use super::openai::{openai_build_body, OPENAI_TOKENS_COUNT_FACTORS}; use super::{ExtraConfig, LocalAIClient, PromptType, SendData, ModelInfo}; use crate::utils::PromptKind; use anyhow::Result; use async_trait::async_trait; use reqwest::{Client as ReqwestClient, RequestBuilder}; use serde::Deserialize; #[derive(Debug, Clone, Deserialize)] pub struct LocalAIConfig { pub name: Option, pub api_base: String, pub api_key: Option, pub chat_endpoint: Option, pub models: Vec, pub extra: Option, } #[derive(Debug, Clone, Deserialize)] pub struct LocalAIModel { name: String, max_tokens: Option, } openai_compatible_client!(LocalAIClient); impl LocalAIClient { config_get_fn!(api_key, get_api_key); pub const PROMPTS: [PromptType<'static>; 4] = [ ("api_base", "API Base:", true, PromptKind::String), ("api_key", "API Key:", false, PromptKind::String), ("models[].name", "Model Name:", true, PromptKind::String), ( "models[].max_tokens", "Max Tokens:", false, PromptKind::Integer, ), ]; pub fn list_models(local_config: &LocalAIConfig, index: usize) -> Vec { let client = Self::name(local_config); local_config .models .iter() .map(|v| { ModelInfo::new(index, client, &v.name) .set_max_tokens(v.max_tokens) .set_tokens_count_factors(OPENAI_TOKENS_COUNT_FACTORS) }) .collect() } fn request_builder(&self, client: &ReqwestClient, data: SendData) -> Result { let api_key = self.get_api_key().ok(); let body = openai_build_body(data, self.model_info.name.clone()); let chat_endpoint = self .config .chat_endpoint .as_deref() .unwrap_or("/chat/completions"); let url = format!("{}{chat_endpoint}", self.config.api_base); let mut builder = client.post(url).json(&body); if let Some(api_key) = api_key { builder = builder.bearer_auth(api_key); } Ok(builder) } }