51 lines
1.9 KiB
Rust
51 lines
1.9 KiB
Rust
use crate::providers::{ChatCompletionRequest, LLMProvider, Message};
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pub async fn generate_topic_description(
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provider: &dyn LLMProvider,
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first_user_message: &str,
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) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
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let system_prompt = "你是一个话题摘要助手。请根据用户的第一句话，用简短的词语（不超过15字）描述这个对话的主题或意图。只输出描述内容，不要任何解释、标点或前缀。";
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let user_prompt = format!("用户消息：{}", first_user_message);
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let request = ChatCompletionRequest {
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messages: vec![
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Message::system(system_prompt),
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Message::user(user_prompt),
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],
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temperature: Some(0.0),
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max_tokens: Some(1024), // 给 reasoning 模型留足思考空间
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tools: None,
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};
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let response = provider.chat(request).await?;
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let description = response.content.trim().to_string();
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if description.is_empty() {
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// 回退：reasoning 模型有时把所有 token 都消耗在推理上，content 为空
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// 此时尝试从 reasoning_content 中提取最后一行有意义的内容作为描述
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if let Some(ref reasoning) = response.reasoning_content {
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let fallback: String = reasoning
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.lines()
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.rev()
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.find(|line| {
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let trimmed = line.trim();
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!trimmed.is_empty() && trimmed.len() <= 50
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})
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.unwrap_or("")
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.trim()
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.to_string();
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if !fallback.is_empty() {
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let truncated: String = fallback.chars().take(50).collect();
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return Ok(truncated);
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}
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}
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return Err("LLM returned empty description".into());
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}
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if description.len() > 50 {
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Ok(description.chars().take(50).collect())
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} else {
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Ok(description)
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}
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} |