diff --git a/rig-core/Cargo.toml b/rig-core/Cargo.toml
index 3dac86b..3c5c7f3 100644
--- a/rig-core/Cargo.toml
+++ b/rig-core/Cargo.toml
@@ -127,3 +127,8 @@ required-features = ["audio"]
 [[example]]
 name = "hyperbolic_audio_generation"
 required-features = ["audio"]
+
+
+[[example]]
+name = "aliyun_embeddings"
+required-features = ["derive"]
diff --git a/rig-core/examples/aliyun_embeddings.rs b/rig-core/examples/aliyun_embeddings.rs
new file mode 100644
index 0000000..eb771f0
--- /dev/null
+++ b/rig-core/examples/aliyun_embeddings.rs
@@ -0,0 +1,30 @@
+use rig::providers::aliyun;
+use rig::Embed;
+
+#[derive(Embed, Debug)]
+struct Greetings {
+    #[embed]
+    message: String,
+}
+
+#[tokio::main]
+async fn main() -> Result<(), anyhow::Error> {
+    // Initialize the Aliyun client
+    let client = aliyun::Client::from_env();
+
+    let embeddings = client
+        .embeddings(aliyun::embedding::EMBEDDING_V1)
+        .document(Greetings {
+            message: "Hello, world!".to_string(),
+        })?
+        .document(Greetings {
+            message: "Goodbye, world!".to_string(),
+        })?
+        .build()
+        .await
+        .expect("Failed to embed documents");
+
+    println!("{:?}", embeddings);
+
+    Ok(())
+}
diff --git a/rig-core/src/providers/aliyun/client.rs b/rig-core/src/providers/aliyun/client.rs
new file mode 100644
index 0000000..3e095b3
--- /dev/null
+++ b/rig-core/src/providers/aliyun/client.rs
@@ -0,0 +1,162 @@
+use crate::{
+    embeddings::{self},
+    Embed,
+};
+use serde::Deserialize;
+
+use super::embedding::EmbeddingModel;
+
+// ================================================================
+// Aliyun Gemini Client
+// ================================================================
+const ALIYUN_API_BASE_URL: &str = "https://dashscope.aliyuncs.com";
+
+#[derive(Clone)]
+pub struct Client {
+    base_url: String,
+    api_key: String,
+    http_client: reqwest::Client,
+}
+
+impl Client {
+    /// Create a new Aliyun client with the given API key.
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::Client;
+    ///
+    /// // Initialize the Aliyun client
+    /// let aliyun = Client::new("your-dashscope-api-key");
+    /// ```
+    pub fn new(api_key: &str) -> Self {
+        Self::from_url(api_key, ALIYUN_API_BASE_URL)
+    }
+
+    /// Create a new Aliyun client with the given API key and base URL.
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::Client;
+    ///
+    /// // Initialize the Aliyun client with a custom base URL
+    /// let aliyun = Client::from_url("your-dashscope-api-key", "https://custom-dashscope-url.com");
+    /// ```
+    pub fn from_url(api_key: &str, base_url: &str) -> Self {
+        Self {
+            base_url: base_url.to_string(),
+            api_key: api_key.to_string(),
+            http_client: reqwest::Client::builder()
+                .default_headers({
+                    let mut headers = reqwest::header::HeaderMap::new();
+                    headers.insert(
+                        reqwest::header::CONTENT_TYPE,
+                        "application/json".parse().unwrap(),
+                    );
+                    headers
+                })
+                .build()
+                .expect("Aliyun reqwest client should build"),
+        }
+    }
+
+    /// Create a new Aliyun client from the `DASHSCOPE_API_KEY` environment variable.
+    /// Panics if the environment variable is not set.
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::Client;
+    ///
+    /// // Initialize the Aliyun client from environment variable
+    /// let aliyun = Client::from_env();
+    /// ```
+    /// # Panics
+    /// This function will panic if the `DASHSCOPE_API_KEY` environment variable is not set.
+    pub fn from_env() -> Self {
+        let api_key = std::env::var("DASHSCOPE_API_KEY").expect("DASHSCOPE_API_KEY not set");
+        Self::new(&api_key)
+    }
+
+    /// Create a POST request to the specified API endpoint path.
+    /// The Authorization header with the API key will be automatically added.
+    ///
+    /// # Arguments
+    /// * `path` - The API endpoint path to append to the base URL
+    ///
+    /// # Returns
+    /// A reqwest::RequestBuilder instance that can be further customized before sending
+    pub fn post(&self, path: &str) -> reqwest::RequestBuilder {
+        let url = format!("{}/{}", self.base_url, path).replace("//", "/");
+
+        tracing::debug!("POST {}/{}", self.base_url, path);
+        self.http_client
+            .post(url)
+            .header("Authorization", format!("Bearer {}", self.api_key))
+    }
+
+    /// Create an embedding model with the given name.
+    /// Note: default embedding dimension of 0 will be used if model is not known.
+    /// If this is the case, it's better to use function `embedding_model_with_ndims`
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::{Client, self};
+    ///
+    /// // Initialize the Aliyun client
+    /// let aliyun = Client::new("your-dashscope-api-key");
+    ///
+    /// let embedding_model = aliyun.embedding_model("your-model-name");
+    /// ```
+    pub fn embedding_model(&self, model: &str) -> EmbeddingModel {
+        EmbeddingModel::new(self.clone(), model, None)
+    }
+
+    /// Create an embedding model with the given name and the number of dimensions in the embedding generated by the model.
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::{Client, self};
+    ///
+    /// // Initialize the Aliyun client
+    /// let aliyun = Client::new("your-dashscope-api-key");
+    ///
+    /// let embedding_model = aliyun.embedding_model_with_ndims("model-unknown-to-rig", 1024);
+    /// ```
+    pub fn embedding_model_with_ndims(&self, model: &str, ndims: usize) -> EmbeddingModel {
+        EmbeddingModel::new(self.clone(), model, Some(ndims))
+    }
+
+    /// Create an embedding builder with the given embedding model.
+    ///
+    /// # Example
+    /// ```
+    /// use rig::providers::aliyun::{Client, self};
+    ///
+    /// // Initialize the Aliyun client
+    /// let aliyun = Client::new("your-dashscope-api-key");
+    ///
+    /// let embeddings = aliyun.embeddings("your-model-name")
+    ///     .simple_document("doc0", "Hello, world!")
+    ///     .simple_document("doc1", "Goodbye, world!")
+    ///     .build()
+    ///     .await
+    ///     .expect("Failed to embed documents");
+    /// ```
+    pub fn embeddings<D: Embed>(
+        &self,
+        model: &str,
+    ) -> embeddings::EmbeddingsBuilder<EmbeddingModel, D> {
+        embeddings::EmbeddingsBuilder::new(self.embedding_model(model))
+    }
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ApiErrorResponse {
+    pub message: String,
+}
+
+#[derive(Debug, Deserialize)]
+#[serde(untagged)]
+pub enum ApiResponse<T> {
+    Ok(T),
+    Err(ApiErrorResponse),
+}
diff --git a/rig-core/src/providers/aliyun/embedding.rs b/rig-core/src/providers/aliyun/embedding.rs
new file mode 100644
index 0000000..bbc765d
--- /dev/null
+++ b/rig-core/src/providers/aliyun/embedding.rs
@@ -0,0 +1,239 @@
+// ================================================================
+//! Aliyun Embedding API Integration
+//! Implementation of Aliyun embedding models for text vectorization
+//! From https://help.aliyun.com/zh/model-studio/developer-reference/text-embedding-synchronous-api
+// ================================================================
+
+use serde_json::json;
+
+use crate::embeddings::{self, EmbeddingError};
+
+use super::client::{ApiResponse, Client};
+
+// Available embedding models provided by Aliyun
+pub const EMBEDDING_V1: &str = "text-embedding-v1";
+pub const EMBEDDING_V2: &str = "text-embedding-v2";
+pub const EMBEDDING_V3: &str = "text-embedding-v3";
+
+/// Aliyun embedding model implementation
+#[derive(Clone)]
+pub struct EmbeddingModel {
+    client: Client,
+    model: String,
+    ndims: Option<usize>,
+}
+
+impl EmbeddingModel {
+    /// Creates a new instance of the Aliyun embedding model
+    ///
+    /// # Arguments
+    /// * `client` - The Aliyun API client
+    /// * `model` - The model identifier (e.g., "text-embedding-v1")
+    /// * `ndims` - Optional custom dimension size for the embedding output
+    pub fn new(client: Client, model: &str, ndims: Option<usize>) -> Self {
+        Self {
+            client,
+            model: model.to_string(),
+            ndims,
+        }
+    }
+}
+
+impl EmbeddingModel {
+    /// Returns the maximum number of documents supported by the model
+    ///
+    /// # Returns
+    /// * For EMBEDDING_V3 model: 10 documents maximum
+    /// * For other models: 25 documents maximum
+    fn max_documents(&self) -> usize {
+        match self.model.as_str() {
+            EMBEDDING_V3 => 10,
+            _ => 25,
+        }
+    }
+
+    /// Returns the maximum number of tokens per document supported by the model
+    ///
+    /// # Returns
+    /// * For EMBEDDING_V3 model: 8192 tokens per document
+    /// * For other models: 2048 tokens per document
+    fn max_tokens(&self) -> usize {
+        match self.model.as_str() {
+            EMBEDDING_V3 => 8192,
+            _ => 2048,
+        }
+    }
+
+    /// Validates if the document list meets the model's constraints
+    ///
+    /// # Validation Checks
+    /// 1. Number of documents doesn't exceed model's maximum capacity
+    /// 2. Each document's token count is within the model's token limit
+    ///
+    /// # Returns
+    /// * `Ok(())` if validation passes
+    /// * `Err(EmbeddingError)` with appropriate error message if validation fails
+    fn validate_documents(&self, documents: &[String]) -> Result<(), EmbeddingError> {
+        const AVG_CHARS_PER_TOKEN: usize = 4;
+
+        if documents.len() > self.max_documents() {
+            return Err(EmbeddingError::ProviderError(format!(
+                "Model {} supports maximum {} documents",
+                self.model,
+                self.max_documents()
+            )));
+        }
+
+        for (i, doc) in documents.iter().enumerate() {
+            let estimated_tokens = doc.len() / AVG_CHARS_PER_TOKEN;
+            if estimated_tokens > self.max_tokens() {
+                return Err(EmbeddingError::ProviderError(format!(
+                    "Document #{} exceeds maximum token limit of {}",
+                    i + 1,
+                    self.max_tokens()
+                )));
+            }
+        }
+
+        Ok(())
+    }
+}
+
+impl embeddings::EmbeddingModel for EmbeddingModel {
+    const MAX_DOCUMENTS: usize = 25;
+
+    /// Returns the default embedding dimension for the current model
+    ///
+    /// # Returns
+    /// * EMBEDDING_V1: 1536 dimensions
+    /// * EMBEDDING_V2: 1536 dimensions
+    /// * EMBEDDING_V3: 1024 dimensions (can be customized)
+    /// * Unknown models: 0 dimensions
+    fn ndims(&self) -> usize {
+        match self.model.as_str() {
+            EMBEDDING_V1 => 1536,
+            EMBEDDING_V2 => 1536,
+
+            // V3 model defaults to 1024 dimensions
+            // Can be customized to [128, 256, 384, 512, 768, 1024]
+            EMBEDDING_V3 => 1024,
+            _ => 0, // Default to 0 for unknown models
+        }
+    }
+
+    /// Generates embeddings for the provided text documents
+    ///
+    /// # Arguments
+    /// * `documents` - Collection of text documents to embed
+    ///
+    /// # Returns
+    /// * `Result<Vec<Embedding>, EmbeddingError>` - Vector of embeddings or error
+    #[cfg_attr(feature = "worker", worker::send)]
+    async fn embed_texts(
+        &self,
+        documents: impl IntoIterator<Item = String> + Send,
+    ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
+        let documents: Vec<String> = documents.into_iter().collect();
+
+        self.validate_documents(&documents)?;
+
+        let request = json!({
+            "model": self.model,
+            "input": documents,
+            "dimension": self.ndims.unwrap_or(self.ndims()),
+            "encoding_format": "float",
+        });
+
+        tracing::info!("{}", serde_json::to_string_pretty(&request).unwrap());
+
+        let response = self
+            .client
+            .post(&format!("/compatible-mode/v1/embeddings"))
+            .json(&request)
+            .send()
+            .await?
+            .error_for_status()?
+            .json::<ApiResponse<aliyun_api_types::EmbeddingResponse>>()
+            .await?;
+
+        match response {
+            ApiResponse::Ok(response) => {
+                let docs = documents
+                    .into_iter()
+                    .zip(response.data)
+                    .map(|(document, embedding)| embeddings::Embedding {
+                        document,
+                        vec: embedding.embedding,
+                    })
+                    .collect();
+
+                Ok(docs)
+            }
+            ApiResponse::Err(err) => Err(EmbeddingError::ProviderError(err.message)),
+        }
+    }
+}
+
+// =================================================================
+// Aliyun API Types
+// =================================================================
+/// Type definitions for Aliyun Embedding API responses
+/// Follows OpenAI-compatible API structure
+#[allow(dead_code)]
+mod aliyun_api_types {
+    use serde::Deserialize;
+
+    /// Response structure for embedding requests
+    #[derive(Debug, Deserialize)]
+    pub struct EmbeddingResponse {
+        pub data: Vec<EmbeddingData>,
+        pub model: String,
+        pub object: String,
+        pub usage: Usage,
+        pub id: String,
+    }
+
+    /// Individual embedding data for a single input document
+    #[derive(Debug, Deserialize)]
+    pub struct EmbeddingData {
+        pub embedding: Vec<f64>,
+        pub index: usize,
+        pub object: String,
+    }
+
+    /// Token usage statistics for the embedding request
+    #[derive(Debug, Deserialize)]
+    pub struct Usage {
+        pub prompt_tokens: usize,
+        pub total_tokens: usize,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::embeddings::embedding::EmbeddingModel as EmbeddingModelTrait;
+
+    #[tokio::test]
+    async fn test_embed_texts() {
+        let client = Client::from_env();
+        let model = EmbeddingModel::new(client, EMBEDDING_V1, None);
+
+        // Test embedding for a single document
+        let documents = vec!["Hello, world!".to_string()];
+        let embeddings = model.embed_texts(documents).await.unwrap();
+
+        assert_eq!(embeddings.len(), 1);
+        assert_eq!(embeddings[0].vec.len(), 1536);
+
+        // Test embedding for multiple documents
+        let documents = vec!["Hello, world!".to_string(), "This is a test".to_string()];
+        let embeddings = model.embed_texts(documents).await.unwrap();
+
+        assert_eq!(embeddings.len(), 2);
+        assert_eq!(embeddings[0].vec.len(), 1536);
+        assert_eq!(embeddings[0].document, "Hello, world!");
+        assert_eq!(embeddings[1].vec.len(), 1536);
+        assert_eq!(embeddings[1].document, "This is a test");
+    }
+}
diff --git a/rig-core/src/providers/aliyun/mod.rs b/rig-core/src/providers/aliyun/mod.rs
new file mode 100644
index 0000000..95e380a
--- /dev/null
+++ b/rig-core/src/providers/aliyun/mod.rs
@@ -0,0 +1,4 @@
+pub mod client;
+pub mod embedding;
+
+pub use client::Client;
diff --git a/rig-core/src/providers/mod.rs b/rig-core/src/providers/mod.rs
index 99f7a94..04c8b21 100644
--- a/rig-core/src/providers/mod.rs
+++ b/rig-core/src/providers/mod.rs
@@ -62,3 +62,4 @@ pub mod openrouter;
 pub mod perplexity;
 pub mod together;
 pub mod xai;
+pub mod aliyun;