v1: accept OpenAI multimodal content shape (array-of-parts)

Modern OpenAI clients (pi-ai, openai SDK 6.x, langchain-js, the official
agents) send `messages[].content` as an array of content parts:
`[{type:"text", text:"..."}, {type:"image_url", ...}]`. Our gateway
typed `content` as plain `String` and 422'd those calls.

Fix: `Message.content` is now `serde_json::Value` so requests
deserialize regardless of shape. `Message::text()` flattens
content-parts arrays (concat'd `text` fields, non-text parts skipped)
for places that need a plain string — Ollama prompt assembly, char
counts, the assistant's own response synthesis. `Message::new_text()`
constructs string-content messages without writing the wrapper at
each call site. Forwarders (openrouter) clone content through
verbatim so providers see exactly what the client sent.

Verified end-to-end: Pi CLI (`pi --print --provider openrouter`)
landed a clean 1902-token request through `/v1/chat/completions`,
routed to OpenRouter as `openai/gpt-oss-120b:free`, response in
1.62s, Langfuse trace `v1.chat:openrouter` recorded with provider
tag. Same path that any tool using the official openai SDK takes.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/gateway/src/v1/openrouter.rs

@@ -59,6 +59,9 @@ pub async fn chat(
 
     let body = ORChatBody {
         model: model.clone(),
+        // Pass content through verbatim — preserves OpenAI's multimodal
+        // content-parts shape (`[{type:"text",text:"..."}, ...]`) so the
+        // upstream provider sees exactly what the client sent.
         messages: req.messages.iter().map(|m| ORMessage {
             role: m.role.clone(),
             content: m.content.clone(),
@@ -102,7 +105,7 @@ pub async fn chat(
     let text = choice.message.content;
 
     let prompt_tokens = parsed.usage.as_ref().map(|u| u.prompt_tokens).unwrap_or_else(|| {
-        let chars: usize = req.messages.iter().map(|m| m.content.chars().count()).sum();
+        let chars: usize = req.messages.iter().map(|m| m.text().chars().count()).sum();
         ((chars + 3) / 4) as u32
     });
     let completion_tokens = parsed.usage.as_ref().map(|u| u.completion_tokens).unwrap_or_else(|| {
@@ -126,7 +129,7 @@ pub async fn chat(
         model,
         choices: vec![Choice {
             index: 0,
-            message: Message { role: "assistant".into(), content: text },
+            message: Message { role: "assistant".into(), content: serde_json::Value::String(text) },
             finish_reason: choice.finish_reason.unwrap_or_else(|| "stop".into()),
         }],
         usage: UsageBlock {
@@ -149,7 +152,7 @@ struct ORChatBody {
 }
 
 #[derive(Serialize)]
-struct ORMessage { role: String, content: String }
+struct ORMessage { role: String, content: serde_json::Value }
 
 #[derive(Deserialize)]
 struct ORChatResponse {