feat: support calling single LLM config

flomo-ai-desktop/src/api/llm_client.rs

@@ -1,4 +1,4 @@
-use crate::config::AppSettings;
+use crate::config::{AppSettings, LLMConfig};
 use serde::{Deserialize, Serialize};
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -23,7 +23,7 @@ pub struct ChatCompletionResponse {
     pub choices: Vec<ChatChoice>,
 }
 
-pub fn call_llm(settings: &AppSettings, user_input: String, selected_prompt: Option<String>) -> Result<String, String> {
+pub fn call_single_llm(config: &LLMConfig, user_input: String, selected_prompt: Option<String>, header_configs: &[crate::config::HeaderConfig]) -> Result<String, String> {
     let full_content = if let Some(prompt) = selected_prompt {
         if !prompt.is_empty() {
             format!("{}{}", prompt, user_input)
@@ -40,7 +40,7 @@ pub fn call_llm(settings: &AppSettings, user_input: String, selected_prompt: Opt
     }];
 
     let request = ChatCompletionRequest {
-        model: settings.llm_config.model.clone(),
+        model: config.model.clone(),
         messages,
     };
 
@@ -50,14 +50,14 @@ pub fn call_llm(settings: &AppSettings, user_input: String, selected_prompt: Opt
         .map_err(|e| format!("Failed to create HTTP client: {}", e))?;
 
     let mut req_builder = client
-        .post(format!("{}/chat/completions", settings.llm_config.base_url))
+        .post(format!("{}/chat/completions", config.base_url))
         .header("Content-Type", "application/json");
 
-    if !settings.llm_config.api_key.is_empty() {
-        req_builder = req_builder.header("Authorization", format!("Bearer {}", settings.llm_config.api_key));
+    if !config.api_key.is_empty() {
+        req_builder = req_builder.header("Authorization", format!("Bearer {}", config.api_key));
     }
 
-    for header in &settings.header_configs {
+    for header in header_configs {
         if !header.key.is_empty() {
             req_builder = req_builder.header(&header.key, &header.value);
         }
@@ -90,3 +90,11 @@ pub fn call_llm(settings: &AppSettings, user_input: String, selected_prompt: Opt
 
     Ok(completion.choices[0].message.content.clone())
 }
+
+pub fn call_llm(settings: &AppSettings, user_input: String, selected_prompt: Option<String>) -> Result<String, String> {
+    settings.llm_configs
+        .iter()
+        .find(|c| c.enabled && !c.api_key.is_empty() && !c.base_url.is_empty())
+        .ok_or_else(|| "没有可用的模型配置".to_string())
+        .and_then(|c| call_single_llm(c, user_input, selected_prompt, &settings.header_configs))
+}