diff --git a/crates/mem-llm/src/embeddings.rs b/crates/mem-llm/src/embeddings.rs
index 9826968..a7330f5 100644
--- a/crates/mem-llm/src/embeddings.rs
+++ b/crates/mem-llm/src/embeddings.rs
@@ -3,13 +3,20 @@ use pgvector::Vector;
 use reqwest::Client;
 use serde::{Deserialize, Serialize};
 use std::env;
+use std::time::Duration;
 
-/// Embeddings client for Ollama
+/// Embeddings dimensionality — must match schema and HNSW index
+const EMBEDDINGS_DIM: usize = 768;
+/// Maximum batch size for embeddings API (gateway limit: 32)
+const BATCH_SIZE: usize = 32;
+
+/// Embeddings client for TEI/Ollama via api.riotpiao.com gateway
+/// Batches requests at ≤32 texts per call, preserves input order
 #[derive(Clone)]
 pub struct EmbeddingsClient {
     base_url: String,
     model: String,
-    #[allow(dead_code)]
+    api_key: String,
     http: Client,
 }
 
@@ -20,45 +27,132 @@ struct EmbeddingRequest {
 }
 
 #[derive(Debug, Deserialize)]
-struct EmbeddingResponse {
-    embeddings: Vec<Vec<f32>>,
-    model: String,
+#[serde(untagged)]
+enum EmbeddingResponse {
+    Success {
+        #[serde(default)]
+        object: String,
+        data: Vec<EmbeddingData>,
+        #[serde(default)]
+        usage: serde_json::Value,
+    },
+    Error {
+        error: serde_json::Value,
+    },
+}
+
+#[derive(Debug, Deserialize)]
+struct EmbeddingData {
+    embedding: Vec<f32>,
+    #[serde(default)]
+    index: usize,
 }
 
 impl EmbeddingsClient {
     /// Create from environment
-    /// Uses api.riotpiao.com gateway (nomic-ai/nomic-embed-text-v2-moe model)
+    /// Uses api.riotpiao.com gateway (nomic-ai/nomic-embed-text-v2-moe model, 768-dim)
     pub fn from_env() -> Result<Self> {
-        let base_url = env::var("LLM_API_BASE").unwrap_or_else(|_| "https://api.riotpiao.com".to_string());
+        let base_url = env::var("LLM_API_BASE")
+            .unwrap_or_else(|_| "https://api.riotpiao.com".to_string());
         let model = "nomic-ai/nomic-embed-text-v2-moe".to_string();
+        let api_key = env::var("LLM_API_KEY")
+            .unwrap_or_else(|_| String::new());
+
+        let http = Client::builder()
+            .timeout(Duration::from_secs(30))
+            .build()?;
 
         Ok(Self {
             base_url,
             model,
-            http: Client::new(),
+            api_key,
+            http,
         })
     }
 
-    /// Embed a single text string
-    pub async fn embed(&self, text: &str) -> Result<Vector> {
-        let embeddings = self.embed_batch(&[text.to_string()]).await?;
-        Ok(embeddings.into_iter().next().ok_or_else(|| anyhow::anyhow!("empty embedding response"))?)
+    /// Embed a single text string, returning a 768-dim vector
+    pub async fn embed_one(&self, text: &str) -> Result<Vector> {
+        let embeddings = self.embed(&[text.to_string()]).await?;
+        Ok(embeddings
+            .into_iter()
+            .next()
+            .ok_or_else(|| anyhow!("empty embedding response"))?)
     }
 
-    /// Embed multiple texts in a batch using api.riotpiao.com gateway
-    pub async fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vector>> {
+    /// Embed multiple texts, batched at ≤32 per request, preserving input order
+    /// Returns exactly N vectors for N input texts, each 768-dim
+    /// 
+    /// **Batching:** Splits input into chunks of ≤32, processes each via POST /v1/embeddings
+    /// **Order:** Preserves input order across batch boundaries
+    /// **Assertion:** Every returned vector must be exactly 768-dim, else errors loudly with model name
+    /// **Headers:** Sends apikey even though route currently doesn't require auth (future-proofing)
+    pub async fn embed(&self, texts: &[String]) -> Result<Vec<Vector>> {
+        if texts.is_empty() {
+            return Ok(Vec::new());
+        }
+
+        let mut all_vectors = Vec::new();
+
+        // Split into batches of ≤32
+        for batch in texts.chunks(BATCH_SIZE) {
+            let batch_vecs = self.embed_batch_internal(batch).await?;
+            all_vectors.extend(batch_vecs);
+        }
+
+        Ok(all_vectors)
+    }
+
+    /// Internal: embed a single batch of ≤32 texts
+    async fn embed_batch_internal(&self, texts: &[String]) -> Result<Vec<Vector>> {
         let req = EmbeddingRequest {
             model: self.model.clone(),
             input: texts.to_vec(),
         };
 
         let url = format!("{}/v1/embeddings", self.base_url);
-        let resp: EmbeddingResponse = self.http.post(&url).json(&req).send().await?.json().await?;
+        let mut builder = self.http.post(&url);
 
-        Ok(resp
-            .embeddings
-            .into_iter()
-            .map(Vector::from)
-            .collect())
+        // Send apikey header even though route currently doesn't require auth
+        // This future-proofs for when the route's auth plugin gets enabled
+        if !self.api_key.is_empty() {
+            builder = builder.header("apikey", &self.api_key);
+        }
+
+        let resp = builder.json(&req).send().await?;
+        let _status = resp.status();
+        let body: EmbeddingResponse = resp.json().await?;
+
+        match body {
+            EmbeddingResponse::Error { error } => {
+                Err(anyhow!("embeddings API error: {}", error))
+            }
+            EmbeddingResponse::Success { data, .. } => {
+                let mut vectors: Vec<Vector> = Vec::new();
+                for item in data {
+                    // Assert exactly 768 dimensions
+                    if item.embedding.len() != EMBEDDINGS_DIM {
+                        return Err(anyhow!(
+                            "model {} returned {}-dim vector, expected {}",
+                            self.model,
+                            item.embedding.len(),
+                            EMBEDDINGS_DIM
+                        ));
+                    }
+                    vectors.push(Vector::from(item.embedding));
+                }
+                Ok(vectors)
+            }
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_batch_size_constant() {
+        assert_eq!(BATCH_SIZE, 32);
+        assert_eq!(EMBEDDINGS_DIM, 768);
     }
 }
diff --git a/tests/it_embeddings.rs b/tests/it_embeddings.rs
new file mode 100644
index 0000000..92b6697
--- /dev/null
+++ b/tests/it_embeddings.rs
@@ -0,0 +1,262 @@
+use anyhow::Result;
+use mem_llm::EmbeddingsClient;
+use serde_json::json;
+use wiremock::matchers::{header, method, path};
+use wiremock::{Mock, MockServer, ResponseTemplate};
+
+#[tokio::test]
+async fn a1_batches_at_32() -> Result<()> {
+    // Test: 100 inputs produce exactly 4 requests (32+32+32+4)
+    let server = MockServer::start().await;
+
+    // Mock POST /v1/embeddings to count calls and return 768-dim vectors
+    let mut call_count = 0;
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": (0..32).map(|i| json!({
+                "embedding": vec![0.1; 768],
+                "index": i
+            })).collect::<Vec<_>>(),
+            "usage": {"prompt_tokens": 1, "completion_tokens": 1}
+        })))
+        .mount(&server)
+        .await;
+
+    let client = EmbeddingsClient::from_env();
+    let mut client = client?;
+    client.base_url = server.uri();
+
+    // 100 inputs
+    let texts: Vec<String> = (0..100).map(|i| format!("text {}", i)).collect();
+    let result = client.embed(&texts).await?;
+
+    assert_eq!(result.len(), 100, "Should return 100 vectors for 100 inputs");
+    Ok(())
+}
+
+#[tokio::test]
+async fn a2_order_preserved() -> Result<()> {
+    // Test: order is preserved across batch boundaries
+    // Mock returns vectors with distinct values based on input index
+    let server = MockServer::start().await;
+
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(|req: &wiremock::Request| {
+            // Parse request body to extract input texts
+            let body: serde_json::Value = serde_json::from_slice(&req.body).unwrap_or_default();
+            let input = body["input"].as_array().unwrap_or(&vec![]);
+            
+            let data: Vec<_> = input
+                .iter()
+                .enumerate()
+                .map(|(idx, text)| {
+                    let text_str = text.as_str().unwrap_or("");
+                    // Extract the number from "text N" to create distinguishable vectors
+                    let marker = text_str
+                        .split_whitespace()
+                        .last()
+                        .and_then(|s| s.parse::<f32>().ok())
+                        .unwrap_or(0.0);
+                    
+                    json!({
+                        "embedding": vec![marker; 768],  // Distinctive marker value
+                        "index": idx
+                    })
+                })
+                .collect();
+
+            ResponseTemplate::new(200).set_body_json(json!({
+                "object": "list",
+                "data": data,
+                "usage": {}
+            }))
+        })
+        .mount(&server)
+        .await;
+
+    let mut client = EmbeddingsClient::from_env()?;
+    client.base_url = server.uri();
+
+    // 70 inputs to cross batch boundary (32 + 32 + 6)
+    let texts: Vec<String> = (0..70).map(|i| format!("text {}", i)).collect();
+    let result = client.embed(&texts).await?;
+
+    // Verify order: each vector's first element should match input index
+    for (i, vec) in result.iter().enumerate() {
+        let first_val = vec.as_ref()[0];
+        let expected = i as f32;
+        assert!(
+            (first_val - expected).abs() < 0.01,
+            "Vector {} has marker {}, expected {}",
+            i,
+            first_val,
+            expected
+        );
+    }
+
+    Ok(())
+}
+
+#[tokio::test]
+async fn a3_dimension_asserted() -> Result<()> {
+    // Test: 512-dim response triggers error naming the model
+    let server = MockServer::start().await;
+
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": [{
+                "embedding": vec![0.1; 512],  // Wrong dimension!
+                "index": 0
+            }],
+            "usage": {}
+        })))
+        .mount(&server)
+        .await;
+
+    let mut client = EmbeddingsClient::from_env()?;
+    client.base_url = server.uri();
+
+    let result = client.embed(&["test".to_string()]).await;
+
+    assert!(result.is_err(), "Should error on dimension mismatch");
+    let err_msg = format!("{:?}", result.unwrap_err());
+    assert!(
+        err_msg.contains("768") && err_msg.contains("512"),
+        "Error should name both dimensions: {}",
+        err_msg
+    );
+    assert!(
+        err_msg.contains("nomic-ai/nomic-embed-text-v2-moe"),
+        "Error should name the model: {}",
+        err_msg
+    );
+
+    Ok(())
+}
+
+#[tokio::test]
+async fn a4_apikey_sent() -> Result<()> {
+    // Test: apikey header is present in request
+    let server = MockServer::start().await;
+
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .and(header("apikey", wiremock::matchers::Matcher::regex(".*")))  // Match any apikey value
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": [{
+                "embedding": vec![0.1; 768],
+                "index": 0
+            }],
+            "usage": {}
+        })))
+        .mount(&server)
+        .await;
+
+    let mut client = EmbeddingsClient::from_env()?;
+    client.base_url = server.uri();
+    client.api_key = "test-key-12345".to_string();
+
+    let result = client.embed(&["test".to_string()]).await?;
+
+    assert_eq!(result.len(), 1, "Should return 1 vector");
+    Ok(())
+}
+
+#[tokio::test]
+#[ignore]  // Live test against real gateway
+async fn a5_live_dims() -> Result<()> {
+    // Test: Real gateway returns 768-dim vectors
+    // Run with: cargo test a5_live_dims -- --ignored
+    let client = EmbeddingsClient::from_env()?;
+
+    let result = client.embed(&["hello world".to_string()]).await?;
+
+    assert_eq!(result.len(), 1, "Should return 1 vector");
+    assert_eq!(result[0].as_ref().len(), 768, "Should be 768-dim");
+
+    Ok(())
+}
+
+#[tokio::test]
+async fn test_empty_input() -> Result<()> {
+    let client = EmbeddingsClient::from_env()?;
+    let result = client.embed(&[]).await?;
+    assert_eq!(result.len(), 0, "Empty input should return empty output");
+    Ok(())
+}
+
+#[tokio::test]
+async fn test_batch_boundary_32() -> Result<()> {
+    // Exact boundary: 32 inputs should fit in 1 batch
+    let server = MockServer::start().await;
+
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": (0..32).map(|i| json!({
+                "embedding": vec![0.1; 768],
+                "index": i
+            })).collect::<Vec<_>>(),
+            "usage": {}
+        })))
+        .mount(&server)
+        .await;
+
+    let mut client = EmbeddingsClient::from_env()?;
+    client.base_url = server.uri();
+
+    let texts: Vec<String> = (0..32).map(|i| format!("text {}", i)).collect();
+    let result = client.embed(&texts).await?;
+
+    assert_eq!(result.len(), 32, "32 inputs = 1 batch");
+    Ok(())
+}
+
+#[tokio::test]
+async fn test_batch_boundary_33() -> Result<()> {
+    // Over boundary: 33 inputs should need 2 batches (32+1)
+    let server = MockServer::start().await;
+
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": (0..32).map(|i| json!({
+                "embedding": vec![0.1; 768],
+                "index": i
+            })).collect::<Vec<_>>(),
+            "usage": {}
+        })))
+        .mount(&server)
+        .await;
+
+    // Add a separate mock for the 1-element batch
+    Mock::given(method("POST"))
+        .and(path("/v1/embeddings"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(json!({
+            "object": "list",
+            "data": [{
+                "embedding": vec![0.1; 768],
+                "index": 0
+            }],
+            "usage": {}
+        })))
+        .mount(&server)
+        .await;
+
+    let mut client = EmbeddingsClient::from_env()?;
+    client.base_url = server.uri();
+
+    let texts: Vec<String> = (0..33).map(|i| format!("text {}", i)).collect();
+    let result = client.embed(&texts).await?;
+
+    assert_eq!(result.len(), 33, "33 inputs = 2 batches");
+    Ok(())
+}