diff --git a/conf/models/aliyun.json b/conf/models/aliyun.json
index b0cad72e4d..32a5221da4 100644
--- a/conf/models/aliyun.json
+++ b/conf/models/aliyun.json
@@ -17,6 +17,20 @@
       "model_types": [
         "chat"
       ]
+    },
+    {
+      "name": "text-embedding-v4",
+      "max_tokens": 8192,
+      "model_types": [
+        "embedding"
+      ]
+    },
+    {
+      "name": "text-embedding-v3",
+      "max_tokens": 8192,
+      "model_types": [
+        "embedding"
+      ]
     }
   ],
   "features": {
diff --git a/internal/entity/models/aliyun.go b/internal/entity/models/aliyun.go
index 2019f1db4b..c1cd7d5564 100644
--- a/internal/entity/models/aliyun.go
+++ b/internal/entity/models/aliyun.go
@@ -19,6 +19,7 @@ package models
 import (
 	"bufio"
 	"bytes"
+	"context"
 	"encoding/json"
 	"fmt"
 	"io"
@@ -350,9 +351,111 @@ func (z *AliyunModel) ChatStreamlyWithSender(modelName string, messages []Messag
 	return scanner.Err()
 }
 
+type aliyunEmbeddingResponse struct {
+	Data []struct {
+		Index     int           `json:"index"`
+		Embedding []interface{} `json:"embedding"`
+	} `json:"data"`
+}
+
 // Encode encodes a list of texts into embeddings
 func (z *AliyunModel) Encode(modelName *string, texts []string, apiConfig *APIConfig, embeddingConfig *EmbeddingConfig) ([][]float64, error) {
-	return nil, fmt.Errorf("%s, no such method", z.Name())
+	if len(texts) == 0 {
+		return [][]float64{}, nil
+	}
+
+	if apiConfig == nil || apiConfig.ApiKey == nil || *apiConfig.ApiKey == "" {
+		return nil, fmt.Errorf("api key is required")
+	}
+
+	if modelName == nil || *modelName == "" {
+		return nil, fmt.Errorf("model name is required")
+	}
+
+	region := "default"
+	if apiConfig.Region != nil && *apiConfig.Region != "" {
+		region = *apiConfig.Region
+	}
+
+	baseURL := z.BaseURL["default"]
+	if region != "default" {
+		if regional, ok := z.BaseURL[region]; ok && regional != "" {
+			baseURL = regional
+		}
+	}
+	if baseURL == "" {
+		return nil, fmt.Errorf("aliyun: no base URL configured for default region")
+	}
+
+	url := fmt.Sprintf("%s/%s", strings.TrimSuffix(baseURL, "/"), z.URLSuffix.Embedding)
+
+	reqBody := map[string]interface{}{
+		"model": *modelName,
+		"input": texts,
+	}
+
+	jsonData, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("failed to marshal request: %w", err)
+	}
+
+	ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
+	defer cancel()
+
+	req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
+	if err != nil {
+		return nil, fmt.Errorf("failed to create request: %w", err)
+	}
+
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
+
+	resp, err := z.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("failed to send request: %w", err)
+	}
+	defer resp.Body.Close()
+
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return nil, fmt.Errorf("failed to read response: %w", err)
+	}
+
+	if resp.StatusCode != http.StatusOK {
+		return nil, fmt.Errorf("Aliyun embeddings API error: %s, body: %s", resp.Status, string(body))
+	}
+
+	var parsed aliyunEmbeddingResponse
+	if err = json.Unmarshal(body, &parsed); err != nil {
+		return nil, fmt.Errorf("failed to parse response: %w", err)
+	}
+
+	embeddings := make([][]float64, len(texts))
+	for _, item := range parsed.Data {
+		if item.Index < 0 || item.Index >= len(texts) {
+			return nil, fmt.Errorf("unexpected embedding index %d for %d inputs", item.Index, len(texts))
+		}
+		vec := make([]float64, len(item.Embedding))
+		for j, v := range item.Embedding {
+			switch val := v.(type) {
+			case float64:
+				vec[j] = val
+			case float32:
+				vec[j] = float64(val)
+			default:
+				return nil, fmt.Errorf("unexpected embedding value type at item %d index %d", item.Index, j)
+			}
+		}
+		embeddings[item.Index] = vec
+	}
+
+	for i, vec := range embeddings {
+		if vec == nil {
+			return nil, fmt.Errorf("missing embedding for input at index %d", i)
+		}
+	}
+
+	return embeddings, nil
 }
 
 // Rerank calculates similarity scores between query and texts