How To Use the NEW Streaming Feature in OpenAI Assistants API
AI Summary
- Overview of OpenAI Assistant API Streaming Feature
- Recently announced streaming support in OpenAI Assistant API.
- Importance of streaming for user experience and engagement.
- Amazon study: 100ms latency cost 1% sales ($900 million annually).
- What is Streaming and Why It Matters
- Streaming allows for real-time interaction with AI models.
- Difference between latency and response time.
- Streaming reduces perceived waiting time for users.
- When to Use Streaming
- Essential for external applications with real-time AI interaction.
- Less critical for internal employee applications.
- Not needed for backend integrations without user interface.
- Implementing Streaming with OpenAI Assistant API
- More complex than previous endpoints due to multiple events.
- Event handler class needed to manage different events.
- Streaming enhances responsiveness and user satisfaction.
- Example Implementation Steps
- Install packages and set API key.
- Create assistant with instructions and capabilities.
- Create a thread and add a message.
- Implement event handler class with methods for different events.
- Use
create and streammethod with event handler.- Handle custom tools and submit tool outputs.
- Using Agency SW Framework for Simplification
- Framework automates assistant creation and message handling.
- Define custom tools and agents.
- Initialize agency and define event handler.
- Run agency with
get completion streammethod.- Production Tips and SAS Platform Implementation
- Review infrastructure for streaming support.
- Transfer application parts to Google Cloud Run for fewer limitations.
- Choose the right protocol (Server-Side Events, WebSockets, GRPC).
- Server-Side Events recommended for simplicity and integration.
- Conclusion
- Infrastructure setup and protocol choice are crucial.
- Streaming significantly impacts user experience.
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