fix: Support for msg[content] as a list

examples/backends/vllm/launch/agg_multimodal.sh

@@ -46,6 +46,8 @@ python -m dynamo.frontend --http-port=8000 &
 EXTRA_ARGS=""
 if [[ "$MODEL_NAME" == "Qwen/Qwen2.5-VL-7B-Instruct" ]]; then
     EXTRA_ARGS="--gpu-memory-utilization 0.85 --max-model-len 2048"
+elif [[ "$MODEL_NAME" == "llava-hf/llava-1.5-7b-hf" ]]; then
+    EXTRA_ARGS="--gpu-memory-utilization 0.85 --max-model-len 2048"
 fi
 
 # Start vLLM worker with vision model

lib/llm/src/preprocessor/prompt/template.rs

@@ -106,6 +106,7 @@ struct HfTokenizerConfigJsonFormatter {
     config: ChatTemplate,
     mixins: Arc<ContextMixins>,
     supports_add_generation_prompt: bool,
+    requires_content_arrays: bool,
 }
 
 // /// OpenAI Standard Prompt Formatter

lib/llm/src/preprocessor/prompt/template/formatters.rs

@@ -6,9 +6,38 @@ use std::sync::Arc;
 use super::tokcfg::{ChatTemplate, raise_exception, strftime_now, tojson};
 use super::{ContextMixins, HfTokenizerConfigJsonFormatter, JinjaEnvironment};
 use either::Either;
-use minijinja::{Environment, Value};
+use minijinja::{Environment, Value, context};
+use serde_json::json;
 use tracing;
 
+/// Detects if a template requires content as arrays (multimodal) vs strings (text-only).
+/// Returns true if the template only works with array format.
+fn detect_content_array_usage(env: &Environment) -> bool {
+    // Test with array format
+    let array_msg = context! {
+        messages => json!([{"role": "user", "content": [{"type": "text", "text": "template_test"}]}]),
+        add_generation_prompt => false,
+    };
+
+    // Test with string format
+    let string_msg = context! {
+        messages => json!([{"role": "user", "content": "template_test"}]),
+        add_generation_prompt => false,
+    };
+
+    let out_array = env
+        .get_template("default")
+        .and_then(|t| t.render(&array_msg))
+        .unwrap_or_default();
+    let out_string = env
+        .get_template("default")
+        .and_then(|t| t.render(&string_msg))
+        .unwrap_or_default();
+
+    // If array works but string doesn't, template requires arrays
+    out_array.contains("template_test") && !out_string.contains("template_test")
+}
+
 /// Remove known non-standard Jinja2 tags from chat templates
 ///
 /// Some models use custom Jinja2 extensions that minijinja doesn't recognize. These tags
@@ -120,11 +149,15 @@ impl HfTokenizerConfigJsonFormatter {
             }
         }
 
+        // Detect at model load time whether this template requires content arrays
+        let requires_content_arrays = detect_content_array_usage(&env);
+
         Ok(HfTokenizerConfigJsonFormatter {
             env,
             config,
             mixins: Arc::new(mixins),
             supports_add_generation_prompt: supports_add_generation_prompt.unwrap_or(false),
+            requires_content_arrays,
         })
     }
 }

lib/llm/src/preprocessor/prompt/template/oai.rs

@@ -73,10 +73,9 @@ fn may_be_fix_tool_schema(tools: serde_json::Value) -> Option<Value> {
     Some(Value::from_serialize(&updated_tools))
 }
 
-fn may_be_fix_msg_content(messages: serde_json::Value) -> Value {
-    // If messages[content] is provided as a list containing ONLY text parts,
-    // concatenate them into a string to match chat template expectations.
-    // Mixed content types are left for chat templates to handle.
+fn may_be_fix_msg_content(messages: serde_json::Value, preserve_arrays: bool) -> Value {
+    // preserve_arrays=true: strings → arrays (multimodal)
+    // preserve_arrays=false: text-only arrays → strings (standard)
 
     let Some(arr) = messages.as_array() else {
         return Value::from_serialize(&messages);
@@ -86,7 +85,20 @@ fn may_be_fix_msg_content(messages: serde_json::Value) -> Value {
         .iter()
         .map(|msg| {
             match msg.get("content") {
-                Some(serde_json::Value::Array(content_array)) => {
+                // Case 1: String to Array (for multimodal templates)
+                Some(serde_json::Value::String(text)) if preserve_arrays => {
+                    let mut modified_msg = msg.clone();
+                    if let Some(msg_object) = modified_msg.as_object_mut() {
+                        let content_array = serde_json::json!([{
+                            "type": "text",
+                            "text": text
+                        }]);
+                        msg_object.insert("content".to_string(), content_array);
+                    }
+                    modified_msg
+                }
+                // Case 2: Array to String (for standard templates)
+                Some(serde_json::Value::Array(content_array)) if !preserve_arrays => {
                     let is_text_only_array = !content_array.is_empty()
                         && content_array.iter().all(|part| {
                             part.get("type")
@@ -114,7 +126,7 @@ fn may_be_fix_msg_content(messages: serde_json::Value) -> Value {
                         msg.clone() // Mixed content or non-text only
                     }
                 }
-                _ => msg.clone(), // String content or missing content - return unchanged
+                _ => msg.clone(), // No conversion needed
             }
         })
         .collect();
@@ -159,19 +171,7 @@ impl OAIChatLikeRequest for NvCreateChatCompletionRequest {
 
     fn messages(&self) -> Value {
         let messages_json = serde_json::to_value(&self.inner.messages).unwrap();
-
-        let needs_fixing = if let Some(arr) = messages_json.as_array() {
-            arr.iter()
-                .any(|msg| msg.get("content").and_then(|c| c.as_array()).is_some())
-        } else {
-            false
-        };
-
-        if needs_fixing {
-            may_be_fix_msg_content(messages_json)
-        } else {
-            Value::from_serialize(&messages_json)
-        }
+        Value::from_serialize(&messages_json)
     }
 
     fn tools(&self) -> Option<Value> {
@@ -301,6 +301,13 @@ impl OAIPromptFormatter for HfTokenizerConfigJsonFormatter {
         let messages_canonical = req.messages();
         let mut messages_for_template: serde_json::Value =
             serde_json::to_value(&messages_canonical).unwrap();
+
+        messages_for_template = serde_json::to_value(may_be_fix_msg_content(
+            messages_for_template,
+            self.requires_content_arrays,
+        ))
+        .unwrap();
+
         normalize_tool_arguments_in_messages(&mut messages_for_template);
 
         let ctx = context! {
@@ -457,7 +464,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Test array → string normalization (preserve_arrays=false for standard templates)
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: text-only array is concatenated into a single string
         assert_eq!(
@@ -500,7 +510,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Test array → string normalization (preserve_arrays=false for standard templates)
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: System message with string content remains unchanged
         assert_eq!(
@@ -541,7 +554,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Empty arrays should be preserved regardless of preserve_arrays setting
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: Empty arrays are preserved as-is
         assert!(messages[0]["content"].is_array());
@@ -562,7 +578,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Test with preserve_arrays=false (standard templates)
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: String content is not modified
         assert_eq!(
@@ -589,7 +608,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Mixed content should be preserved regardless of preserve_arrays setting
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: Mixed content types are preserved as array for template handling
         assert!(messages[0]["content"].is_array());
@@ -617,7 +639,10 @@ mod tests {
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Non-text arrays should be preserved regardless of preserve_arrays setting
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Verify: Non-text content arrays are preserved for template handling
         assert!(messages[0]["content"].is_array());
@@ -713,7 +738,8 @@ NORMAL MODE
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Mixed types should preserve array structure
         assert!(messages[0]["content"].is_array());
@@ -735,7 +761,8 @@ NORMAL MODE
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         // Unknown types mixed with text should preserve array
         assert!(messages[0]["content"].is_array());
@@ -873,11 +900,15 @@ NORMAL MODE
         }"#;
 
         let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
-        let mut messages = serde_json::to_value(request.messages()).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Apply content normalization with preserve_arrays=false (standard templates)
+        let mut messages =
+            serde_json::to_value(may_be_fix_msg_content(messages_raw, false)).unwrap();
 
         normalize_tool_arguments_in_messages(&mut messages);
 
-        // Multimodal content preserved as array
+        // Multimodal content preserved as array (mixed types not flattened)
         assert!(messages[0]["content"].is_array());
         assert_eq!(messages[0]["content"].as_array().unwrap().len(), 3);
 
@@ -889,6 +920,63 @@ NORMAL MODE
         );
     }
 
+    /// Tests string → array normalization for multimodal templates
+    #[test]
+    fn test_may_be_fix_msg_content_string_to_array() {
+        let json_str = r#"{
+            "model": "gpt-4o",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": "Hello, how are you?"
+                }
+            ]
+        }"#;
+
+        let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Test with preserve_arrays=true (multimodal templates)
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, true)).unwrap();
+
+        // Verify: String is converted to array format
+        assert!(messages[0]["content"].is_array());
+        let content_array = messages[0]["content"].as_array().unwrap();
+        assert_eq!(content_array.len(), 1);
+        assert_eq!(content_array[0]["type"], "text");
+        assert_eq!(content_array[0]["text"], "Hello, how are you?");
+    }
+
+    /// Tests that arrays are preserved when preserve_arrays=true
+    #[test]
+    fn test_may_be_fix_msg_content_array_preserved_with_multimodal() {
+        let json_str = r#"{
+            "model": "gpt-4o",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "part 1"},
+                        {"type": "text", "text": "part 2"}
+                    ]
+                }
+            ]
+        }"#;
+
+        let request: NvCreateChatCompletionRequest = serde_json::from_str(json_str).unwrap();
+        let messages_raw = serde_json::to_value(request.messages()).unwrap();
+
+        // Test with preserve_arrays=true (multimodal templates)
+        let messages = serde_json::to_value(may_be_fix_msg_content(messages_raw, true)).unwrap();
+
+        // Verify: Array is preserved as-is
+        assert!(messages[0]["content"].is_array());
+        let content_array = messages[0]["content"].as_array().unwrap();
+        assert_eq!(content_array.len(), 2);
+        assert_eq!(content_array[0]["text"], "part 1");
+        assert_eq!(content_array[1]["text"], "part 2");
+    }
+
     fn user() -> Msg {
         Msg::User(Default::default())
     }

tests/serve/test_vllm.py

@@ -217,6 +217,42 @@ class VLLMConfig(EngineConfig):
             ),
         ],
     ),
+    "multimodal_agg_llava": VLLMConfig(
+        name="multimodal_agg_llava",
+        directory=vllm_dir,
+        script_name="agg_multimodal.sh",
+        marks=[
+            pytest.mark.gpu_2,
+            # https://github.com/ai-dynamo/dynamo/issues/4501
+            pytest.mark.xfail(strict=False),
+        ],
+        model="llava-hf/llava-1.5-7b-hf",
+        script_args=["--model", "llava-hf/llava-1.5-7b-hf"],
+        delayed_start=0,
+        timeout=360,
+        request_payloads=[
+            # HTTP URL test
+            chat_payload(
+                [
+                    {"type": "text", "text": "What is in this image?"},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
+                        },
+                    },
+                ],
+                repeat_count=1,
+                expected_response=["bus"],
+                temperature=0.0,
+            ),
+            # String content test - verifies string → array conversion for multimodal templates
+            chat_payload_default(
+                repeat_count=1,
+                expected_response=[],  # Just validate no error
+            ),
+        ],
+    ),
     # TODO: Update this test case when we have video multimodal support in vllm official components
     "multimodal_video_agg": VLLMConfig(
         name="multimodal_video_agg",