(Feat): Initial Commit

This commit is contained in:
2025-12-12 15:31:27 +00:00
commit 5b13236b80
48 changed files with 13146 additions and 0 deletions

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.gitignore vendored Normal file
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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
discord_bot/.venv
discord_bot/config/config.toml
discord_bot/logs/
backend_api/target/
backend_api/logs/
backend_api/data/
backend_api/src/config/config.toml
.vscode
.qodo
.target
things-todo.md

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backend_api/Cargo.lock generated Normal file

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[package]
name = "backend_api"
version = "0.1.0"
edition = "2021"
[dependencies]
poem = "3.1.12"
poem-openapi = { version = "5.1.16", features = ["swagger-ui"] }
tokio = { version = "1.48.0", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
reqwest = { version = "0.11", default-features = false, features = ["json", "rustls-tls", "multipart"] }
sha2 = "0.10"
hex = "0.4"
chrono = { version = "0.4", features = ["serde"] }
toml = "0.8"
anyhow = "1.0"
thiserror = "1.0"
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter"] }
image = "0.25"
base64 = "0.22"
tesseract = { version = "0.14", optional = true }
validator = { version = "0.18", features = ["derive"] }
strum = { version = "0.26", features = ["derive"] }
sqlx = { version = "0.8", features = ["runtime-tokio-rustls", "sqlite", "postgres", "chrono"] }
[features]
default = []
ocr = ["tesseract"]

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backend_api/README.md Normal file
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backend_api/src/cache.rs Normal file
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use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use chrono::{DateTime, Utc, Duration};
use crate::models::CacheEntry;
#[derive(Clone)]
pub struct CacheItem {
pub entry: CacheEntry,
pub expires_at: DateTime<Utc>,
}
pub struct Cache {
store: Arc<RwLock<HashMap<String, CacheItem>>>,
ttl_seconds: i64,
max_size: usize,
hits: Arc<RwLock<u64>>,
misses: Arc<RwLock<u64>>,
}
impl Cache {
pub fn new(ttl_seconds: i64, max_size: usize) -> Self {
Self {
store: Arc::new(RwLock::new(HashMap::new())),
ttl_seconds,
max_size,
hits: Arc::new(RwLock::new(0)),
misses: Arc::new(RwLock::new(0)),
}
}
pub async fn get(&self, checksum: &str) -> Option<CacheEntry> {
let mut store = self.store.write().await;
if let Some(item) = store.get_mut(checksum) {
// Check if expired
if Utc::now() > item.expires_at {
store.remove(checksum);
let mut misses = self.misses.write().await;
*misses += 1;
return None;
}
// Update access count and last accessed
item.entry.access_count += 1;
item.entry.last_accessed = Utc::now().to_rfc3339();
let mut hits = self.hits.write().await;
*hits += 1;
return Some(item.entry.clone());
}
let mut misses = self.misses.write().await;
*misses += 1;
None
}
pub async fn insert(&self, checksum: String, entry: CacheEntry) {
let mut store = self.store.write().await;
// If cache is full, remove oldest entry
if store.len() >= self.max_size {
if let Some(oldest_key) = store
.iter()
.min_by_key(|(_, item)| &item.entry.created_at)
.map(|(k, _)| k.clone())
{
store.remove(&oldest_key);
}
}
let expires_at = Utc::now() + Duration::seconds(self.ttl_seconds);
store.insert(checksum, CacheItem { entry, expires_at });
}
pub async fn get_all(&self, limit: Option<usize>, offset: Option<usize>) -> Vec<CacheEntry> {
let store = self.store.read().await;
let now = Utc::now();
let mut entries: Vec<CacheEntry> = store
.values()
.filter(|item| now <= item.expires_at)
.map(|item| item.entry.clone())
.collect();
// Sort by created_at descending
entries.sort_by(|a, b| b.created_at.cmp(&a.created_at));
let offset = offset.unwrap_or(0);
let limit = limit.unwrap_or(entries.len());
entries.into_iter().skip(offset).take(limit).collect()
}
pub async fn get_by_guild(&self, guild_id: &str) -> Vec<CacheEntry> {
let store = self.store.read().await;
let now = Utc::now();
let mut entries: Vec<CacheEntry> = store
.values()
.filter(|item| now <= item.expires_at && item.entry.guild_id == guild_id)
.map(|item| item.entry.clone())
.collect();
entries.sort_by(|a, b| b.created_at.cmp(&a.created_at));
entries
}
pub async fn get_by_user(&self, user_id: &str) -> Vec<CacheEntry> {
let store = self.store.read().await;
let now = Utc::now();
let mut entries: Vec<CacheEntry> = store
.values()
.filter(|item| now <= item.expires_at && item.entry.user_id == user_id)
.map(|item| item.entry.clone())
.collect();
entries.sort_by(|a, b| b.created_at.cmp(&a.created_at));
entries
}
pub async fn delete_by_id(&self, id: i64) -> u64 {
let mut store = self.store.write().await;
let initial_len = store.len();
store.retain(|_, item| item.entry.id != id);
(initial_len - store.len()) as u64
}
pub async fn delete_by_guild(&self, guild_id: &str) -> u64 {
let mut store = self.store.write().await;
let initial_len = store.len();
store.retain(|_, item| item.entry.guild_id != guild_id);
(initial_len - store.len()) as u64
}
pub async fn delete_by_user(&self, user_id: &str) -> u64 {
let mut store = self.store.write().await;
let initial_len = store.len();
store.retain(|_, item| item.entry.user_id != user_id);
(initial_len - store.len()) as u64
}
pub async fn delete_all(&self) -> u64 {
let mut store = self.store.write().await;
let count = store.len() as u64;
store.clear();
count
}
pub async fn get_stats(&self) -> (u64, u64, usize) {
let hits = *self.hits.read().await;
let misses = *self.misses.read().await;
let size = self.store.read().await.len();
(hits, misses, size)
}
pub async fn cleanup_expired(&self) {
let mut store = self.store.write().await;
let now = Utc::now();
store.retain(|_, item| now <= item.expires_at);
}
}

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use sha2::{Sha256, Digest};
use crate::models::SummarizeRequest;
pub fn calculate_checksum(request: &SummarizeRequest) -> String {
let mut hasher = Sha256::new();
// Include all relevant fields in the checksum
hasher.update(request.text.as_bytes());
hasher.update(format!("{:?}", request.provider).as_bytes());
if let Some(model) = &request.model {
hasher.update(model.as_bytes());
}
if let Some(temp) = request.temperature {
hasher.update(temp.to_string().as_bytes());
}
if let Some(max_tokens) = request.max_tokens {
hasher.update(max_tokens.to_string().as_bytes());
}
if let Some(top_p) = request.top_p {
hasher.update(top_p.to_string().as_bytes());
}
if let Some(system_prompt) = &request.system_prompt {
hasher.update(system_prompt.as_bytes());
}
let result = hasher.finalize();
hex::encode(result)
}

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use serde::{Deserialize, Serialize};
use std::fmt;
/// Claude model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClaudeModel {
/// Model identifier used in API calls
pub id: String,
/// Human-readable model name
pub name: String,
/// Input token cost per million tokens (USD)
pub input_price_per_million: f64,
/// Output token cost per million tokens (USD)
pub output_price_per_million: f64,
/// Maximum context window size
pub max_context_tokens: u32,
/// Whether this model is currently available
pub available: bool,
}
/// Enum of available Claude models
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ClaudeModelType {
/// Claude Sonnet 4.5 (2025-09-29) - Balanced performance and cost
Sonnet4_5,
/// Claude Haiku 4.5 (2025-10-01) - Fast and cost-effective
Haiku4_5,
/// Claude Opus 4.5 (2025-10-01) - Most capable, highest cost
Opus4_5,
/// Claude Sonnet 3.7 (2025-02-19) - Previous generation balanced model
Sonnet3_7,
/// Custom model (for flexibility)
Custom(String),
}
impl ClaudeModelType {
/// Get the model ID string for API calls
pub fn id(&self) -> String {
match self {
ClaudeModelType::Sonnet4_5 => "claude-sonnet-4-5-20250929".to_string(),
ClaudeModelType::Haiku4_5 => "claude-haiku-4-5-20251001".to_string(),
ClaudeModelType::Opus4_5 => "claude-opus-4-5-20251001".to_string(),
ClaudeModelType::Sonnet3_7 => "claude-3-7-sonnet-20250219".to_string(),
ClaudeModelType::Custom(id) => id.clone(),
}
}
/// Get full model information including pricing
pub fn info(&self) -> ClaudeModel {
match self {
ClaudeModelType::Sonnet4_5 => ClaudeModel {
id: self.id(),
name: "Claude Sonnet 4.5".to_string(),
input_price_per_million: 3.0,
output_price_per_million: 15.0,
max_context_tokens: 200_000,
available: true,
},
ClaudeModelType::Haiku4_5 => ClaudeModel {
id: self.id(),
name: "Claude Haiku 4.5".to_string(),
input_price_per_million: 0.8,
output_price_per_million: 4.0,
max_context_tokens: 200_000,
available: true,
},
ClaudeModelType::Opus4_5 => ClaudeModel {
id: self.id(),
name: "Claude Opus 4.5".to_string(),
input_price_per_million: 15.0,
output_price_per_million: 75.0,
max_context_tokens: 200_000,
available: true,
},
ClaudeModelType::Sonnet3_7 => ClaudeModel {
id: self.id(),
name: "Claude 3.7 Sonnet".to_string(),
input_price_per_million: 3.0,
output_price_per_million: 15.0,
max_context_tokens: 200_000,
available: true,
},
ClaudeModelType::Custom(id) => ClaudeModel {
id: id.clone(),
name: format!("Custom Model: {}", id),
input_price_per_million: 3.0, // Default to Sonnet pricing
output_price_per_million: 15.0,
max_context_tokens: 200_000,
available: true,
},
}
}
/// Parse a model ID string into a ClaudeModelType
pub fn from_id(id: &str) -> Self {
match id {
"claude-sonnet-4-5-20250929" => ClaudeModelType::Sonnet4_5,
"claude-haiku-4-5-20251001" => ClaudeModelType::Haiku4_5,
"claude-opus-4-5-20251001" => ClaudeModelType::Opus4_5,
"claude-3-7-sonnet-20250219" => ClaudeModelType::Sonnet3_7,
_ => ClaudeModelType::Custom(id.to_string()),
}
}
/// Get a list of all available Claude models
pub fn all_available() -> Vec<ClaudeModelType> {
vec![
ClaudeModelType::Sonnet4_5,
ClaudeModelType::Haiku4_5,
ClaudeModelType::Opus4_5,
ClaudeModelType::Sonnet3_7,
]
}
/// Calculate cost for token usage
pub fn calculate_cost(&self, input_tokens: u32, output_tokens: u32) -> f64 {
let info = self.info();
let input_cost = (input_tokens as f64 / 1_000_000.0) * info.input_price_per_million;
let output_cost = (output_tokens as f64 / 1_000_000.0) * info.output_price_per_million;
input_cost + output_cost
}
}
impl fmt::Display for ClaudeModelType {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "{}", self.id())
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_model_id() {
assert_eq!(ClaudeModelType::Sonnet4_5.id(), "claude-sonnet-4-5-20250929");
assert_eq!(ClaudeModelType::Haiku4_5.id(), "claude-haiku-4-5-20251001");
}
#[test]
fn test_from_id() {
assert_eq!(
ClaudeModelType::from_id("claude-sonnet-4-5-20250929"),
ClaudeModelType::Sonnet4_5
);
}
#[test]
fn test_cost_calculation() {
let cost = ClaudeModelType::Sonnet4_5.calculate_cost(1_000_000, 1_000_000);
assert_eq!(cost, 18.0); // 3.0 + 15.0
}
}

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use serde::{Deserialize, Serialize};
use std::fs;
use anyhow::Result;
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct Config {
pub server: ServerConfig,
pub database: DatabaseConfig,
pub anthropic: Option<AnthropicConfig>,
pub ollama: Option<OllamaConfig>,
pub lmstudio: Option<LmStudioConfig>,
pub rate_limiting: RateLimitingConfig,
pub cache: CacheConfig,
pub credits: CreditsConfig,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct CreditsConfig {
/// Whether the credit system is enabled
pub enabled: bool,
/// User IDs that bypass the credit system (bot owners)
#[serde(default)]
pub bypass_user_ids: Vec<String>,
/// Credits cost per summarization
#[serde(default = "default_credits_per_summary")]
pub credits_per_summary: i64,
/// Credits cost per OCR summarization
#[serde(default = "default_credits_per_ocr")]
pub credits_per_ocr: i64,
}
fn default_credits_per_summary() -> i64 { 1 }
fn default_credits_per_ocr() -> i64 { 2 }
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct ServerConfig {
pub host: String,
pub port: u16,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
#[serde(rename_all = "lowercase")]
pub enum DatabaseType {
Sqlite,
Postgres,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct DatabaseConfig {
#[serde(rename = "type")]
pub db_type: DatabaseType,
pub url: String,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct AnthropicConfig {
pub api_key: String,
pub base_url: String,
pub model: String,
pub max_tokens: u32,
pub temperature: f32,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct OllamaConfig {
pub base_url: String,
pub model: String,
pub temperature: f32,
pub max_tokens: u32,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct LmStudioConfig {
pub base_url: String,
pub model: String,
pub temperature: f32,
pub max_tokens: u32,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct RateLimitingConfig {
pub enabled: bool,
pub guild_rate_limit: u32,
pub user_rate_limit: u32,
pub window_seconds: Option<i64>,
}
#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct CacheConfig {
pub enabled: bool,
pub ttl_seconds: Option<i64>,
pub max_size: Option<usize>,
}
impl Config {
pub fn load(path: &str) -> Result<Self> {
let content = fs::read_to_string(path)?;
let config: Config = toml::from_str(&content)?;
Ok(config)
}
}

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[server]
host = "127.0.0.1"
port = 3001
[anthropic]
api_key = "your-anthropic-api-key-here"
base_url = "https://api.anthropic.com/v1"
model = "claude-3-5-sonnet-20241022"
max_tokens = 4096
temperature = 1.0
[ollama]
base_url = "http://localhost:11434"
model = "llama3.2"
temperature = 0.7
max_tokens = 4096
[rate_limiting]
enabled = true
# Requests per minute per guild
guild_rate_limit = 30
# Requests per minute per user
user_rate_limit = 10
# Rate limit window in seconds (default: 60)
window_seconds = 60
[cache]
enabled = true
# Cache TTL in seconds (default: 3600 = 1 hour)
ttl_seconds = 3600
# Maximum number of cache entries (default: 1000)
max_size = 1000
[credits]
# Enable credit-based monetization
enabled = true
# User IDs that bypass the credit system (bot owners)
bypass_user_ids = ["YOUR_DISCORD_USER_ID_HERE"]
# Credits cost per summarization (default: 1)
credits_per_summary = 1
# Credits cost per OCR summarization (default: 2)
credits_per_ocr = 2
[database]
type = "sqlite"
url = "sqlite:summarizer.db"

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//! Credit system for monetization
//!
//! Handles user credits, tiers, transactions, and bypass functionality.
use anyhow::Result;
use chrono::Utc;
use serde::{Deserialize, Serialize};
use sqlx::Row;
use tracing::{debug, info};
use crate::database::DatabasePool;
/// User tier levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
#[serde(rename_all = "lowercase")]
#[allow(dead_code)]
pub enum TierLevel {
Free = 1,
Basic = 2,
Pro = 3,
Unlimited = 4,
}
#[allow(dead_code)]
impl TierLevel {
pub fn from_id(id: i32) -> Self {
match id {
1 => TierLevel::Free,
2 => TierLevel::Basic,
3 => TierLevel::Pro,
4 => TierLevel::Unlimited,
_ => TierLevel::Free,
}
}
pub fn name(&self) -> &'static str {
match self {
TierLevel::Free => "Free",
TierLevel::Basic => "Basic",
TierLevel::Pro => "Pro",
TierLevel::Unlimited => "Unlimited",
}
}
pub fn monthly_credits(&self) -> i64 {
match self {
TierLevel::Free => 50,
TierLevel::Basic => 500,
TierLevel::Pro => 2000,
TierLevel::Unlimited => -1, // Unlimited
}
}
pub fn max_messages(&self) -> i32 {
match self {
TierLevel::Free => 50,
TierLevel::Basic => 100,
TierLevel::Pro => 250,
TierLevel::Unlimited => 500,
}
}
pub fn price_usd(&self) -> f64 {
match self {
TierLevel::Free => 0.0,
TierLevel::Basic => 4.99,
TierLevel::Pro => 9.99,
TierLevel::Unlimited => 19.99,
}
}
}
/// User credit information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserCredits {
pub user_id: String,
pub credits: i64,
pub tier_id: i32,
pub lifetime_credits_used: i64,
pub created_at: String,
pub updated_at: String,
}
impl UserCredits {
pub fn tier(&self) -> TierLevel {
TierLevel::from_id(self.tier_id)
}
pub fn has_unlimited(&self) -> bool {
self.tier() == TierLevel::Unlimited
}
}
/// Credit transaction record
#[derive(Debug, Clone, Serialize, Deserialize)]
#[allow(dead_code)]
pub struct CreditTransaction {
pub id: i64,
pub user_id: String,
pub guild_id: Option<String>,
pub amount: i64,
pub transaction_type: String,
pub description: String,
pub balance_after: i64,
pub created_at: String,
}
/// Result of a credit check
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CreditCheckResult {
pub allowed: bool,
pub reason: Option<String>,
pub credits_remaining: i64,
pub is_bypass: bool,
pub tier: String,
}
/// Credit system manager
pub struct CreditSystem {
bypass_user_ids: Vec<String>,
}
#[allow(dead_code)]
impl CreditSystem {
pub fn new(bypass_user_ids: Vec<String>) -> Self {
info!("Credit system initialized with {} bypass users", bypass_user_ids.len());
Self { bypass_user_ids }
}
/// Check if a user can bypass the credit system
pub fn is_bypass_user(&self, user_id: &str) -> bool {
self.bypass_user_ids.contains(&user_id.to_string())
}
/// Check if user has sufficient credits
pub async fn check_credits(
&self,
pool: &DatabasePool,
user_id: &str,
credits_needed: i64,
) -> Result<CreditCheckResult> {
// Check bypass first
if self.is_bypass_user(user_id) {
debug!("User {} is a bypass user, allowing request", user_id);
return Ok(CreditCheckResult {
allowed: true,
reason: None,
credits_remaining: -1,
is_bypass: true,
tier: "Bypass".to_string(),
});
}
// Get or create user credits
let user_credits = self.get_or_create_user(pool, user_id).await?;
// Unlimited tier users always pass
if user_credits.has_unlimited() {
return Ok(CreditCheckResult {
allowed: true,
reason: None,
credits_remaining: -1,
is_bypass: false,
tier: user_credits.tier().name().to_string(),
});
}
// Check if user has enough credits
if user_credits.credits >= credits_needed {
Ok(CreditCheckResult {
allowed: true,
reason: None,
credits_remaining: user_credits.credits,
is_bypass: false,
tier: user_credits.tier().name().to_string(),
})
} else {
Ok(CreditCheckResult {
allowed: false,
reason: Some(format!(
"Insufficient credits. You have {} but need {}",
user_credits.credits, credits_needed
)),
credits_remaining: user_credits.credits,
is_bypass: false,
tier: user_credits.tier().name().to_string(),
})
}
}
/// Deduct credits from a user
pub async fn deduct_credits(
&self,
pool: &DatabasePool,
user_id: &str,
amount: i64,
guild_id: Option<&str>,
description: &str,
) -> Result<i64> {
// Bypass users don't get charged
if self.is_bypass_user(user_id) {
debug!("Bypass user {}, not deducting credits", user_id);
return Ok(-1);
}
let user_credits = self.get_or_create_user(pool, user_id).await?;
// Unlimited tier users don't get charged
if user_credits.has_unlimited() {
debug!("Unlimited tier user {}, not deducting credits", user_id);
return Ok(-1);
}
let new_balance = (user_credits.credits - amount).max(0);
let now = Utc::now().to_rfc3339();
match pool {
DatabasePool::Sqlite(p) => {
sqlx::query(
"UPDATE user_credits SET credits = ?, lifetime_credits_used = lifetime_credits_used + ?, updated_at = ? WHERE user_id = ?"
)
.bind(new_balance)
.bind(amount)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
// Record transaction
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES (?, ?, ?, ?, ?, ?, ?)"
)
.bind(user_id)
.bind(guild_id)
.bind(-amount)
.bind("usage")
.bind(description)
.bind(new_balance)
.bind(&now)
.execute(p)
.await?;
}
DatabasePool::Postgres(p) => {
sqlx::query(
"UPDATE user_credits SET credits = $1, lifetime_credits_used = lifetime_credits_used + $2, updated_at = $3 WHERE user_id = $4"
)
.bind(new_balance)
.bind(amount)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES ($1, $2, $3, $4, $5, $6, $7)"
)
.bind(user_id)
.bind(guild_id)
.bind(-amount)
.bind("usage")
.bind(description)
.bind(new_balance)
.bind(&now)
.execute(p)
.await?;
}
}
debug!("Deducted {} credits from user {}, new balance: {}", amount, user_id, new_balance);
Ok(new_balance)
}
/// Add credits to a user (for refunds, purchases, etc.)
pub async fn add_credits(
&self,
pool: &DatabasePool,
user_id: &str,
amount: i64,
guild_id: Option<&str>,
transaction_type: &str,
description: &str,
) -> Result<i64> {
let user_credits = self.get_or_create_user(pool, user_id).await?;
let new_balance = user_credits.credits + amount;
let now = Utc::now().to_rfc3339();
match pool {
DatabasePool::Sqlite(p) => {
sqlx::query(
"UPDATE user_credits SET credits = ?, updated_at = ? WHERE user_id = ?"
)
.bind(new_balance)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES (?, ?, ?, ?, ?, ?, ?)"
)
.bind(user_id)
.bind(guild_id)
.bind(amount)
.bind(transaction_type)
.bind(description)
.bind(new_balance)
.bind(&now)
.execute(p)
.await?;
}
DatabasePool::Postgres(p) => {
sqlx::query(
"UPDATE user_credits SET credits = $1, updated_at = $2 WHERE user_id = $3"
)
.bind(new_balance)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES ($1, $2, $3, $4, $5, $6, $7)"
)
.bind(user_id)
.bind(guild_id)
.bind(amount)
.bind(transaction_type)
.bind(description)
.bind(new_balance)
.bind(&now)
.execute(p)
.await?;
}
}
info!("Added {} credits to user {}, new balance: {}", amount, user_id, new_balance);
Ok(new_balance)
}
/// Get or create user credits
pub async fn get_or_create_user(&self, pool: &DatabasePool, user_id: &str) -> Result<UserCredits> {
let now = Utc::now().to_rfc3339();
let default_credits = TierLevel::Free.monthly_credits();
match pool {
DatabasePool::Sqlite(p) => {
// Try to get existing user
let row = sqlx::query(
"SELECT user_id, credits, tier_id, lifetime_credits_used, created_at, updated_at FROM user_credits WHERE user_id = ?"
)
.bind(user_id)
.fetch_optional(p)
.await?;
if let Some(r) = row {
return Ok(UserCredits {
user_id: r.get(0),
credits: r.get(1),
tier_id: r.get(2),
lifetime_credits_used: r.get(3),
created_at: r.get(4),
updated_at: r.get(5),
});
}
// Create new user
sqlx::query(
"INSERT INTO user_credits (user_id, credits, tier_id, lifetime_credits_used, created_at, updated_at) VALUES (?, ?, 1, 0, ?, ?)"
)
.bind(user_id)
.bind(default_credits)
.bind(&now)
.bind(&now)
.execute(p)
.await?;
// Record initial grant
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES (?, NULL, ?, ?, ?, ?, ?)"
)
.bind(user_id)
.bind(default_credits)
.bind("grant")
.bind("Initial free tier credits")
.bind(default_credits)
.bind(&now)
.execute(p)
.await?;
Ok(UserCredits {
user_id: user_id.to_string(),
credits: default_credits,
tier_id: 1,
lifetime_credits_used: 0,
created_at: now.clone(),
updated_at: now,
})
}
DatabasePool::Postgres(p) => {
let row = sqlx::query(
"SELECT user_id, credits, tier_id, lifetime_credits_used, created_at, updated_at FROM user_credits WHERE user_id = $1"
)
.bind(user_id)
.fetch_optional(p)
.await?;
if let Some(r) = row {
return Ok(UserCredits {
user_id: r.get(0),
credits: r.get(1),
tier_id: r.get(2),
lifetime_credits_used: r.get(3),
created_at: r.get(4),
updated_at: r.get(5),
});
}
sqlx::query(
"INSERT INTO user_credits (user_id, credits, tier_id, lifetime_credits_used, created_at, updated_at) VALUES ($1, $2, 1, 0, $3, $4)"
)
.bind(user_id)
.bind(default_credits)
.bind(&now)
.bind(&now)
.execute(p)
.await?;
sqlx::query(
"INSERT INTO credit_transactions (user_id, guild_id, amount, transaction_type, description, balance_after, created_at) VALUES ($1, NULL, $2, $3, $4, $5, $6)"
)
.bind(user_id)
.bind(default_credits)
.bind("grant")
.bind("Initial free tier credits")
.bind(default_credits)
.bind(&now)
.execute(p)
.await?;
Ok(UserCredits {
user_id: user_id.to_string(),
credits: default_credits,
tier_id: 1,
lifetime_credits_used: 0,
created_at: now.clone(),
updated_at: now,
})
}
}
}
/// Get user's transaction history
pub async fn get_transactions(
&self,
pool: &DatabasePool,
user_id: &str,
limit: i64,
) -> Result<Vec<CreditTransaction>> {
match pool {
DatabasePool::Sqlite(p) => {
let rows = sqlx::query(
"SELECT id, user_id, guild_id, amount, transaction_type, description, balance_after, created_at FROM credit_transactions WHERE user_id = ? ORDER BY created_at DESC LIMIT ?"
)
.bind(user_id)
.bind(limit)
.fetch_all(p)
.await?;
Ok(rows.into_iter().map(|r| CreditTransaction {
id: r.get(0),
user_id: r.get(1),
guild_id: r.get(2),
amount: r.get(3),
transaction_type: r.get(4),
description: r.get(5),
balance_after: r.get(6),
created_at: r.get(7),
}).collect())
}
DatabasePool::Postgres(p) => {
let rows = sqlx::query(
"SELECT id, user_id, guild_id, amount, transaction_type, description, balance_after, created_at FROM credit_transactions WHERE user_id = $1 ORDER BY created_at DESC LIMIT $2"
)
.bind(user_id)
.bind(limit)
.fetch_all(p)
.await?;
Ok(rows.into_iter().map(|r| CreditTransaction {
id: r.get(0),
user_id: r.get(1),
guild_id: r.get(2),
amount: r.get(3),
transaction_type: r.get(4),
description: r.get(5),
balance_after: r.get(6),
created_at: r.get(7),
}).collect())
}
}
}
/// Set user tier
pub async fn set_user_tier(
&self,
pool: &DatabasePool,
user_id: &str,
tier: TierLevel,
) -> Result<()> {
let now = Utc::now().to_rfc3339();
match pool {
DatabasePool::Sqlite(p) => {
sqlx::query(
"UPDATE user_credits SET tier_id = ?, updated_at = ? WHERE user_id = ?"
)
.bind(tier as i32)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
}
DatabasePool::Postgres(p) => {
sqlx::query(
"UPDATE user_credits SET tier_id = $1, updated_at = $2 WHERE user_id = $3"
)
.bind(tier as i32)
.bind(&now)
.bind(user_id)
.execute(p)
.await?;
}
}
info!("Set user {} tier to {:?}", user_id, tier);
Ok(())
}
}
/// Create credit system tables
pub async fn create_credit_tables(pool: &DatabasePool) -> Result<()> {
match pool {
DatabasePool::Sqlite(p) => {
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS user_credits (
user_id TEXT PRIMARY KEY,
credits INTEGER NOT NULL DEFAULT 50,
tier_id INTEGER NOT NULL DEFAULT 1,
lifetime_credits_used INTEGER NOT NULL DEFAULT 0,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
)
"#
)
.execute(p)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS credit_transactions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id TEXT NOT NULL,
guild_id TEXT,
amount INTEGER NOT NULL,
transaction_type TEXT NOT NULL,
description TEXT NOT NULL,
balance_after INTEGER NOT NULL,
created_at TEXT NOT NULL
)
"#
)
.execute(p)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_credit_transactions_user ON credit_transactions(user_id)")
.execute(p)
.await?;
}
DatabasePool::Postgres(p) => {
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS user_credits (
user_id TEXT PRIMARY KEY,
credits BIGINT NOT NULL DEFAULT 50,
tier_id INTEGER NOT NULL DEFAULT 1,
lifetime_credits_used BIGINT NOT NULL DEFAULT 0,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
)
"#
)
.execute(p)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS credit_transactions (
id BIGSERIAL PRIMARY KEY,
user_id TEXT NOT NULL,
guild_id TEXT,
amount BIGINT NOT NULL,
transaction_type TEXT NOT NULL,
description TEXT NOT NULL,
balance_after BIGINT NOT NULL,
created_at TEXT NOT NULL
)
"#
)
.execute(p)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_credit_transactions_user ON credit_transactions(user_id)")
.execute(p)
.await?;
}
}
info!("Credit system tables created");
Ok(())
}

781
backend_api/src/database.rs Normal file
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@@ -0,0 +1,781 @@
use anyhow::Result;
use chrono::Utc;
use sqlx::{sqlite::SqlitePool, postgres::PgPool, Row};
use crate::models::{CacheEntry, CacheStats};
use crate::config::DatabaseType;
pub enum DatabasePool {
Sqlite(SqlitePool),
Postgres(PgPool),
}
pub struct Database {
pool: DatabasePool,
}
impl Database {
/// Get a reference to the underlying database pool
pub fn pool(&self) -> &DatabasePool {
&self.pool
}
pub async fn new(database_url: &str, db_type: DatabaseType) -> Result<Self> {
let pool = match db_type {
DatabaseType::Sqlite => {
let pool = SqlitePool::connect(database_url).await?;
DatabasePool::Sqlite(pool)
}
DatabaseType::Postgres => {
let pool = PgPool::connect(database_url).await?;
DatabasePool::Postgres(pool)
}
};
let db = Self { pool };
db.create_tables().await?;
db.initialize_stats().await?;
db.create_indexes().await?;
Ok(db)
}
async fn create_tables(&self) -> Result<()> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS cache (
id INTEGER PRIMARY KEY AUTOINCREMENT,
checksum TEXT NOT NULL UNIQUE,
text TEXT NOT NULL,
summary TEXT NOT NULL,
provider TEXT NOT NULL,
model TEXT NOT NULL,
guild_id TEXT NOT NULL,
user_id TEXT NOT NULL,
channel_id TEXT,
temperature REAL,
max_tokens INTEGER,
top_p REAL,
system_prompt TEXT,
created_at TEXT NOT NULL,
last_accessed TEXT NOT NULL,
access_count INTEGER DEFAULT 1
)
"#,
)
.execute(pool)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS rate_limits (
id INTEGER PRIMARY KEY AUTOINCREMENT,
guild_id TEXT NOT NULL,
user_id TEXT NOT NULL,
window_start TEXT NOT NULL,
request_count INTEGER DEFAULT 1,
UNIQUE(guild_id, user_id, window_start)
)
"#,
)
.execute(pool)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS stats (
id INTEGER PRIMARY KEY CHECK (id = 1),
total_hits INTEGER DEFAULT 0,
total_misses INTEGER DEFAULT 0
)
"#,
)
.execute(pool)
.await?;
}
DatabasePool::Postgres(pool) => {
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS cache (
id BIGSERIAL PRIMARY KEY,
checksum TEXT NOT NULL UNIQUE,
text TEXT NOT NULL,
summary TEXT NOT NULL,
provider TEXT NOT NULL,
model TEXT NOT NULL,
guild_id TEXT NOT NULL,
user_id TEXT NOT NULL,
channel_id TEXT,
temperature REAL,
max_tokens INTEGER,
top_p REAL,
system_prompt TEXT,
created_at TEXT NOT NULL,
last_accessed TEXT NOT NULL,
access_count BIGINT DEFAULT 1
)
"#,
)
.execute(pool)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS rate_limits (
id BIGSERIAL PRIMARY KEY,
guild_id TEXT NOT NULL,
user_id TEXT NOT NULL,
window_start TEXT NOT NULL,
request_count BIGINT DEFAULT 1,
UNIQUE(guild_id, user_id, window_start)
)
"#,
)
.execute(pool)
.await?;
sqlx::query(
r#"
CREATE TABLE IF NOT EXISTS stats (
id INTEGER PRIMARY KEY CHECK (id = 1),
total_hits BIGINT DEFAULT 0,
total_misses BIGINT DEFAULT 0
)
"#,
)
.execute(pool)
.await?;
}
}
Ok(())
}
async fn initialize_stats(&self) -> Result<()> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
sqlx::query("INSERT OR IGNORE INTO stats (id, total_hits, total_misses) VALUES (1, 0, 0)")
.execute(pool)
.await?;
}
DatabasePool::Postgres(pool) => {
sqlx::query("INSERT INTO stats (id, total_hits, total_misses) VALUES (1, 0, 0) ON CONFLICT (id) DO NOTHING")
.execute(pool)
.await?;
}
}
Ok(())
}
async fn create_indexes(&self) -> Result<()> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_checksum ON cache(checksum)")
.execute(pool)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_guild ON cache(guild_id)")
.execute(pool)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_user ON cache(user_id)")
.execute(pool)
.await?;
}
DatabasePool::Postgres(pool) => {
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_checksum ON cache(checksum)")
.execute(pool)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_guild ON cache(guild_id)")
.execute(pool)
.await?;
sqlx::query("CREATE INDEX IF NOT EXISTS idx_cache_user ON cache(user_id)")
.execute(pool)
.await?;
}
}
Ok(())
}
pub async fn get_cache_by_checksum(&self, checksum: &str) -> Result<Option<CacheEntry>> {
let now = Utc::now().to_rfc3339();
match &self.pool {
DatabasePool::Sqlite(pool) => {
// Update access count and last_accessed
sqlx::query(
"UPDATE cache SET access_count = access_count + 1, last_accessed = ? WHERE checksum = ?"
)
.bind(&now)
.bind(checksum)
.execute(pool)
.await?;
// Increment cache hit
sqlx::query("UPDATE stats SET total_hits = total_hits + 1 WHERE id = 1")
.execute(pool)
.await?;
let row = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE checksum = ?"
)
.bind(checksum)
.fetch_optional(pool)
.await?;
Ok(row.map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}))
}
DatabasePool::Postgres(pool) => {
// Update access count and last_accessed
sqlx::query(
"UPDATE cache SET access_count = access_count + 1, last_accessed = $1 WHERE checksum = $2"
)
.bind(&now)
.bind(checksum)
.execute(pool)
.await?;
// Increment cache hit
sqlx::query("UPDATE stats SET total_hits = total_hits + 1 WHERE id = 1")
.execute(pool)
.await?;
let row = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE checksum = $1"
)
.bind(checksum)
.fetch_optional(pool)
.await?;
Ok(row.map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}))
}
}
}
pub async fn insert_cache(
&self,
checksum: &str,
text: &str,
summary: &str,
provider: &str,
model: &str,
guild_id: &str,
user_id: &str,
channel_id: Option<&str>,
temperature: Option<f32>,
max_tokens: Option<u32>,
top_p: Option<f32>,
system_prompt: Option<&str>,
) -> Result<i64> {
let now = Utc::now().to_rfc3339();
match &self.pool {
DatabasePool::Sqlite(pool) => {
// Increment cache miss
sqlx::query("UPDATE stats SET total_misses = total_misses + 1 WHERE id = 1")
.execute(pool)
.await?;
let result = sqlx::query(
r#"
INSERT INTO cache (checksum, text, summary, provider, model, guild_id, user_id, channel_id, temperature, max_tokens, top_p, system_prompt, created_at, last_accessed)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
"#
)
.bind(checksum)
.bind(text)
.bind(summary)
.bind(provider)
.bind(model)
.bind(guild_id)
.bind(user_id)
.bind(channel_id)
.bind(temperature)
.bind(max_tokens.map(|v| v as i64))
.bind(top_p)
.bind(system_prompt)
.bind(&now)
.bind(&now)
.execute(pool)
.await?;
Ok(result.last_insert_rowid())
}
DatabasePool::Postgres(pool) => {
// Increment cache miss
sqlx::query("UPDATE stats SET total_misses = total_misses + 1 WHERE id = 1")
.execute(pool)
.await?;
let result = sqlx::query(
r#"
INSERT INTO cache (checksum, text, summary, provider, model, guild_id, user_id, channel_id, temperature, max_tokens, top_p, system_prompt, created_at, last_accessed)
VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14)
RETURNING id
"#
)
.bind(checksum)
.bind(text)
.bind(summary)
.bind(provider)
.bind(model)
.bind(guild_id)
.bind(user_id)
.bind(channel_id)
.bind(temperature)
.bind(max_tokens.map(|v| v as i32))
.bind(top_p)
.bind(system_prompt)
.bind(&now)
.bind(&now)
.fetch_one(pool)
.await?;
Ok(result.get(0))
}
}
}
pub async fn get_all_cache_entries(&self, limit: Option<i64>, offset: Option<i64>) -> Result<Vec<CacheEntry>> {
let limit = limit.unwrap_or(100);
let offset = offset.unwrap_or(0);
match &self.pool {
DatabasePool::Sqlite(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache ORDER BY last_accessed DESC LIMIT ? OFFSET ?"
)
.bind(limit)
.bind(offset)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
DatabasePool::Postgres(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache ORDER BY last_accessed DESC LIMIT $1 OFFSET $2"
)
.bind(limit)
.bind(offset)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
}
}
pub async fn get_cache_by_guild(&self, guild_id: &str) -> Result<Vec<CacheEntry>> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE guild_id = ? ORDER BY last_accessed DESC"
)
.bind(guild_id)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
DatabasePool::Postgres(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE guild_id = $1 ORDER BY last_accessed DESC"
)
.bind(guild_id)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
}
}
pub async fn get_cache_by_user(&self, user_id: &str) -> Result<Vec<CacheEntry>> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE user_id = ? ORDER BY last_accessed DESC"
)
.bind(user_id)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
DatabasePool::Postgres(pool) => {
let rows = sqlx::query(
"SELECT id, checksum, text, summary, provider, model, guild_id, user_id, channel_id, created_at, last_accessed, access_count FROM cache WHERE user_id = $1 ORDER BY last_accessed DESC"
)
.bind(user_id)
.fetch_all(pool)
.await?;
Ok(rows.into_iter().map(|r| CacheEntry {
id: r.get(0),
checksum: r.get(1),
text: r.get(2),
summary: r.get(3),
provider: r.get(4),
model: r.get(5),
guild_id: r.get(6),
user_id: r.get(7),
channel_id: r.get(8),
created_at: r.get(9),
last_accessed: r.get(10),
access_count: r.get(11),
}).collect())
}
}
}
pub async fn delete_cache_by_id(&self, id: i64) -> Result<u64> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE id = ?")
.bind(id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
DatabasePool::Postgres(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE id = $1")
.bind(id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
}
}
pub async fn delete_cache_by_guild(&self, guild_id: &str) -> Result<u64> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE guild_id = ?")
.bind(guild_id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
DatabasePool::Postgres(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE guild_id = $1")
.bind(guild_id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
}
}
pub async fn delete_cache_by_user(&self, user_id: &str) -> Result<u64> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE user_id = ?")
.bind(user_id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
DatabasePool::Postgres(pool) => {
let result = sqlx::query("DELETE FROM cache WHERE user_id = $1")
.bind(user_id)
.execute(pool)
.await?;
Ok(result.rows_affected())
}
}
}
pub async fn delete_all_cache(&self) -> Result<u64> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let result = sqlx::query("DELETE FROM cache")
.execute(pool)
.await?;
Ok(result.rows_affected())
}
DatabasePool::Postgres(pool) => {
let result = sqlx::query("DELETE FROM cache")
.execute(pool)
.await?;
Ok(result.rows_affected())
}
}
}
pub async fn get_cache_stats(&self) -> Result<CacheStats> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
let total_entries: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM cache")
.fetch_one(pool)
.await?;
let stats_row = sqlx::query("SELECT total_hits, total_misses FROM stats WHERE id = 1")
.fetch_one(pool)
.await?;
let total_hits: i64 = stats_row.get(0);
let total_misses: i64 = stats_row.get(1);
let total_requests = total_hits + total_misses;
let hit_rate = if total_requests > 0 {
(total_hits as f64 / total_requests as f64) * 100.0
} else {
0.0
};
let total_size: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(LENGTH(text) + LENGTH(summary)), 0) FROM cache"
)
.fetch_one(pool)
.await?;
Ok(CacheStats {
total_entries,
total_hits,
total_misses,
hit_rate,
total_size_bytes: total_size,
})
}
DatabasePool::Postgres(pool) => {
let total_entries: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM cache")
.fetch_one(pool)
.await?;
let stats_row = sqlx::query("SELECT total_hits, total_misses FROM stats WHERE id = 1")
.fetch_one(pool)
.await?;
let total_hits: i64 = stats_row.get(0);
let total_misses: i64 = stats_row.get(1);
let total_requests = total_hits + total_misses;
let hit_rate = if total_requests > 0 {
(total_hits as f64 / total_requests as f64) * 100.0
} else {
0.0
};
let total_size: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(LENGTH(text) + LENGTH(summary)), 0) FROM cache"
)
.fetch_one(pool)
.await?;
Ok(CacheStats {
total_entries,
total_hits,
total_misses,
hit_rate,
total_size_bytes: total_size,
})
}
}
}
pub async fn _check_rate_limit(&self, guild_id: &str, user_id: &str, guild_limit: u32, user_limit: u32) -> Result<(bool, bool)> {
let now = Utc::now();
let window_start = now.format("%Y-%m-%d %H:%M").to_string();
match &self.pool {
DatabasePool::Sqlite(pool) => {
// Clean old rate limit entries (older than 1 minute)
sqlx::query("DELETE FROM rate_limits WHERE window_start < datetime('now', '-1 minute')")
.execute(pool)
.await?;
// Check guild rate limit
let guild_count: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(request_count), 0) FROM rate_limits WHERE guild_id = ? AND window_start = ?"
)
.bind(guild_id)
.bind(&window_start)
.fetch_one(pool)
.await?;
// Check user rate limit
let user_count: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(request_count), 0) FROM rate_limits WHERE user_id = ? AND window_start = ?"
)
.bind(user_id)
.bind(&window_start)
.fetch_one(pool)
.await?;
let guild_limited = guild_count >= guild_limit as i64;
let user_limited = user_count >= user_limit as i64;
if !guild_limited && !user_limited {
// Increment rate limit counter
sqlx::query(
r#"
INSERT INTO rate_limits (guild_id, user_id, window_start, request_count)
VALUES (?, ?, ?, 1)
ON CONFLICT(guild_id, user_id, window_start) DO UPDATE SET request_count = request_count + 1
"#
)
.bind(guild_id)
.bind(user_id)
.bind(&window_start)
.execute(pool)
.await?;
}
Ok((guild_limited, user_limited))
}
DatabasePool::Postgres(pool) => {
// Clean old rate limit entries (older than 1 minute)
sqlx::query("DELETE FROM rate_limits WHERE window_start < (NOW() - INTERVAL '1 minute')::TEXT")
.execute(pool)
.await?;
// Check guild rate limit
let guild_count: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(request_count), 0) FROM rate_limits WHERE guild_id = $1 AND window_start = $2"
)
.bind(guild_id)
.bind(&window_start)
.fetch_one(pool)
.await?;
// Check user rate limit
let user_count: i64 = sqlx::query_scalar(
"SELECT COALESCE(SUM(request_count), 0) FROM rate_limits WHERE user_id = $1 AND window_start = $2"
)
.bind(user_id)
.bind(&window_start)
.fetch_one(pool)
.await?;
let guild_limited = guild_count >= guild_limit as i64;
let user_limited = user_count >= user_limit as i64;
if !guild_limited && !user_limited {
// Increment rate limit counter
sqlx::query(
r#"
INSERT INTO rate_limits (guild_id, user_id, window_start, request_count)
VALUES ($1, $2, $3, 1)
ON CONFLICT(guild_id, user_id, window_start) DO UPDATE SET request_count = rate_limits.request_count + 1
"#
)
.bind(guild_id)
.bind(user_id)
.bind(&window_start)
.execute(pool)
.await?;
}
Ok((guild_limited, user_limited))
}
}
}
/// Check if the database connection is healthy
pub async fn health_check(&self) -> Result<bool> {
match &self.pool {
DatabasePool::Sqlite(pool) => {
sqlx::query("SELECT 1")
.fetch_one(pool)
.await?;
Ok(true)
}
DatabasePool::Postgres(pool) => {
sqlx::query("SELECT 1")
.fetch_one(pool)
.await?;
Ok(true)
}
}
}
}

402
backend_api/src/llm.rs Normal file
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use anyhow::{anyhow, Result};
use reqwest::Client;
use serde::{Deserialize, Serialize};
use crate::config::Config;
use crate::models::{SummarizeRequest, TokenUsage};
use crate::v1::enums::LlmProvider;
use crate::claude_models::ClaudeModelType;
pub struct LlmClient {
client: Client,
config: Config,
}
#[derive(Debug, Serialize, Deserialize)]
struct AnthropicRequest {
model: String,
max_tokens: u32,
temperature: f32,
messages: Vec<AnthropicMessage>,
#[serde(skip_serializing_if = "Option::is_none")]
system: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
top_p: Option<f32>,
}
#[derive(Debug, Serialize, Deserialize)]
struct AnthropicMessage {
role: String,
content: String,
}
#[derive(Debug, Serialize, Deserialize)]
struct AnthropicResponse {
content: Vec<AnthropicContent>,
model: String,
usage: AnthropicUsage,
}
#[derive(Debug, Serialize, Deserialize)]
struct AnthropicUsage {
input_tokens: u32,
output_tokens: u32,
}
#[derive(Debug, Serialize, Deserialize)]
struct AnthropicContent {
text: String,
}
#[derive(Debug, Serialize, Deserialize)]
struct OllamaRequest {
model: String,
prompt: String,
stream: bool,
#[serde(skip_serializing_if = "Option::is_none")]
system: Option<String>,
options: OllamaOptions,
}
#[derive(Debug, Serialize, Deserialize)]
struct OllamaOptions {
temperature: f32,
#[serde(skip_serializing_if = "Option::is_none")]
top_p: Option<f32>,
#[serde(skip_serializing_if = "Option::is_none")]
num_predict: Option<i32>,
}
#[derive(Debug, Serialize, Deserialize)]
struct OllamaResponse {
response: String,
model: String,
prompt_eval_count: Option<u32>,
eval_count: Option<u32>,
}
#[derive(Debug, Serialize, Deserialize)]
pub struct _OllamaModelInfo {
pub name: String,
pub modified_at: String,
pub size: i64,
}
#[derive(Debug, Serialize, Deserialize)]
struct _OllamaListResponse {
models: Vec<_OllamaModelInfo>,
}
impl LlmClient {
pub fn new(config: Config) -> Self {
Self {
client: Client::new(),
config,
}
}
pub async fn summarize(&self, request: &SummarizeRequest) -> Result<(String, String, TokenUsage)> {
match request.provider {
LlmProvider::Anthropic => self.summarize_anthropic(request).await,
LlmProvider::Ollama => self.summarize_ollama(request).await,
LlmProvider::Lmstudio => self.summarize_lmstudio(request).await,
}
}
async fn summarize_anthropic(&self, request: &SummarizeRequest) -> Result<(String, String, TokenUsage)> {
let anthropic_config = self.config.anthropic.as_ref()
.ok_or_else(|| anyhow!("Anthropic config not found"))?;
let model = request.model.clone()
.unwrap_or_else(|| anthropic_config.model.clone());
let max_tokens = request.max_tokens
.unwrap_or(anthropic_config.max_tokens);
let temperature = request.temperature
.unwrap_or(anthropic_config.temperature);
let system_prompt = if let Some(ref style) = request.style {
style.to_system_prompt()
} else {
request.system_prompt.clone()
.unwrap_or_else(|| "You are a helpful assistant that summarizes text concisely and accurately. Provide a clear, well-structured summary of the given text.".to_string())
};
let anthropic_request = AnthropicRequest {
model: model.clone(),
max_tokens,
temperature,
messages: vec![
AnthropicMessage {
role: "user".to_string(),
content: format!("Please summarize the following text:\n\n{}", request.text),
}
],
system: Some(system_prompt),
top_p: request.top_p,
};
let response = self.client
.post(format!("{}/messages", anthropic_config.base_url))
.header("x-api-key", &anthropic_config.api_key)
.header("anthropic-version", "2023-06-01")
.header("content-type", "application/json")
.json(&anthropic_request)
.send()
.await?;
if !response.status().is_success() {
let error_text = response.text().await?;
return Err(anyhow!("Anthropic API error: {}", error_text));
}
let anthropic_response: AnthropicResponse = response.json().await?;
let summary = anthropic_response.content
.first()
.map(|c| c.text.clone())
.ok_or_else(|| anyhow!("No content in Anthropic response"))?;
let token_usage = TokenUsage {
input_tokens: anthropic_response.usage.input_tokens,
output_tokens: anthropic_response.usage.output_tokens,
total_tokens: anthropic_response.usage.input_tokens + anthropic_response.usage.output_tokens,
estimated_cost_usd: Some(calculate_anthropic_cost(
&model,
anthropic_response.usage.input_tokens,
anthropic_response.usage.output_tokens,
)),
};
Ok((summary, model, token_usage))
}
async fn summarize_ollama(&self, request: &SummarizeRequest) -> Result<(String, String, TokenUsage)> {
let ollama_config = self.config.ollama.as_ref()
.ok_or_else(|| anyhow!("Ollama config not found"))?;
let model = request.model.clone()
.unwrap_or_else(|| ollama_config.model.clone());
let temperature = request.temperature
.unwrap_or(ollama_config.temperature);
let system_prompt = if let Some(ref style) = request.style {
style.to_system_prompt()
} else {
request.system_prompt.clone()
.unwrap_or_else(|| "You are a helpful assistant that summarizes text concisely and accurately. Provide a clear, well-structured summary of the given text.".to_string())
};
let prompt = format!("Please summarize the following text:\n\n{}", request.text);
let ollama_request = OllamaRequest {
model: model.clone(),
prompt,
stream: false,
system: Some(system_prompt),
options: OllamaOptions {
temperature,
top_p: request.top_p,
num_predict: request.max_tokens.map(|v| v as i32),
},
};
let response = self.client
.post(format!("{}/api/generate", ollama_config.base_url))
.json(&ollama_request)
.send()
.await?;
if !response.status().is_success() {
let error_text = response.text().await?;
return Err(anyhow!("Ollama API error: {}", error_text));
}
let ollama_response: OllamaResponse = response.json().await?;
let token_usage = TokenUsage {
input_tokens: ollama_response.prompt_eval_count.unwrap_or(0),
output_tokens: ollama_response.eval_count.unwrap_or(0),
total_tokens: ollama_response.prompt_eval_count.unwrap_or(0) + ollama_response.eval_count.unwrap_or(0),
estimated_cost_usd: None, // Ollama is local, no cost
};
Ok((ollama_response.response, model, token_usage))
}
pub async fn _list_ollama_models(&self) -> Result<Vec<_OllamaModelInfo>> {
let ollama_config = self.config.ollama.as_ref()
.ok_or_else(|| anyhow!("Ollama config not found"))?;
let response = self.client
.get(format!("{}/api/tags", ollama_config.base_url))
.send()
.await?;
if !response.status().is_success() {
return Err(anyhow!("Failed to list Ollama models"));
}
let list_response: _OllamaListResponse = response.json().await?;
Ok(list_response.models)
}
pub fn _get_anthropic_models(&self) -> Vec<String> {
// Return known Claude models from claude_models module
ClaudeModelType::all_available()
.into_iter()
.map(|m| m.id())
.collect()
}
async fn summarize_lmstudio(&self, request: &SummarizeRequest) -> Result<(String, String, TokenUsage)> {
let lmstudio_config = self.config.lmstudio.as_ref()
.ok_or_else(|| anyhow!("LMStudio config not found"))?;
let model = request.model.clone()
.unwrap_or_else(|| lmstudio_config.model.clone());
let temperature = request.temperature
.unwrap_or(lmstudio_config.temperature);
let max_tokens = request.max_tokens
.unwrap_or(lmstudio_config.max_tokens);
let system_prompt = if let Some(ref style) = request.style {
style.to_system_prompt()
} else {
request.system_prompt.clone()
.unwrap_or_else(|| "You are a helpful assistant that summarizes text concisely and accurately. Provide a clear, well-structured summary of the given text.".to_string())
};
// LMStudio uses OpenAI-compatible API
#[derive(Serialize)]
struct LmStudioRequest {
model: String,
messages: Vec<LmStudioMessage>,
temperature: f32,
max_tokens: u32,
}
#[derive(Serialize)]
struct LmStudioMessage {
role: String,
content: String,
}
#[derive(Deserialize)]
struct LmStudioResponse {
choices: Vec<LmStudioChoice>,
usage: Option<LmStudioUsage>,
}
#[derive(Deserialize)]
struct LmStudioChoice {
message: LmStudioResponseMessage,
}
#[derive(Deserialize)]
struct LmStudioResponseMessage {
content: String,
}
#[derive(Deserialize)]
struct LmStudioUsage {
prompt_tokens: u32,
completion_tokens: u32,
total_tokens: u32,
}
let lmstudio_request = LmStudioRequest {
model: model.clone(),
messages: vec![
LmStudioMessage {
role: "system".to_string(),
content: system_prompt,
},
LmStudioMessage {
role: "user".to_string(),
content: format!("Please summarize the following text:\n\n{}", request.text),
},
],
temperature,
max_tokens,
};
let response = self.client
.post(format!("{}/chat/completions", lmstudio_config.base_url))
.json(&lmstudio_request)
.send()
.await?;
if !response.status().is_success() {
let error_text = response.text().await?;
return Err(anyhow!("LMStudio API error: {}", error_text));
}
let lmstudio_response: LmStudioResponse = response.json().await?;
let summary = lmstudio_response.choices
.first()
.map(|c| c.message.content.clone())
.ok_or_else(|| anyhow!("No content in LMStudio response"))?;
let token_usage = if let Some(usage) = lmstudio_response.usage {
TokenUsage {
input_tokens: usage.prompt_tokens,
output_tokens: usage.completion_tokens,
total_tokens: usage.total_tokens,
estimated_cost_usd: None, // LMStudio is local, no cost
}
} else {
TokenUsage {
input_tokens: 0,
output_tokens: 0,
total_tokens: 0,
estimated_cost_usd: None,
}
};
Ok((summary, model, token_usage))
}
pub async fn _list_lmstudio_models(&self) -> Result<Vec<_OllamaModelInfo>> {
let lmstudio_config = self.config.lmstudio.as_ref()
.ok_or_else(|| anyhow!("LMStudio config not found"))?;
let response = self.client
.get(format!("{}/models", lmstudio_config.base_url))
.send()
.await?;
if !response.status().is_success() {
return Err(anyhow!("Failed to list LMStudio models"));
}
#[derive(Deserialize)]
struct LmStudioModelsResponse {
data: Vec<LmStudioModel>,
}
#[derive(Deserialize)]
struct LmStudioModel {
id: String,
}
let list_response: LmStudioModelsResponse = response.json().await?;
// Convert to OllamaModelInfo format for compatibility
Ok(list_response.data.into_iter().map(|m| _OllamaModelInfo {
name: m.id,
modified_at: "".to_string(),
size: 0,
}).collect())
}
}
// Calculate Anthropic API costs based on model and token usage using ClaudeModelType
fn calculate_anthropic_cost(model: &str, input_tokens: u32, output_tokens: u32) -> f64 {
let claude_model = ClaudeModelType::from_id(model);
claude_model.calculate_cost(input_tokens, output_tokens)
}

300
backend_api/src/main.rs Normal file
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mod config;
mod models;
mod llm;
mod checksum;
mod ocr;
mod cache;
mod database;
mod rate_limiter;
mod claude_models;
mod credits;
mod v1;
use anyhow::Result;
use poem::{listener::TcpListener, Route, EndpointExt};
use poem::middleware::Tracing;
use poem_openapi::OpenApiService;
use std::sync::Arc;
use tracing::{info, warn, error};
use config::Config;
use v1::{AppState, create_apis};
/// Result of LLM provider health check
struct LlmHealthResult {
available: bool,
error_message: Option<String>,
}
/// Performs startup health checks and returns whether the server can start.
/// Returns Err if no LLM provider is available.
async fn perform_startup_health_checks(state: &Arc<AppState>) -> Result<()> {
// Check database
match state.database.health_check().await {
Ok(_) => info!("✓ Database connection healthy"),
Err(e) => {
error!("✗ Database connection failed: {}", e);
return Err(anyhow::anyhow!("Database connection failed: {}", e));
}
}
let mut anthropic_available = false;
let mut ollama_available = false;
let mut lmstudio_available = false;
let mut anthropic_error: Option<String> = None;
// Check Anthropic API if configured
if let Some(ref config) = state.config.anthropic {
info!("Checking Anthropic API...");
let result = check_anthropic_health(config).await;
anthropic_available = result.available;
anthropic_error = result.error_message.clone();
if result.available {
info!("✓ Anthropic API healthy");
} else {
error!("✗ Anthropic API check failed: {}", result.error_message.unwrap_or_default());
}
} else {
warn!("Anthropic API not configured");
}
// Check Ollama API if configured
if let Some(ref config) = state.config.ollama {
info!("Checking Ollama API...");
let result = check_ollama_health(config).await;
ollama_available = result.available;
if result.available {
info!("✓ Ollama API healthy");
} else {
error!("✗ Ollama API check failed: {}", result.error_message.unwrap_or_default());
}
} else {
warn!("Ollama API not configured");
}
// Check LMStudio API if configured
if let Some(ref config) = state.config.lmstudio {
info!("Checking LMStudio API...");
let result = check_lmstudio_health(config).await;
lmstudio_available = result.available;
if result.available {
info!("✓ LMStudio API healthy");
} else {
error!("✗ LMStudio API check failed: {}", result.error_message.unwrap_or_default());
}
} else {
warn!("LMStudio API not configured");
}
// Check if at least one LLM provider is available
if !anthropic_available && !ollama_available && !lmstudio_available {
error!("═══════════════════════════════════════════════════════════════");
error!("FATAL: No LLM provider is available!");
error!("═══════════════════════════════════════════════════════════════");
if state.config.anthropic.is_some() {
if let Some(ref err) = anthropic_error {
if err.contains("401") || err.contains("invalid") || err.contains("authentication") {
error!("Anthropic API key appears to be invalid");
} else if err.contains("insufficient") || err.contains("credit") || err.contains("balance") {
error!("Anthropic API credits may be exhausted");
}
}
error!("- Anthropic: UNAVAILABLE");
} else {
error!("- Anthropic: NOT CONFIGURED");
}
if state.config.ollama.is_some() {
error!("- Ollama: UNAVAILABLE (is the server running?)");
} else {
error!("- Ollama: NOT CONFIGURED");
}
if state.config.lmstudio.is_some() {
error!("- LMStudio: UNAVAILABLE (is the server running?)");
} else {
error!("- LMStudio: NOT CONFIGURED");
}
error!("═══════════════════════════════════════════════════════════════");
error!("Please configure at least one working LLM provider in config.toml");
error!("═══════════════════════════════════════════════════════════════");
return Err(anyhow::anyhow!("No LLM provider available. Server cannot start."));
}
info!("Startup health checks completed successfully");
Ok(())
}
async fn check_anthropic_health(config: &config::AnthropicConfig) -> LlmHealthResult {
let client = reqwest::Client::new();
#[derive(serde::Serialize)]
struct TestRequest {
model: String,
max_tokens: u32,
messages: Vec<TestMessage>,
}
#[derive(serde::Serialize)]
struct TestMessage {
role: String,
content: String,
}
let test_request = TestRequest {
model: "claude-haiku-4-5-20251001".to_string(),
max_tokens: 10,
messages: vec![TestMessage {
role: "user".to_string(),
content: "Hello".to_string(),
}],
};
match client
.post(format!("{}/messages", config.base_url))
.header("x-api-key", &config.api_key)
.header("anthropic-version", "2023-06-01")
.header("content-type", "application/json")
.json(&test_request)
.send()
.await
{
Ok(resp) if resp.status().is_success() => LlmHealthResult {
available: true,
error_message: None,
},
Ok(resp) => {
let status = resp.status();
let error_text = resp.text().await.unwrap_or_else(|_| "Unknown error".to_string());
LlmHealthResult {
available: false,
error_message: Some(format!("HTTP {} - {}", status, error_text)),
}
}
Err(e) => LlmHealthResult {
available: false,
error_message: Some(format!("Connection failed: {}", e)),
},
}
}
async fn check_ollama_health(config: &config::OllamaConfig) -> LlmHealthResult {
let client = reqwest::Client::new();
match client
.get(format!("{}/api/tags", config.base_url))
.send()
.await
{
Ok(resp) if resp.status().is_success() => LlmHealthResult {
available: true,
error_message: None,
},
Ok(resp) => LlmHealthResult {
available: false,
error_message: Some(format!("HTTP {}", resp.status())),
},
Err(e) => LlmHealthResult {
available: false,
error_message: Some(format!("Connection failed: {}", e)),
},
}
}
async fn check_lmstudio_health(config: &config::LmStudioConfig) -> LlmHealthResult {
let client = reqwest::Client::new();
match client
.get(format!("{}/models", config.base_url))
.send()
.await
{
Ok(resp) if resp.status().is_success() => LlmHealthResult {
available: true,
error_message: None,
},
Ok(resp) => LlmHealthResult {
available: false,
error_message: Some(format!("HTTP {}", resp.status())),
},
Err(e) => LlmHealthResult {
available: false,
error_message: Some(format!("Connection failed: {}", e)),
},
}
}
#[tokio::main]
async fn main() -> Result<()> {
// Initialize tracing
tracing_subscriber::fmt()
.with_env_filter(
tracing_subscriber::EnvFilter::try_from_default_env()
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
)
.init();
info!("Starting Summarizer API v1...");
// Load configuration
let config = Config::load("./src/config/config.toml")?;
info!("Configuration loaded");
// Create app state with in-memory cache, database, and rate limiter
let state = Arc::new(AppState::new(config.clone()).await?);
info!("App state initialized with hybrid cache (memory + database) and rate limiter");
// Perform startup health checks - exit if no LLM is available
info!("Performing startup health checks...");
perform_startup_health_checks(&state).await?;
// Create versioned APIs
let (summarize_api, cache_api, models_api, ocr_api, health_api, credits_api) = create_apis(state.clone());
// Combine all APIs into a single OpenAPI service
let api_service = OpenApiService::new(
(summarize_api, cache_api, models_api, ocr_api, health_api, credits_api),
"Summarizer API",
"1.0"
)
.server(format!("http://{}:{}/api/v1", config.server.host, config.server.port));
let ui = api_service.swagger_ui();
let spec = api_service.spec_endpoint();
// Build routes
let app = Route::new()
.nest("/api/v1", api_service)
.nest("/docs", ui)
.nest("/spec", spec)
.with(Tracing);
let addr = format!("{}:{}", config.server.host, config.server.port);
info!("Starting server on {}", addr);
info!("API documentation available at http://{}/docs", addr);
info!("API v1 endpoints available at http://{}/api/v1", addr);
// Start background cleanup task
let cleanup_state = state.clone();
tokio::spawn(async move {
let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(300)); // 5 minutes
loop {
interval.tick().await;
cleanup_state.cache.cleanup_expired().await;
cleanup_state.rate_limiter.cleanup_expired().await;
info!("Cleaned up expired cache and rate limit entries");
}
});
poem::Server::new(TcpListener::bind(&addr))
.run(app)
.await?;
Ok(())
}

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use poem_openapi::Object;
use serde::{Deserialize, Serialize};
use validator::Validate;
pub use crate::v1::enums::{LlmProvider, SummarizationStyle};
#[derive(Debug, Clone, Serialize, Deserialize, Object, Validate)]
pub struct SummarizeRequest {
/// The text to summarize
#[validate(length(min = 1, max = 100000, message = "Text must be between 1 and 100000 characters"))]
pub text: String,
/// LLM provider (anthropic or ollama)
pub provider: LlmProvider,
/// Model name (optional, uses default from config)
#[validate(length(min = 1, max = 100))]
pub model: Option<String>,
/// Temperature (0.0 to 2.0)
#[validate(range(min = 0.0, max = 2.0))]
pub temperature: Option<f32>,
/// Max tokens for the response
#[validate(range(min = 1, max = 100000))]
pub max_tokens: Option<u32>,
/// Top P sampling (0.0 to 1.0)
#[validate(range(min = 0.0, max = 1.0))]
pub top_p: Option<f32>,
/// Predefined summarization style (overrides system_prompt if provided)
pub style: Option<SummarizationStyle>,
/// Custom system prompt (optional, ignored if style is set)
#[validate(length(max = 5000))]
pub system_prompt: Option<String>,
/// Discord guild ID
#[validate(length(min = 1, max = 50))]
pub guild_id: String,
/// Discord user ID
#[validate(length(min = 1, max = 50))]
pub user_id: String,
/// Discord channel ID (optional)
#[validate(length(min = 1, max = 50))]
pub channel_id: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct SummarizeResponse {
/// The generated summary
pub summary: String,
/// Model used
pub model: String,
/// Provider used
pub provider: String,
/// Whether this was served from cache
pub from_cache: bool,
/// Request checksum
pub checksum: String,
/// Timestamp
pub timestamp: String,
/// Token usage information
pub token_usage: Option<TokenUsage>,
/// Processing time in milliseconds
pub processing_time_ms: Option<u64>,
/// Summarization style used
pub style_used: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct TokenUsage {
/// Input tokens (prompt)
pub input_tokens: u32,
/// Output tokens (completion)
pub output_tokens: u32,
/// Total tokens used
pub total_tokens: u32,
/// Estimated cost in USD (if available)
pub estimated_cost_usd: Option<f64>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct LlmInfo {
/// Provider name
pub provider: String,
/// Model name
pub model: String,
/// Model version (if available)
pub version: Option<String>,
/// Whether this model is available
pub available: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct CacheEntry {
/// Cache entry ID
pub id: i64,
/// Request checksum
pub checksum: String,
/// Original text
pub text: String,
/// Generated summary
pub summary: String,
/// Provider used
pub provider: String,
/// Model used
pub model: String,
/// Guild ID
pub guild_id: String,
/// User ID
pub user_id: String,
/// Channel ID
pub channel_id: Option<String>,
/// Created timestamp
pub created_at: String,
/// Last accessed timestamp
pub last_accessed: String,
/// Access count
pub access_count: i64,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct CacheStats {
/// Total cache entries
pub total_entries: i64,
/// Total cache hits
pub total_hits: i64,
/// Total cache misses
pub total_misses: i64,
/// Cache hit rate (percentage)
pub hit_rate: f64,
/// Total size in bytes (approximate)
pub total_size_bytes: i64,
}
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct RateLimitInfo {
/// Guild ID
pub guild_id: String,
/// User ID
pub user_id: String,
/// Requests in current window
pub requests_count: i64,
/// Window start time
pub window_start: String,
/// Whether rate limit is exceeded
pub is_limited: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct DeleteCacheRequest {
/// Cache entry ID to delete (optional)
pub id: Option<i64>,
/// Delete all entries for a guild (optional)
pub guild_id: Option<String>,
/// Delete all entries for a user (optional)
pub user_id: Option<String>,
/// Delete all entries (use with caution)
pub delete_all: Option<bool>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct ErrorResponse {
pub success: bool,
pub error: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct SuccessResponse {
pub success: bool,
pub message: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object, Validate)]
pub struct OcrSummarizeRequest {
/// LLM provider (anthropic or ollama)
pub provider: LlmProvider,
/// Model name (optional, uses default from config)
#[validate(length(min = 1, max = 100))]
pub model: Option<String>,
/// Temperature (0.0 to 2.0)
#[validate(range(min = 0.0, max = 2.0))]
pub temperature: Option<f32>,
/// Max tokens for the response
#[validate(range(min = 1, max = 100000))]
pub max_tokens: Option<u32>,
/// Top P sampling (0.0 to 1.0)
#[validate(range(min = 0.0, max = 1.0))]
pub top_p: Option<f32>,
/// Predefined summarization style
pub style: Option<SummarizationStyle>,
/// Custom system prompt (optional)
#[validate(length(max = 5000))]
pub system_prompt: Option<String>,
/// Discord guild ID
#[validate(length(min = 1, max = 50))]
pub guild_id: String,
/// Discord user ID
#[validate(length(min = 1, max = 50))]
pub user_id: String,
/// Discord channel ID (optional)
#[validate(length(min = 1, max = 50))]
pub channel_id: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct OcrSummarizeResponse {
/// Extracted text from OCR
pub extracted_text: String,
/// The generated summary
pub summary: String,
/// Model used
pub model: String,
/// Provider used
pub provider: String,
/// Whether summary was served from cache
pub from_cache: bool,
/// Request checksum
pub checksum: String,
/// Timestamp
pub timestamp: String,
/// Token usage information
pub token_usage: Option<TokenUsage>,
/// OCR processing time in milliseconds
pub ocr_time_ms: u64,
/// Summarization processing time in milliseconds
pub summarization_time_ms: Option<u64>,
/// Total processing time in milliseconds
pub total_time_ms: u64,
/// Summarization style used
pub style_used: Option<String>,
/// Image dimensions
pub image_info: ImageInfo,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct ImageInfo {
/// Image width in pixels
pub width: u32,
/// Image height in pixels
pub height: u32,
/// Image format (e.g., PNG, JPEG)
pub format: String,
/// File size in bytes
pub size_bytes: usize,
}

69
backend_api/src/ocr.rs Normal file
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use anyhow::{anyhow, Result};
use crate::models::ImageInfo;
#[cfg(feature = "ocr")]
use tesseract::Tesseract;
#[cfg(feature = "ocr")]
use image::ImageFormat;
#[cfg(feature = "ocr")]
use std::io::Cursor;
pub struct OcrProcessor;
impl OcrProcessor {
pub fn new() -> Self {
Self
}
#[cfg(feature = "ocr")]
pub async fn extract_text_from_image(&self, image_data: &[u8]) -> Result<(String, ImageInfo, u64)> {
let start = std::time::Instant::now();
let img = image::load_from_memory(image_data)
.map_err(|e| anyhow!("Failed to load image: {}", e))?;
let format = image::guess_format(image_data)
.map_err(|e| anyhow!("Failed to detect image format: {}", e))?;
let image_info = ImageInfo {
width: img.width(),
height: img.height(),
format: format!("{:?}", format),
size_bytes: image_data.len(),
};
let gray_img = img.to_luma8();
// Save to temporary buffer for Tesseract
let mut buffer = Vec::new();
let mut cursor = Cursor::new(&mut buffer);
gray_img.write_to(&mut cursor, ImageFormat::Png)
.map_err(|e| anyhow!("Failed to encode image: {}", e))?;
let tesseract = Tesseract::new(None, Some("eng"))
.map_err(|e| anyhow!("Failed to initialize Tesseract: {}", e))?;
let text = tesseract
.set_image_from_mem(&buffer)
.map_err(|e| anyhow!("Failed to set image: {}", e))?
.get_text()
.map_err(|e| anyhow!("Failed to extract text: {}", e))?;
let ocr_time_ms = start.elapsed().as_millis() as u64;
let cleaned_text = text.trim().to_string();
if cleaned_text.is_empty() {
return Err(anyhow!("No text found in image"));
}
Ok((cleaned_text, image_info, ocr_time_ms))
}
#[cfg(not(feature = "ocr"))]
pub async fn extract_text_from_image(&self, _image_data: &[u8]) -> Result<(String, ImageInfo, u64)> {
Err(anyhow!("OCR feature is not enabled. Rebuild with --features ocr"))
}
}

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use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use chrono::{DateTime, Utc, Duration};
#[derive(Clone)]
struct RateLimitEntry {
count: u32,
window_start: DateTime<Utc>,
}
pub struct RateLimiter {
guild_limits: Arc<RwLock<HashMap<String, RateLimitEntry>>>,
user_limits: Arc<RwLock<HashMap<String, RateLimitEntry>>>,
window_seconds: i64,
}
impl RateLimiter {
pub fn new(window_seconds: i64) -> Self {
Self {
guild_limits: Arc::new(RwLock::new(HashMap::new())),
user_limits: Arc::new(RwLock::new(HashMap::new())),
window_seconds,
}
}
pub async fn check_rate_limit(
&self,
guild_id: &str,
user_id: &str,
guild_limit: u32,
user_limit: u32,
) -> (bool, bool) {
let now = Utc::now();
let window_duration = Duration::seconds(self.window_seconds);
// Check guild rate limit
let mut guild_limits = self.guild_limits.write().await;
let guild_limited = if let Some(entry) = guild_limits.get_mut(guild_id) {
if now - entry.window_start > window_duration {
// Reset window
entry.count = 1;
entry.window_start = now;
false
} else if entry.count >= guild_limit {
true
} else {
entry.count += 1;
false
}
} else {
guild_limits.insert(
guild_id.to_string(),
RateLimitEntry {
count: 1,
window_start: now,
},
);
false
};
// Check user rate limit
let mut user_limits = self.user_limits.write().await;
let user_limited = if let Some(entry) = user_limits.get_mut(user_id) {
if now - entry.window_start > window_duration {
// Reset window
entry.count = 1;
entry.window_start = now;
false
} else if entry.count >= user_limit {
true
} else {
entry.count += 1;
false
}
} else {
user_limits.insert(
user_id.to_string(),
RateLimitEntry {
count: 1,
window_start: now,
},
);
false
};
(guild_limited, user_limited)
}
pub async fn cleanup_expired(&self) {
let now = Utc::now();
let window_duration = Duration::seconds(self.window_seconds);
let mut guild_limits = self.guild_limits.write().await;
guild_limits.retain(|_, entry| now - entry.window_start <= window_duration);
let mut user_limits = self.user_limits.write().await;
user_limits.retain(|_, entry| now - entry.window_start <= window_duration);
}
}

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use poem_openapi::Enum;
use serde::{Deserialize, Serialize};
#[derive(Debug, Clone, Serialize, Deserialize, Enum)]
#[oai(rename_all = "lowercase")]
pub enum LlmProvider {
Anthropic,
Ollama,
Lmstudio,
}
#[derive(Debug, Clone, Serialize, Deserialize, Enum)]
#[oai(rename_all = "lowercase")]
pub enum SummarizationStyle {
/// Brief, concise summary focusing on key points
Brief,
/// Detailed, comprehensive summary with context
Detailed,
/// Funny, humorous take on the content
Funny,
/// Professional, formal business-style summary
Professional,
/// Technical, focusing on technical details and terminology
Technical,
/// ELI5 (Explain Like I'm 5) - simple, easy to understand
Eli5,
/// Bullet points format
Bullets,
/// Academic style with citations and formal language
Academic,
/// Roast style
Roast,
}
impl SummarizationStyle {
pub fn to_system_prompt(&self) -> String {
match self {
SummarizationStyle::Brief => {
"You are a concise summarization assistant. Create brief, to-the-point summaries that capture only the most essential information. Keep it under 3-4 sentences.".to_string()
}
SummarizationStyle::Detailed => {
"You are a comprehensive summarization assistant. Create detailed summaries that include context, nuances, and important details. Organize information logically and ensure nothing significant is missed.".to_string()
}
SummarizationStyle::Funny => {
"You are a humorous summarization assistant. Create entertaining summaries that capture the essence of the content while adding wit, humor, and playful commentary. Make it fun to read while staying accurate.".to_string()
}
SummarizationStyle::Professional => {
"You are a professional business summarization assistant. Create formal, well-structured summaries suitable for corporate environments. Use professional language and focus on actionable insights.".to_string()
}
SummarizationStyle::Technical => {
"You are a technical summarization assistant. Focus on technical details, specifications, methodologies, and precise terminology. Maintain accuracy and include relevant technical context.".to_string()
}
SummarizationStyle::Eli5 => {
"You are an ELI5 (Explain Like I'm 5) summarization assistant. Break down complex topics into simple, easy-to-understand language that anyone can grasp. Use analogies and simple examples.".to_string()
}
SummarizationStyle::Bullets => {
"You are a bullet-point summarization assistant. Create clear, organized summaries using bullet points. Each point should be concise and capture a distinct idea or fact. Use proper formatting with • or - for bullets.".to_string()
}
SummarizationStyle::Academic => {
"You are an academic summarization assistant. Create scholarly summaries with formal language, proper structure, and attention to methodology and evidence. Maintain objectivity and academic rigor.".to_string()
}
SummarizationStyle::Roast => {
"You are a roast summarization assistant. Create humorous summaries that capture the essence of the content while adding wit, dark humor, and harshly criticizing the content. Make it fun to read while staying accurate.".to_string()
}
}
}
}

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pub mod routes;
pub mod enums;
pub mod responses;
use std::sync::Arc;
use crate::config::Config;
use crate::cache::Cache;
use crate::database::Database;
use crate::rate_limiter::RateLimiter;
use crate::llm::LlmClient;
use crate::ocr::OcrProcessor;
use crate::credits::{CreditSystem, create_credit_tables};
pub struct AppState {
pub cache: Cache,
pub database: Database,
pub rate_limiter: RateLimiter,
pub llm_client: LlmClient,
pub ocr_processor: OcrProcessor,
pub credit_system: CreditSystem,
pub config: Config,
}
impl AppState {
pub async fn new(config: Config) -> anyhow::Result<Self> {
let cache = Cache::new(
config.cache.ttl_seconds.unwrap_or(3600),
config.cache.max_size.unwrap_or(1000),
);
// Initialize database
let database = Database::new(
&config.database.url,
config.database.db_type.clone(),
).await?;
// Create credit system tables
create_credit_tables(&database.pool()).await?;
let rate_limiter = RateLimiter::new(
config.rate_limiting.window_seconds.unwrap_or(60),
);
let llm_client = LlmClient::new(config.clone());
let ocr_processor = OcrProcessor::new();
// Initialize credit system with bypass users
let credit_system = CreditSystem::new(config.credits.bypass_user_ids.clone());
Ok(Self {
cache,
database,
rate_limiter,
llm_client,
ocr_processor,
credit_system,
config,
})
}
}
pub fn create_apis(state: Arc<AppState>) -> (
routes::SummarizeApi,
routes::CacheApi,
routes::ModelsApi,
routes::OcrApi,
routes::HealthApi,
routes::CreditsApi,
) {
(
routes::SummarizeApi { state: state.clone() },
routes::CacheApi { state: state.clone() },
routes::ModelsApi { state: state.clone() },
routes::OcrApi { state: state.clone() },
routes::HealthApi { state: state.clone() },
routes::CreditsApi { state },
)
}

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use poem_openapi::{ApiResponse, payload::Json};
use crate::models::*;
#[derive(ApiResponse)]
pub enum SummarizeResult {
#[oai(status = 200)]
Ok(Json<SummarizeResponse>),
#[oai(status = 400)]
Error(Json<ErrorResponse>),
}
#[derive(ApiResponse)]
pub enum CacheStatsResult {
#[oai(status = 200)]
Ok(Json<CacheStats>),
#[oai(status = 500)]
Error(Json<ErrorResponse>),
}
#[derive(ApiResponse)]
pub enum CacheEntriesResult {
#[oai(status = 200)]
Ok(Json<Vec<CacheEntry>>),
#[oai(status = 500)]
Error(Json<ErrorResponse>),
}
#[derive(ApiResponse)]
pub enum DeleteResult {
#[oai(status = 200)]
Ok(Json<SuccessResponse>),
#[oai(status = 400)]
Error(Json<ErrorResponse>),
}
#[derive(ApiResponse)]
pub enum OcrSummarizeResult {
#[oai(status = 200)]
Ok(Json<OcrSummarizeResponse>),
#[oai(status = 400)]
Error(Json<ErrorResponse>),
}

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use poem_openapi::{payload::Json, param::{Path, Query}, OpenApi};
use std::sync::Arc;
use tracing::warn;
use crate::models::{CacheStats, CacheEntry, DeleteCacheRequest, SuccessResponse};
use super::super::AppState;
pub struct CacheApi {
pub state: Arc<AppState>,
}
#[OpenApi(prefix_path = "/cache")]
impl CacheApi {
/// Get cache statistics from database (persistent stats)
#[oai(path = "/stats", method = "get")]
pub async fn get_cache_stats(&self) -> Json<CacheStats> {
// Get stats from database for accurate persistent statistics
match self.state.database.get_cache_stats().await {
Ok(stats) => Json(stats),
Err(e) => {
warn!("Failed to get cache stats from database: {}", e);
// Fallback to in-memory stats
let (hits, misses, size) = self.state.cache.get_stats().await;
Json(CacheStats {
total_entries: size as i64,
total_hits: hits as i64,
total_misses: misses as i64,
hit_rate: if hits + misses > 0 {
(hits as f64 / (hits + misses) as f64) * 100.0
} else {
0.0
},
total_size_bytes: 0,
})
}
}
}
/// List cache entries with pagination from database
#[oai(path = "/entries", method = "get")]
pub async fn list_cache_entries(
&self,
limit: Query<Option<i64>>,
offset: Query<Option<i64>>,
) -> Json<Vec<CacheEntry>> {
// Fetch from database for complete persistent data
match self.state.database.get_all_cache_entries(limit.0, offset.0).await {
Ok(entries) => Json(entries),
Err(e) => {
warn!("Failed to list cache entries from database: {}", e);
// Fallback to in-memory cache
let limit_usize = limit.0.map(|l| l as usize);
let offset_usize = offset.0.map(|o| o as usize);
let entries = self.state.cache.get_all(limit_usize, offset_usize).await;
Json(entries)
}
}
}
/// Get cache entries for a specific guild from database
#[oai(path = "/guild/:guild_id", method = "get")]
pub async fn get_guild_cache(
&self,
guild_id: Path<String>,
) -> Json<Vec<CacheEntry>> {
// Fetch from database for complete persistent data
match self.state.database.get_cache_by_guild(&guild_id.0).await {
Ok(entries) => Json(entries),
Err(e) => {
warn!("Failed to get guild cache from database: {}", e);
// Fallback to in-memory cache
let entries = self.state.cache.get_by_guild(&guild_id.0).await;
Json(entries)
}
}
}
/// Get cache entries for a specific user from database
#[oai(path = "/user/:user_id", method = "get")]
pub async fn get_user_cache(
&self,
user_id: Path<String>,
) -> Json<Vec<CacheEntry>> {
// Fetch from database for complete persistent data
match self.state.database.get_cache_by_user(&user_id.0).await {
Ok(entries) => Json(entries),
Err(e) => {
warn!("Failed to get user cache from database: {}", e);
// Fallback to in-memory cache
let entries = self.state.cache.get_by_user(&user_id.0).await;
Json(entries)
}
}
}
/// Delete cache entries from both memory and database
#[oai(path = "/delete", method = "post")]
pub async fn delete_cache(
&self,
request: Json<DeleteCacheRequest>,
) -> Json<SuccessResponse> {
let request = request.0;
// Delete from both memory cache and database
let (memory_deleted, db_deleted) = if let Some(id) = request.id {
let mem = self.state.cache.delete_by_id(id).await;
let db = self.state.database.delete_cache_by_id(id).await.unwrap_or(0);
(mem, db)
} else if let Some(guild_id) = request.guild_id {
let mem = self.state.cache.delete_by_guild(&guild_id).await;
let db = self.state.database.delete_cache_by_guild(&guild_id).await.unwrap_or(0);
(mem, db)
} else if let Some(user_id) = request.user_id {
let mem = self.state.cache.delete_by_user(&user_id).await;
let db = self.state.database.delete_cache_by_user(&user_id).await.unwrap_or(0);
(mem, db)
} else if request.delete_all == Some(true) {
let mem = self.state.cache.delete_all().await;
let db = self.state.database.delete_all_cache().await.unwrap_or(0);
(mem, db)
} else {
(0, 0)
};
let total_deleted = memory_deleted + db_deleted;
Json(SuccessResponse {
success: true,
message: format!(
"Deleted {} cache entries (memory: {}, database: {})",
total_deleted, memory_deleted, db_deleted
),
})
}
}

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@@ -0,0 +1,357 @@
//! Credits API routes for managing user credits and tiers.
use poem_openapi::{param::Path, param::Query, payload::Json, Object, OpenApi, Tags};
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use crate::credits::TierLevel;
use crate::v1::AppState;
#[derive(Tags)]
enum ApiTags {
/// Credit management endpoints
Credits,
}
/// Credits API for managing user credits
pub struct CreditsApi {
pub state: Arc<AppState>,
}
// ==================== Request/Response Models ====================
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct UserCreditsResponse {
pub success: bool,
pub user_id: String,
pub credits: i64,
pub tier_id: i32,
pub tier_name: String,
pub lifetime_credits_used: i64,
pub is_unlimited: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct AddCreditsRequest {
pub user_id: String,
pub amount: i64,
#[oai(default)]
pub transaction_type: Option<String>,
#[oai(default)]
pub description: Option<String>,
#[oai(default)]
pub guild_id: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct AddCreditsResponse {
pub success: bool,
pub user_id: String,
pub new_balance: i64,
pub amount_added: i64,
#[oai(default)]
pub error: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct DeductCreditsRequest {
pub user_id: String,
pub amount: i64,
#[oai(default)]
pub description: Option<String>,
#[oai(default)]
pub guild_id: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct DeductCreditsResponse {
pub success: bool,
pub user_id: String,
pub new_balance: i64,
pub amount_deducted: i64,
#[oai(default)]
pub error: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct SetTierRequest {
pub user_id: String,
pub tier_id: i32,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct SetTierResponse {
pub success: bool,
pub user_id: String,
pub tier_id: i32,
pub tier_name: String,
#[oai(default)]
pub error: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct TierInfo {
pub id: i32,
pub name: String,
pub monthly_credits: i64,
pub price_usd: f64,
pub max_messages_per_summary: i32,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct TiersResponse {
pub success: bool,
pub tiers: Vec<TierInfo>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct TransactionInfo {
pub id: i64,
pub user_id: String,
pub guild_id: Option<String>,
pub amount: i64,
pub transaction_type: String,
pub description: String,
pub balance_after: i64,
pub created_at: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct TransactionsResponse {
pub success: bool,
pub transactions: Vec<TransactionInfo>,
#[oai(default)]
pub error: Option<String>,
}
// ==================== API Implementation ====================
#[OpenApi(prefix_path = "/credits", tag = "ApiTags::Credits")]
impl CreditsApi {
/// Get user credits
#[oai(path = "/:user_id", method = "get")]
pub async fn get_credits(&self, user_id: Path<String>) -> Json<UserCreditsResponse> {
let pool = self.state.database.pool();
match self.state.credit_system.get_or_create_user(pool, &user_id.0).await {
Ok(user) => {
let tier_name = match user.tier_id {
1 => "free",
2 => "basic",
3 => "pro",
4 => "unlimited",
_ => "free",
};
Json(UserCreditsResponse {
success: true,
user_id: user.user_id,
credits: user.credits,
tier_id: user.tier_id,
tier_name: tier_name.to_string(),
lifetime_credits_used: user.lifetime_credits_used,
is_unlimited: user.tier_id == 4,
})
}
Err(_e) => Json(UserCreditsResponse {
success: false,
user_id: user_id.0.clone(),
credits: 0,
tier_id: 1,
tier_name: "free".to_string(),
lifetime_credits_used: 0,
is_unlimited: false,
}),
}
}
/// Add credits to a user
#[oai(path = "/add", method = "post")]
pub async fn add_credits(&self, request: Json<AddCreditsRequest>) -> Json<AddCreditsResponse> {
let pool = self.state.database.pool();
let tx_type = request.transaction_type.as_deref().unwrap_or("add");
let description = request.description.as_deref().unwrap_or("Credits added");
match self.state.credit_system.add_credits(
pool,
&request.user_id,
request.amount,
request.guild_id.as_deref(),
tx_type,
description,
).await {
Ok(new_balance) => Json(AddCreditsResponse {
success: true,
user_id: request.user_id.clone(),
new_balance,
amount_added: request.amount,
error: None,
}),
Err(e) => Json(AddCreditsResponse {
success: false,
user_id: request.user_id.clone(),
new_balance: 0,
amount_added: 0,
error: Some(format!("{}", e)),
}),
}
}
/// Deduct credits from a user
#[oai(path = "/deduct", method = "post")]
pub async fn deduct_credits(&self, request: Json<DeductCreditsRequest>) -> Json<DeductCreditsResponse> {
let pool = self.state.database.pool();
let description = request.description.as_deref().unwrap_or("Credits deducted");
let user = match self.state.credit_system.get_or_create_user(pool, &request.user_id).await {
Ok(u) => u,
Err(e) => {
return Json(DeductCreditsResponse {
success: false,
user_id: request.user_id.clone(),
new_balance: 0,
amount_deducted: 0,
error: Some(format!("Failed to get user: {}", e)),
});
}
};
if user.tier_id == 4 || self.state.credit_system.is_bypass_user(&request.user_id) {
return Json(DeductCreditsResponse {
success: true,
user_id: request.user_id.clone(),
new_balance: user.credits,
amount_deducted: 0,
error: None,
});
}
if user.credits < request.amount {
return Json(DeductCreditsResponse {
success: false,
user_id: request.user_id.clone(),
new_balance: user.credits,
amount_deducted: 0,
error: Some(format!("Insufficient credits. Current balance: {}", user.credits)),
});
}
match self.state.credit_system.deduct_credits(
pool,
&request.user_id,
request.amount,
request.guild_id.as_deref(),
description,
).await {
Ok(new_balance) => Json(DeductCreditsResponse {
success: true,
user_id: request.user_id.clone(),
new_balance,
amount_deducted: request.amount,
error: None,
}),
Err(e) => Json(DeductCreditsResponse {
success: false,
user_id: request.user_id.clone(),
new_balance: user.credits,
amount_deducted: 0,
error: Some(format!("{}", e)),
}),
}
}
/// Set user tier
#[oai(path = "/tier", method = "post")]
pub async fn set_tier(&self, request: Json<SetTierRequest>) -> Json<SetTierResponse> {
let pool = self.state.database.pool();
let tier = match request.tier_id {
1 => TierLevel::Free,
2 => TierLevel::Basic,
3 => TierLevel::Pro,
4 => TierLevel::Unlimited,
_ => {
return Json(SetTierResponse {
success: false,
user_id: request.user_id.clone(),
tier_id: request.tier_id,
tier_name: "invalid".to_string(),
error: Some("Invalid tier ID. Must be 1-4.".to_string()),
});
}
};
let tier_name = match request.tier_id {
1 => "free",
2 => "basic",
3 => "pro",
4 => "unlimited",
_ => "unknown",
};
if let Err(e) = self.state.credit_system.get_or_create_user(pool, &request.user_id).await {
return Json(SetTierResponse {
success: false,
user_id: request.user_id.clone(),
tier_id: request.tier_id,
tier_name: tier_name.to_string(),
error: Some(format!("Failed to get/create user: {}", e)),
});
}
match self.state.credit_system.set_user_tier(pool, &request.user_id, tier).await {
Ok(_) => Json(SetTierResponse {
success: true,
user_id: request.user_id.clone(),
tier_id: request.tier_id,
tier_name: tier_name.to_string(),
error: None,
}),
Err(e) => Json(SetTierResponse {
success: false,
user_id: request.user_id.clone(),
tier_id: request.tier_id,
tier_name: tier_name.to_string(),
error: Some(format!("{}", e)),
}),
}
}
/// Get all available tiers
#[oai(path = "/tiers", method = "get")]
pub async fn get_tiers(&self) -> Json<TiersResponse> {
let tiers = vec![
TierInfo { id: 1, name: "free".to_string(), monthly_credits: 50, price_usd: 0.0, max_messages_per_summary: 50 },
TierInfo { id: 2, name: "basic".to_string(), monthly_credits: 500, price_usd: 4.99, max_messages_per_summary: 100 },
TierInfo { id: 3, name: "pro".to_string(), monthly_credits: 2000, price_usd: 9.99, max_messages_per_summary: 250 },
TierInfo { id: 4, name: "unlimited".to_string(), monthly_credits: -1, price_usd: 19.99, max_messages_per_summary: 500 },
];
Json(TiersResponse { success: true, tiers })
}
/// Get user transaction history
#[oai(path = "/:user_id/transactions", method = "get")]
pub async fn get_transactions(&self, user_id: Path<String>, limit: Query<Option<i64>>) -> Json<TransactionsResponse> {
let pool = self.state.database.pool();
let limit_val = limit.0.unwrap_or(20);
match self.state.credit_system.get_transactions(pool, &user_id.0, limit_val).await {
Ok(transactions) => {
let tx_infos: Vec<TransactionInfo> = transactions.into_iter().map(|tx| TransactionInfo {
id: tx.id,
user_id: tx.user_id,
guild_id: tx.guild_id,
amount: tx.amount,
transaction_type: tx.transaction_type,
description: tx.description,
balance_after: tx.balance_after,
created_at: tx.created_at,
}).collect();
Json(TransactionsResponse { success: true, transactions: tx_infos, error: None })
}
Err(e) => Json(TransactionsResponse { success: false, transactions: vec![], error: Some(format!("{}", e)) }),
}
}
}

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@@ -0,0 +1,363 @@
use poem_openapi::{payload::Json, OpenApi, Object, Tags};
use std::sync::Arc;
use serde::{Deserialize, Serialize};
use reqwest::Client;
use crate::models::SuccessResponse;
use crate::claude_models::ClaudeModelType;
use super::super::AppState;
#[derive(Tags)]
enum ApiTags {
/// Health check endpoints
Health,
}
pub struct HealthApi {
pub state: Arc<AppState>,
}
/// Simple health status response for bot health checks
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct SimpleHealthResponse {
/// Status string ("healthy" or "unhealthy")
pub status: String,
/// Version of the API
pub version: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct HealthCheckResponse {
/// Overall health status
pub healthy: bool,
/// Health check timestamp
pub timestamp: String,
/// Database health
pub database: ServiceHealth,
/// Anthropic API health (if configured)
pub anthropic: Option<ServiceHealth>,
/// Ollama API health (if configured)
pub ollama: Option<ServiceHealth>,
/// LMStudio API health (if configured)
pub lmstudio: Option<ServiceHealth>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Object)]
pub struct ServiceHealth {
/// Whether the service is available
pub available: bool,
/// Status message
pub message: String,
/// Response time in milliseconds
pub response_time_ms: Option<u64>,
/// Available models (if applicable)
pub models: Option<Vec<String>>,
}
#[OpenApi(prefix_path = "/health", tag = "ApiTags::Health")]
impl HealthApi {
/// Simple health check - returns status for bot startup checks
#[oai(path = "/", method = "get")]
pub async fn health(&self) -> Json<SimpleHealthResponse> {
// Quick check if at least one LLM is available
let has_anthropic = self.state.config.anthropic.is_some();
let has_ollama = self.state.config.ollama.is_some();
let has_lmstudio = self.state.config.lmstudio.is_some();
let status = if has_anthropic || has_ollama || has_lmstudio {
"healthy"
} else {
"unhealthy"
};
Json(SimpleHealthResponse {
status: status.to_string(),
version: env!("CARGO_PKG_VERSION").to_string(),
})
}
/// Comprehensive health check endpoint with detailed service status
#[oai(path = "/detailed", method = "get")]
pub async fn health_detailed(&self) -> Json<HealthCheckResponse> {
let _start_time = std::time::Instant::now();
// Check Database
let database = self.check_database().await;
// Check Anthropic API if configured
let anthropic = if self.state.config.anthropic.is_some() {
Some(self.check_anthropic().await)
} else {
None
};
// Check Ollama API if configured
let ollama = if self.state.config.ollama.is_some() {
Some(self.check_ollama().await)
} else {
None
};
// Check LMStudio API if configured
let lmstudio = if self.state.config.lmstudio.is_some() {
Some(self.check_lmstudio().await)
} else {
None
};
// Determine overall health
let mut healthy = database.available;
// At least one LLM provider should be available
let llm_available = anthropic.as_ref().map(|s| s.available).unwrap_or(false)
|| ollama.as_ref().map(|s| s.available).unwrap_or(false)
|| lmstudio.as_ref().map(|s| s.available).unwrap_or(false);
healthy = healthy && llm_available;
Json(HealthCheckResponse {
healthy,
timestamp: chrono::Utc::now().to_rfc3339(),
database,
anthropic,
ollama,
lmstudio,
})
}
/// Simple health check endpoint (just returns OK)
#[oai(path = "/simple", method = "get")]
pub async fn health_simple(&self) -> Json<SuccessResponse> {
Json(SuccessResponse {
success: true,
message: "API is healthy".to_string(),
})
}
async fn check_database(&self) -> ServiceHealth {
let start = std::time::Instant::now();
match self.state.database.health_check().await {
Ok(_) => ServiceHealth {
available: true,
message: "Database is healthy".to_string(),
response_time_ms: Some(start.elapsed().as_millis() as u64),
models: None,
},
Err(e) => ServiceHealth {
available: false,
message: format!("Database error: {}", e),
response_time_ms: Some(start.elapsed().as_millis() as u64),
models: None,
},
}
}
async fn check_anthropic(&self) -> ServiceHealth {
let start = std::time::Instant::now();
let config = match &self.state.config.anthropic {
Some(c) => c,
None => return ServiceHealth {
available: false,
message: "Anthropic not configured".to_string(),
response_time_ms: None,
models: None,
},
};
let client = Client::new();
// Try to make a minimal API call to verify the API key
#[derive(serde::Serialize)]
struct TestRequest {
model: String,
max_tokens: u32,
messages: Vec<TestMessage>,
}
#[derive(serde::Serialize)]
struct TestMessage {
role: String,
content: String,
}
let test_request = TestRequest {
model: "claude-haiku-4-5-20251001".to_string(), // Use cheapest model for health check
max_tokens: 10,
messages: vec![TestMessage {
role: "user".to_string(),
content: "Hello".to_string(),
}],
};
let response = client
.post(format!("{}/messages", config.base_url))
.header("x-api-key", &config.api_key)
.header("anthropic-version", "2023-06-01")
.header("content-type", "application/json")
.json(&test_request)
.send()
.await;
let response_time = start.elapsed().as_millis() as u64;
match response {
Ok(resp) if resp.status().is_success() => {
let models = ClaudeModelType::all_available()
.into_iter()
.map(|m| m.id())
.collect();
ServiceHealth {
available: true,
message: "Anthropic API is healthy".to_string(),
response_time_ms: Some(response_time),
models: Some(models),
}
}
Ok(resp) => {
let status = resp.status();
let error_text = resp.text().await.unwrap_or_else(|_| "Unknown error".to_string());
ServiceHealth {
available: false,
message: format!("Anthropic API error ({}): {}", status, error_text),
response_time_ms: Some(response_time),
models: None,
}
}
Err(e) => ServiceHealth {
available: false,
message: format!("Failed to connect to Anthropic API: {}", e),
response_time_ms: Some(response_time),
models: None,
},
}
}
async fn check_ollama(&self) -> ServiceHealth {
let start = std::time::Instant::now();
let config = match &self.state.config.ollama {
Some(c) => c,
None => return ServiceHealth {
available: false,
message: "Ollama not configured".to_string(),
response_time_ms: None,
models: None,
},
};
let client = Client::new();
// Try to list models
let response = client
.get(format!("{}/api/tags", config.base_url))
.send()
.await;
let response_time = start.elapsed().as_millis() as u64;
match response {
Ok(resp) if resp.status().is_success() => {
#[derive(serde::Deserialize)]
struct OllamaListResponse {
models: Vec<OllamaModel>,
}
#[derive(serde::Deserialize)]
struct OllamaModel {
name: String,
}
let models_list = resp.json::<OllamaListResponse>().await.ok()
.map(|m| m.models.into_iter().map(|model| model.name).collect());
ServiceHealth {
available: true,
message: "Ollama API is healthy".to_string(),
response_time_ms: Some(response_time),
models: models_list,
}
}
Ok(resp) => {
let status = resp.status();
ServiceHealth {
available: false,
message: format!("Ollama API error ({})", status),
response_time_ms: Some(response_time),
models: None,
}
}
Err(e) => ServiceHealth {
available: false,
message: format!("Failed to connect to Ollama API: {}", e),
response_time_ms: Some(response_time),
models: None,
},
}
}
async fn check_lmstudio(&self) -> ServiceHealth {
let start = std::time::Instant::now();
let config = match &self.state.config.lmstudio {
Some(c) => c,
None => return ServiceHealth {
available: false,
message: "LMStudio not configured".to_string(),
response_time_ms: None,
models: None,
},
};
let client = Client::new();
// Try to list models (OpenAI-compatible endpoint)
let response = client
.get(format!("{}/models", config.base_url))
.send()
.await;
let response_time = start.elapsed().as_millis() as u64;
match response {
Ok(resp) if resp.status().is_success() => {
#[derive(serde::Deserialize)]
struct LmStudioModelsResponse {
data: Vec<LmStudioModel>,
}
#[derive(serde::Deserialize)]
struct LmStudioModel {
id: String,
}
let models_list = resp.json::<LmStudioModelsResponse>().await.ok()
.map(|m| m.data.into_iter().map(|model| model.id).collect());
ServiceHealth {
available: true,
message: "LMStudio API is healthy".to_string(),
response_time_ms: Some(response_time),
models: models_list,
}
}
Ok(resp) => {
let status = resp.status();
ServiceHealth {
available: false,
message: format!("LMStudio API error ({})", status),
response_time_ms: Some(response_time),
models: None,
}
}
Err(e) => ServiceHealth {
available: false,
message: format!("Failed to connect to LMStudio API: {}", e),
response_time_ms: Some(response_time),
models: None,
},
}
}
}

View File

@@ -0,0 +1,13 @@
pub mod summarize;
pub mod cache;
pub mod models;
pub mod ocr;
pub mod health;
pub mod credits;
pub use summarize::SummarizeApi;
pub use cache::CacheApi;
pub use models::ModelsApi;
pub use ocr::OcrApi;
pub use health::HealthApi;
pub use credits::CreditsApi;

View File

@@ -0,0 +1,40 @@
use poem_openapi::{payload::Json, OpenApi};
use std::sync::Arc;
use crate::models::LlmInfo;
use super::super::AppState;
pub struct ModelsApi {
pub state: Arc<AppState>,
}
#[OpenApi(prefix_path = "/models")]
impl ModelsApi {
/// List available LLM models
#[oai(path = "/", method = "get")]
pub async fn list_models(&self) -> Json<Vec<LlmInfo>> {
let mut models = Vec::new();
// Add Anthropic models
if let Some(ref anthropic_config) = self.state.config.anthropic {
models.push(LlmInfo {
provider: "anthropic".to_string(),
model: anthropic_config.model.clone(),
version: None,
available: true,
});
}
// Add Ollama models
if let Some(ref ollama_config) = self.state.config.ollama {
models.push(LlmInfo {
provider: "ollama".to_string(),
model: ollama_config.model.clone(),
version: None,
available: true,
});
}
Json(models)
}
}

View File

@@ -0,0 +1,217 @@
use poem_openapi::{payload::{Json, Binary}, param::Query, OpenApi};
use std::sync::Arc;
use std::time::Instant;
use chrono::Utc;
use tracing::info;
use validator::Validate;
use crate::models::*;
use crate::v1::responses::OcrSummarizeResult;
use crate::v1::enums::{LlmProvider, SummarizationStyle};
use crate::checksum;
use super::super::AppState;
pub struct OcrApi {
pub state: Arc<AppState>,
}
#[OpenApi(prefix_path = "/ocr")]
impl OcrApi {
/// OCR and summarize image (send image as binary in request body, params as query/form)
#[oai(path = "/summarize", method = "post")]
pub async fn ocr_summarize(
&self,
provider: Query<String>,
guild_id: Query<String>,
user_id: Query<String>,
channel_id: Query<Option<String>>,
model: Query<Option<String>>,
temperature: Query<Option<f32>>,
max_tokens: Query<Option<u32>>,
top_p: Query<Option<f32>>,
style: Query<Option<String>>,
image: Binary<Vec<u8>>,
) -> OcrSummarizeResult {
// Parse provider
let provider_enum = match provider.0.to_lowercase().as_str() {
"anthropic" => LlmProvider::Anthropic,
"ollama" => LlmProvider::Ollama,
_ => {
return OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: "Invalid provider. Must be 'anthropic' or 'ollama'".to_string(),
}));
}
};
// Parse style if provided
let style_enum = if let Some(ref s) = style.0 {
match s.to_lowercase().as_str() {
"brief" => Some(SummarizationStyle::Brief),
"detailed" => Some(SummarizationStyle::Detailed),
"funny" => Some(SummarizationStyle::Funny),
"professional" => Some(SummarizationStyle::Professional),
"technical" => Some(SummarizationStyle::Technical),
"eli5" => Some(SummarizationStyle::Eli5),
"bullets" => Some(SummarizationStyle::Bullets),
"academic" => Some(SummarizationStyle::Academic),
_ => None,
}
} else {
None
};
let params = OcrSummarizeRequest {
provider: provider_enum.clone(),
model: model.0.clone(),
temperature: temperature.0,
max_tokens: max_tokens.0,
top_p: top_p.0,
style: style_enum.clone(),
system_prompt: None,
guild_id: guild_id.0.clone(),
user_id: user_id.0.clone(),
channel_id: channel_id.0.clone(),
};
// Validate request
if let Err(e) = params.validate() {
return OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("Validation error: {}", e),
}));
}
let total_start = Instant::now();
// Check rate limiting if enabled
if self.state.config.rate_limiting.enabled {
let (guild_limited, user_limited) = self.state.rate_limiter.check_rate_limit(
&params.guild_id,
&params.user_id,
self.state.config.rate_limiting.guild_rate_limit,
self.state.config.rate_limiting.user_rate_limit,
).await;
if guild_limited {
return OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: "Guild rate limit exceeded".to_string(),
}));
}
if user_limited {
return OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: "User rate limit exceeded".to_string(),
}));
}
}
// Get image data
let image_data = image.0;
// Perform OCR
let (extracted_text, image_info, ocr_time_ms) = match self.state.ocr_processor.extract_text_from_image(&image_data).await {
Ok(result) => result,
Err(e) => {
return OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("OCR failed: {}", e),
}));
}
};
info!("OCR extracted {} characters in {}ms", extracted_text.len(), ocr_time_ms);
// Create summarize request from extracted text
let summarize_request = SummarizeRequest {
text: extracted_text.clone(),
provider: params.provider.clone(),
model: params.model.clone(),
temperature: params.temperature,
max_tokens: params.max_tokens,
top_p: params.top_p,
style: params.style.clone(),
system_prompt: params.system_prompt.clone(),
guild_id: params.guild_id.clone(),
user_id: params.user_id.clone(),
channel_id: params.channel_id.clone(),
};
// Calculate checksum for caching
let checksum = checksum::calculate_checksum(&summarize_request);
let summarization_start = Instant::now();
// Check cache if enabled
if self.state.config.cache.enabled {
if let Some(cached) = self.state.cache.get(&checksum).await {
info!("Cache hit for OCR summary checksum: {}", checksum);
let total_time = total_start.elapsed().as_millis() as u64;
return OcrSummarizeResult::Ok(Json(OcrSummarizeResponse {
extracted_text,
summary: cached.summary,
model: cached.model,
provider: cached.provider,
from_cache: true,
checksum: cached.checksum,
timestamp: Utc::now().to_rfc3339(),
token_usage: None,
ocr_time_ms,
summarization_time_ms: Some(0),
total_time_ms: total_time,
style_used: params.style.as_ref().map(|s| format!("{:?}", s).to_lowercase()),
image_info,
}));
}
}
// Call LLM for summarization
match self.state.llm_client.summarize(&summarize_request).await {
Ok((summary, model, token_usage)) => {
let provider = format!("{:?}", params.provider).to_lowercase();
let summarization_time_ms = summarization_start.elapsed().as_millis() as u64;
// Store in cache if enabled
if self.state.config.cache.enabled {
let cache_entry = CacheEntry {
id: chrono::Utc::now().timestamp(),
checksum: checksum.clone(),
text: extracted_text.clone(),
summary: summary.clone(),
provider: provider.clone(),
model: model.clone(),
guild_id: params.guild_id.clone(),
user_id: params.user_id.clone(),
channel_id: params.channel_id.clone(),
created_at: Utc::now().to_rfc3339(),
last_accessed: Utc::now().to_rfc3339(),
access_count: 1,
};
self.state.cache.insert(checksum.clone(), cache_entry).await;
}
let total_time = total_start.elapsed().as_millis() as u64;
OcrSummarizeResult::Ok(Json(OcrSummarizeResponse {
extracted_text,
summary,
model,
provider,
from_cache: false,
checksum,
timestamp: Utc::now().to_rfc3339(),
token_usage: Some(token_usage),
ocr_time_ms,
summarization_time_ms: Some(summarization_time_ms),
total_time_ms: total_time,
style_used: params.style.as_ref().map(|s| format!("{:?}", s).to_lowercase()),
image_info,
}))
}
Err(e) => OcrSummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("Summarization failed: {}", e),
})),
}
}
}

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use poem_openapi::{payload::Json, OpenApi};
use std::sync::Arc;
use std::time::Instant;
use chrono::Utc;
use tracing::info;
use validator::Validate;
use crate::models::*;
use crate::v1::responses::SummarizeResult;
use crate::checksum;
use super::super::AppState;
pub struct SummarizeApi {
pub state: Arc<AppState>,
}
#[OpenApi(prefix_path = "/summarize")]
impl SummarizeApi {
/// Summarize text using specified LLM provider
#[oai(path = "/", method = "post")]
pub async fn summarize(
&self,
request: Json<SummarizeRequest>,
) -> SummarizeResult {
let request = request.0;
// Validate request
if let Err(e) = request.validate() {
return SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("Validation error: {}", e),
}));
}
let start_time = Instant::now();
// Check credits if enabled
let credits_needed = self.state.config.credits.credits_per_summary;
if self.state.config.credits.enabled {
match self.state.credit_system.check_credits(
self.state.database.pool(),
&request.user_id,
credits_needed,
).await {
Ok(result) => {
if !result.allowed {
return SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: result.reason.unwrap_or_else(|| "Insufficient credits".to_string()),
}));
}
}
Err(e) => {
return SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("Credit check failed: {}", e),
}));
}
}
}
// Check rate limiting if enabled
if self.state.config.rate_limiting.enabled {
let (guild_limited, user_limited) = self.state.rate_limiter.check_rate_limit(
&request.guild_id,
&request.user_id,
self.state.config.rate_limiting.guild_rate_limit,
self.state.config.rate_limiting.user_rate_limit,
).await;
if guild_limited {
return SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: "Guild rate limit exceeded".to_string(),
}));
}
if user_limited {
return SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: "User rate limit exceeded".to_string(),
}));
}
}
// Calculate checksum for caching
let checksum = checksum::calculate_checksum(&request);
// Check cache if enabled (hybrid: memory first, then database)
if self.state.config.cache.enabled {
// First check in-memory cache
if let Some(cached) = self.state.cache.get(&checksum).await {
info!("Memory cache hit for checksum: {}", checksum);
let processing_time = start_time.elapsed().as_millis() as u64;
return SummarizeResult::Ok(Json(SummarizeResponse {
summary: cached.summary,
model: cached.model,
provider: cached.provider,
from_cache: true,
checksum: cached.checksum,
timestamp: Utc::now().to_rfc3339(),
token_usage: None,
processing_time_ms: Some(processing_time),
style_used: None,
}));
}
// If not in memory, check database
if let Ok(Some(cached)) = self.state.database.get_cache_by_checksum(&checksum).await {
info!("Database cache hit for checksum: {}", checksum);
// Add to memory cache for faster future access
self.state.cache.insert(checksum.clone(), cached.clone()).await;
let processing_time = start_time.elapsed().as_millis() as u64;
return SummarizeResult::Ok(Json(SummarizeResponse {
summary: cached.summary,
model: cached.model,
provider: cached.provider,
from_cache: true,
checksum: cached.checksum,
timestamp: Utc::now().to_rfc3339(),
token_usage: None,
processing_time_ms: Some(processing_time),
style_used: None,
}));
}
}
// Call LLM
match self.state.llm_client.summarize(&request).await {
Ok((summary, model, token_usage)) => {
let provider = format!("{:?}", request.provider).to_lowercase();
// Deduct credits after successful summarization
if self.state.config.credits.enabled {
if let Err(e) = self.state.credit_system.deduct_credits(
self.state.database.pool(),
&request.user_id,
credits_needed,
Some(&request.guild_id),
&format!("Summarization ({})", provider),
).await {
tracing::warn!("Failed to deduct credits: {}", e);
}
}
// Store in cache if enabled (hybrid: both memory and database)
if self.state.config.cache.enabled {
// Store in database first
let db_result = self.state.database.insert_cache(
&checksum,
&request.text,
&summary,
&provider,
&model,
&request.guild_id,
&request.user_id,
request.channel_id.as_deref(),
request.temperature,
request.max_tokens,
request.top_p,
request.system_prompt.as_deref(),
).await;
let entry_id = db_result.unwrap_or_else(|e| {
tracing::warn!("Failed to store in database: {}", e);
chrono::Utc::now().timestamp()
});
// Also store in memory cache
let cache_entry = CacheEntry {
id: entry_id,
checksum: checksum.clone(),
text: request.text.clone(),
summary: summary.clone(),
provider: provider.clone(),
model: model.clone(),
guild_id: request.guild_id.clone(),
user_id: request.user_id.clone(),
channel_id: request.channel_id.clone(),
created_at: Utc::now().to_rfc3339(),
last_accessed: Utc::now().to_rfc3339(),
access_count: 1,
};
self.state.cache.insert(checksum.clone(), cache_entry).await;
}
let processing_time = start_time.elapsed().as_millis() as u64;
SummarizeResult::Ok(Json(SummarizeResponse {
summary,
model,
provider,
from_cache: false,
checksum,
timestamp: Utc::now().to_rfc3339(),
token_usage: Some(token_usage),
processing_time_ms: Some(processing_time),
style_used: request.style.as_ref().map(|s| format!("{:?}", s).to_lowercase()),
}))
}
Err(e) => SummarizeResult::Error(Json(ErrorResponse {
success: false,
error: format!("LLM error: {}", e),
})),
}
}
}

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3.12

0
discord_bot/README.md Normal file
View File

View File

@@ -0,0 +1,28 @@
[Bot]
# Discord bot token from https://discord.com/developers/applications
token = "YOUR_BOT_TOKEN_HERE"
# Discord application ID
application_id = "YOUR_APPLICATION_ID_HERE"
# Discord user IDs of bot owners (can use admin commands)
owner_ids = ["YOUR_USER_ID_HERE"]
# Logging configuration
log_level = "INFO"
log_format = "<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>"
# HTTP client settings
http_timeout = 30.0
max_retries = 3
# Command prefix for text commands (slash commands don't need this)
command_prefix = ["s!", "S!"]
# Debug mode (enables verbose logging)
debug = false
# Backend API URL (the Rust summarizer API)
# Must include the full path to the API version, e.g., /api/v1/
api_url = "http://127.0.0.1:3001/api/v1/"

100
discord_bot/pyproject.toml Normal file
View File

@@ -0,0 +1,100 @@
[project]
name = "discord-bot"
version = "0.1.0"
description = "Discord Summarizer Bot with credit-based monetization"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"attrs==25.4.0",
"disnake==2.11.0",
"httpx==0.28.1",
"loguru==0.7.3",
"pydantic==2.12.3",
]
[dependency-groups]
dev = [
"black==25.9.0",
"isort==7.0.0",
"pyright==1.1.407",
"ruff==0.14.3",
]
[tool.black]
line-length = 88
target-version = ["py312"]
[tool.ruff.isort]
combine-as-imports = true
[tool.slotscheck]
strict-imports = true
require-superclass = true
require-subclass = false
[tool.pyright]
typeCheckingMode = "strict"
pythonVersion = "3.12"
[tool.ruff]
select = [
"E",
"F",
"W",
"I",
"D",
"UP",
"YTT",
"ANN",
"S",
"BLE",
"FBT",
"B",
"C4",
"DTZ",
"T10",
"EM",
"ISC",
"G",
"PIE",
"T20",
"Q",
"RSE",
"RET",
"SIM",
"ARG",
"PGH",
"PLC",
"PLE",
"PLR",
"PLW",
"TRY",
"RUF",
]
ignore = [
"F403",
"F405",
"D107",
"D203",
"D213",
"RET505",
"RET506",
"RET507",
"RET508",
"PLR2004",
"PLR0912",
"PLR0913",
"PLR0915",
"UP006",
"UP007",
"UP035",
"S311",
]
unfixable = ["RET502", "RET503"]
[tool.ruff.pydocstyle]
convention = "numpy"
[tool.ruff.per-file-ignores]
"__init__.py" = ["F401"]

View File

@@ -0,0 +1,69 @@
"""Summarizer API Python SDK.
A fully typed async client for the Summarizer API.
Usage:
from sdk import SummarizerClient, LlmProvider
async with SummarizerClient() as client:
response = await client.summarize(
text="Long text to summarize...",
provider=LlmProvider.ANTHROPIC,
guild_id="123456789",
user_id="987654321",
)
print(response.summary)
"""
from .client import SummarizerClient
from .exceptions import (
SummarizerAPIError,
SummarizerBadRequestError,
SummarizerConnectionError,
SummarizerRateLimitError,
SummarizerServerError,
SummarizerTimeoutError,
)
from .models import (
CacheEntry,
CacheStats,
DeleteCacheRequest,
ErrorResponse,
ImageInfo,
LlmInfo,
LlmProvider,
OcrSummarizeResponse,
SuccessResponse,
SummarizationStyle,
SummarizeRequest,
SummarizeResponse,
TokenUsage,
)
__all__ = [
# Client
"SummarizerClient",
# Exceptions
"SummarizerAPIError",
"SummarizerBadRequestError",
"SummarizerConnectionError",
"SummarizerRateLimitError",
"SummarizerServerError",
"SummarizerTimeoutError",
# Enums
"LlmProvider",
"SummarizationStyle",
# Request models
"SummarizeRequest",
"DeleteCacheRequest",
# Response models
"SummarizeResponse",
"OcrSummarizeResponse",
"CacheEntry",
"CacheStats",
"LlmInfo",
"SuccessResponse",
"ErrorResponse",
"TokenUsage",
"ImageInfo",
]

374
discord_bot/sdk/client.py Normal file
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"""Async client for the Summarizer API."""
from __future__ import annotations
from types import TracebackType
from typing import Any, Self
import httpx
from .exceptions import (
SummarizerAPIError,
SummarizerBadRequestError,
SummarizerConnectionError,
SummarizerRateLimitError,
SummarizerServerError,
SummarizerTimeoutError,
)
from .models import (
CacheEntry,
CacheStats,
DeleteCacheRequest,
ErrorResponse,
HealthResponse,
LlmInfo,
LlmProvider,
OcrSummarizeResponse,
SuccessResponse,
SummarizationStyle,
SummarizeRequest,
SummarizeResponse,
)
DEFAULT_BASE_URL = "http://127.0.0.1:3001/api"
DEFAULT_TIMEOUT = 60.0
class SummarizerClient:
"""Async client for the Summarizer API.
Usage:
async with SummarizerClient() as client:
response = await client.summarize(
text="Long text to summarize...",
provider=LlmProvider.ANTHROPIC,
guild_id="123456789",
user_id="987654321",
)
print(response.summary)
"""
def __init__(
self,
base_url: str = DEFAULT_BASE_URL,
timeout: float = DEFAULT_TIMEOUT,
headers: dict[str, str] | None = None,
) -> None:
"""Initialize the client.
Args:
base_url: Base URL for the API (default: http://127.0.0.1:3001/api)
timeout: Request timeout in seconds (default: 60.0)
headers: Additional headers to include in requests
"""
self._base_url = base_url.rstrip("/")
self._timeout = timeout
self._headers = headers or {}
self._client: httpx.AsyncClient | None = None
async def __aenter__(self) -> Self:
"""Enter async context manager and create HTTP session."""
self._client = httpx.AsyncClient(
base_url=self._base_url,
timeout=self._timeout,
headers=self._headers,
)
return self
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
"""Exit async context manager and close HTTP session."""
if self._client:
await self._client.aclose()
self._client = None
def _ensure_client(self) -> httpx.AsyncClient:
"""Ensure client is initialized."""
if self._client is None:
msg = (
"Client not initialized. Use 'async with SummarizerClient() as client:'"
)
raise RuntimeError(msg)
return self._client
async def _handle_response(self, response: httpx.Response) -> httpx.Response:
"""Handle HTTP response and raise appropriate exceptions."""
if response.status_code == 200:
return response
try:
error_data = response.json()
error_response = ErrorResponse.model_validate(error_data)
error_message = error_response.error
except Exception: # noqa: BLE001
error_message = response.text or f"HTTP {response.status_code}"
if response.status_code == 400:
raise SummarizerBadRequestError(error_message, response)
if response.status_code == 429:
raise SummarizerRateLimitError(error_message)
if response.status_code >= 500:
raise SummarizerServerError(error_message, response.status_code)
raise SummarizerAPIError(error_message, response.status_code)
async def _request(
self,
method: str,
path: str,
*,
json: dict[str, Any] | None = None,
params: dict[str, str | int | float] | None = None,
content: bytes | None = None,
content_type: str | None = None,
) -> httpx.Response:
"""Make an HTTP request."""
client = self._ensure_client()
headers: dict[str, str] = {}
if content_type:
headers["Content-Type"] = content_type
try:
response = await client.request(
method,
path,
json=json,
params=params,
content=content,
headers=headers if headers else None,
)
return await self._handle_response(response)
except httpx.ConnectError as e:
raise SummarizerConnectionError(str(e)) from e
except httpx.TimeoutException as e:
raise SummarizerTimeoutError(str(e)) from e
except httpx.HTTPError as e:
raise SummarizerAPIError(str(e)) from e
async def summarize(
self,
text: str,
provider: LlmProvider,
guild_id: str,
user_id: str,
*,
model: str | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
top_p: float | None = None,
style: SummarizationStyle | None = None,
system_prompt: str | None = None,
channel_id: str | None = None,
) -> SummarizeResponse:
"""Summarize text using the specified LLM provider.
Args:
text: The text to summarize
provider: LLM provider (anthropic, ollama, or lmstudio)
guild_id: Discord guild ID
user_id: Discord user ID
model: Model name (optional, uses default from config)
temperature: Temperature (0.0 to 2.0)
max_tokens: Max tokens for the response
top_p: Top P sampling (0.0 to 1.0)
style: Predefined summarization style
system_prompt: Custom system prompt (ignored if style is set)
channel_id: Discord channel ID
Returns:
SummarizeResponse with the generated summary
"""
request = SummarizeRequest(
text=text,
provider=provider,
guild_id=guild_id,
user_id=user_id,
model=model,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
style=style,
system_prompt=system_prompt,
channel_id=channel_id,
)
response = await self._request(
"POST",
"/summarize",
json=request.model_dump(exclude_none=True),
)
return SummarizeResponse.model_validate(response.json())
async def ocr_summarize(
self,
image_data: bytes,
provider: LlmProvider,
guild_id: str,
user_id: str,
*,
model: str | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
top_p: float | None = None,
style: SummarizationStyle | None = None,
channel_id: str | None = None,
) -> OcrSummarizeResponse:
"""OCR and summarize an image.
Args:
image_data: Raw image bytes
provider: LLM provider
guild_id: Discord guild ID
user_id: Discord user ID
model: Model name (optional)
temperature: Temperature (0.0 to 2.0)
max_tokens: Max tokens for the response
top_p: Top P sampling (0.0 to 1.0)
style: Predefined summarization style
channel_id: Discord channel ID
Returns:
OcrSummarizeResponse with extracted text and summary
"""
params: dict[str, str | int | float] = {
"provider": provider.value,
"guild_id": guild_id,
"user_id": user_id,
}
if model is not None:
params["model"] = model
if temperature is not None:
params["temperature"] = temperature
if max_tokens is not None:
params["max_tokens"] = max_tokens
if top_p is not None:
params["top_p"] = top_p
if style is not None:
params["style"] = style.value
if channel_id is not None:
params["channel_id"] = channel_id
response = await self._request(
"POST",
"/ocr-summarize",
params=params,
content=image_data,
content_type="application/octet-stream",
)
return OcrSummarizeResponse.model_validate(response.json())
async def list_models(self) -> list[LlmInfo]:
"""List available LLM models.
Returns:
List of available LLM models
"""
response = await self._request("GET", "/models")
data = response.json()
return [LlmInfo.model_validate(item) for item in data]
async def get_cache_stats(self) -> CacheStats:
"""Get cache statistics.
Returns:
CacheStats with cache metrics
"""
response = await self._request("GET", "/cache/stats")
return CacheStats.model_validate(response.json())
async def list_cache_entries(
self,
*,
limit: int | None = None,
offset: int | None = None,
) -> list[CacheEntry]:
"""List cache entries with pagination.
Args:
limit: Maximum number of entries to return
offset: Number of entries to skip
Returns:
List of cache entries
"""
params: dict[str, str | int | float] = {}
if limit is not None:
params["limit"] = limit
if offset is not None:
params["offset"] = offset
response = await self._request("GET", "/cache/entries", params=params)
data = response.json()
return [CacheEntry.model_validate(item) for item in data]
async def get_guild_cache(self, guild_id: str) -> list[CacheEntry]:
"""Get cache entries for a specific guild.
Args:
guild_id: Discord guild ID
Returns:
List of cache entries for the guild
"""
response = await self._request("GET", f"/cache/guild/{guild_id}")
data = response.json()
return [CacheEntry.model_validate(item) for item in data]
async def get_user_cache(self, user_id: str) -> list[CacheEntry]:
"""Get cache entries for a specific user.
Args:
user_id: Discord user ID
Returns:
List of cache entries for the user
"""
response = await self._request("GET", f"/cache/user/{user_id}")
data = response.json()
return [CacheEntry.model_validate(item) for item in data]
async def delete_cache(
self,
*,
entry_id: int | None = None,
guild_id: str | None = None,
user_id: str | None = None,
delete_all: bool = False,
) -> SuccessResponse:
"""Delete cache entries.
Args:
entry_id: Specific cache entry ID to delete
guild_id: Delete all entries for a guild
user_id: Delete all entries for a user
delete_all: Delete all entries (use with caution)
Returns:
SuccessResponse confirming deletion
"""
request = DeleteCacheRequest(
id=entry_id,
guild_id=guild_id,
user_id=user_id,
delete_all=delete_all if delete_all else None,
)
response = await self._request(
"POST",
"/cache/delete",
json=request.model_dump(exclude_none=True),
)
return SuccessResponse.model_validate(response.json())
async def health_check(self) -> HealthResponse:
"""Check API health.
Returns:
HealthResponse with status and version
"""
response = await self._request("GET", "/health")
return HealthResponse.model_validate(response.json())

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"""Exception classes for Summarizer API errors."""
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from httpx import Response
class SummarizerAPIError(Exception):
"""Base exception for Summarizer API errors."""
def __init__(self, message: str, status_code: int | None = None) -> None:
super().__init__(message)
self.message = message
self.status_code = status_code
def __str__(self) -> str:
if self.status_code:
return f"[{self.status_code}] {self.message}"
return self.message
class SummarizerConnectionError(SummarizerAPIError):
"""Raised when connection to the API fails."""
def __init__(self, message: str = "Failed to connect to Summarizer API") -> None:
super().__init__(message)
class SummarizerTimeoutError(SummarizerAPIError):
"""Raised when API request times out."""
def __init__(self, message: str = "Request to Summarizer API timed out") -> None:
super().__init__(message)
class SummarizerBadRequestError(SummarizerAPIError):
"""Raised for 400 Bad Request errors."""
def __init__(self, message: str, response: Response | None = None) -> None:
super().__init__(message, status_code=400)
self.response = response
class SummarizerServerError(SummarizerAPIError):
"""Raised for 5xx server errors."""
def __init__(self, message: str, status_code: int = 500) -> None:
super().__init__(message, status_code=status_code)
class SummarizerRateLimitError(SummarizerAPIError):
"""Raised when rate limit is exceeded."""
def __init__(self, message: str = "Rate limit exceeded") -> None:
super().__init__(message, status_code=429)

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discord_bot/sdk/models.py Normal file
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"""Models for Summarizer API."""
from __future__ import annotations
from enum import StrEnum
from typing import Annotated
from pydantic import BaseModel, Field
class LlmProvider(StrEnum):
"""LLM provider options."""
ANTHROPIC = "anthropic"
OLLAMA = "ollama"
LMSTUDIO = "lmstudio"
class SummarizationStyle(StrEnum):
"""Predefined summarization styles."""
BRIEF = "brief"
DETAILED = "detailed"
FUNNY = "funny"
PROFESSIONAL = "professional"
TECHNICAL = "technical"
ELI5 = "eli5"
BULLETS = "bullets"
ACADEMIC = "academic"
ROAST = "roast"
class TokenUsage(BaseModel):
"""Token usage information from LLM response."""
input_tokens: Annotated[int, Field(description="Input tokens (prompt)")]
output_tokens: Annotated[int, Field(description="Output tokens (completion)")]
total_tokens: Annotated[int, Field(description="Total tokens used")]
estimated_cost_usd: Annotated[
float | None, Field(default=None, description="Estimated cost in USD")
]
class ImageInfo(BaseModel):
"""Image metadata from OCR processing."""
width: Annotated[int, Field(description="Image width in pixels")]
height: Annotated[int, Field(description="Image height in pixels")]
format: Annotated[str, Field(description="Image format (e.g., PNG, JPEG)")]
size_bytes: Annotated[int, Field(description="File size in bytes")]
class SummarizeRequest(BaseModel):
"""Request payload for text summarization."""
text: Annotated[
str, Field(min_length=1, max_length=100000, description="Text to summarize")
]
provider: Annotated[LlmProvider, Field(description="LLM provider")]
guild_id: Annotated[
str, Field(min_length=1, max_length=50, description="Discord guild ID")
]
user_id: Annotated[
str, Field(min_length=1, max_length=50, description="Discord user ID")
]
model: Annotated[
str | None, Field(default=None, max_length=100, description="Model name")
]
temperature: Annotated[
float | None,
Field(default=None, ge=0.0, le=2.0, description="Temperature (0.0 to 2.0)"),
]
max_tokens: Annotated[
int | None,
Field(default=None, ge=1, le=100000, description="Max tokens for response"),
]
top_p: Annotated[
float | None,
Field(default=None, ge=0.0, le=1.0, description="Top P sampling (0.0 to 1.0)"),
]
style: Annotated[
SummarizationStyle | None,
Field(default=None, description="Summarization style"),
]
system_prompt: Annotated[
str | None,
Field(default=None, max_length=5000, description="Custom system prompt"),
]
channel_id: Annotated[
str | None, Field(default=None, max_length=50, description="Discord channel ID")
]
class SummarizeResponse(BaseModel):
"""Response from text summarization endpoint."""
summary: Annotated[str, Field(description="Generated summary")]
model: Annotated[str, Field(description="Model used")]
provider: Annotated[str, Field(description="Provider used")]
from_cache: Annotated[bool, Field(description="Whether served from cache")]
checksum: Annotated[str, Field(description="Request checksum")]
timestamp: Annotated[str, Field(description="Timestamp")]
token_usage: Annotated[
TokenUsage | None, Field(default=None, description="Token usage info")
]
processing_time_ms: Annotated[
int | None, Field(default=None, description="Processing time in milliseconds")
]
style_used: Annotated[
str | None, Field(default=None, description="Summarization style used")
]
class OcrSummarizeResponse(BaseModel):
"""Response from OCR summarization endpoint."""
extracted_text: Annotated[str, Field(description="Extracted text from OCR")]
summary: Annotated[str, Field(description="Generated summary")]
model: Annotated[str, Field(description="Model used")]
provider: Annotated[str, Field(description="Provider used")]
from_cache: Annotated[bool, Field(description="Whether served from cache")]
checksum: Annotated[str, Field(description="Request checksum")]
timestamp: Annotated[str, Field(description="Timestamp")]
token_usage: Annotated[
TokenUsage | None, Field(default=None, description="Token usage info")
]
ocr_time_ms: Annotated[
int, Field(description="OCR processing time in milliseconds")
]
summarization_time_ms: Annotated[
int | None,
Field(default=None, description="Summarization time in milliseconds"),
]
total_time_ms: Annotated[
int, Field(description="Total processing time in milliseconds")
]
style_used: Annotated[
str | None, Field(default=None, description="Summarization style used")
]
image_info: Annotated[ImageInfo, Field(description="Image metadata")]
class LlmInfo(BaseModel):
"""Information about an available LLM model."""
provider: Annotated[str, Field(description="Provider name")]
model: Annotated[str, Field(description="Model name")]
version: Annotated[str | None, Field(default=None, description="Model version")]
available: Annotated[bool, Field(description="Whether model is available")]
class CacheEntry(BaseModel):
"""A single cache entry."""
id: Annotated[int, Field(description="Cache entry ID")]
checksum: Annotated[str, Field(description="Request checksum")]
text: Annotated[str, Field(description="Original text")]
summary: Annotated[str, Field(description="Generated summary")]
provider: Annotated[str, Field(description="Provider used")]
model: Annotated[str, Field(description="Model used")]
guild_id: Annotated[str, Field(description="Guild ID")]
user_id: Annotated[str, Field(description="User ID")]
channel_id: Annotated[str | None, Field(default=None, description="Channel ID")]
created_at: Annotated[str, Field(description="Created timestamp")]
last_accessed: Annotated[str, Field(description="Last accessed timestamp")]
access_count: Annotated[int, Field(description="Access count")]
class CacheStats(BaseModel):
"""Cache statistics."""
total_entries: Annotated[int, Field(description="Total cache entries")]
total_hits: Annotated[int, Field(description="Total cache hits")]
total_misses: Annotated[int, Field(description="Total cache misses")]
hit_rate: Annotated[float, Field(description="Cache hit rate (percentage)")]
total_size_bytes: Annotated[int, Field(description="Total size in bytes")]
class DeleteCacheRequest(BaseModel):
"""Request payload for deleting cache entries."""
id: Annotated[
int | None, Field(default=None, description="Cache entry ID to delete")
]
guild_id: Annotated[
str | None, Field(default=None, description="Delete all for guild")
]
user_id: Annotated[
str | None, Field(default=None, description="Delete all for user")
]
delete_all: Annotated[
bool | None, Field(default=None, description="Delete all entries")
]
class HealthResponse(BaseModel):
"""Health check response."""
status: Annotated[str, Field(description="Health status (e.g., 'healthy')")]
version: Annotated[str, Field(description="API version")]
class SuccessResponse(BaseModel):
"""Generic success response."""
success: Annotated[bool, Field(description="Operation success status")]
message: Annotated[str, Field(description="Success message")]
class ErrorResponse(BaseModel):
"""Error response from API."""
success: Annotated[bool, Field(description="Always False for errors")]
error: Annotated[str, Field(description="Error message")]

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"""Discord bot source package."""

235
discord_bot/src/bot.py Normal file
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"""Main Discord bot class and entry point."""
from __future__ import annotations
import asyncio
import pkgutil
import platform
import sys
from datetime import UTC, datetime
from pathlib import Path
import disnake
from disnake.ext import commands
from loguru import logger
from sdk.client import SummarizerClient
from sdk.exceptions import SummarizerAPIError, SummarizerConnectionError
from src.config import Config, load_config
# Version info
__version__ = "1.0.0"
class SummarizerBot(commands.InteractionBot):
"""Summarizer Discord bot with slash commands."""
def __init__(self, config: Config) -> None:
self.config = config
intents = disnake.Intents.default()
super().__init__(
intents=intents,
test_guilds=None, # Use global commands
owner_ids={int(uid) for uid in config.bot.owner_ids},
)
self._setup_logging()
def _setup_logging(self) -> None:
"""Configure loguru logging with file rotation and formatting."""
log_dir = Path(__file__).parent.parent / "logs"
log_dir.mkdir(exist_ok=True)
logger.remove()
# Console handler with colors
logger.add(
sys.stderr,
format=self.config.bot.log_format,
level=self.config.bot.log_level,
colorize=True,
backtrace=True,
diagnose=self.config.bot.debug,
)
# Main log file with rotation
logger.add(
log_dir / "bot.log",
rotation="10 MB",
retention="7 days",
compression="gz",
level="DEBUG" if self.config.bot.debug else "INFO",
format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} - {message}",
backtrace=True,
diagnose=True,
)
# Error log file (errors only)
logger.add(
log_dir / "errors.log",
rotation="5 MB",
retention="30 days",
compression="gz",
level="ERROR",
format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} - {message}",
backtrace=True,
diagnose=True,
)
logger.debug("Logging initialized")
async def on_ready(self) -> None:
"""Called when bot is ready - display startup banner."""
self._print_startup_banner()
def _print_startup_banner(self) -> None:
"""Print a startup banner with bot information."""
total_users = sum(g.member_count or 0 for g in self.guilds)
total_channels = sum(len(g.channels) for g in self.guilds)
banner = f"""
╔══════════════════════════════════════════════════════════════════╗
║ ║
║ ███████╗██╗ ██╗███╗ ███╗███╗ ███╗ █████╗ ██████╗ ██╗ ██║
║ ██╔════╝██║ ██║████╗ ████║████╗ ████║██╔══██╗██╔══██╗╚██╗ ██╔║
║ ███████╗██║ ██║██╔████╔██║██╔████╔██║███████║██████╔╝ ╚████╔╝║
║ ╚════██║██║ ██║██║╚██╔╝██║██║╚██╔╝██║██╔══██║██╔══██╗ ╚██╔╝ ║
║ ███████║╚██████╔╝██║ ╚═╝ ██║██║ ╚═╝ ██║██║ ██║██║ ██║ ██║ ║
║ ╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ║
║ SUMMARIZER BOT ║
║ ║
╠══════════════════════════════════════════════════════════════════╣
║ Bot Information ║
╠══════════════════════════════════════════════════════════════════╣
║ Bot User : {self.user!s:<49}
║ Bot ID : {self.user.id if self.user else 'N/A'!s:<49}
║ Version : {__version__:<49}
║ disnake : {disnake.__version__:<49}
║ Python : {platform.python_version():<49}
╠══════════════════════════════════════════════════════════════════╣
║ Statistics ║
╠══════════════════════════════════════════════════════════════════╣
║ Guilds : {len(self.guilds):<49}
║ Users : {total_users:<49}
║ Channels : {total_channels:<49}
║ Commands : {len(self.all_slash_commands):<49}
╠══════════════════════════════════════════════════════════════════╣
║ Configuration ║
╠══════════════════════════════════════════════════════════════════╣
║ API URL : {self.config.bot.api_url:<49}
║ Log Level : {self.config.bot.log_level:<49}
║ Debug Mode : {self.config.bot.debug!s:<49}
╠══════════════════════════════════════════════════════════════════╣
║ Started at : {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC'):<49}
╚══════════════════════════════════════════════════════════════════╝
"""
# Print to console (bypass loguru for clean banner)
print(banner)
# Also log key info
logger.info(
f"Bot ready: {self.user} (ID: {self.user.id if self.user else 'N/A'})"
)
logger.info(f"Connected to {len(self.guilds)} guilds with {total_users} users")
logger.info(f"Loaded {len(self.all_slash_commands)} slash commands")
async def on_slash_command_error(
self,
interaction: disnake.ApplicationCommandInteraction[SummarizerBot],
exception: commands.CommandError,
) -> None:
"""Handle slash command errors with admin notification."""
from src.errors import handle_command_error
await handle_command_error(self, interaction, exception)
def setup_extensions(self) -> None:
"""Load all extensions using pkgutil."""
from src import extensions
for module_info in pkgutil.iter_modules(
extensions.__path__, prefix="src.extensions."
):
if module_info.name.split(".")[-1].startswith("_"):
continue
try:
self.load_extension(module_info.name)
logger.info(f"Loaded extension: {module_info.name}")
except Exception as e:
logger.error(f"Failed to load extension {module_info.name}: {e}")
async def check_api_health(api_url: str) -> bool:
"""Check if the backend API is available.
Args:
api_url: The API base URL to check
Returns
-------
True if API is healthy, False otherwise
"""
try:
async with SummarizerClient(base_url=api_url, timeout=10.0) as client:
await client.health_check()
return True
except SummarizerConnectionError:
logger.error(f"Cannot connect to API at {api_url}")
return False
except SummarizerAPIError as e:
logger.error(f"API health check failed: {e}")
return False
except Exception as e:
logger.error(f"Unexpected error during API health check: {e}")
return False
def setup_early_logging() -> None:
"""Setup minimal logging before config is loaded."""
logger.remove()
logger.add(
sys.stderr,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <level>{message}</level>",
level="INFO",
colorize=True,
)
async def main() -> None:
"""Main entry point."""
setup_early_logging()
# Load configuration
try:
config = load_config()
except FileNotFoundError as e:
logger.error(f"Configuration error: {e}")
logger.info(
"Copy config/config.toml.example to config/config.toml and fill in your settings."
)
sys.exit(1)
# Check API availability before starting
logger.info(f"Checking API availability at {config.bot.api_url}...")
if not await check_api_health(config.bot.api_url):
logger.critical(
"Backend API is not available. Bot cannot start without the API."
)
logger.info("Please ensure the Summarizer API is running and accessible.")
logger.info(f"Expected API URL: {config.bot.api_url}")
sys.exit(1)
logger.success("API health check passed!")
# Initialize and start bot
bot = SummarizerBot(config)
bot.setup_extensions()
logger.info("Starting Discord bot...")
await bot.start(config.bot.token)
if __name__ == "__main__":
asyncio.run(main())

70
discord_bot/src/config.py Normal file
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"""Configuration management for the Discord bot."""
from __future__ import annotations
import tomllib
from pathlib import Path
from typing import Annotated
from pydantic import BaseModel, Field
class BotConfig(BaseModel):
"""Bot configuration loaded from TOML."""
token: Annotated[str, Field(description="Discord bot token")]
application_id: Annotated[int | str, Field(description="Discord application ID")]
owner_ids: Annotated[
list[int | str], Field(default_factory=list, description="Bot owner user IDs")
]
log_level: Annotated[str, Field(default="INFO", description="Logging level")]
log_format: Annotated[
str,
Field(
default="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan> - <level>{message}</level>",
description="Loguru format string",
),
]
http_timeout: Annotated[
float, Field(default=30.0, description="HTTP request timeout")
]
max_retries: Annotated[int, Field(default=3, description="Max HTTP retries")]
command_prefix: Annotated[
list[str],
Field(default_factory=lambda: ["s!"], description="Text command prefixes"),
]
debug: Annotated[bool, Field(default=False, description="Debug mode")]
api_url: Annotated[
str, Field(default="http://127.0.0.1:3001/api", description="Backend API URL")
]
class Config(BaseModel):
"""Root configuration model."""
bot: Annotated[BotConfig, Field(alias="Bot")]
def load_config(path: Path | str | None = None) -> Config:
"""Load configuration from TOML file.
Args:
path: Path to config file. Defaults to config/config.toml
Returns
-------
Parsed Config object
"""
if path is None:
path = Path(__file__).parent.parent / "config" / "config.toml"
elif isinstance(path, str):
path = Path(path)
if not path.exists():
msg = f"Config file not found: {path}"
raise FileNotFoundError(msg)
with path.open("rb") as f:
data = tomllib.load(f)
return Config.model_validate(data)

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discord_bot/src/embeds.py Normal file
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"""Pastel-themed embed builders for the Summarizer bot."""
from __future__ import annotations
from datetime import UTC, datetime
from typing import TYPE_CHECKING
import disnake
if TYPE_CHECKING:
from sdk import (
CacheEntry,
CacheStats,
LlmInfo,
OcrSummarizeResponse,
SummarizeResponse,
)
class Pastel:
"""Pastel color palette."""
LAVENDER = 0xE6E6FA
MINT = 0x98FF98
PEACH = 0xFFDAB9
SKY = 0x87CEEB
ROSE = 0xFFB6C1
LEMON = 0xFFFACD
CORAL = 0xF08080
LILAC = 0xDDA0DD
SAGE = 0xBCB88A
BLUSH = 0xDE5D83
def summary_embed(response: SummarizeResponse, original_text: str) -> disnake.Embed:
"""Create embed for summarization result."""
embed = disnake.Embed(
title="Summary",
description=response.summary,
color=Pastel.LAVENDER,
timestamp=datetime.now(UTC),
)
# Truncate original text preview
preview = original_text[:200] + "..." if len(original_text) > 200 else original_text
embed.add_field(name="Original", value=f"```{preview}```", inline=False)
# Metadata in a clean format
meta_parts = [f"**Model** {response.model}", f"**Provider** {response.provider}"]
if response.style_used:
meta_parts.append(f"**Style** {response.style_used}")
if response.from_cache:
meta_parts.append("**Cached**")
embed.add_field(name="Details", value=" · ".join(meta_parts), inline=False)
# Token usage if available
if response.token_usage:
usage = response.token_usage
tokens_text = f"In: {usage.input_tokens} · Out: {usage.output_tokens} · Total: {usage.total_tokens}"
if usage.estimated_cost_usd:
tokens_text += f" · ${usage.estimated_cost_usd:.4f}"
embed.set_footer(text=tokens_text)
elif response.processing_time_ms:
embed.set_footer(text=f"Processed in {response.processing_time_ms}ms")
return embed
def ocr_summary_embed(response: OcrSummarizeResponse) -> disnake.Embed:
"""Create embed for OCR summarization result."""
embed = disnake.Embed(
title="Image Summary",
description=response.summary,
color=Pastel.MINT,
timestamp=datetime.now(UTC),
)
# Extracted text preview
extracted = response.extracted_text
preview = extracted[:300] + "..." if len(extracted) > 300 else extracted
if preview.strip():
embed.add_field(name="Extracted Text", value=f"```{preview}```", inline=False)
# Image info
img = response.image_info
img_text = f"{img.width}x{img.height} {img.format} · {img.size_bytes / 1024:.1f} KB"
embed.add_field(name="Image", value=img_text, inline=True)
# Timing
timing = f"OCR: {response.ocr_time_ms}ms · Total: {response.total_time_ms}ms"
embed.add_field(name="Processing", value=timing, inline=True)
if response.from_cache:
embed.set_footer(text="Served from cache")
return embed
def error_embed(title: str, message: str) -> disnake.Embed:
"""Create embed for error messages."""
return disnake.Embed(
title=title,
description=message,
color=Pastel.CORAL,
timestamp=datetime.now(UTC),
)
def success_embed(title: str, message: str) -> disnake.Embed:
"""Create embed for success messages."""
return disnake.Embed(
title=title,
description=message,
color=Pastel.MINT,
timestamp=datetime.now(UTC),
)
def info_embed(title: str, message: str) -> disnake.Embed:
"""Create embed for informational messages."""
return disnake.Embed(
title=title,
description=message,
color=Pastel.SKY,
timestamp=datetime.now(UTC),
)
def loading_embed(message: str = "Processing your request...") -> disnake.Embed:
"""Create embed for loading/processing state."""
return disnake.Embed(
title="Working on it",
description=message,
color=Pastel.LEMON,
timestamp=datetime.now(UTC),
)
def cache_stats_embed(stats: CacheStats) -> disnake.Embed:
"""Create embed for cache statistics."""
embed = disnake.Embed(
title="Cache Statistics",
color=Pastel.LILAC,
timestamp=datetime.now(UTC),
)
embed.add_field(name="Entries", value=f"{stats.total_entries:,}", inline=True)
embed.add_field(name="Hit Rate", value=f"{stats.hit_rate:.1f}%", inline=True)
embed.add_field(
name="Size", value=f"{stats.total_size_bytes / 1024:.1f} KB", inline=True
)
embed.add_field(name="Hits", value=f"{stats.total_hits:,}", inline=True)
embed.add_field(name="Misses", value=f"{stats.total_misses:,}", inline=True)
return embed
def cache_entry_embed(entry: CacheEntry) -> disnake.Embed:
"""Create embed for a single cache entry."""
embed = disnake.Embed(
title=f"Cache Entry #{entry.id}",
color=Pastel.PEACH,
timestamp=datetime.now(UTC),
)
# Summary preview
summary_preview = (
entry.summary[:500] + "..." if len(entry.summary) > 500 else entry.summary
)
embed.description = summary_preview
embed.add_field(name="Model", value=entry.model, inline=True)
embed.add_field(name="Provider", value=entry.provider, inline=True)
embed.add_field(name="Accesses", value=str(entry.access_count), inline=True)
embed.add_field(name="Created", value=entry.created_at, inline=True)
embed.add_field(name="Last Used", value=entry.last_accessed, inline=True)
embed.set_footer(text=f"Checksum: {entry.checksum[:16]}...")
return embed
def models_embed(models: list[LlmInfo]) -> disnake.Embed:
"""Create embed for available models list."""
embed = disnake.Embed(
title="Available Models",
color=Pastel.SAGE,
timestamp=datetime.now(UTC),
)
# Group by provider
by_provider: dict[str, list[LlmInfo]] = {}
for model in models:
if model.provider not in by_provider:
by_provider[model.provider] = []
by_provider[model.provider].append(model)
for provider, provider_models in by_provider.items():
model_lines: list[str] = []
for m in provider_models:
status = "available" if m.available else "unavailable"
version = f" ({m.version})" if m.version else ""
model_lines.append(f"`{m.model}`{version} - {status}")
embed.add_field(
name=provider.title(), value="\n".join(model_lines), inline=False
)
return embed
def help_embed() -> disnake.Embed:
"""Create help embed with command overview."""
embed = disnake.Embed(
title="Summarizer Bot",
description="Transform long text into concise summaries using AI.",
color=Pastel.LAVENDER,
timestamp=datetime.now(UTC),
)
commands = [
("`/summarize`", "Summarize text with customizable style and model"),
("`/ocr`", "Extract and summarize text from images"),
("`/models`", "View available AI models"),
("`/cache stats`", "View cache statistics"),
("`/cache clear`", "Clear your cached summaries"),
]
for name, desc in commands:
embed.add_field(name=name, value=desc, inline=False)
embed.set_footer(text="Use /help <command> for detailed usage")
return embed

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discord_bot/src/errors.py Normal file
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"""Error handling utilities for the bot."""
from __future__ import annotations
import contextlib
import traceback
from datetime import UTC, datetime
import disnake
from disnake.ext import commands
from loguru import logger
from src.bot import SummarizerBot
from src.embeds import Pastel, error_embed
from src.pagination import Paginator, paginate_text
def create_error_report_embeds(
interaction: disnake.ApplicationCommandInteraction[SummarizerBot],
exception: BaseException,
) -> list[disnake.Embed]:
"""Create detailed error report embeds for admins/owners."""
error_id = f"{interaction.id}"
timestamp = datetime.now(UTC)
# Format the traceback
tb_lines = traceback.format_exception(
type(exception), exception, exception.__traceback__
)
tb_text = "".join(tb_lines)
# Build context info
guild_info = (
f"{interaction.guild.name} ({interaction.guild_id})"
if interaction.guild
else "DM"
)
channel_info = f"#{interaction.channel}" if interaction.channel else "Unknown"
context = (
f"**Error ID:** `{error_id}`\n"
f"**Command:** `/{interaction.application_command.name}`\n"
f"**User:** {interaction.author} (`{interaction.author.id}`)\n"
f"**Guild:** {guild_info}\n"
f"**Channel:** {channel_info}\n"
f"**Time:** <t:{int(timestamp.timestamp())}:F>"
)
# First embed with context
embeds: list[disnake.Embed] = []
header_embed = disnake.Embed(
title="Error Report",
description=context,
color=Pastel.CORAL,
timestamp=timestamp,
)
header_embed.add_field(
name="Exception Type",
value=f"`{type(exception).__name__}`",
inline=True,
)
header_embed.add_field(
name="Message",
value=str(exception)[:1024] or "No message",
inline=False,
)
embeds.append(header_embed)
# Paginate traceback if needed
if len(tb_text) > 4000:
chunks = paginate_text(tb_text, max_length=4000)
for i, chunk in enumerate(chunks, 1):
tb_embed = disnake.Embed(
title=f"Traceback ({i}/{len(chunks)})",
description=f"```python\n{chunk}\n```",
color=Pastel.CORAL,
)
embeds.append(tb_embed)
else:
tb_embed = disnake.Embed(
title="Traceback",
description=f"```python\n{tb_text}\n```",
color=Pastel.CORAL,
)
embeds.append(tb_embed)
# Add footer to last embed
embeds[-1].set_footer(text=f"Error ID: {error_id}")
return embeds
async def notify_owners(
bot: SummarizerBot,
embeds: list[disnake.Embed],
) -> None:
"""Send error report to all bot owners via DM."""
for owner_id in bot.config.bot.owner_ids:
try:
user = await bot.fetch_user(int(owner_id))
if len(embeds) == 1:
await user.send(embed=embeds[0])
else:
# Use paginator for multiple embeds
view = Paginator(embeds, author_id=user.id)
view.update_page_indicator()
await user.send(embed=embeds[0], view=view)
except disnake.Forbidden:
logger.warning(f"Cannot DM owner {owner_id} - DMs disabled")
except disnake.NotFound:
logger.warning(f"Owner {owner_id} not found")
except Exception as e: # noqa: BLE001
logger.error(f"Failed to notify owner {owner_id}: {e}")
def is_privileged_user(
bot: SummarizerBot,
user_id: int,
) -> bool:
"""Check if user is an owner or admin."""
return str(user_id) in bot.config.bot.owner_ids
async def handle_command_error(
bot: SummarizerBot,
interaction: disnake.ApplicationCommandInteraction[SummarizerBot],
exception: commands.CommandError,
) -> None:
"""Handle command errors with proper user feedback and admin notification."""
# Handle known error types with specific messages
if isinstance(exception, commands.CheckFailure):
await _send_error_response(
interaction,
"Permission Denied",
"You don't have permission to use this command.",
)
return
if isinstance(exception, commands.CommandOnCooldown):
await _send_error_response(
interaction,
"Cooldown",
f"Please wait {exception.retry_after:.1f}s before using this command again.",
)
return
if isinstance(exception, commands.MissingPermissions):
perms = ", ".join(exception.missing_permissions)
await _send_error_response(
interaction,
"Missing Permissions",
f"You need the following permissions: {perms}",
)
return
if isinstance(exception, commands.BotMissingPermissions):
perms = ", ".join(exception.missing_permissions)
await _send_error_response(
interaction,
"Bot Missing Permissions",
f"I need the following permissions: {perms}",
)
return
# Log the error
logger.exception(
f"Unhandled error in /{interaction.application_command.name}: {exception}"
)
# For privileged users, show the actual error
if is_privileged_user(bot, interaction.author.id):
error_msg = f"```\n{type(exception).__name__}: {exception}\n```"
if len(error_msg) > 4000:
error_msg = error_msg[:3997] + "```"
await _send_error_response(
interaction,
"Error (Admin View)",
error_msg,
)
else:
# Generic message for regular users
await _send_error_response(
interaction,
"Something went wrong",
"An unexpected error occurred. The bot administrators have been notified.",
)
# Create and send error report to owners
try:
error_embeds = create_error_report_embeds(interaction, exception)
await notify_owners(bot, error_embeds)
except Exception as e: # noqa: BLE001
logger.error(f"Failed to create/send error report: {e}")
async def _send_error_response(
interaction: disnake.ApplicationCommandInteraction[SummarizerBot],
title: str,
message: str,
) -> None:
"""Send an error response to the user."""
embed = error_embed(title, message)
try:
if interaction.response.is_done():
await interaction.edit_original_response(embed=embed)
else:
await interaction.response.send_message(embed=embed, ephemeral=True)
except Exception: # noqa: BLE001
# Last resort - try followup
with contextlib.suppress(Exception):
await interaction.followup.send(embed=embed, ephemeral=True)

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"""Bot extensions package."""

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"""Admin commands cog for bot owners."""
from __future__ import annotations
import contextlib
from typing import Any
import disnake
from disnake.ext import commands
from loguru import logger
from sdk import SummarizerAPIError, SummarizerClient, SummarizerRateLimitError
from src.bot import SummarizerBot
from src.embeds import error_embed, help_embed, info_embed, success_embed
from src.errors import create_error_report_embeds, notify_owners
# Rate limit for admin commands: 5 uses per 30 seconds per user
ADMIN_COOLDOWN = commands.CooldownMapping.from_cooldown(
5, 30.0, commands.BucketType.user
)
def check_cooldown(
mapping: commands.CooldownMapping,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
) -> float | None:
"""Check if user is on cooldown. Returns retry_after if on cooldown."""
bucket = mapping.get_bucket(inter)
if bucket:
return bucket.update_rate_limit()
return None
def is_owner() -> Any: # noqa: ANN401
"""Check if user is a bot owner."""
async def predicate(
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
) -> bool:
return str(inter.author.id) in inter.bot.config.bot.owner_ids
return commands.check(predicate) # type: ignore[arg-type]
class AdminCog(commands.Cog):
"""Admin-only commands for bot management."""
def __init__(self, bot: SummarizerBot) -> None:
self.bot = bot
@commands.slash_command(name="help", description="Show bot help and commands")
async def help_command(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Display help information."""
await inter.response.send_message(embed=help_embed())
@commands.slash_command(name="ping", description="Check bot latency")
async def ping(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Check bot and API latency."""
# Check rate limit
retry_after = check_cooldown(ADMIN_COOLDOWN, inter)
if retry_after:
await inter.response.send_message(
embed=error_embed(
"Cooldown",
f"Please wait {retry_after:.1f}s before using this command again.",
),
ephemeral=True,
)
return
await inter.response.defer()
bot_latency = round(self.bot.latency * 1000)
# Check API health
api_status = "offline"
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
await client.health_check()
api_status = "online"
except SummarizerAPIError:
api_status = "error"
except Exception: # noqa: BLE001
api_status = "unreachable"
embed = info_embed(
"Pong",
f"**Bot** {bot_latency}ms\n**API** {api_status}",
)
await inter.edit_original_response(embed=embed)
@commands.slash_command(name="admin", description="Admin commands")
@is_owner()
async def admin(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Parent command for admin operations."""
@admin.sub_command(
name="clear-guild-cache", description="Clear all cache for this server"
)
async def clear_guild_cache(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Clear all cache entries for the current guild."""
if not inter.guild_id:
await inter.response.send_message(
embed=error_embed(
"Server Only", "This command can only be used in a server."
),
ephemeral=True,
)
return
await inter.response.defer(ephemeral=True)
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
result = await client.delete_cache(guild_id=str(inter.guild_id))
await inter.edit_original_response(
embed=success_embed("Guild Cache Cleared", result.message)
)
except SummarizerRateLimitError as e:
logger.warning(f"Rate limit clearing guild cache: {e}")
await inter.edit_original_response(
embed=error_embed(
"Rate Limited",
"API rate limit exceeded. Please try again later.",
)
)
except SummarizerAPIError as e:
logger.warning(f"API error clearing guild cache: {e}")
await inter.edit_original_response(embed=error_embed("Failed", str(e)))
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error clearing guild cache")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
)
)
# Notify owners
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
@admin.sub_command(
name="clear-all-cache", description="Clear entire cache (dangerous)"
)
async def clear_all_cache(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Clear all cache entries globally."""
await inter.response.defer(ephemeral=True)
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
result = await client.delete_cache(delete_all=True)
await inter.edit_original_response(
embed=success_embed("All Cache Cleared", result.message)
)
except SummarizerRateLimitError as e:
logger.warning(f"Rate limit clearing all cache: {e}")
await inter.edit_original_response(
embed=error_embed(
"Rate Limited",
"API rate limit exceeded. Please try again later.",
)
)
except SummarizerAPIError as e:
logger.warning(f"API error clearing all cache: {e}")
await inter.edit_original_response(embed=error_embed("Failed", str(e)))
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error clearing all cache")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
)
)
# Notify owners
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
@admin.sub_command(name="status", description="Show bot status")
async def status(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Show detailed bot status."""
await inter.response.defer(ephemeral=True)
guilds = len(self.bot.guilds)
users = sum(g.member_count or 0 for g in self.bot.guilds)
# Get cache stats
cache_info = "unavailable"
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
stats = await client.get_cache_stats()
cache_info = (
f"{stats.total_entries:,} entries · "
f"{stats.hit_rate:.1f}% hit rate"
)
except Exception: # noqa: BLE001
logger.debug("Failed to get cache stats for status command")
embed = info_embed(
"Bot Status",
f"**Servers** {guilds:,}\n"
f"**Users** {users:,}\n"
f"**Cache** {cache_info}\n"
f"**API** {self.bot.config.bot.api_url}",
)
await inter.edit_original_response(embed=embed)
def setup(bot: SummarizerBot) -> None:
"""Load the cog."""
bot.add_cog(AdminCog(bot))

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"""Cache management commands cog."""
from __future__ import annotations
import disnake
from disnake.ext import commands
from loguru import logger
from sdk import SummarizerAPIError, SummarizerClient
from src.bot import SummarizerBot
from src.embeds import cache_entry_embed, cache_stats_embed, error_embed, success_embed
class CacheCog(commands.Cog):
"""Commands for cache management."""
def __init__(self, bot: SummarizerBot) -> None:
self.bot = bot
@commands.slash_command(name="cache", description="Cache management commands")
async def cache(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Parent command for cache operations."""
@cache.sub_command(name="stats", description="View cache statistics")
async def cache_stats(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Display cache statistics."""
await inter.response.defer()
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
stats = await client.get_cache_stats()
await inter.edit_original_response(embed=cache_stats_embed(stats))
except SummarizerAPIError as e:
logger.warning(f"API error getting cache stats: {e}")
await inter.edit_original_response(
embed=error_embed("Failed to Get Stats", str(e))
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error getting cache stats")
await inter.edit_original_response(
embed=error_embed("Something went wrong", str(e))
)
@cache.sub_command(name="list", description="List your cached summaries")
async def cache_list(
self,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
limit: int = commands.Param(
description="Number of entries to show", default=5, ge=1, le=25
),
) -> None:
"""List user's cached summaries."""
await inter.response.defer(ephemeral=True)
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
entries = await client.get_user_cache(str(inter.author.id))
if not entries:
await inter.edit_original_response(
embed=success_embed(
"No Cache Entries", "You don't have any cached summaries yet."
)
)
return
# Show limited entries
entries = entries[:limit]
embeds = [cache_entry_embed(entry) for entry in entries]
await inter.edit_original_response(embeds=embeds[:10]) # Discord limit
except SummarizerAPIError as e:
logger.warning(f"API error listing cache: {e}")
await inter.edit_original_response(
embed=error_embed("Failed to List Cache", str(e))
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error listing cache")
await inter.edit_original_response(
embed=error_embed("Something went wrong", str(e))
)
@cache.sub_command(name="clear", description="Clear your cached summaries")
async def cache_clear(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""Clear user's cached summaries."""
await inter.response.defer(ephemeral=True)
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
result = await client.delete_cache(user_id=str(inter.author.id))
await inter.edit_original_response(
embed=success_embed("Cache Cleared", result.message)
)
except SummarizerAPIError as e:
logger.warning(f"API error clearing cache: {e}")
await inter.edit_original_response(
embed=error_embed("Failed to Clear Cache", str(e))
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error clearing cache")
await inter.edit_original_response(
embed=error_embed("Something went wrong", str(e))
)
def setup(bot: SummarizerBot) -> None:
"""Load the cog."""
bot.add_cog(CacheCog(bot))

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"""Summarization commands cog."""
from __future__ import annotations
import contextlib
from datetime import UTC, datetime
import disnake
from disnake.ext import commands
from loguru import logger
from sdk import (
LlmProvider,
SummarizationStyle,
SummarizerAPIError,
SummarizerClient,
SummarizerRateLimitError,
)
from src.bot import SummarizerBot
from src.embeds import error_embed, models_embed, ocr_summary_embed, summary_embed
from src.errors import create_error_report_embeds, notify_owners
from src.views import QuickSummarizeView
# Rate limit: 5 uses per 60 seconds per user
SUMMARIZE_COOLDOWN = commands.CooldownMapping.from_cooldown(
5, 60.0, commands.BucketType.user
)
# Rate limit for bulk: 3 uses per 120 seconds per user
BULK_COOLDOWN = commands.CooldownMapping.from_cooldown(
3, 120.0, commands.BucketType.user
)
# Rate limit for OCR: 3 uses per 60 seconds per user
OCR_COOLDOWN = commands.CooldownMapping.from_cooldown(3, 60.0, commands.BucketType.user)
def check_cooldown(
mapping: commands.CooldownMapping,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
) -> float | None:
"""Check if user is on cooldown. Returns retry_after if on cooldown."""
bucket = mapping.get_bucket(inter)
if bucket:
return bucket.update_rate_limit()
return None
class SummarizeCog(commands.Cog):
"""Commands for text and image summarization."""
def __init__(self, bot: SummarizerBot) -> None:
self.bot = bot
@commands.slash_command(
name="summarize", description="Summarize a message by ID or reply."
)
async def summarize(
self,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
message_id: str = commands.Param(
description="Message ID to summarize (or reply to a message)",
default=None,
),
style: str = commands.Param(
description="Summary style",
default=None,
choices=[
"brief",
"detailed",
"funny",
"professional",
"technical",
"eli5",
"bullets",
"academic",
"roast",
],
),
) -> None:
"""Summarize a message. Provide message_id or reply to a message."""
# Check rate limit
retry_after = check_cooldown(SUMMARIZE_COOLDOWN, inter)
if retry_after:
await inter.response.send_message(
embed=error_embed(
"Cooldown",
f"Please wait {retry_after:.1f}s before using this command again.",
),
ephemeral=True,
)
return
# Determine which message to summarize first (before deferring)
target_message: disnake.Message | None = None
# Check if this is a reply to a message
if hasattr(inter, "message") and inter.message and inter.message.reference:
ref = inter.message.reference
if ref.message_id:
with contextlib.suppress(disnake.NotFound):
target_message = await inter.channel.fetch_message(ref.message_id)
# If no reply, try message_id parameter
if target_message is None and message_id:
try:
msg_id = int(message_id)
target_message = await inter.channel.fetch_message(msg_id)
except ValueError:
await inter.response.send_message(
embed=error_embed(
"Invalid Message ID",
"Please provide a valid message ID (numbers only).\n\n"
"**Tip:** Enable Developer Mode in Discord settings "
"to copy message IDs, or reply to the message.",
),
ephemeral=True,
)
return
except disnake.NotFound:
await inter.response.send_message(
embed=error_embed(
"Message Not Found",
"Could not find a message with that ID in this channel.\n\n"
"Make sure the message exists and is in this channel.",
),
ephemeral=True,
)
return
except disnake.Forbidden:
await inter.response.send_message(
embed=error_embed(
"Access Denied",
"I don't have permission to read that message.",
),
ephemeral=True,
)
return
# No message found
if target_message is None:
await inter.response.send_message(
embed=error_embed(
"No Message Specified",
"Please provide a message ID or reply to a message.\n\n"
"**Usage:**\n"
"• `/summarize message_id:123456789` - Summarize by ID\n"
"• Reply to a message and use `/summarize`\n\n"
"**Tip:** Right-click → Copy Message ID (Developer Mode)",
),
ephemeral=True,
)
return
# Check if message has content
text = target_message.content
if not text or not text.strip():
await inter.response.send_message(
embed=error_embed(
"Empty Message",
"That message has no text content to summarize.",
),
ephemeral=True,
)
return
llm_style = SummarizationStyle(style) if style else None
# Create callback for summarization
async def do_summarize(provider: str, model: str | None) -> None:
try:
llm_provider = LlmProvider(provider)
async with SummarizerClient(
base_url=self.bot.config.bot.api_url
) as client:
response = await client.summarize(
text=text,
provider=llm_provider,
guild_id=str(inter.guild_id or 0),
user_id=str(inter.author.id),
channel_id=str(inter.channel_id) if inter.channel_id else None,
style=llm_style,
model=model,
)
embed = summary_embed(response, text)
await inter.edit_original_response(embed=embed, view=None)
except SummarizerRateLimitError as e:
logger.warning(f"Rate limit from API in summarize: {e}")
await inter.edit_original_response(
embed=error_embed(
"Rate Limited",
"The API rate limit has been exceeded. Please try again later.",
),
view=None,
)
except SummarizerAPIError as e:
logger.warning(f"API error in summarize: {e}")
await inter.edit_original_response(
embed=error_embed("Summarization Failed", str(e)), view=None
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error in summarize")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
),
view=None,
)
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
# Show provider/model selection view
view = QuickSummarizeView(self.bot, do_summarize, author_id=inter.author.id)
preview = text[:200] + ("..." if len(text) > 200 else "")
embed = disnake.Embed(
title="📝 Summarize Message",
description=(
f"Ready to summarize message from "
f"**{target_message.author.display_name}**\n\n"
f"**Preview:** {preview}\n\n"
f"**Style:** {style or 'Default'}"
),
color=disnake.Color.blurple(),
)
await inter.response.send_message(embed=embed, view=view)
@commands.slash_command(
name="bulk-summarize",
description="Summarize multiple messages from the channel.",
)
async def bulk_summarize(
self,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
messages: int = commands.Param(
description="Number of messages to summarize (max 100)",
ge=1,
le=100,
),
style: str = commands.Param(
description="Summary style",
default=None,
choices=[
"brief",
"detailed",
"funny",
"professional",
"technical",
"eli5",
"bullets",
"academic",
"roast",
],
),
user: disnake.User | None = None,
after: str = commands.Param(
description="Only messages after date/time (YYYY-MM-DD HH:MM)",
default=None,
),
before: str = commands.Param(
description="Only messages before date/time (YYYY-MM-DD HH:MM)",
default=None,
),
include_bots: bool = False, # noqa: FBT001, FBT002
) -> None:
"""Summarize multiple messages with filtering options."""
# Check rate limit
retry_after = check_cooldown(BULK_COOLDOWN, inter)
if retry_after:
await inter.response.send_message(
embed=error_embed(
"Cooldown",
f"Please wait {retry_after:.1f}s before using this command again.",
),
ephemeral=True,
)
return
# Parse date filters first (before any response)
after_dt: datetime | None = None
before_dt: datetime | None = None
if after:
after_dt = self._parse_datetime(after)
if after_dt is None:
await inter.response.send_message(
embed=error_embed(
"Invalid Date Format",
f"Could not parse 'after' date: `{after}`\n\n"
"Use format: `YYYY-MM-DD` or `YYYY-MM-DD HH:MM`",
),
ephemeral=True,
)
return
if before:
before_dt = self._parse_datetime(before)
if before_dt is None:
await inter.response.send_message(
embed=error_embed(
"Invalid Date Format",
f"Could not parse 'before' date: `{before}`\n\n"
"Use format: `YYYY-MM-DD` or `YYYY-MM-DD HH:MM`",
),
ephemeral=True,
)
return
# Fetch messages with filters
collected_messages: list[disnake.Message] = []
fetch_limit = messages * 3 # Fetch more to account for filtering
async for msg in inter.channel.history(
limit=fetch_limit,
after=after_dt,
before=before_dt,
):
# Skip bots if not included
if not include_bots and msg.author.bot:
continue
# Filter by user if specified
if user and msg.author.id != user.id:
continue
# Skip empty messages
if not msg.content or not msg.content.strip():
continue
collected_messages.append(msg)
if len(collected_messages) >= messages:
break
if not collected_messages:
filter_info: list[str] = []
if user:
filter_info.append(f"from {user.mention}")
if after_dt:
filter_info.append(f"after {after}")
if before_dt:
filter_info.append(f"before {before}")
if not include_bots:
filter_info.append("excluding bots")
filter_str = (
", ".join(filter_info) if filter_info else "with current filters"
)
await inter.response.send_message(
embed=error_embed(
"No Messages Found",
f"No messages found {filter_str}.",
),
ephemeral=True,
)
return
# Build text from messages (oldest first)
collected_messages.reverse()
text_parts: list[str] = []
for msg in collected_messages:
timestamp = msg.created_at.strftime("%Y-%m-%d %H:%M")
text_parts.append(f"[{timestamp}] {msg.author.display_name}: {msg.content}")
text = "\n".join(text_parts)
llm_style = SummarizationStyle(style) if style else None
msg_count = len(collected_messages)
# Create callback for summarization
async def do_bulk_summarize(provider: str, model: str | None) -> None:
try:
llm_provider = LlmProvider(provider)
async with SummarizerClient(
base_url=self.bot.config.bot.api_url
) as client:
response = await client.summarize(
text=text,
provider=llm_provider,
guild_id=str(inter.guild_id or 0),
user_id=str(inter.author.id),
channel_id=str(inter.channel_id) if inter.channel_id else None,
style=llm_style,
model=model,
)
embed = summary_embed(response, text)
embed.set_footer(text=f"Summarized {msg_count} messages")
await inter.edit_original_response(embed=embed, view=None)
except SummarizerRateLimitError as e:
logger.warning(f"Rate limit from API in bulk_summarize: {e}")
await inter.edit_original_response(
embed=error_embed(
"Rate Limited",
"The API rate limit has been exceeded. Please try again later.",
),
view=None,
)
except SummarizerAPIError as e:
logger.warning(f"API error in bulk_summarize: {e}")
await inter.edit_original_response(
embed=error_embed("Summarization Failed", str(e)), view=None
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error in bulk_summarize")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
),
view=None,
)
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
# Show provider/model selection view
view = QuickSummarizeView(
self.bot, do_bulk_summarize, author_id=inter.author.id
)
# Build filter description
filter_parts: list[str] = []
if user:
filter_parts.append(f"From: {user.mention}")
if after:
filter_parts.append(f"After: {after}")
if before:
filter_parts.append(f"Before: {before}")
if include_bots:
filter_parts.append("Including bots")
filter_desc = "\n".join(filter_parts) if filter_parts else "No filters"
embed = disnake.Embed(
title="📚 Bulk Summarize",
description=(
f"Ready to summarize **{msg_count}** messages\n\n"
f"**Filters:**\n{filter_desc}\n\n"
f"**Style:** {style or 'Default'}"
),
color=disnake.Color.blurple(),
)
await inter.response.send_message(embed=embed, view=view)
def _parse_datetime(self, date_str: str) -> datetime | None:
"""Parse a date/datetime string into a datetime object."""
formats = [
"%Y-%m-%d %H:%M",
"%Y-%m-%d %H:%M:%S",
"%Y-%m-%d",
"%d/%m/%Y %H:%M",
"%d/%m/%Y",
]
for fmt in formats:
try:
dt = datetime.strptime(date_str.strip(), fmt) # noqa: DTZ007
return dt.replace(tzinfo=UTC)
except ValueError:
continue
return None
@commands.slash_command(
name="ocr", description="Extract and summarize text from an image"
)
async def ocr_summarize(
self,
inter: disnake.ApplicationCommandInteraction[SummarizerBot],
image: disnake.Attachment = commands.Param( # noqa: B008
description="Image to process"
),
style: str = commands.Param(
description="Summary style",
default=None,
choices=[
"brief",
"detailed",
"funny",
"professional",
"technical",
"eli5",
"bullets",
"academic",
"roast",
],
),
) -> None:
"""OCR and summarize an image."""
# Check rate limit
retry_after = check_cooldown(OCR_COOLDOWN, inter)
if retry_after:
await inter.response.send_message(
embed=error_embed(
"Cooldown",
f"Please wait {retry_after:.1f}s before using this command again.",
),
ephemeral=True,
)
return
# Validate image
if not image.content_type or not image.content_type.startswith("image/"):
await inter.response.send_message(
embed=error_embed("Invalid File", "Please upload an image file."),
ephemeral=True,
)
return
# Read image data before showing the view
image_data = await image.read()
llm_style = SummarizationStyle(style) if style else None
# Create callback for OCR summarization
async def do_ocr_summarize(provider: str, model: str | None) -> None:
try:
llm_provider = LlmProvider(provider)
async with SummarizerClient(
base_url=self.bot.config.bot.api_url
) as client:
response = await client.ocr_summarize(
image_data=image_data,
provider=llm_provider,
guild_id=str(inter.guild_id or 0),
user_id=str(inter.author.id),
channel_id=str(inter.channel_id) if inter.channel_id else None,
style=llm_style,
model=model,
)
embed = ocr_summary_embed(response)
await inter.edit_original_response(embed=embed, view=None)
except SummarizerRateLimitError as e:
logger.warning(f"Rate limit from API in ocr: {e}")
await inter.edit_original_response(
embed=error_embed(
"Rate Limited",
"The API rate limit has been exceeded. Please try again later.",
),
view=None,
)
except SummarizerAPIError as e:
logger.warning(f"API error in ocr: {e}")
await inter.edit_original_response(
embed=error_embed("OCR Failed", str(e)), view=None
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error in ocr")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
),
view=None,
)
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
# Show provider/model selection view
view = QuickSummarizeView(self.bot, do_ocr_summarize, author_id=inter.author.id)
# Format file size
size_kb = image.size / 1024 if image.size else 0
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb/1024:.1f} MB"
embed = disnake.Embed(
title="🖼️ OCR & Summarize",
description=(
f"Ready to extract and summarize text from image\n\n"
f"**File:** {image.filename}\n"
f"**Size:** {size_str}\n"
f"**Style:** {style or 'Default'}"
),
color=disnake.Color.blurple(),
)
if image.url:
embed.set_thumbnail(url=image.url)
await inter.response.send_message(embed=embed, view=view)
@commands.slash_command(name="models", description="List available AI models")
async def list_models(
self, inter: disnake.ApplicationCommandInteraction[SummarizerBot]
) -> None:
"""List available LLM models."""
await inter.response.defer()
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
models_list = await client.list_models()
await inter.edit_original_response(embed=models_embed(models_list))
except SummarizerAPIError as e:
logger.warning(f"API error listing models: {e}")
await inter.edit_original_response(
embed=error_embed("Failed to List Models", str(e))
)
except Exception as e: # noqa: BLE001
logger.exception("Unexpected error listing models")
await inter.edit_original_response(
embed=error_embed(
"Something went wrong", "An unexpected error occurred."
)
)
# Notify owners
with contextlib.suppress(Exception):
error_embeds = create_error_report_embeds(inter, e)
await notify_owners(self.bot, error_embeds)
def setup(bot: SummarizerBot) -> None:
"""Load the cog."""
bot.add_cog(SummarizeCog(bot))

View File

@@ -0,0 +1,225 @@
"""Pagination utilities for long content."""
from __future__ import annotations
import disnake
class Paginator(disnake.ui.View):
"""Interactive paginator for long content."""
def __init__(
self,
embeds: list[disnake.Embed],
*,
author_id: int | None = None,
timeout: float = 180.0,
) -> None:
super().__init__(timeout=timeout)
self.embeds = embeds
self.author_id = author_id
self.current_page = 0
self.total_pages = len(embeds)
self._update_buttons()
def _update_buttons(self) -> None:
"""Update button states based on current page."""
self.first_button.disabled = self.current_page == 0
self.prev_button.disabled = self.current_page == 0
self.next_button.disabled = self.current_page >= self.total_pages - 1
self.last_button.disabled = self.current_page >= self.total_pages - 1
async def interaction_check(self, interaction: disnake.MessageInteraction) -> bool:
"""Check if the user can interact with the paginator."""
if self.author_id and interaction.author.id != self.author_id:
await interaction.response.send_message(
"This paginator isn't for you.", ephemeral=True
)
return False
return True
@disnake.ui.button(label="", style=disnake.ButtonStyle.secondary)
async def first_button(
self,
button: disnake.ui.Button[Paginator],
interaction: disnake.MessageInteraction,
) -> None:
"""Go to first page."""
self.current_page = 0
self._update_buttons()
await interaction.response.edit_message(
embed=self.embeds[self.current_page], view=self
)
@disnake.ui.button(label="", style=disnake.ButtonStyle.primary)
async def prev_button(
self,
button: disnake.ui.Button[Paginator],
interaction: disnake.MessageInteraction,
) -> None:
"""Go to previous page."""
self.current_page = max(0, self.current_page - 1)
self._update_buttons()
await interaction.response.edit_message(
embed=self.embeds[self.current_page], view=self
)
@disnake.ui.button(label="", style=disnake.ButtonStyle.secondary, disabled=True)
async def page_indicator(
self,
button: disnake.ui.Button[Paginator],
interaction: disnake.MessageInteraction,
) -> None:
"""Page indicator (non-interactive)."""
@disnake.ui.button(label="", style=disnake.ButtonStyle.primary)
async def next_button(
self,
button: disnake.ui.Button[Paginator],
interaction: disnake.MessageInteraction,
) -> None:
"""Go to next page."""
self.current_page = min(self.total_pages - 1, self.current_page + 1)
self._update_buttons()
await interaction.response.edit_message(
embed=self.embeds[self.current_page], view=self
)
@disnake.ui.button(label="", style=disnake.ButtonStyle.secondary)
async def last_button(
self,
button: disnake.ui.Button[Paginator],
interaction: disnake.MessageInteraction,
) -> None:
"""Go to last page."""
self.current_page = self.total_pages - 1
self._update_buttons()
await interaction.response.edit_message(
embed=self.embeds[self.current_page], view=self
)
def update_page_indicator(self) -> None:
"""Update the page indicator label."""
self.page_indicator.label = f"{self.current_page + 1}/{self.total_pages}"
async def on_timeout(self) -> None:
"""Disable all buttons on timeout."""
for item in self.children:
if isinstance(item, disnake.ui.Button):
item.disabled = True
def paginate_text(text: str, max_length: int = 4000) -> list[str]:
"""Split text into chunks that fit within Discord's limits.
Tries to split on paragraph boundaries, then sentences, then words.
"""
if len(text) <= max_length:
return [text]
chunks: list[str] = []
remaining = text
while remaining:
if len(remaining) <= max_length:
chunks.append(remaining)
break
# Find a good split point
split_at = max_length
# Try to split at paragraph boundary
para_break = remaining.rfind("\n\n", 0, max_length)
if para_break > max_length // 2:
split_at = para_break + 2
else:
# Try to split at line boundary
line_break = remaining.rfind("\n", 0, max_length)
if line_break > max_length // 2:
split_at = line_break + 1
else:
# Try to split at sentence boundary
for sep in (". ", "! ", "? "):
sent_break = remaining.rfind(sep, 0, max_length)
if sent_break > max_length // 2:
split_at = sent_break + len(sep)
break
else:
# Try to split at word boundary
word_break = remaining.rfind(" ", 0, max_length)
if word_break > max_length // 2:
split_at = word_break + 1
chunks.append(remaining[:split_at].rstrip())
remaining = remaining[split_at:].lstrip()
return chunks
def create_paginated_embeds(
title: str,
content: str,
*,
color: int = 0xE6E6FA,
max_length: int = 4000,
footer_prefix: str = "",
) -> list[disnake.Embed]:
"""Create a list of embeds from long content."""
chunks = paginate_text(content, max_length)
if len(chunks) == 1:
embed = disnake.Embed(title=title, description=chunks[0], color=color)
if footer_prefix:
embed.set_footer(text=footer_prefix)
return [embed]
embeds: list[disnake.Embed] = []
for i, chunk in enumerate(chunks, 1):
embed = disnake.Embed(
title=f"{title} ({i}/{len(chunks)})" if len(chunks) > 1 else title,
description=chunk,
color=color,
)
footer = f"Page {i}/{len(chunks)}"
if footer_prefix:
footer = f"{footer_prefix} · {footer}"
embed.set_footer(text=footer)
embeds.append(embed)
return embeds
async def send_paginated(
target: disnake.abc.Messageable | disnake.Interaction,
embeds: list[disnake.Embed],
*,
author_id: int | None = None,
ephemeral: bool = False,
) -> disnake.Message | None:
"""Send paginated embeds with navigation buttons if needed."""
if not embeds:
return None
if len(embeds) == 1:
# Single page, no pagination needed
if isinstance(target, disnake.Interaction):
if target.response.is_done():
return await target.edit_original_response(embed=embeds[0])
await target.response.send_message(embed=embeds[0], ephemeral=ephemeral)
return await target.original_response()
return await target.send(embed=embeds[0])
# Multiple pages, use paginator
view = Paginator(embeds, author_id=author_id)
view.update_page_indicator()
if isinstance(target, disnake.Interaction):
if target.response.is_done():
return await target.edit_original_response(embed=embeds[0], view=view)
await target.response.send_message(
embed=embeds[0], view=view, ephemeral=ephemeral
)
return await target.original_response()
return await target.send(embed=embeds[0], view=view)

294
discord_bot/src/views.py Normal file
View File

@@ -0,0 +1,294 @@
"""Discord UI components for summarization commands."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable, Coroutine
import disnake
from loguru import logger
from sdk import LlmInfo, SummarizerClient
if TYPE_CHECKING:
from src.bot import SummarizerBot
class ProviderSelect(disnake.ui.StringSelect):
"""Dropdown for selecting LLM provider."""
def __init__(self, selected: str = "lmstudio") -> None:
options = [
disnake.SelectOption(
label="Anthropic (Claude)",
value="anthropic",
description="Claude AI models",
emoji="🤖",
default=(selected == "anthropic"),
),
disnake.SelectOption(
label="Ollama",
value="ollama",
description="Local Ollama models",
emoji="🦙",
default=(selected == "ollama"),
),
disnake.SelectOption(
label="LM Studio",
value="lmstudio",
description="Local LM Studio models",
emoji="💻",
default=(selected == "lmstudio"),
),
]
super().__init__(
placeholder="Select AI Provider",
options=options,
custom_id="provider_select",
)
async def callback(self, inter: disnake.MessageInteraction) -> None:
"""Handle provider selection - triggers model list refresh."""
# The view will handle this
self.view.selected_provider = self.values[0] # type: ignore[union-attr]
await self.view.refresh_models(inter) # type: ignore[union-attr]
class ModelSelect(disnake.ui.StringSelect):
"""Dropdown for selecting LLM model."""
def __init__(self, models: list[LlmInfo] | None = None) -> None:
if models:
options = [
disnake.SelectOption(
label=m.model[:100],
value=m.model,
description=f"{m.provider}"
+ (f" v{m.version}" if m.version else ""), # noqa: E501
)
for m in models[:25] # Discord limit
]
else:
options = [
disnake.SelectOption(
label="Select provider first",
value="none",
description="Choose a provider to see available models",
)
]
super().__init__(
placeholder="Select Model (optional)",
options=options,
custom_id="model_select",
disabled=not models,
)
async def callback(self, inter: disnake.MessageInteraction) -> None:
"""Handle model selection."""
self.view.selected_model = self.values[0] if self.values[0] != "none" else None # type: ignore[union-attr]
await inter.response.defer()
class ProviderModelView(disnake.ui.View):
"""View for selecting provider and model before summarization."""
def __init__(
self,
bot: SummarizerBot,
on_confirm: Callable[[str, str | None], Coroutine[Any, Any, None]],
*,
timeout: float = 120.0,
author_id: int,
) -> None:
super().__init__(timeout=timeout)
self.bot = bot
self.on_confirm = on_confirm
self.author_id = author_id
self.selected_provider: str = "lmstudio"
self.selected_model: str | None = None
self._models_cache: dict[str, list[LlmInfo]] = {}
# Add components
self.provider_select = ProviderSelect()
self.model_select = ModelSelect()
self.add_item(self.provider_select)
self.add_item(self.model_select)
async def interaction_check( # type: ignore[override]
self, interaction: disnake.MessageInteraction
) -> bool:
"""Only allow the command author to interact."""
if interaction.author.id != self.author_id:
await interaction.response.send_message(
"Only the command author can use these controls.", ephemeral=True
)
return False
return True
async def fetch_models(self) -> list[LlmInfo]:
"""Fetch available models from API."""
try:
async with SummarizerClient(base_url=self.bot.config.bot.api_url) as client:
return await client.list_models()
except Exception as e: # noqa: BLE001
logger.warning(f"Failed to fetch models: {e}")
return []
async def prefetch_models(self) -> None:
"""Prefetch and cache all models."""
if not self._models_cache:
all_models = await self.fetch_models()
for model in all_models:
provider = model.provider.lower()
if provider not in self._models_cache:
self._models_cache[provider] = []
self._models_cache[provider].append(model)
def get_provider_models(self, provider: str) -> list[LlmInfo]:
"""Get available models for a specific provider."""
return [m for m in self._models_cache.get(provider, []) if m.available]
async def refresh_models(self, inter: disnake.MessageInteraction) -> None:
"""Refresh model list based on selected provider."""
await inter.response.defer()
# Fetch models if not cached
if not self._models_cache:
all_models = await self.fetch_models()
for model in all_models:
provider = model.provider.lower()
if provider not in self._models_cache:
self._models_cache[provider] = []
self._models_cache[provider].append(model)
# Filter models for selected provider
provider_models = self._models_cache.get(self.selected_provider, [])
available_models = [m for m in provider_models if m.available]
# Update provider select to show current selection
self.remove_item(self.provider_select)
self.provider_select = ProviderSelect(selected=self.selected_provider)
self.add_item(self.provider_select)
# Update model select
self.remove_item(self.model_select)
self.model_select = ModelSelect(available_models if available_models else None)
self.add_item(self.model_select)
self.selected_model = None
await inter.edit_original_response(view=self)
@disnake.ui.button(label="Use Defaults", style=disnake.ButtonStyle.secondary, row=2)
async def use_defaults(
self, _button: disnake.ui.Button, _inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Use default provider and model."""
self.stop()
await self.on_confirm("lmstudio", None)
@disnake.ui.button(label="Confirm", style=disnake.ButtonStyle.primary, row=2)
async def confirm(
self, _button: disnake.ui.Button, _inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Confirm selection and proceed."""
self.stop()
await self.on_confirm(self.selected_provider, self.selected_model)
@disnake.ui.button(label="Cancel", style=disnake.ButtonStyle.danger, row=2)
async def cancel(
self, _button: disnake.ui.Button, inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Cancel the operation."""
self.stop()
await inter.response.edit_message(
content="Operation cancelled.", embed=None, view=None
)
class QuickSummarizeView(disnake.ui.View):
"""View with quick summarize button and advanced options."""
def __init__(
self,
bot: SummarizerBot,
summarize_callback: Callable[[str, str | None], Coroutine[Any, Any, None]],
*,
timeout: float = 60.0,
author_id: int,
) -> None:
super().__init__(timeout=timeout)
self.bot = bot
self.summarize_callback = summarize_callback
self.author_id = author_id
async def interaction_check( # type: ignore[override]
self, interaction: disnake.MessageInteraction
) -> bool:
"""Only allow the command author to interact."""
if interaction.author.id != self.author_id:
await interaction.response.send_message(
"Only the command author can use these controls.", ephemeral=True
)
return False
return True
@disnake.ui.button(
label="Summarize (Default)", style=disnake.ButtonStyle.primary, emoji=""
)
async def quick_summarize(
self, _button: disnake.ui.Button, inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Quick summarize with defaults."""
self.stop()
await inter.response.defer()
await self.summarize_callback("lmstudio", None)
@disnake.ui.button(
label="Choose Provider/Model", style=disnake.ButtonStyle.secondary, emoji="⚙️"
)
async def advanced_options(
self, _button: disnake.ui.Button, inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Show advanced provider/model selection."""
self.stop()
async def on_confirm(provider: str, model: str | None) -> None:
await self.summarize_callback(provider, model)
view = ProviderModelView(self.bot, on_confirm, author_id=self.author_id)
# Prefetch models
await view.prefetch_models()
# Set up initial model select with lmstudio models
lmstudio_models = view.get_provider_models("lmstudio")
view.remove_item(view.model_select)
view.model_select = ModelSelect(lmstudio_models if lmstudio_models else None)
view.add_item(view.model_select)
embed = disnake.Embed(
title="🔧 Advanced Options",
description="Select your preferred AI provider and model.",
color=disnake.Color.blurple(),
)
embed.add_field(
name="Provider",
value="Choose the AI service to use for summarization.",
inline=False,
)
embed.add_field(
name="Model",
value="Optionally select a specific model (leave empty for default).",
inline=False,
)
await inter.response.edit_message(embed=embed, view=view)
@disnake.ui.button(label="Cancel", style=disnake.ButtonStyle.danger, emoji="✖️")
async def cancel(
self, _button: disnake.ui.Button, inter: disnake.MessageInteraction # type: ignore[type-arg]
) -> None:
"""Cancel the operation."""
self.stop()
await inter.response.edit_message(
content="Operation cancelled.", embed=None, view=None
)

895
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{"reason":"compiler-message","package_id":"path+file:///home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api#0.1.0","manifest_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/Cargo.toml","target":{"kind":["bin"],"crate_types":["bin"],"name":"backend_api","src_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/src/main.rs","edition":"2021","doc":true,"doctest":false,"test":true},"message":{"$message_type":"diagnostic","message":"methods `list_ollama_models`, `get_anthropic_models`, and `list_lmstudio_models` are never used","code":{"code":"dead_code","explanation":null},"level":"warning","spans":[{"file_name":"src/llm.rs","byte_start":2016,"byte_end":2030,"line_start":89,"line_end":89,"column_start":1,"column_end":15,"is_primary":false,"text":[{"text":"impl LlmClient {","highlight_start":1,"highlight_end":15}],"label":"methods in this implementation","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":7463,"byte_end":7481,"line_start":228,"line_end":228,"column_start":18,"column_end":36,"is_primary":true,"text":[{"text":" pub async fn list_ollama_models(&self) -> Result<Vec<_OllamaModelInfo>> {","highlight_start":18,"highlight_end":36}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":8041,"byte_end":8061,"line_start":245,"line_end":245,"column_start":12,"column_end":32,"is_primary":true,"text":[{"text":" pub fn get_anthropic_models(&self) -> Vec<String> {","highlight_start":12,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":11901,"byte_end":11921,"line_start":364,"line_end":364,"column_start":18,"column_end":38,"is_primary":true,"text":[{"text":" pub async fn list_lmstudio_models(&self) -> Result<Vec<_OllamaModelInfo>> {","highlight_start":18,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"`#[warn(dead_code)]` on by default","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: methods `list_ollama_models`, `get_anthropic_models`, and `list_lmstudio_models` are never used\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0msrc/llm.rs:228:18\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m89\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mimpl LlmClient {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--------------\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12mmethods in this implementation\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m228\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub async fn list_ollama_models(&self) -> Result<Vec<_OllamaModelInfo>> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m245\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub fn get_anthropic_models(&self) -> Vec<String> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m364\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub async fn list_lmstudio_models(&self) -> Result<Vec<_OllamaModelInfo>> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m= \u001b[0m\u001b[0m\u001b[1mnote\u001b[0m\u001b[0m: `#[warn(dead_code)]` on by default\u001b[0m\n\n"}}
{"reason":"compiler-message","package_id":"path+file:///home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api#0.1.0","manifest_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/Cargo.toml","target":{"kind":["bin"],"crate_types":["bin"],"name":"backend_api","src_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/src/main.rs","edition":"2021","doc":true,"doctest":false,"test":true},"message":{"$message_type":"diagnostic","message":"method `check_rate_limit` is never used","code":{"code":"dead_code","explanation":null},"level":"warning","spans":[{"file_name":"src/database.rs","byte_start":303,"byte_end":316,"line_start":16,"line_end":16,"column_start":1,"column_end":14,"is_primary":false,"text":[{"text":"impl Database {","highlight_start":1,"highlight_end":14}],"label":"method in this implementation","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/database.rs","byte_start":24371,"byte_end":24387,"line_start":661,"line_end":661,"column_start":18,"column_end":34,"is_primary":true,"text":[{"text":" pub async fn check_rate_limit(&self, guild_id: &str, user_id: &str, guild_limit: u32, user_limit: u32) -> Result<(bool, bool)> {","highlight_start":18,"highlight_end":34}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: method `check_rate_limit` is never used\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0msrc/database.rs:661:18\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m16\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mimpl Database {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m-------------\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12mmethod in this implementation\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m661\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub async fn check_rate_limit(&self, guild_id: &str, user_id: &str, guild_limit: u32, user_limit: u32) -> Result<(bool, bool)> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^\u001b[0m\n\n"}}
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{"reason":"compiler-message","package_id":"path+file:///home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api#0.1.0","manifest_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/Cargo.toml","target":{"kind":["bin"],"crate_types":["bin"],"name":"backend_api","src_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/src/main.rs","edition":"2021","doc":true,"doctest":false,"test":true},"message":{"$message_type":"diagnostic","message":"unused import: `anyhow`","code":{"code":"unused_imports","explanation":null},"level":"warning","spans":[{"file_name":"src/database.rs","byte_start":21,"byte_end":27,"line_start":1,"line_end":1,"column_start":22,"column_end":28,"is_primary":true,"text":[{"text":"use anyhow::{Result, anyhow};","highlight_start":22,"highlight_end":28}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"`#[warn(unused_imports)]` on by default","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"remove the unused import","code":null,"level":"help","spans":[{"file_name":"src/database.rs","byte_start":19,"byte_end":27,"line_start":1,"line_end":1,"column_start":20,"column_end":28,"is_primary":true,"text":[{"text":"use anyhow::{Result, anyhow};","highlight_start":20,"highlight_end":28}],"label":null,"suggested_replacement":"","suggestion_applicability":"MachineApplicable","expansion":null},{"file_name":"src/database.rs","byte_start":12,"byte_end":13,"line_start":1,"line_end":1,"column_start":13,"column_end":14,"is_primary":true,"text":[{"text":"use anyhow::{Result, anyhow};","highlight_start":13,"highlight_end":14}],"label":null,"suggested_replacement":"","suggestion_applicability":"MachineApplicable","expansion":null},{"file_name":"src/database.rs","byte_start":27,"byte_end":28,"line_start":1,"line_end":1,"column_start":28,"column_end":29,"is_primary":true,"text":[{"text":"use anyhow::{Result, anyhow};","highlight_start":28,"highlight_end":29}],"label":null,"suggested_replacement":"","suggestion_applicability":"MachineApplicable","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: unused import: `anyhow`\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0msrc/database.rs:1:22\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m1\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0muse anyhow::{Result, anyhow};\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m= \u001b[0m\u001b[0m\u001b[1mnote\u001b[0m\u001b[0m: `#[warn(unused_imports)]` on by default\u001b[0m\n\n"}}
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{"reason":"compiler-message","package_id":"path+file:///home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api#0.1.0","manifest_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/Cargo.toml","target":{"kind":["bin"],"crate_types":["bin"],"name":"backend_api","src_path":"/home/zanewalker/Development/Programming/PythonProjects/DiscordBots/Summarizer_Bot/backend_api/src/main.rs","edition":"2021","doc":true,"doctest":false,"test":true},"message":{"$message_type":"diagnostic","message":"methods `list_ollama_models`, `get_anthropic_models`, and `list_lmstudio_models` are never used","code":{"code":"dead_code","explanation":null},"level":"warning","spans":[{"file_name":"src/llm.rs","byte_start":2016,"byte_end":2030,"line_start":89,"line_end":89,"column_start":1,"column_end":15,"is_primary":false,"text":[{"text":"impl LlmClient {","highlight_start":1,"highlight_end":15}],"label":"methods in this implementation","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":7463,"byte_end":7481,"line_start":228,"line_end":228,"column_start":18,"column_end":36,"is_primary":true,"text":[{"text":" pub async fn list_ollama_models(&self) -> Result<Vec<_OllamaModelInfo>> {","highlight_start":18,"highlight_end":36}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":8041,"byte_end":8061,"line_start":245,"line_end":245,"column_start":12,"column_end":32,"is_primary":true,"text":[{"text":" pub fn get_anthropic_models(&self) -> Vec<String> {","highlight_start":12,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"src/llm.rs","byte_start":11901,"byte_end":11921,"line_start":364,"line_end":364,"column_start":18,"column_end":38,"is_primary":true,"text":[{"text":" pub async fn list_lmstudio_models(&self) -> Result<Vec<_OllamaModelInfo>> {","highlight_start":18,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"`#[warn(dead_code)]` on by default","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: methods `list_ollama_models`, `get_anthropic_models`, and `list_lmstudio_models` are never used\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0msrc/llm.rs:228:18\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m89\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mimpl LlmClient {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--------------\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12mmethods in this implementation\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m228\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub async fn list_ollama_models(&self) -> Result<Vec<_OllamaModelInfo>> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m245\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub fn get_anthropic_models(&self) -> Vec<String> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m...\u001b[0m\n\u001b[0m\u001b[1m\u001b[38;5;12m364\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m pub async fn list_lmstudio_models(&self) -> Result<Vec<_OllamaModelInfo>> {\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m= \u001b[0m\u001b[0m\u001b[1mnote\u001b[0m\u001b[0m: `#[warn(dead_code)]` on by default\u001b[0m\n\n"}}
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