AI Gateway: Enhancing LLM Integrations for Application Development

About this video

This comprehensive video guide demonstrates how Portkey's AI Gateway can simplify LLM integrations within applications. The tutorial explores how to interact with multiple AI providers such as Mistral, Perplexity, and OpenAI using a universal API provided by the AI Gateway. The advantages discussed include caching, automatic retries, and fallbacks for error management. The video also presents how to use canary testing, creating virtual keys, load balancing, and creating configurations. Lastly, it delves into how canary testing can allow a specific weight for how many queries are sent to a different LLM and how you can create a configuration for retrying queries. Finally, the speaker demonstrates how to use the platform’s logging feature. 00:00 Introduction to Portkey's AI Gateway 00:12 Understanding the Universal API 00:37 Benefits of Caching in AI Gateway 00:37 Exploring Supported AI Providers 00:59 Cost and Speed Advantages of Caching 01:22 Fallbacks and Automatic Retries 02:03 Load Balancing Across Models 02:42 Canary Testing for New Models 03:21 Creating and Using Virtual Keys 03:57 Exploring Portkey's Platform Features 04:14 Using the Observability Platform 05:19 Creating a Configuration with the GUI 05:49 Setting Up Virtual Keys 06:13 Starting a New Project with Bun or Node.js 08:39 Logging and Caching in Action 10:26 Conclusion and Final Thoughts 🔥 Don't forget to like, share, and subscribe for more! 🔗 Relevant Links: https://github.com/Portkey-AI/gateway https://portkey.ai/features/ai-gateway 👉 Follow me on Twitter for updates: [@dev__digest]