Changelog
See the latest new features released in Arize
Last updated
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See the latest new features released in Arize
Last updated
Was this helpful?
We've increased the row limit for datasets in the playground, so you can run prompts in parallel on up to 100 examples.
You can now create and run evals on your experiments from the UI. Compare performance across different prompt templates, models, or configurations without code. Learn more →
When running evaluations using background tasks, you can now cancel them mid-flight while observing task logs. Learn more →
We've made it easier to view, test, and validate your tool calls in prompt playground. Learn more →
Compare the outputs of a new prompt and the original prompt side-by-side. Tweak model parameters and compare results across your datasets. Learn more →
We now support logging image segmentation to Arize. Log your segmentation coordinates and compare your predictions vs. your actuals.
We’ve made it way easier to drill into specific time ranges, with quick presets like "last 15 minutes" and custom shorthand for specific dates and times, such as 10d
,4/1 - 4/6
, 4/1 3:00am
.
Learn more →
Access and manage your prompts in code with support for OpenAI and VertexAI. Learn more
Get full visbility into your evaluation task runs, including when it ran, what triggered it, and if there were errors. Learn more →
Easily run your online evaluation tasks over historical data.
Quickly debug and refine your prompts used by your online evaluators by loading them prefilled into prompt playground. Learn more →
Dynamically select the fields you want to see in your sessions view.
Use Arize to annotate your data with 3rd parties. Learn more →
You can now collapse rows to see more data at a glance or expand them to view more text.
Schedule for monitors to run hourly, daily, weekly, or monthly.
Specify which columns of data you'd like to export when exporting data via the ArizeExportClient by specifying columns
.
You can now create datasets through many methods, from traces, code, manually in the UI, or CSV upload. Read more
Support for HTTP when sending traces to Arize! See GitHub for more info.
Audio tracing: Capture, process, and send audio data to Arize and observe your application behavior.
Evaluation: Assess how well your models identify emotional tones like frustration, joy, or neutrality.
We’ve added new ways to plot your charts, with custom colors and better UX!
Manage, iterate, and deploy your prompts in one place. Version control your templates and use them across playground, tasks, and APIs. Read more
Use our pre-built, off-the-shelf evaluators to evaluate spans without requiring requests to an LLM-as-a-Judge. These include Regex matching, JSON validation, Contains keyword, and more!
Quickly experiment with your prompts across your datasets. All you have to do is click "Save as experiment" Read more
See exactly how and when your monitors are triggered
Support for sessions
via LangChain native thread tracking in TypeScript is now available. Easily track multi-turn conversations / threads using LangChain.js.
Extract key insights quickly from your spans instead of trying to decipher meaning in hundreds of spans. Ask questions and run evals right in the trace view.
Building dashboard plots just got way easier. Create time series plots and even translate code into ready to go visualizations.
The Custom Metric skill now supports a conversational flow, making it easier for users to iterate and refine metrics dynamically
Experiment traces for a dataset are now consolidated accessed under "Experiment Projects".
For your multi-class ML models, you can see how your model is calibrated in one visualization
You can now log experiment data manually using a dataframe, instead of running an experiment. This is useful if you already have the data you need, and re-running the query would be expensive. SDK Reference
Users can generate their desired metric by having copilot translate natural language descriptions or existing code (e.g., SQL, Python) into AQL. Learn more →
Copilot now works for embeddings! Users can select embedding data point and Copilot will analyze for patterns and insights. Learn more →
Local Explainability is now live, providing both a table view and waterfall style plot for detailed, per-feature SHAP values on individual predictions. Learn more →
Visualize specific evaluations over time in dashboards. Learn more →
Now users can follow the full function calling tutorial from OpenAI and iterate on different functions in different messages from within the Prompt Playground.
User can now ingest traces created by the Vercel AI SDK into Arize. Learn more →
You can add metadata and context that will be picked up by all of our auto instrumentations and added to spans. Learn more →
Users now have the option to to test a task, such as online eval, by running it once on existing data, or apply evaluation labels to older traces. Learn more →
Users can now filter experiments based on dataset attributes or experiment results, making it easy to identify areas for improvement and track their experiment progress with more precision. Learn more →
With Embeddings Tracing, you can effortlessly select embedding spans and dive straight into the UMAP visualizer, simplifying troubleshooting for your genAI applications. Learn more →
Users can now view a detailed breakdown of labels for their experiments on the Experiments Details page.
We've added full support for all available OpenAI models in the playground including the o1-mini
and o1-preview
.
We've added better input variable behavior, autocompletion enhancements, support for mustache/f-string input variables, and more.
We now store the last three filters used by a user! Users can easily access their filter history in the query filters dropdown, making it simpler to reuse filters for future queries.
Apply filters directly from the table by hovering over the text to reveal the filter icon.
We made it way simpler to add automatic tracing to your applications! It's now just a few lines of code to use OpenTelemetry to trace your LLM application. Check out our new quickstart guide which uses our arize-otel package.
Easily add spans to a dataset from the Traces page using the "Add to Dataset" button.