🚀 Proposal: Add Streaming Support for Chat Bot Responses

[LexioJ]

Describe the feature you'd like to request

Summary

Add Server-Sent Events (SSE) streaming support to the Chat Bot to display LLM responses in real-time as tokens are generated, rather than waiting for the complete response. This would significantly improve user experience, especially for longer responses.

Current Limitations

User Experience Issues

1. Long wait times with no feedback: Users must wait for the entire response to be generated before seeing any output
2. Polling overhead: Frontend polls every 2 seconds, adding average 1-second latency to response delivery
3. Timeout risks: Long responses can trigger timeouts before completion (as noted in Use streaming endpoint for text responses #41)
4. Unnecessary server load: Constant polling creates repeated HTTP requests while tasks are running

Current Architecture

The Chat Bot currently uses a polling-based approach:

User sends message → TaskProcessing schedules task → Frontend polls every 2s →
Task completes → Result saved to DB → Next poll returns complete message

File references:

• Polling implementation: src/components/ChattyLLM/ChattyLLMInputForm.vue:716-760
• Task checking: lib/Controller/ChattyLLMController.php:680-724
• 2-second polling interval defined at src/components/ChattyLLM/ChattyLLMInputForm.vue:753

Why Streaming is Now Feasible

Addressing Previous Concerns

In #41, streaming was marked as "technically not realistic" due to concerns about model compatibility and Nextcloud platform limitations. However, I believe these concerns can be addressed:

1. Model Compatibility

Concern: "We won't be able to use many models anymore"

Reality: Virtually all modern LLM providers support streaming:

• ✅ OpenAI API (GPT-3.5, GPT-4, GPT-4o) - stream: true parameter
• ✅ Anthropic Claude - Server-Sent Events streaming
• ✅ Ollama (local models) - Streaming endpoint
• ✅ LocalAI - OpenAI-compatible streaming API
• ✅ Azure OpenAI - Streaming support
• ✅ Google Gemini - Streaming responses
• ✅ Hugging Face Inference API - Streaming generators
• ✅ Together AI, Groq, Perplexity - All support streaming

Providers that don't support streaming can gracefully fallback to the current polling approach.

2. Nextcloud Platform Limitations

Current constraint: The TaskProcessing framework is indeed incompatible with streaming:

• ISynchronousProvider::process() is a blocking method that returns complete output
• Task status is binary: "running" (HTTP 417) or "complete" (HTTP 200)
• No support for partial/chunked output delivery
• $reportProgress callback exists but is never utilized by any provider

Solution: Bypass TaskProcessing for streaming-capable providers by creating a dedicated streaming endpoint that communicates directly with LLM APIs.

Proposed Implementation

Architecture Overview

Create a parallel streaming path that coexists with the current TaskProcessing approach:

┌──────────────────────────────────────────────────────────────┐
│                    Chat Message Request                       │
└───────────────────────┬──────────────────────────────────────┘
                        │
                        ▼
              ┌─────────────────────┐
              │ Provider Capability │
              │     Detection       │
              └─────────────────────┘
                        │
          ┌─────────────┴─────────────┐
          ▼                           ▼
┌──────────────────┐        ┌──────────────────┐
│ Streaming Path   │        │ Legacy Path      │
│ (New)            │        │ (Current)        │
├──────────────────┤        ├──────────────────┤
│ Direct API call  │        │ TaskProcessing   │
│ Server-Sent      │        │ Poll every 2s    │
│ Events (SSE)     │        │ Complete output  │
│ Real-time chunks │        │                  │
└──────────────────┘        └──────────────────┘
          │                           │
          └─────────────┬─────────────┘
                        ▼
              ┌──────────────────┐
              │ Message saved    │
              │ to database      │
              └──────────────────┘

Implementation Steps

1. Backend: New Streaming Endpoint

┌─────────────┐         ┌──────────────────┐         ┌─────────────────┐
│   Frontend  │         │   Streaming      │         │   LLM Provider  │
│  (EventSrc) │         │   Controller     │         │   (Direct API)  │
└─────────────┘         └──────────────────┘         └─────────────────┘
       │                         │                            │
       │ 1. POST /chat/stream    │                            │
       ├────────────────────────>│                            │
       │                         │ Save user message to DB    │
       │                         │                            │
       │ 2. EventSource connect  │                            │
       │ GET /chat/stream/{id}   │                            │
       ├────────────────────────>│                            │
       │                         │ 3. Call LLM API (streaming)│
       │                         ├───────────────────────────>│
       │                         │                            │
       │                         │<── chunk: "Hello"──────────┤
       │<── data: "Hello"────────┤                            │
       │                         │<── chunk: " world"─────────┤
       │<── data: " world"───────┤                            │
       │                         │<── chunk: "!"──────────────┤
       │<── data: "!"────────────┤                            │
       │                         │                            │
       │<── data: [DONE]─────────┤ Save complete message      │
       │                         │                            │

File: lib/Controller/ChattyLLMController.php

Add a new method:

/**
 * Stream LLM response in real-time using Server-Sent Events
 *
 * @param int $sessionId
 * @param int $messageId User's message ID
 * @return Http\StreamResponse
 */
#[NoAdminRequired]
public function streamGenerate(int $sessionId, int $messageId): Response {
    // 1. Validate session ownership
    // 2. Get conversation history
    // 3. Detect provider streaming capability
    // 4. If streaming supported:
    //    - Set headers: Content-Type: text/event-stream
    //    - Call provider API with streaming enabled
    //    - Yield chunks as Server-Sent Events
    //    - Save complete message to DB when done
    // 5. If streaming not supported:
    //    - Return error, frontend falls back to polling
}

2. Provider Integration Layer

New file: lib/Service/StreamingService.php

class StreamingService {
    /**
     * Check if configured provider supports streaming
     */
    public function providerSupportsStreaming(): bool;

    /**
     * Stream chat completion from provider
     * Yields string chunks as they arrive
     */
    public function streamChatCompletion(array $messages): \Generator;

    /**
     * Get provider-specific streaming configuration
     */
    private function getProviderConfig(): array;
}

This service would:

• Read provider settings (already configured in Assistant settings)
• Make direct HTTP requests to provider APIs with streaming enabled
• Parse streaming response format (SSE or JSON streaming)
• Yield chunks as they arrive
• Handle errors and reconnection

3. Frontend: EventSource Integration

File: src/components/ChattyLLM/ChattyLLMInputForm.vue

Replace polling with EventSource:

// New method (replaces pollGenerationTask)
async streamMessageGeneration(sessionId, messageId) {
    const url = generateUrl('/apps/assistant/api/v1/chat/stream')
    const params = new URLSearchParams({ sessionId, messageId })

    const eventSource = new EventSource(`${url}?${params}`)
    let fullMessage = ''

    eventSource.onmessage = (event) => {
        if (event.data === '[DONE]') {
            eventSource.close()
            this.loadingMessage = false
            return
        }

        // Append chunk to display
        fullMessage += event.data
        this.updateStreamingMessage(fullMessage)
    }

    eventSource.onerror = (error) => {
        eventSource.close()
        // Fallback to polling if streaming fails
        this.pollGenerationTask(taskId, sessionId)
    }
}

// New method: Update message display in real-time
updateStreamingMessage(content) {
    // Find or create placeholder message and update its content
    // This provides real-time visual feedback as tokens arrive
}

4. Route Configuration

File: appinfo/routes.php

Add new route:

[
    'name' => 'chattyLLM#streamGenerate',
    'url' => '/api/v1/chat/stream',
    'verb' => 'GET',
],

5. Graceful Fallback

The implementation should:

• ✅ Detect provider streaming capability at runtime
• ✅ Use streaming when available
• ✅ Automatically fallback to polling for non-streaming providers
• ✅ Maintain full backward compatibility
• ✅ No configuration required from users

Configuration

No user action required. The system should:

1. Auto-detect if the configured provider supports streaming
2. Use streaming endpoint if available
3. Fall back to current polling if not

This could be exposed as an optional toggle in admin settings:

☑ Enable streaming responses (recommended for supported providers)

Benefits

User Experience

• Immediate feedback: Users see responses as they're generated, like ChatGPT/Claude web interfaces
• Perceived performance: Even if total time is the same, streaming feels faster
• Better for long responses: Progress is visible instead of a loading spinner
• Reduced timeouts: Streaming keeps connection alive, preventing timeout issues

Technical Benefits

• Reduced server load: One SSE connection vs. polling requests every 2 seconds
• Lower latency: No 0-2 second polling delay
• More efficient: Less HTTP overhead, fewer database queries
• Modern standard: SSE is well-supported in all modern browsers
• Progressive enhancement: Works alongside existing system

Competitive Parity

All major AI chat interfaces use streaming:

• ChatGPT web interface
• Claude web interface
• Google Gemini
• Microsoft Copilot

Users expect this behavior from AI assistants.

Backward Compatibility

✅ Fully backward compatible:

• Existing polling mechanism remains untouched
• Non-streaming providers continue to work
• Gradual rollout possible (enable per-provider)
• No database schema changes required
• No breaking API changes

Security Considerations

• Same authentication/authorization as current chat endpoints
• Session ownership validation
• Rate limiting (inherit from existing chat endpoints)
• Input sanitization (already handled)
• SSE is one-way (server→client), no additional attack surface

Open Questions for Maintainers

1. Architecture approval: Is bypassing TaskProcessing acceptable for this use case? Or would you prefer exploring $reportProgress callback implementation in TaskProcessing framework?

2. Provider integration: Should streaming be implemented in:

   • Option A: Assistant app directly (proposed above)
   • Option B: Individual provider apps (e.g., integration_openai)
   • Option C: Both with an interface/contract

3. Feature flag: Should this be:

   • Auto-enabled when provider supports it
   • Opt-in via admin setting
   • Opt-in per user

4. Scope: Should we also add streaming for:

   • Title generation?
   • Other TaskProcessing task types?

5. Testing: What providers should be tested in CI/CD?

Alternative Considered: WebSockets

WebSockets would also enable streaming but:

• ❌ More complex (bi-directional when we only need server→client)
• ❌ Requires more infrastructure (persistent connections, state management)
• ❌ SSE is simpler and sufficient for this use case
• ✅ SSE auto-reconnects and is easier to debug

Request for Feedback

I'd love to hear thoughts from @julien-nc, @marcelklehr, and the Nextcloud community:

1. Does this architectural approach make sense?
2. Are there Nextcloud platform considerations I'm missing?
3. Would you accept a PR implementing this? If so, any specific requirements?
4. Should this be coordinated with provider apps (e.g., integration_openai)?

Related Issues

• Use streaming endpoint for text responses #41 - Original streaming request (closed as not feasible)
• Making intuitive output display with pause functionality #150 - Related discussion mentioned in Use streaming endpoint for text responses #41

References

• Current polling implementation: src/components/ChattyLLM/ChattyLLMInputForm.vue:716-760
• Task processing controller: lib/Controller/ChattyLLMController.php:680-724
• TaskProcessing listener: lib/Listener/ChattyLLMTaskListener.php:68
• OpenAI Streaming API: https://platform.openai.com/docs/api-reference/streaming
• MDN Server-Sent Events: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events

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I'm willing to implement this feature if there's interest from the maintainers. Please let me know your thoughts!

Describe the solution you'd like

see above

Describe alternatives you've considered

see above

[janepie]

Thank you for submitting this! We have streaming chat responses in our roadmap for Assistant. An implementation should be using the taskprocessing API that is included in the server code.

[Tokix]

Is there already a pre-alpha implementation of this already? I would be willing to test or contribute :) A developer description would be interesting on how to manually test and upgrade the plugin on the nextcloud instance would be interesting if you have any documentation please link it.