Deep-Chat Custom Handlers: Role Not Updating? Here's Why!

Hey Deep-Chat Devs: Understanding the signals.onResponse Role Conundrum

Alright, folks, let's talk deep-chat and a tricky little behavior many of us encounter, especially when diving into custom handlers: why does signals.onResponse seem to ignore MessageContent.role after the very first response? If you've been banging your head against the wall trying to make your chatbot emit multiple distinct messages, each with its own fancy role like reasoning, text, or tool_calls, only to see everything lump into one big blob, then you're in the right place. This isn't a bug, guys, it's actually deep-chat working exactly as designed for a specific use case, but it can definitely throw a wrench in your plans if you're trying to achieve something more granular. We're aiming to assign a custom role to each response type so we can style or hide them independently, giving our users a richer, more structured chat experience. Imagine being able to show a 'thinking' message, then the actual 'answer', and then perhaps a 'tool call' status, all in their own separate, beautifully styled bubbles. That's the dream, right? The challenge is that deep-chat's onResponse signal, when used repeatedly in a streaming fashion, is designed primarily to append text to an existing message, not necessarily to create a brand new message every time you tweak the role parameter. This fundamental understanding is key to unlocking the full potential of your custom handlers and making your chatbot truly shine. Stick around, and we'll unpack this together, offering practical solutions and insights.

The Core of the Problem: Why Roles Seem to Get Ignored

The heart of this deep-chat mystery lies in how the library manages streaming responses versus distinct messages. When you use signals.onResponse multiple times within a single streaming session – that is, between an signals.onOpen() and an signals.onClose() call – deep-chat generally tries to be smart and efficient. Its default behavior is to aggregate all incoming text chunks into a single message bubble. This is incredibly useful for streaming long AI responses word by word, as it creates a smooth, continuous flow of text without cluttering the UI with a new message bubble for every single word. The role parameter you provide in signals.onResponse is primarily considered for the initial creation of this message. Once that first message bubble is established with its designated role, subsequent calls to onResponse within that same streaming context will append their text content to this already-created message, effectively ignoring any new role you try to pass. It's like trying to change the color of a car after it's already painted and driven off the lot – you need to start with a fresh car! This design choice optimizes for a common scenario, but it directly conflicts with our desire to have separate, distinct messages for different types of content, each with its own role. We want a new message when the content type or semantic role changes, not just when new words arrive for the same type. This is where the confusion often sets in, as developers expect the role attribute to be a strong signal for message separation, but in a streaming context, it functions more as an initial identifier.

Diving Deeper: How deep-chat Handles Streaming Messages

Let's get a little under the hood, shall we? When signals.onResponse is invoked within your custom handler, deep-chat checks its internal state. Is there an active, open message currently being streamed? If the answer is yes, then any text provided in the onResponse call is simply appended to that existing message. The role property, at this point, becomes largely irrelevant for changing the message's identity. It was used when that message was first instantiated. If, however, there is no active message (perhaps because onOpen() was just called, or the previous message was explicitly closed with onClose()), then deep-chat proceeds to create a new message bubble, and that's when the role parameter in your onResponse call truly takes effect. It dictates the role of this brand-new message. Think of it like this: onOpen() signals, "Hey, get ready to start a new conversation turn!" The first onResponse following that onOpen() determines the role and initial content of that turn. Every onResponse thereafter, until onClose(), is simply adding more content to that same turn. This mechanism is crucial for displaying long-form, dynamically generated text from, say, a large language model. It prevents the user interface from becoming a chaotic mess of tiny, rapidly appearing message bubbles, which would be a terrible user experience. The goal is a single, evolving message that flows naturally, which is great for single-purpose streaming, but not for multi-purpose, distinct message types.

The Expected vs. The Actual: What We Want vs. What We Get

So, here's the rub, guys. What we expect from signals.onResponse when we change the role is for deep-chat to go, "Aha! A new role! Time for a brand-new message bubble!" Our mental model often leads us to believe that a change in role is a strong enough signal to delineate a new message. For instance, if our API first streams a reasoning chunk, then an ai response, then a tool_call result, we naturally want three distinct message bubbles, each with its appropriate role and styling. However, what we actually get is typically all that content appended to the first message bubble that was created, and it only inherits the role of that very first onResponse call. This can be super frustrating because it makes styling and organizing different semantic parts of your AI's output incredibly difficult. You can't easily make the reasoning section slightly grayed out, the ai section prominent, and the tool_call section a distinct, actionable component if they're all jammed into one bubble. Understanding this fundamental disconnect between our expectation (new role = new message) and deep-chat's default streaming behavior (new text = append to existing message) is the first big step towards fixing this issue and building the highly structured, dynamic chatbot UI we all crave. We need to actively tell deep-chat when a message is complete and when it's time to start a fresh one.

Unpacking Your Custom Handler: A Closer Look at the Dummy Code

Let's put on our developer hats and dissect that dummy handler code you provided, which perfectly illustrates the challenge at hand. It's a fantastic example because it clearly shows the intent: to alternate roles (ai and user) for each word being streamed. On the surface, it makes perfect sense, right? You'd think that by specifying a new role every other word, deep-chat would obediently pop out new messages. But as we've discussed, the internal mechanics don't quite work that way for streaming. The code is concise and effectively simulates a streaming API, which is why it's such a great test case for this specific problem. We'll walk through signals.onOpen(), the words.forEach loop with its setTimeout magic, and the repeated signals.onResponse calls to pinpoint exactly why deep-chat behaves the way it does in this scenario. By understanding the flow of this code in relation to deep-chat's signals, we'll gain critical insights into how to structure our handlers correctly for emitting multiple messages with distinct roles. This isn't about blaming the code; it's about understanding the API's design and then adapting our implementation to work with it, not against it, to achieve the desired granular control over our chat messages.

Breaking Down the signals.onResponse Call

Let's trace the execution of your dummy handler. First, signals.onOpen() kicks things off. This is deep-chat signaling that a new conversation turn or stream is about to begin. It prepares the UI to receive messages. Then, we hit the words.forEach loop, which, coupled with setTimeout, simulates a continuous stream of data arriving over time, like a real-time API. Crucially, each iteration of this loop calls signals.onResponse. Now, here's the kicker: because signals.onOpen() was called only once at the very beginning and signals.onClose() is called only once at the very end of the entire word sequence, deep-chat interprets all these onResponse calls as part of the same ongoing streaming message. It sees the initial onOpen() as the start of one big message block, and every onResponse until the final onClose() as just adding more content to that single block. The text content (word + (index < l ? ' ' : '')) is diligently appended. So, from deep-chat's perspective, it's just one long message being built up, word by word, until onClose() says, "Okay, that's all, folks! This message is complete." This single streaming context is the root cause of the behavior we're observing, where roles are ignored.

The index % 2 ? 'ai' : 'user' Logic and Its Impact

Your alternating role: index % 2 ? 'ai' : 'user' logic is a clever way to demonstrate the problem, and it perfectly highlights the misunderstanding. The intent is clear: you want to switch between ai and user roles for successive words, implying distinct messages. For instance, you might expect "Lorem" to be 'ai', "ipsum" to be 'user', "dolor" to be 'ai', and so on, each in its own bubble. However, because of what we just discussed about deep-chat's streaming behavior, this role assignment only matters for the very first signals.onResponse call after signals.onOpen(). If index is 0, role: 'user' is set. If index is 1, role: 'ai' is set. Whichever role is assigned during that initial onResponse call, that's the role the entire aggregated message will inherit. All subsequent onResponse calls within that same stream, despite their differing role values, will simply append their text to the message that was initiated with the first role. The role property in subsequent onResponse calls becomes effectively redundant in a streaming context where a message is already actively being built. This is the crucial point of friction between developer expectation and deep-chat's internal logic for efficient text streaming.

Why Appending Text is the Default Behavior

Let's be real, guys, deep-chat's default behavior of appending text during streaming isn't some arbitrary design choice; it's a fundamental optimization for a smooth user experience. Imagine if every single signals.onResponse call, especially from a fast-streaming API, created a brand-new message bubble. Your chat interface would quickly become a chaotic, flickering mess of hundreds of tiny, one-word messages, each popping up and disappearing, making it utterly unreadable and jarring. This would be a nightmare for users trying to follow a continuous conversation. The library prioritizes a cohesive, single-message display for evolving content, which is the most common use case for generative AI responses. When an AI is thinking and then streaming its response, users typically want to see that response unfold within a single, logical message bubble, not segmented into micro-messages. So, while it causes a bit of head-scratching when you want distinct role-based messages, this appending behavior is actually a sensible default that ensures a superior user experience for the majority of streaming scenarios. Our task now is to learn how to override or properly guide this default behavior when our specific use case demands separate, distinct messages.

The Aha! Moment: Emitting Multiple Messages with Distinct Roles

Okay, so we've identified the problem: deep-chat wants to aggregate, and we want to separate. The Aha! moment comes when we realize that to achieve multiple messages with distinct roles, we need to explicitly tell deep-chat when one message ends and another begins. It's all about creating clear boundaries for each logical piece of information. This isn't about hacking the system; it's about using the signals API exactly as intended for different scenarios. For streaming a single, continuous text, repeated onResponse calls between onOpen and onClose work great. But for streaming multiple distinct types of content, each with its own role, we need to treat each type as its own mini-stream or complete message. This means a different sequence of signal calls, ensuring that deep-chat has the opportunity to instantiate a new message object for each desired role. It's a subtle but powerful distinction that, once grasped, will transform how you build complex deep-chat custom handlers. Prepare for some code, because seeing is believing when it comes to taming those deep-chat signals and getting them to do exactly what you want!

The Key Insight: When Does deep-chat Create a New Message?

This is the million-dollar question, guys! deep-chat creates a new message under a couple of primary conditions, and understanding these is critical. First, a new message is created when signals.onResponse is called, and there is no message currently streaming or open. This typically happens right after signals.onOpen() has been invoked but before any onResponse has had a chance to start appending to an existing message. The first onResponse call in a new onOpen() session will always instantiate a new message. Second, and this is the vital part for our use case, a new message is created after the signals.onClose() method has been successfully called for the previous message. When onClose() is invoked, it finalizes the current streaming message, signaling to deep-chat that that particular conversation turn is complete. Once that message is closed, the slate is clean. Any subsequent signals.onOpen() followed by signals.onResponse will then trigger the creation of a brand-new message bubble, complete with its own role and initial content. So, to explicitly force deep-chat to recognize a new message boundary for each distinct role or content type, you need to manage those onOpen() and onClose() calls carefully. Each logical message that you want to appear in its own bubble must be wrapped in its own onOpen() and onClose() pair.

Solution 1: Leveraging signals.onResponse for Complete Messages (with a trick!)

The most straightforward way to emit multiple messages, each with its own distinct role, is to treat each logical content block (e.g., reasoning, AI text, tool call) as a complete, separate message and manage the streaming signals accordingly. Instead of streaming individual words of different logical message types within one continuous onOpen()-onClose() session, you need to initiate and complete each distinct message separately. This means for every chunk of information that should get its own message bubble, you'll follow this pattern: signals.onOpen(), then signals.onResponse() with the full text (or primary content) for that specific message and its designated role, and then immediately signals.onClose(). The onClose() call is the crucial trick here. It tells deep-chat that the current message is finished and sealed. Only after onClose() has been called can the next signals.onOpen() and subsequent signals.onResponse create a new, distinct message with its own role. If you're still wanting to stream each of those distinct logical messages (e.g., stream the reasoning word-by-word, then stream the AI response word-by-word), you would simply enclose each internal streaming process within its own onOpen() and onClose() pair. This disciplined approach ensures that deep-chat correctly interprets your intentions and renders each semantic piece of your AI's response in its own dedicated, role-assigned bubble.

Solution 2: Exploring Alternative Signal Methods (if available or needed)

While the onOpen() -> onResponse() -> onClose() cycle is your go-to for creating distinct text messages, it's always smart to keep an eye on deep-chat's documentation for any specialized signals. Sometimes, specific content types might have their own dedicated signals or MessageContent properties that implicitly trigger new message creation. For instance, if deep-chat has explicit support for tool_code or card messages, these might be designed to inherently create new, distinct message components without needing the explicit onOpen()/onClose() cycle for each individual part of a structured response. Always check if there are specific MessageContent properties like type: 'tool_code' or component: { type: 'card', ... } that deep-chat interprets as unique entities, possibly causing a new message to be rendered automatically. However, for generic text content that you simply want to separate by role, sticking to the onClose() pattern for demarcation is usually the most robust and universally applicable solution. The key is to know when deep-chat expects a complete message and when it's prepared to create a new one based on the signals it receives.

Example Code: Implementing the Fix for Your Handler

Alright, let's get down to business and demonstrate the fix with an updated handler. The key here is to call signals.onClose() after each logical message is fully sent, even if it's just a single onResponse call, before starting the next one. This forces deep-chat to recognize each as a separate entity.

{
  // ... other deep-chat configurations
  handler: (body, signals) => {
    const REASONING_MESSAGE = '***Thinking Process:***\nI first considered the user\'s intent, broke down the request, and then planned the execution steps. This reasoning helps structure a precise response.';
    const AI_RESPONSE_MESSAGE = 'Hello there! Based on my thorough analysis, here is the main part of your answer. I focused on clarity and directness, ensuring all key points are addressed effectively. Feel free to ask if you need further details or elaboration on any specific aspect of my response.';
    const TOOL_CALL_MESSAGE = '***Tool Call Initiated:***\n```json\n{\n  "tool": "search_web",\n  "query": "deep-chat custom handler documentation"\n}\n```\n_Executing external search..._';

    let currentDelay = 0;

    // 1. Send the 'reasoning' message
    signals.onOpen();
    signals.onResponse({
      role: 'reasoning',
      text: REASONING_MESSAGE
    });
    signals.onClose(); // <-- CRUCIAL: Closes the reasoning message
    currentDelay += 1000; // Add a delay before the next message for better UX

    // 2. Send the main 'ai' response message after a short delay
    setTimeout(() => {
      signals.onOpen();
      signals.onResponse({
        role: 'ai',
        text: AI_RESPONSE_MESSAGE
      });
      signals.onClose(); // <-- CRUCIAL: Closes the AI response message
      currentDelay += 1000;
    }, currentDelay);

    // 3. Send a 'tool_call' message after another delay
    setTimeout(() => {
      signals.onOpen();
      signals.onResponse({
        role: 'tool_call',
        text: TOOL_CALL_MESSAGE
      });
      signals.onClose(); // <-- CRUCIAL: Closes the tool call message
    }, currentDelay + 1000);

    // If your actual API streams word-by-word *for each type*,
    // you'd wrap *each word-by-word stream* in its own onOpen/onClose pair:
    // Example for streaming REASONING_WORDS word by word:
    // signals.onOpen();
    // REASONING_WORDS.forEach((word, index) => {
    //   setTimeout(() => {
    //     signals.onResponse({ role: 'reasoning', text: word + ' ' });
    //     if (index === REASONING_WORDS.length - 1) signals.onClose();
    //   }, index * 100);
    // });
    // Then, after a delay, repeat for AI_RESPONSE_WORDS, and so on.
  },
  // ... rest of deep-chat config
}

In this updated example, we're not trying to stream individual words with alternating roles. Instead, we're sending each complete, distinct message type (reasoning, AI response, tool call) as its own separate entity. The signals.onClose() call after each message's onResponse is the absolute game-changer. It explicitly tells deep-chat, "This message is done. The next onOpen() will start a brand new one." This way, each piece of content gets its own bubble and correctly assigned role, allowing for independent styling and management. If you truly need to stream word-by-word for each distinct message type, you'd nest your word-streaming logic within an onOpen() and onClose() pair for each type, as hinted in the commented section.

Best Practices for Custom Deep-Chat Handlers

Beyond just getting those roles right, building robust custom deep-chat handlers involves a few more best practices that'll save you headaches and elevate your chatbot's performance and user experience. It's not just about what signals you send, but how you manage the entire interaction from your custom API to the chat interface. A well-designed handler anticipates various scenarios, from smooth sailing to unexpected turbulence, ensuring your chatbot remains responsive and informative. We're talking about things like handling intermediate data, gracefully dealing with errors, and making sure your UI stays snappy. Remember, your custom handler is the bridge between your complex backend logic and the simple, intuitive chat interface, so making that bridge strong and efficient is paramount. By adopting these best practices, you'll not only solve immediate problems but also build a foundation for scalable, maintainable, and highly effective chatbot applications using deep-chat. Let's explore how to make your handlers truly resilient and user-friendly, pushing beyond basic functionality to deliver a premium chat experience.

Managing State in Streaming Responses

When you're dealing with streaming responses from your custom API, managing internal state within your handler becomes incredibly important. Your backend might send fragmented data, or pieces of information that need to be reassembled or processed before they're ready to be sent to deep-chat as a coherent message. Never rely on deep-chat's internal message state for your API's business logic. Your handler should maintain its own temporary buffers or state variables if, for example, your custom API sends `{