feat(embed): MiniCPM4 embed/embed_batch + MiniCPM4GenerateModel embed_text/embed_text_batch

- MiniCPMModel::embed: run forward through layers + norm, mean pool, L2 normalize
- MiniCPMModel::embed_batch: batch version
- MiniCPM4GenerateModel::embed_text: tokenize + embed + return Vec<f32>
- MiniCPM4GenerateModel::embed_text_batch: batch version

src/models/minicpm4/generate.rs

@@ -42,6 +42,25 @@ impl<'a> MiniCPM4GenerateModel<'a> {
             model_name,
         })
     }
+
+    /// 文本向量嵌入。
+    ///
+    /// 将输入文本编码为固定长度向量（mean pool + L2 normalize）。
+    pub fn embed_text(&mut self, text: &str) -> Result<Vec<f32>> {
+        let input_ids = self.tokenizer.text_encode(text.to_string(), &self.device)?;
+        let embedding = self.model.embed(&input_ids)?;
+        let vec: Vec<f32> = embedding.flatten_all()?.to_vec1()?;
+        Ok(vec)
+    }
+
+    /// 批量文本向量嵌入。
+    pub fn embed_text_batch(&mut self, texts: &[&str]) -> Result<Vec<Vec<f32>>> {
+        let mut results = Vec::with_capacity(texts.len());
+        for text in texts {
+            results.push(self.embed_text(text)?);
+        }
+        Ok(results)
+    }
 }
 
 impl<'a> GenerationDataProvider for MiniCPM4GenerateModel<'a> {