feat: Translate various strings and comments to Spanish for better localization
- Updated error messages and comments in main.rs, openapi.rs, portfolio.rs, predictive.rs, ai.rs, health.rs, middleware.rs, models.rs, token_limits.rs, and webhooks.rs to Spanish. - Enhanced user experience by providing localized content for Spanish-speaking users.
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-41
@@ -1,33 +1,33 @@
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//! AI Utilities for OpenCCB
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//! Provides embedding generation and other AI helper functions
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//! Utilidades de IA para OpenCCB
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//! Proporciona generación de embeddings y otras funciones de ayuda de IA
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use serde::{Deserialize, Serialize};
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use thiserror::Error;
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/// Default embedding model for Ollama
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/// Modelo de embedding por defecto para Ollama
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pub const DEFAULT_EMBEDDING_MODEL: &str = "nomic-embed-text";
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/// Default Ollama URL
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/// URL de Ollama por defecto
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pub const DEFAULT_OLLAMA_URL: &str = "http://localhost:11434";
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/// Embedding dimensions for nomic-embed-text
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/// Dimensiones del embedding para nomic-embed-text
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pub const EMBEDDING_DIMENSIONS: usize = 768;
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/// Model selection for different use cases
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/// Selección de modelo para diferentes casos de uso
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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pub enum ModelType {
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/// Fast conversational AI (chat, tutor, Q&A)
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/// IA conversacional rápida (chat, tutor, preguntas y respuestas)
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Chat,
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/// Complex reasoning (analysis, recommendations, feedback)
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/// Razonamiento complejo (análisis, recomendaciones, retroalimentación)
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Complex,
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/// Advanced tasks (course generation, detailed analysis)
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/// Tareas avanzadas (generación de cursos, análisis detallado)
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Advanced,
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/// Embedding generation
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/// Generación de embeddings
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Embedding,
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}
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impl ModelType {
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/// Get the model name for this type from environment
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/// Obtener el nombre del modelo para este tipo desde el entorno
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pub fn get_model(&self) -> String {
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match self {
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ModelType::Chat => {
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@@ -45,34 +45,34 @@ impl ModelType {
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}
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}
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/// Get recommended temperature for this model type
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/// Obtener la temperatura recomendada para este tipo de modelo
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pub fn get_temperature(&self) -> f32 {
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match self {
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ModelType::Chat => 0.7, // Balanced creativity/accuracy
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ModelType::Complex => 0.5, // More focused reasoning
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ModelType::Advanced => 0.6, // Balanced for analysis
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ModelType::Embedding => 0.0, // Deterministic for embeddings
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ModelType::Chat => 0.7, // Equilibrio entre creatividad y precisión
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ModelType::Complex => 0.5, // Razonamiento más enfocado
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ModelType::Advanced => 0.6, // Equilibrado para análisis
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ModelType::Embedding => 0.0, // Determinista para embeddings
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}
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}
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/// Get max tokens for this model type
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/// Obtener los tokens máximos para este tipo de modelo
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pub fn get_max_tokens(&self) -> u32 {
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match self {
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ModelType::Chat => 1024,
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ModelType::Complex => 2048,
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ModelType::Advanced => 4096,
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ModelType::Embedding => 0, // Not applicable
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ModelType::Embedding => 0, // No aplicable
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}
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}
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}
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#[derive(Error, Debug)]
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pub enum AiError {
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#[error("Ollama request failed: {0}")]
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#[error("Solicitud a Ollama fallida: {0}")]
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OllamaRequest(String),
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#[error("Invalid embedding response: {0}")]
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#[error("Respuesta de embedding inválida: {0}")]
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InvalidResponse(String),
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#[error("Model not available: {0}")]
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#[error("Modelo no disponible: {0}")]
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ModelNotAvailable(String),
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}
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@@ -83,19 +83,19 @@ pub struct EmbeddingResponse {
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pub model: String,
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}
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/// Get Ollama URL from environment or default
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/// Obtener la URL de Ollama desde el entorno o por defecto
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pub fn get_ollama_url() -> String {
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std::env::var("LOCAL_OLLAMA_URL").unwrap_or_else(|_| DEFAULT_OLLAMA_URL.to_string())
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}
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/// Get embedding model from environment or default
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/// Obtener el modelo de embedding desde el entorno o por defecto
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pub fn get_embedding_model() -> String {
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std::env::var("EMBEDDING_MODEL").unwrap_or_else(|_| DEFAULT_EMBEDDING_MODEL.to_string())
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}
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/// Get the best model for a specific task
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/// Obtener el mejor modelo para una tarea específica
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pub fn get_model_for_task(task: &str) -> String {
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// Task-based model selection
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// Selección de modelo basada en la tarea
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match task.to_lowercase().as_str() {
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t if t.contains("chat") || t.contains("tutor") || t.contains("conversation") => {
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ModelType::Chat.get_model()
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@@ -116,22 +116,22 @@ pub fn get_model_for_task(task: &str) -> String {
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}
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}
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/// Create a reqwest client that accepts invalid certificates (for dev with self-signed certs)
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/// Crear un cliente reqwest que acepte certificados inválidos (para desarrollo con certificados autofirmados)
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fn create_insecure_client() -> Result<reqwest::Client, AiError> {
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reqwest::Client::builder()
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.danger_accept_invalid_certs(true)
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.danger_accept_invalid_hostnames(true)
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.build()
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.map_err(|e| AiError::OllamaRequest(format!("Failed to create HTTP client: {}", e)))
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.map_err(|e| AiError::OllamaRequest(format!("Error al crear el cliente HTTP: {}", e)))
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}
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/// Generate embedding for text using Ollama
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/// Generar embedding para texto usando Ollama
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///
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/// # Arguments
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/// * `client` - reqwest::Client instance
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/// * `ollama_url` - Base URL for Ollama (e.g., "http://localhost:11434")
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/// * `model` - Embedding model name (default: "nomic-embed-text")
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/// * `text` - Text to embed
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/// # Argumentos
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/// * `client` - instancia de reqwest::Client
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/// * `ollama_url` - URL base para Ollama (e.g., "http://localhost:11434")
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/// * `model` - Nombre del modelo de embedding (por defecto: "nomic-embed-text")
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/// * `text` - Texto a incrustar
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pub async fn generate_embedding(
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client: &reqwest::Client,
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ollama_url: &str,
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@@ -148,25 +148,25 @@ pub async fn generate_embedding(
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}))
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.send()
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.await
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.map_err(|e| AiError::OllamaRequest(format!("Request failed: {}", e)))?;
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.map_err(|e| AiError::OllamaRequest(format!("Solicitud fallida: {}", e)))?;
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if !response.status().is_success() {
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let status = response.status();
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let error_text = response.text().await.unwrap_or_default();
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return Err(AiError::OllamaRequest(
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format!("Ollama API error ({}): {}", status, error_text)
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format!("Error de la API de Ollama ({}): {}", status, error_text)
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));
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}
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let embedding_response: EmbeddingResponse = response
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.json()
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.await
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.map_err(|e| AiError::InvalidResponse(format!("Failed to parse response: {}", e)))?;
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.map_err(|e| AiError::InvalidResponse(format!("Error al analizar la respuesta: {}", e)))?;
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Ok(embedding_response)
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}
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/// Generate embeddings for multiple texts in batch
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/// Generar embeddings para múltiples textos en lote
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pub async fn generate_embeddings_batch(
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client: &reqwest::Client,
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ollama_url: &str,
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@@ -183,8 +183,8 @@ pub async fn generate_embeddings_batch(
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Ok(embeddings)
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}
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/// Convert a vector of f32 to pgvector-compatible format
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/// PostgreSQL vector format: "[0.1,0.2,0.3,...]"
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/// Convertir un vector de f32 a un formato compatible con pgvector
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/// Formato de vector de PostgreSQL: "[0.1,0.2,0.3,...]"
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pub fn embedding_to_pgvector(embedding: &[f32]) -> String {
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let formatted: Vec<String> = embedding
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.iter()
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@@ -193,12 +193,12 @@ pub fn embedding_to_pgvector(embedding: &[f32]) -> String {
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format!("[{}]", formatted.join(","))
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}
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/// Parse pgvector format back to Vec<f32>
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/// Analizar el formato pgvector de vuelta a Vec<f32>
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pub fn pgvector_to_embedding(pgvector: &str) -> Result<Vec<f32>, String> {
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let trimmed = pgvector.trim().trim_start_matches('[').trim_end_matches(']');
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trimmed
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.split(',')
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.map(|s| s.trim().parse::<f32>().map_err(|e| format!("Parse error: {}", e)))
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.map(|s| s.trim().parse::<f32>().map_err(|e| format!("Error de análisis: {}", e)))
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.collect()
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}
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