Changelog
ModernBert, more DSPy support and Qwen 2.5
New models and features have landed in Relevance AI, expanding support for advanced AI capabilities.
When working with AI-driven applications, having optimized models and training tools is key. These new updates include:
✅ ModernBERT for Advanced RAG – Now leveraging ModernBERT Embed (released by the answer.ai team), a model trained from ModernBERT-base, bringing new embedding advancements to AI workflows. More details on the model are available here: https://huggingface.co/nomic-ai/modernbert-embed-base
✅ Expanded DSPy Support – We've added DSPy get training data, DSPy train, and DSPy run steps to train DSPy systems for response generation based on gold set answers.
✅ Qwen 2.5 for AI Agents – Our tool-calling model suite now includes Qwen 2.5, known for exceptional coding & tool execution performance, achieving benchmarks comparable to GPT-4o.
To get started, explore these new models and capabilities inside Relevance AI.
Before using these updates, you’ll have the opportunity to review model specifications and tool integrations to ensure they align with your workflows.
These enhancements bring more power, flexibility, and precision to AI agents, making your automation workflows even stronger.
If you're interested in learning more, you can sign up to get started or book a demo to see it in action.
Other improvements
A small one but a good one! --> The entry point for viewing agent queues has been restyled!
At a glance you can now see how many active queues your agent has and how many upcoming items are going to be processed in those active queues. For better user experience, the button is now also hidden if the agent has no queues (active or paused). The scheduled triggers queue is now only created when the agent enables that ability instead of by default on agent creation.
