> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# November 2024

> New Releases, Enhancements, + Changes

## What's New

November 7, 2024

#### New Copilot Skills

* **Custom Metric Skill**: Copilot now writes custom metrics! Users can generate their desired metric by having copilot translate natural language descriptions or existing code (e.g., SQL, Python) into AQL. [Learn more →](/docs/ax/machine-learning/machine-learning/how-to-ml/custom-metrics-api/arizeql-generator)

  <Frame caption="Copilot Custom Metric Skill">
    <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/11-07-2024-image1.gif" />
  </Frame>

* **Embedding Summarization Skill**: Copilot now works for embeddings! Users can select embedding data point and Copilot will analyze for patterns and insights. [Learn more →](/docs/ax/machine-learning/computer-vision/how-to-cv/embedding-summarization)

  <Frame caption="Copilot Embedding Summarization Skill">
    <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/11-07-2024-image2.gif" />
  </Frame>

## Enhancements

November 7, 2024

#### Local Explainability Support

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 →](/docs/ax/machine-learning/machine-learning/how-to-ml/explainability/interpreting-and-analyzing-feature-importance-values#local-feature-importance)

<Frame caption="Local Explainability Support">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/11-07-2024-image3.gif" />
</Frame>

#### Experiment Over Time Widget

This widget allows users to integrate experiment data directly into their dashboards for ongoing visibility and analysis. Users can now:

* Select dataset of interest

* Choose specific evaluations they want to visualize over time

* Complete with direct connectivity to experiment details, making it easy to access the individual experiment results

[Learn more →](/docs/ax/observe/dashboards/widgets)

<Frame caption="Experiment Over Time Widget">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/11-07-2024-image4.gif" />
</Frame>

#### Full Function Calling Replay in Prompt Playground

Now users can follow the full [function calling tutorial](https://platform.openai.com/docs/guides/function-calling) from OpenAI and iterate on different functions in different messages from within the Prompt Playground.

<Frame caption="Full Function Calling Replay">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/92dd4f20-image.jpeg" />
</Frame>

#### Instrumentation Enhancements

* **Context Attribute Propagation** - Arize now has a set of utilities (eg: `using_session`) that allow users to set properties on context. All of these properties will be picked up by *all* of our auto instrumentations and added to spans. [Learn more →](/docs/ax/instrument/manual-instrumentation)

* **Typescript Trace Configuration** - Typescript auto instrumentations now accept a trace configuration which allows for data masking and configuration of attribute values on spans. [Learn more →](/docs/ax/instrument/mask-and-redact-data)

* **Vercel AI SDK** - User can now ingest traces created by the Vercel AI SDK into Arize. [Learn more →](/docs/ax/integrations/ts-js-agent-frameworks/vercel/vercel-ai-sdk-v6-tracing)

* **LangChain Auto Instrumentation**: Arize's LangChain auto instrumentation now supports langchain.js version 0.3 and is backwards compatible with all previous versions. [Learn more →](/docs/ax/integrations/python-agent-frameworks/langchain/langchain-tracing)

## 📚 New Content

November 7, 2024

The latest video tutorials, paper readings, ebooks, self-guided learning modules, and technical posts:

🧑‍🏫 [Prompt Optimization Course](https://arize.com/course/prompt-optimization/)

📊 [Evaluation Workflows to Accelerate Generative App Development and AI ROI](https://arize.com/blog/arize-vertex-ai-api/)

🐝 [Swarm: OpenAI's Experimental Approach to Multi-Agent Systems](https://arize.com/blog/swarm-openai-experimental-approach-to-multi-agent-systems/)

✏️ [LLM Evaluation Course](https://arize.com/llm-evaluation/)

🤖 [Techniques for Self-Improving LLM Evals](https://arize.com/blog/techniques-for-self-improving-llm-evals/)