitet
        if create_mentions_edge(db, stdb, node_id, entity_id, created_by).await? {
            created_edges += 1;
        }
    }

    let result = serde_json::json!({
        "status": "completed",
        "topics_created": created_topics,
        "edges_created": created_edges,
        "suggestions": {
            "topics": suggestion.topics,
            "mentions": suggestion.mentions.iter().map(|m| &m.name).collect::<Vec<_>>()
        }
    });

    tracing::info!(
        node_id = %node_id,
        topics_created = created_topics,
        edges_created = created_edges,
        "AI edge-forslag fullført"
    );

    Ok(result)
}

/// Kall LiteLLM (OpenAI-kompatibelt API) for å analysere innhold.
async fn call_llm(user_content: &str) -> Result<AiSuggestion, String> {
    let gateway_url = std::env::var("AI_GATEWAY_URL")
        .unwrap_or_else(|_| "http://localhost:4000".to_string());
    let api_key = std::env::var("LITELLM_MASTER_KEY")
        .unwrap_or_default();

    // Bruk sidelinja/rutine (billig modell) for edge-forslag
    let model = std::env::var("AI_EDGES_MODEL")
        .unwrap_or_else(|_| "sidelinja/rutine".to_string());

    let request = ChatRequest {
        model,
        messages: vec![
            ChatMessage {
                role: "system".to_string(),
                content: SYSTEM_PROMPT.to_string(),
            },
            ChatMessage {
                role: "user".to_string(),
                content: user_content.to_string(),
            },
        ],
        temperature: 0.2,
        response_format: ResponseFormat {
            r#type: "json_object".to_string(),
        },
    };

    let client = reqwest::Client::new();
    let url = format!("{gateway_url}/v1/chat/completions");

    let resp = client
        .post(&url)
        .header("Authorization", format!("Bearer {api_key}"))
        .header("Content-Type", "application/json")
        .json(&request)
        .timeout(std::time::Duration::from_secs(30))
        .send()
        .await
        .map_err(|e| format!("LiteLLM-kall feilet: {e}"))?;

    if !resp.status().is_success() {
        let status = resp.status();
        let body = resp.text().await.unwrap_or_default();
        return Err(format!("LiteLLM returnerte {status}: {body}"));
    }

    let chat_resp: ChatResponse = resp
        .json()
        .await
        .map_err(|e| format!("Kunne ikke parse LiteLLM-respons: {e}"))?;

    let content = chat_resp
        .choices
        .first()
        .and_then(|c| c.message.content.as_deref())
        .ok_or("LiteLLM returnerte ingen content")?;

    // Parse JSON fra LLM-output
    let suggestion: AiSuggestion = serde_json::from_str(content)
        .map_err(|e| format!("Kunne ikke parse LLM JSON: {e}. Rå output: {content}"))?;

    Ok(suggestion)
}

/// Opprett en topic-node i PG og STDB.
async fn create_topic_node(
    db: &PgPool,
    stdb: &StdbClient,
    id: Uuid,
    title: &str,
    created_by: Uuid,
) -> Result<(), String> {
    let metadata = serde_json::json!({"ai_generated": true});

    // STDB først
    stdb.create_node(
        &id.to_string(),
        "topic",
        title,
        "",
        "discoverable",
        &metadata.to_string(),
        &created_by.to_string(),
    )
    .await
    .map_err(|e| format!("STDB create_node (topic) feilet: {e}"))?;

    // PG
    sqlx::query(
        r#"
        INSERT INTO nodes (id, node_kind, title, content, visibility, metadata, created_by)
        VALUES ($1, 'topic', $2, '', 'discoverable', $3, $4)
        ON CONFLICT (id) DO NOTHING
        "#,
    )
    .bind(id)
    .bind(title)
    .bind(&metadata)
    .bind(created_by)
    .execute(db)
    .await
    .map_err(|e| format!("PG insert topic feilet: {e}"))?;

    tracing::info!(topic_id = %id, title = %title, "Ny topic-node opprettet (AI)");
    Ok(())
}

/// Opprett en entitet-node (person, org, sted) i PG og STDB.
async fn create_entity_node(
    db: &PgPool,
    stdb: &StdbClient,
    id: Uuid,
    name: &str,
    entity_type: &str,
    created_by: Uuid,
) -> Result<(), String> {
    let metadata = serde_json::json!({
        "ai_generated": true,
        "entity_type": entity_type
    });

    stdb.create_node(
        &id.to_string(),
        "topic",
        name,
        "",
        "discoverable",
        &metadata.to_string(),
        &created_by.to_string(),
    )
    .await
    .map_err(|e| format!("STDB create_node (entity) feilet: {e}"))?;

    sqlx::query(
        r#"
        INSERT INTO nodes (id, node_kind, title, content, visibility, metadata, created_by)
        VALUES ($1, 'topic', $2, '', 'discoverable', $3, $4)
        ON CONFLICT (id) DO NOTHING
        "#,
    )
    .bind(id)
    .bind(name)
    .bind(&metadata)
    .bind(created_by)
    .execute(db)
    .await
    .map_err(|e| format!("PG insert entity feilet: {e}"))?;

    tracing::info!(entity_id = %id, name = %name, entity_type = %entity_type, "Ny entitet-node opprettet (AI)");
    Ok(())
}

/// Opprett en mentions-edge fra innholdsnode til target.
/// Returnerer true hvis ny edge ble opprettet, false hvis den allerede eksisterte.
async fn create_mentions_edge(
    db: &PgPool,
    stdb: &StdbClient,
    source_id: Uuid,
    target_id: Uuid,
    created_by: Uuid,
) -> Result<bool, String> {
    // Sjekk om edge allerede finnes
    let exists = sqlx::query_scalar::<_, bool>(
        "SELECT EXISTS(SELECT 1 FROM edges WHERE source_id = $1 AND target_id = $2 AND edge_type = 'mentions')",
    )
    .bind(source_id)
    .bind(target_id)
    .fetch_one(db)
    .await
    .map_err(|e| format!("PG-feil ved edge-sjekk: {e}"))?;

    if exists {
        return Ok(false);
    }

    let edge_id = Uuid::now_v7();
    let metadata = serde_json::json!({"origin": "ai"});

    // STDB først
    stdb.create_edge(
        &edge_id.to_string(),
        &source_id.to_string(),
        &target_id.to_string(),
        "mentions",
        &metadata.to_string(),
        false,
        &created_by.to_string(),
    )
    .await
    .map_err(|e| format!("STDB create_edge (mentions) feilet: {e}"))?;

    // PG
    sqlx::query(
        r#"
        INSERT INTO edges (id, source_id, target_id, edge_type, metadata, system, created_by)
        VALUES ($1, $2, $3, 'mentions', $4, false, $5)
        ON CONFLICT (source_id, target_id, edge_type) DO NOTHING
        "#,
    )
    .bind(edge_id)
    .bind(source_id)
    .bind(target_id)
    .bind(&metadata)
    .bind(created_by)
    .execute(db)
    .await
    .map_err(|e| format!("PG insert mentions-edge feilet: {e}"))?;

    tracing::info!(
        edge_id = %edge_id,
        source = %source_id,
        target = %target_id,
        "Mentions-edge opprettet (AI)"
    );

    Ok(true)
}