LLM API Gateway
Continuous Streaming

How Continuous Streaming Works

Our LLM API gateway implements a robust architecture for maintaining long-duration streaming sessions. The system combines persistent connection management with intelligent state preservation to enable AI interactions spanning hours or days.

Unlike traditional streaming solutions that assume short-lived connections, our infrastructure is designed from the ground up for extended sessions. Automatic checkpointing, reconnection handling, and progress tracking ensure reliable operation throughout lengthy AI interactions.

• Session Management: Unique identifiers for each streaming session
• State Serialization: Efficient checkpointing of conversation state
• Connection Pooling: Maintained backends for instant reconnection
• Flow Control: Adaptive buffering prevents client overflow
• Monitoring: Real-time metrics for session health
• Recovery: Automatic restoration from checkpoints

Technical Documentation

# Initialize continuous streaming gateway
from continuous_gateway import ContinuousStream

stream = ContinuousStream(
    session_id="analysis-2024-001",
    checkpoint_interval=300,  # 5 minutes
    max_duration_hours=72,
    resume_on_disconnect=True
)

# Start or resume session
async def continuous_generate(prompt):
    if stream.has_checkpoint():
        await stream.restore_from_checkpoint()
    
    async for chunk in stream.generate(
        model="gpt-4-turbo",
        prompt=prompt,
        streaming=True
    ):
        # Process chunk
        handle_chunk(chunk)
        
        # Auto-checkpoint handled internally
        if chunk.is_checkpoint:
            log_progress(
                stream.tokens_generated,
                stream.duration
            )