Real-Time & Streaming AI¶

Overview¶

Learn how to build real-time AI applications with streaming responses, WebSocket connections, and progressive loading.

Duration: 8 hours (4 notebooks + materials)

Topics Covered:

Streaming LLM Responses
WebSocket Connections
Real-Time RAG
Production Streaming Systems
Realtime voice and multimodal interactions

Learning Objectives¶

By the end of this phase, you will be able to:

Implement Server-Sent Events (SSE) for streaming
Build WebSocket-based real-time chat applications
Understand when WebRTC is a better fit than SSE/WebSockets
Handle progressive loading and chunked responses
Create streaming RAG pipelines
Deploy production-ready streaming systems
Optimize for latency and throughput
Design interruption-safe realtime voice loops

Prerequisites¶

Strong Python programming skills
Understanding of LLMs and APIs
Basic knowledge of async/await
Familiarity with web technologies
Completed Phases 1-10

Course Content¶

1. Streaming Responses (90 minutes)¶

File: 01_streaming_responses.ipynb

Topics:

OpenAI streaming API (stream=True)
Server-Sent Events (SSE) protocol
Handling stream chunks
Real-time token processing
Error handling in streams
Progress indicators

Key Code:

# OpenAI Streaming
for chunk in client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
):
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# FastAPI SSE
from fastapi import FastAPI
from fastapi.responses import StreamingResponse

@app.get("/stream")
async def stream_response():
    async def generate():
        for chunk in get_llm_stream():
            yield f"data: {chunk}\\n\\n"
    return StreamingResponse(generate(), media_type="text/event-stream")

2. WebSocket Connections (90 minutes)¶

File: 02_websocket_connections.ipynb

Topics:

WebSocket protocol basics
Bidirectional communication
FastAPI WebSocket endpoints
Client-side WebSocket handling
Connection management
Heartbeat and reconnection

Key Code:

# Server
from fastapi import WebSocket

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()
        response = await process_message(data)
        await websocket.send_text(response)

# Client
import websockets

async with websockets.connect("ws://localhost:8000/ws") as ws:
    await ws.send("Hello")
    response = await ws.recv()

3. Real-Time RAG (90 minutes)¶

File: 03_real_time_rag.ipynb

Topics:

Streaming search results
Progressive context loading
Incremental vector search
Streaming summarization
Real-time document processing
Hybrid search streaming

Architecture:

        flowchart TD
    A[User Query] --> B[Vector Search - stream]
    B --> C[Document Retrieval - progressive]
    C --> D[Context Assembly - incremental]
    D --> E[LLM Generation - streaming]
    E --> F[Response - real-time]

4. Production Streaming (120 minutes)¶

File: 04_production_streaming.ipynb

Topics:

Load balancing streaming connections
Connection pooling
Rate limiting
Backpressure handling
Monitoring and metrics
Error recovery
Scaling strategies

Production Considerations:

Connection limits
Timeout management
Memory management
Graceful degradation
Observability

Technical Stack¶

Backend:

FastAPI
OpenAI Python SDK
WebSockets library
asyncio

Frontend:

HTML/CSS/JavaScript
EventSource API
WebSocket API
React (optional)

Infrastructure:

Nginx (reverse proxy)
Redis (connection management)
Prometheus (monitoring)
Docker
WebRTC / LiveKit style realtime media transport

2026 Realtime Topics To Know¶

Realtime APIs for voice and multimodal assistants
Turn-taking, interruption, and low-latency audio streaming
WebRTC for browser-to-browser media and live copilot experiences
Disaggregated retrieval + generation pipelines to keep end-to-end latency low

Best Practices¶

Performance¶

Use connection pooling
Implement backpressure
Buffer appropriately
Monitor latency

Reliability¶

Handle disconnections gracefully
Implement retry logic
Timeout management
Circuit breakers

Security¶

Rate limiting per user
Input validation
Authentication tokens
CORS configuration

User Experience¶

Loading indicators
Smooth animations
Error messages
Offline support

Common Patterns¶

Pattern 1: Simple SSE Streaming¶

async def stream_generator():
    async for chunk in llm_stream():
        yield f"data: {json.dumps({'text': chunk})}\\n\\n"

Pattern 2: WebSocket with Heartbeat¶

async def heartbeat(websocket):
    while True:
        await asyncio.sleep(30)
        await websocket.send_json({"type": "ping"})

Pattern 3: Streaming RAG¶

async def streaming_rag(query):
    # Search
    docs = await vector_search(query)
    yield {"type": "sources", "data": docs}
    
    # Generate
    async for chunk in llm_generate(query, docs):
        yield {"type": "text", "data": chunk}

Real-World Examples¶

ChatGPT-style Interface
- Streaming responses
- Typing indicators
- Stop generation
- Copy/retry
Live Document Q&A
- Upload and index
- Real-time search
- Streaming answers
- Source citations
Multi-User Chat
- WebSocket rooms
- Broadcast messages
- User presence
- Typing indicators

Resources¶

Documentation¶

Libraries¶

fastapi - Modern Python web framework
websockets - WebSocket client/server
sse-starlette - SSE for Starlette/FastAPI
httpx - Async HTTP client

Tools¶

Postman - API testing with WebSocket support
k6 - Load testing
WebSocket King - WebSocket client tester

Troubleshooting¶

Issue: Stream stops unexpectedly¶

Solution: Check timeout settings, implement heartbeat

Issue: High latency¶

Solution: Optimize chunk size, reduce buffering, check network

Issue: Connection drops¶

Solution: Implement reconnection logic, use exponential backoff

Issue: Memory leaks¶

Solution: Close connections properly, cleanup event listeners

Next Steps¶

After completing this phase:

Review Phase 19 (AI Safety) for securing streaming apps
Explore Phase 15 (AI Agents) for multi-agent streaming
Check Phase 18 (Low-Code) for Gradio/Streamlit streaming
Build your own production streaming application

Time Estimates¶

Total Duration: 8 hours
Notebooks: 6-7 hours
Assignment: 4-6 hours
Challenges: 6-8 hours
Total with Practice: 16-20 hours

Success Criteria¶

✅ Implement SSE and WebSocket endpoints
✅ Build real-time chat interface
✅ Create streaming RAG pipeline
✅ Handle 100+ concurrent connections
✅ Deploy production streaming app
✅ Monitor and optimize performance

Note: This is a foundational module for building modern AI applications. Master these concepts to create responsive, real-time user experiences.