Chat Application Gateway

Build production-ready chat applications with AI API gateway. Handle streaming, context, and user experience.

Streaming

Real-time token-by-token response delivery

Context

Automatic conversation history management

Rate Limits

Per-user usage controls

Moderation

Content safety filters

Implementation

// Chat endpoint with streaming app.post('/chat', async (req, res) => { const { messages, userId } = req.body; // Check rate limits await rateLimiter.check(userId); // Stream response const stream = await openai.chat.completions.create({ model: 'gpt-4', messages: messages, stream: true }); // Forward streaming response res.setHeader('Content-Type', 'text/event-stream'); for await (const chunk of stream) { res.write(chunk.choices[0].delta.content); } res.end(); });

Conversation Flow

User
What's the weather today?
Assistant
I'd be happy to check the weather for you! Could you please share your location?
User
I'm in San Francisco
Assistant
Currently, San Francisco is 68°F (20°C) with partly cloudy skies. Perfect weather for a walk!

Best Practices

Implement Streaming

Use Server-Sent Events (SSE) for real-time responses. Reduces perceived latency and improves UX.

Manage Context Window

Automatically truncate or summarize older messages to stay within token limits.

Add Rate Limiting

Per-user limits prevent abuse and ensure fair usage across all customers.

Include Content Moderation

Filter both input and output for safety compliance and user protection.

FAQ

How do I handle long conversations?
Implement automatic context truncation. Keep recent messages, summarize or drop older ones when approaching limits.
Should I use streaming?
Yes! Streaming significantly improves perceived latency for chat applications. Users see responses as they're generated.
How do I prevent abuse?
Implement per-user rate limits, track usage, and add content moderation filters.

Related Resources

AI Assistants

Assistant API

Content Generation

Text generation

Prompt Engineering

Chat prompts

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