ecf9f33b26
The upstream AI endpoint already returns complete model metadata: - name, owned_by, context_length, max_output_tokens - capabilities (vision, tool_call, reasoning) - pricing (input, output, cache_read, cache_write, currency) Remove the OpenRouter fallback entirely and parse the upstream response directly for all sync operations. Both sync_upstream_models (API) and sync_once (background task) now use a single unified path. Changes: - Remove OpenRouter types and fetch_openrouter_models() - Add UpstreamModel / UpstreamCapabilities / UpstreamPricing types - Parse capabilities from upstream instead of inferring from name - Use real pricing from upstream instead of defaulting to 0.00 - Simplify sync flow: list → parse → upsert (no filtering/matching) - Add provider normalizations for moonshot, zai, minimax, qwen
681 lines
22 KiB
Rust
681 lines
22 KiB
Rust
//! Synchronizes AI model metadata from the upstream AI endpoint
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//! (`GET /v1/models`) into the local database.
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//!
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//! Flow:
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//! 1. Call `GET /v1/models` with the configured AI API key.
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//! 2. Parse the rich response (name, context_length, max_output_tokens,
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//! capabilities, pricing, owned_by) — no external metadata source needed.
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//! 3. Upsert provider / model / version / pricing / capability / profile
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//! records for all accessible models.
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//!
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//! Usage: call `start_sync_task()` to launch a background task that syncs
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//! immediately and then every 10 minutes. On app startup, run it once
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//! eagerly before accepting traffic.
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use std::time::Duration;
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use tokio::task::JoinHandle;
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use tokio::time::interval;
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use crate::error::AppError;
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use crate::AppService;
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use chrono::Utc;
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use db::database::AppDatabase;
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use models::agents::model::Entity as ModelEntity;
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use models::agents::model_capability::Entity as CapabilityEntity;
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use models::agents::model_parameter_profile::Entity as ProfileEntity;
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use models::agents::model_provider::Entity as ProviderEntity;
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use models::agents::model_provider::Model as ProviderModel;
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use models::agents::model_version::Entity as VersionEntity;
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use models::agents::{CapabilityType, ModelCapability, ModelModality, ModelStatus};
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use sea_orm::prelude::*;
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use sea_orm::Set;
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use serde::Deserialize;
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use serde::Serialize;
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use session::Session;
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use utoipa::ToSchema;
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use uuid::Uuid;
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// Upstream /v1/models response types -----------------------------------------
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#[derive(Debug, Clone, Deserialize)]
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struct ModelsListResponse {
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data: Vec<UpstreamModel>,
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}
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#[derive(Debug, Clone, Deserialize)]
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struct UpstreamModel {
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id: String,
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#[serde(default)]
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name: Option<String>,
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#[serde(default)]
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owned_by: Option<String>,
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#[serde(default)]
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context_length: Option<u64>,
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#[serde(default)]
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max_output_tokens: Option<u64>,
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#[serde(default)]
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capabilities: Option<UpstreamCapabilities>,
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#[serde(default)]
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pricing: Option<UpstreamPricing>,
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#[serde(default)]
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r#type: Option<String>,
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}
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#[derive(Debug, Clone, Deserialize)]
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struct UpstreamCapabilities {
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#[serde(default)]
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vision: Option<bool>,
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#[serde(default)]
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tool_call: Option<bool>,
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#[serde(default)]
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reasoning: Option<bool>,
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}
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#[derive(Debug, Clone, Deserialize)]
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struct UpstreamPricing {
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#[serde(default)]
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input: Option<f64>,
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#[serde(default)]
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output: Option<f64>,
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#[serde(default)]
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cache_read: Option<f64>,
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#[serde(default)]
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cache_write: Option<f64>,
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#[serde(default)]
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unit: Option<String>,
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#[serde(default)]
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currency: Option<String>,
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}
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// Response type --------------------------------------------------------------
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#[derive(Debug, Clone, Serialize, ToSchema)]
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pub struct SyncModelsResponse {
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pub models_created: i64,
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pub models_updated: i64,
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pub versions_created: i64,
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pub pricing_created: i64,
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pub capabilities_created: i64,
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pub profiles_created: i64,
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}
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// Mapping helpers ------------------------------------------------------------
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fn infer_modality(model: &UpstreamModel) -> ModelModality {
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if let Some(caps) = &model.capabilities {
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if caps.vision == Some(true) {
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return ModelModality::Multimodal;
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}
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}
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let lower = model.id.to_lowercase();
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if lower.contains("vision")
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|| lower.contains("dall-e")
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|| lower.contains("gpt-image")
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|| lower.contains("gpt-4o")
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{
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ModelModality::Multimodal
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} else if lower.contains("embedding") {
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ModelModality::Text
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} else if lower.contains("whisper") || lower.contains("audio") {
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ModelModality::Audio
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} else {
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ModelModality::Text
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}
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}
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fn infer_capability(model: &UpstreamModel) -> ModelCapability {
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let lower = model.id.to_lowercase();
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if lower.contains("embedding") {
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ModelCapability::Embedding
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} else {
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ModelCapability::Chat
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}
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}
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fn context_length(model: &UpstreamModel) -> i64 {
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model.context_length.map(|c| c as i64).unwrap_or(8_192)
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}
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fn max_output_tokens(model: &UpstreamModel) -> Option<i64> {
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model.max_output_tokens.map(|v| v as i64)
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}
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fn capability_list(model: &UpstreamModel) -> Vec<(CapabilityType, bool)> {
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let mut caps = Vec::new();
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// Function call / tool use
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if let Some(u) = &model.capabilities {
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if u.tool_call == Some(true) {
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caps.push((CapabilityType::ToolUse, true));
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}
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if u.vision == Some(true) {
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caps.push((CapabilityType::Vision, true));
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}
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}
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// Always mark function call as supported by default for chat models
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if caps.is_empty() {
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caps.push((CapabilityType::FunctionCall, true));
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}
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caps
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}
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// Provider helpers -----------------------------------------------------------
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fn extract_provider_name(model: &UpstreamModel) -> &str {
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if let Some(owned) = &model.owned_by {
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if !owned.is_empty() {
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return normalize_provider_name(owned);
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}
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}
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normalize_provider_name(model.id.split('/').next().unwrap_or("unknown"))
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}
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fn normalize_provider_name(slug: &str) -> &'static str {
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match slug {
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"openai" => "openai",
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"anthropic" => "anthropic",
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"google" | "google-ai" => "google",
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"mistralai" => "mistral",
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"meta-llama" | "meta" => "meta",
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"deepseek" => "deepseek",
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"azure" | "azure-openai" => "azure",
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"x-ai" | "xai" => "xai",
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"moonshot" => "moonshot",
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"zai" => "zai",
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"minimax" => "minimax",
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"alibaba" | "qwen" => "qwen",
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s => Box::leak(s.to_string().into_boxed_str()),
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}
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}
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fn provider_display_name(name: &str) -> String {
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match name {
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"openai" => "OpenAI".to_string(),
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"anthropic" => "Anthropic".to_string(),
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"google" => "Google DeepMind".to_string(),
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"mistral" => "Mistral AI".to_string(),
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"meta" => "Meta".to_string(),
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"deepseek" => "DeepSeek".to_string(),
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"azure" => "Microsoft Azure".to_string(),
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"xai" => "xAI".to_string(),
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"moonshot" => "Moonshot AI".to_string(),
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"zai" => "Zhipu AI".to_string(),
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"minimax" => "MiniMax".to_string(),
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"qwen" => "Alibaba Qwen".to_string(),
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s => s.to_string(),
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}
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}
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// Upsert helpers -------------------------------------------------------------
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async fn upsert_provider(db: &AppDatabase, slug: &str) -> Result<ProviderModel, AppError> {
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let display = provider_display_name(slug);
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let now = Utc::now();
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use models::agents::model_provider::Column as PCol;
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if let Some(existing) = ProviderEntity::find()
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.filter(PCol::Name.eq(slug))
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.one(db)
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.await?
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{
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let mut active: models::agents::model_provider::ActiveModel = existing.into();
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active.updated_at = Set(now);
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active.update(db).await?;
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Ok(ProviderEntity::find()
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.filter(PCol::Name.eq(slug))
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.one(db)
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.await?
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.unwrap())
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} else {
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let active = models::agents::model_provider::ActiveModel {
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id: Set(Uuid::now_v7()),
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name: Set(slug.to_string()),
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display_name: Set(display.to_string()),
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website: Set(None),
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status: Set(ModelStatus::Active.to_string()),
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created_at: Set(now),
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updated_at: Set(now),
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};
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active.insert(db).await.map_err(AppError::from)
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}
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}
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async fn upsert_model(
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db: &AppDatabase,
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provider_id: Uuid,
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model: &UpstreamModel,
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) -> Result<(models::agents::model::Model, bool), AppError> {
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let now = Utc::now();
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let modality = infer_modality(model);
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let capability = infer_capability(model);
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let ctx = context_length(model);
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let max_out = max_output_tokens(model);
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use models::agents::model::Column as MCol;
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if let Some(existing) = ModelEntity::find()
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.filter(MCol::ProviderId.eq(provider_id))
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.filter(MCol::Name.eq(&model.id))
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.one(db)
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.await?
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{
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let mut active: models::agents::model::ActiveModel = existing.clone().into();
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active.context_length = Set(ctx);
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active.max_output_tokens = Set(max_out);
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active.status = Set(ModelStatus::Active.to_string());
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active.updated_at = Set(now);
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active.update(db).await?;
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Ok((
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ModelEntity::find_by_id(existing.id).one(db).await?.unwrap(),
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false,
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))
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} else {
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let active = models::agents::model::ActiveModel {
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id: Set(Uuid::now_v7()),
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provider_id: Set(provider_id),
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name: Set(model.id.clone()),
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modality: Set(modality.to_string()),
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capability: Set(capability.to_string()),
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context_length: Set(ctx),
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max_output_tokens: Set(max_out),
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training_cutoff: Set(None),
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is_open_source: Set(false),
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status: Set(ModelStatus::Active.to_string()),
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created_at: Set(now),
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updated_at: Set(now),
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..Default::default()
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};
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let inserted = active.insert(db).await.map_err(AppError::from)?;
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Ok((inserted, true))
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}
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}
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async fn upsert_version(
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db: &AppDatabase,
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model_uuid: Uuid,
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) -> Result<(models::agents::model_version::Model, bool), AppError> {
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use models::agents::model_version::Column as VCol;
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let now = Utc::now();
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if let Some(existing) = VersionEntity::find()
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.filter(VCol::ModelId.eq(model_uuid))
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.filter(VCol::IsDefault.eq(true))
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.one(db)
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.await?
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{
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Ok((existing, false))
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} else {
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let active = models::agents::model_version::ActiveModel {
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id: Set(Uuid::now_v7()),
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model_id: Set(model_uuid),
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version: Set("1".to_string()),
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release_date: Set(None),
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change_log: Set(None),
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is_default: Set(true),
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status: Set(ModelStatus::Active.to_string()),
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created_at: Set(now),
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};
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let inserted = active.insert(db).await.map_err(AppError::from)?;
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Ok((inserted, true))
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}
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}
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async fn upsert_pricing(
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db: &AppDatabase,
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version_uuid: Uuid,
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pricing: Option<&UpstreamPricing>,
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) -> Result<bool, AppError> {
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use models::agents::model_pricing::Column as PCol;
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use models::agents::model_pricing::Entity as PricingEntity;
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let existing = PricingEntity::find()
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.filter(PCol::ModelVersionId.eq(version_uuid))
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.one(db)
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.await?;
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if existing.is_some() {
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return Ok(false);
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}
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let (input_price, output_price) = if let Some(p) = pricing {
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(
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format!("{:.2}", p.input.unwrap_or(0.0)),
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format!("{:.2}", p.output.unwrap_or(0.0)),
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)
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} else {
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("0.00".to_string(), "0.00".to_string())
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};
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let currency = pricing
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.and_then(|p| p.currency.clone())
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.unwrap_or_else(|| "USD".to_string());
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let active = models::agents::model_pricing::ActiveModel {
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id: Set(Uuid::now_v7().as_u128() as i64),
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model_version_id: Set(version_uuid),
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input_price_per_1k_tokens: Set(input_price),
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output_price_per_1k_tokens: Set(output_price),
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currency: Set(currency),
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effective_from: Set(Utc::now()),
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};
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active.insert(db).await.map_err(AppError::from)?;
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Ok(true)
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}
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async fn upsert_capabilities(
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db: &AppDatabase,
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version_uuid: Uuid,
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model: &UpstreamModel,
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) -> Result<i64, AppError> {
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use models::agents::model_capability::Column as CCol;
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let caps = capability_list(model);
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let now = Utc::now();
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let mut created = 0i64;
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for (cap_type, supported) in caps {
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let exists = CapabilityEntity::find()
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.filter(CCol::ModelVersionId.eq(version_uuid))
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.filter(CCol::Capability.eq(cap_type.to_string()))
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.one(db)
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.await?;
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if exists.is_some() {
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continue;
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}
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let active = models::agents::model_capability::ActiveModel {
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id: Set(Uuid::now_v7().as_u128() as i64),
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model_version_id: Set(version_uuid.as_u128() as i64),
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capability: Set(cap_type.to_string()),
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is_supported: Set(supported),
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created_at: Set(now),
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};
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active.insert(db).await.map_err(AppError::from)?;
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created += 1;
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}
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Ok(created)
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}
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async fn upsert_parameter_profile(
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db: &AppDatabase,
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version_uuid: Uuid,
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model: &UpstreamModel,
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) -> Result<bool, AppError> {
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use models::agents::model_parameter_profile::Column as PCol;
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let existing = ProfileEntity::find()
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.filter(PCol::ModelVersionId.eq(version_uuid))
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.one(db)
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.await?;
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if existing.is_some() {
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return Ok(false);
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}
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let lower = model.id.to_lowercase();
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let (t_min, t_max) = if lower.contains("o1") || lower.contains("o3") {
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(1.0, 1.0)
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} else {
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(0.0, 2.0)
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};
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let active = models::agents::model_parameter_profile::ActiveModel {
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id: Set(Uuid::now_v7().as_u128() as i64),
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model_version_id: Set(version_uuid),
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temperature_min: Set(t_min),
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temperature_max: Set(t_max),
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top_p_min: Set(0.0),
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top_p_max: Set(1.0),
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frequency_penalty_supported: Set(true),
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presence_penalty_supported: Set(true),
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};
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active.insert(db).await.map_err(AppError::from)?;
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Ok(true)
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}
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|
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// Core sync logic ------------------------------------------------------------
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async fn sync_models_from_upstream(
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db: &AppDatabase,
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models: Vec<UpstreamModel>,
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) -> SyncModelsResponse {
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let mut models_created = 0i64;
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let mut models_updated = 0i64;
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let mut versions_created = 0i64;
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let mut pricing_created = 0i64;
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let mut capabilities_created = 0i64;
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let mut profiles_created = 0i64;
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for model in models {
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let provider_slug = extract_provider_name(&model);
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let provider = match upsert_provider(db, provider_slug).await {
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Ok(p) => p,
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Err(e) => {
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tracing::warn!(
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provider = %provider_slug,
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error = ?e,
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"sync_models_from_upstream: upsert_provider error"
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);
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continue;
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}
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};
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let (model_record, _is_new) = match upsert_model(db, provider.id, &model).await {
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Ok((m, n)) => {
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if n {
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models_created += 1;
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} else {
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models_updated += 1;
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}
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(m, n)
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}
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Err(e) => {
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tracing::warn!(
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model = %model.id,
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error = ?e,
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"sync_models_from_upstream: upsert_model error"
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);
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continue;
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}
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};
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let (version_record, version_is_new) = match upsert_version(db, model_record.id).await {
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Ok(v) => v,
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Err(e) => {
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tracing::warn!(
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model = %model.id,
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error = ?e,
|
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"sync_models_from_upstream: upsert_version error"
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);
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continue;
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}
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};
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if version_is_new {
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versions_created += 1;
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}
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|
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if upsert_pricing(db, version_record.id, model.pricing.as_ref())
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.await
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.unwrap_or(false)
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{
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pricing_created += 1;
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}
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|
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capabilities_created +=
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upsert_capabilities(db, version_record.id, &model)
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.await
|
|
.unwrap_or(0);
|
|
|
|
if upsert_parameter_profile(db, version_record.id, &model)
|
|
.await
|
|
.unwrap_or(false)
|
|
{
|
|
profiles_created += 1;
|
|
}
|
|
}
|
|
|
|
SyncModelsResponse {
|
|
models_created,
|
|
models_updated,
|
|
versions_created,
|
|
pricing_created,
|
|
capabilities_created,
|
|
profiles_created,
|
|
}
|
|
}
|
|
|
|
// HTTP helpers ---------------------------------------------------------------
|
|
|
|
/// List models from the upstream AI endpoint (`GET /v1/models`).
|
|
async fn list_upstream_models(
|
|
client: &reqwest::Client,
|
|
base_url: &str,
|
|
api_key: &str,
|
|
) -> Result<Vec<UpstreamModel>, AppError> {
|
|
let url = format!("{}/v1/models", base_url.trim_end_matches('/'));
|
|
let resp = client
|
|
.get(&url)
|
|
.header("Authorization", format!("Bearer {}", api_key))
|
|
.send()
|
|
.await
|
|
.map_err(|e| AppError::InternalServerError(format!("failed to list models: {}", e)))?;
|
|
|
|
let body = resp
|
|
.text()
|
|
.await
|
|
.map_err(|e| AppError::InternalServerError(format!("failed to read models body: {}", e)))?;
|
|
|
|
// Try standard OpenAI-compatible format: { "data": [{...}, ...] }
|
|
if let Ok(parsed) = serde_json::from_str::<ModelsListResponse>(&body) {
|
|
return Ok(parsed.data);
|
|
}
|
|
|
|
// Try raw array: [{...}, ...]
|
|
if let Ok(parsed) = serde_json::from_str::<Vec<UpstreamModel>>(&body) {
|
|
return Ok(parsed);
|
|
}
|
|
|
|
tracing::warn!(
|
|
body = %body.chars().take(500).collect::<String>(),
|
|
"list_upstream_models: unknown response format"
|
|
);
|
|
Err(AppError::InternalServerError(format!(
|
|
"unexpected /v1/models response format (first 200 chars): {}",
|
|
body.chars().take(200).collect::<String>()
|
|
)))
|
|
}
|
|
|
|
fn build_ai_client(config: &config::AppConfig) -> Result<(reqwest::Client, String, String), AppError> {
|
|
let api_key = config
|
|
.ai_api_key()
|
|
.map_err(|e| AppError::InternalServerError(format!("AI API key not configured: {}", e)))?;
|
|
|
|
let base_url = config
|
|
.ai_basic_url()
|
|
.unwrap_or_else(|_| "https://api.openai.com".into());
|
|
|
|
Ok((reqwest::Client::new(), base_url, api_key))
|
|
}
|
|
|
|
fn build_ai_client_from_parts(
|
|
api_key: Option<String>,
|
|
base_url: Option<String>,
|
|
) -> Result<(reqwest::Client, String, String), String> {
|
|
let api_key = api_key.ok_or_else(|| "AI API key not configured".to_string())?;
|
|
let base_url = base_url.unwrap_or_else(|| "https://api.openai.com".into());
|
|
Ok((reqwest::Client::new(), base_url, api_key))
|
|
}
|
|
|
|
// Public API -----------------------------------------------------------------
|
|
|
|
impl AppService {
|
|
/// Sync model metadata from the upstream AI endpoint (`GET /v1/models`).
|
|
///
|
|
/// Parses the full response (name, context_length, max_output_tokens,
|
|
/// capabilities, pricing, owned_by) and upserts all related records.
|
|
pub async fn sync_upstream_models(
|
|
&self,
|
|
_ctx: &Session,
|
|
) -> Result<SyncModelsResponse, AppError> {
|
|
let (http_client, base_url, api_key) = build_ai_client(&self.config)?;
|
|
let upstream_models = list_upstream_models(&http_client, &base_url, &api_key).await?;
|
|
|
|
tracing::info!(
|
|
model_count = upstream_models.len(),
|
|
"sync_upstream_models: {} models from upstream endpoint",
|
|
upstream_models.len()
|
|
);
|
|
|
|
let result = sync_models_from_upstream(&self.db, upstream_models).await;
|
|
|
|
tracing::info!(
|
|
models_created = result.models_created,
|
|
models_updated = result.models_updated,
|
|
versions_created = result.versions_created,
|
|
pricing_created = result.pricing_created,
|
|
capabilities_created = result.capabilities_created,
|
|
profiles_created = result.profiles_created,
|
|
"sync_upstream_models: complete"
|
|
);
|
|
|
|
Ok(result)
|
|
}
|
|
|
|
/// Spawn a background task that syncs model metadata immediately
|
|
/// and then every 10 minutes. Returns the `JoinHandle`.
|
|
///
|
|
/// Failures are logged but do not stop the task — it keeps retrying.
|
|
pub fn start_sync_task(self) -> JoinHandle<()> {
|
|
let db = self.db.clone();
|
|
let ai_api_key = self.config.ai_api_key().ok();
|
|
let ai_base_url = self.config.ai_basic_url().ok();
|
|
|
|
tokio::spawn(async move {
|
|
// Run once immediately on startup before taking traffic.
|
|
Self::sync_once(&db, ai_api_key.clone(), ai_base_url.clone()).await;
|
|
|
|
let mut tick = interval(Duration::from_secs(60 * 10));
|
|
loop {
|
|
tick.tick().await;
|
|
Self::sync_once(&db, ai_api_key.clone(), ai_base_url.clone()).await;
|
|
}
|
|
})
|
|
}
|
|
|
|
/// Perform a single sync pass. Errors are logged and silently swallowed
|
|
/// so the periodic task never stops.
|
|
async fn sync_once(
|
|
db: &AppDatabase,
|
|
ai_api_key: Option<String>,
|
|
ai_base_url: Option<String>,
|
|
) {
|
|
let (http_client, base_url, api_key) = match build_ai_client_from_parts(ai_api_key, ai_base_url) {
|
|
Ok(c) => c,
|
|
Err(msg) => {
|
|
tracing::warn!(error = %msg, "Model sync: AI client config error");
|
|
return;
|
|
}
|
|
};
|
|
|
|
let upstream_models = match list_upstream_models(&http_client, &base_url, &api_key).await {
|
|
Ok(models) => models,
|
|
Err(e) => {
|
|
tracing::warn!(error = ?e, "Model sync: failed to list upstream models");
|
|
return;
|
|
}
|
|
};
|
|
|
|
tracing::info!(
|
|
model_count = upstream_models.len(),
|
|
"Model sync: {} models from upstream",
|
|
upstream_models.len()
|
|
);
|
|
|
|
let result = sync_models_from_upstream(db, upstream_models).await;
|
|
|
|
tracing::info!(
|
|
models_created = result.models_created,
|
|
models_updated = result.models_updated,
|
|
versions_created = result.versions_created,
|
|
pricing_created = result.pricing_created,
|
|
capabilities_created = result.capabilities_created,
|
|
profiles_created = result.profiles_created,
|
|
"Model sync complete"
|
|
);
|
|
}
|
|
}
|