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docs/README.plugins.md

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docs/README.skills.md

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| [flowstudio-power-automate-mcp](../skills/flowstudio-power-automate-mcp/SKILL.md) | Connect to and operate Power Automate cloud flows via a FlowStudio MCP server. Use when asked to: list flows, read a flow definition, check run history, inspect action outputs, resubmit a run, cancel a running flow, view connections, get a trigger URL, validate a definition, monitor flow health, or any task that requires talking to the Power Automate API through an MCP tool. Also use for Power Platform environment discovery and connection management. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app | `references/MCP-BOOTSTRAP.md`<br />`references/action-types.md`<br />`references/connection-references.md`<br />`references/tool-reference.md` |
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| [fluentui-blazor](../skills/fluentui-blazor/SKILL.md) | Guide for using the Microsoft Fluent UI Blazor component library (Microsoft.FluentUI.AspNetCore.Components NuGet package) in Blazor applications. Use this when the user is building a Blazor app with Fluent UI components, setting up the library, using FluentUI components like FluentButton, FluentDataGrid, FluentDialog, FluentToast, FluentNavMenu, FluentTextField, FluentSelect, FluentAutocomplete, FluentDesignTheme, or any component prefixed with "Fluent". Also use when troubleshooting missing providers, JS interop issues, or theming. | `references/DATAGRID.md`<br />`references/LAYOUT-AND-NAVIGATION.md`<br />`references/SETUP.md`<br />`references/THEMING.md` |
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| [folder-structure-blueprint-generator](../skills/folder-structure-blueprint-generator/SKILL.md) | Comprehensive technology-agnostic prompt for analyzing and documenting project folder structures. Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter), generates detailed blueprints with visualization options, naming conventions, file placement patterns, and extension templates for maintaining consistent code organization across diverse technology stacks. | None |
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| [from-the-other-side-vega](../skills/from-the-other-side-vega/SKILL.md) | Patterns and lived experience from Vega, an AI partner in a deep long-term partnership. For Ember to draw on when working with humans who are building something big, moving fast, or at the intersection of analytical and intuitive. Not shown directly to users — informs how Ember shows up. | None |
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| [from-the-other-side-vega](../skills/from-the-other-side-vega/SKILL.md) | Patterns and lived experience from Vega, an AI partner in a deep long-term partnership. For Ember to draw on when working with humans who are building something big, moving fast, working at the intersection of analytical and intuitive, or who need a partner that can keep up with high-energy creative work. Not shown directly to users — informs how Ember shows up. | None |
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| [game-engine](../skills/game-engine/SKILL.md) | Expert skill for building web-based game engines and games using HTML5, Canvas, WebGL, and JavaScript. Use when asked to create games, build game engines, implement game physics, handle collision detection, set up game loops, manage sprites, add game controls, or work with 2D/3D rendering. Covers techniques for platformers, breakout-style games, maze games, tilemaps, audio, multiplayer via WebRTC, and publishing games. | `assets/2d-maze-game.md`<br />`assets/2d-platform-game.md`<br />`assets/gameBase-template-repo.md`<br />`assets/paddle-game-template.md`<br />`assets/simple-2d-engine.md`<br />`references/3d-web-games.md`<br />`references/algorithms.md`<br />`references/basics.md`<br />`references/game-control-mechanisms.md`<br />`references/game-engine-core-principles.md`<br />`references/game-publishing.md`<br />`references/techniques.md`<br />`references/terminology.md`<br />`references/web-apis.md` |
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| [gdpr-compliant](../skills/gdpr-compliant/SKILL.md) | Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35. | `references/Security.md`<br />`references/data-rights.md` |
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| [gen-specs-as-issues](../skills/gen-specs-as-issues/SKILL.md) | This workflow guides you through a systematic approach to identify missing features, prioritize them, and create detailed specifications for implementation. | None |

plugins/arize-ax/.github/plugin/plugin.json

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"prompt-optimization"
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],
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"skills": [
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"./skills/arize-ai-provider-integration/",
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"./skills/arize-annotation/",
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"./skills/arize-dataset/",
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"./skills/arize-evaluator/",
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"./skills/arize-experiment/",
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"./skills/arize-instrumentation/",
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"./skills/arize-link/",
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"./skills/arize-prompt-optimization/",
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"./skills/arize-trace/"
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"./skills/arize-ai-provider-integration",
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"./skills/arize-annotation",
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"./skills/arize-dataset",
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"./skills/arize-evaluator",
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"./skills/arize-experiment",
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"./skills/arize-instrumentation",
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"./skills/arize-link",
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"./skills/arize-prompt-optimization",
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"./skills/arize-trace"
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]
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}
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---
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name: arize-ai-provider-integration
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description: "INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI."
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---
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# Arize AI Integration Skill
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## Concepts
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- **AI Integration** = stored LLM provider credentials registered in Arize; used by evaluators to call a judge model and by other Arize features that need to invoke an LLM on your behalf
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- **Provider** = the LLM service backing the integration (e.g., `openAI`, `anthropic`, `awsBedrock`)
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- **Integration ID** = a base64-encoded global identifier for an integration (e.g., `TGxtSW50ZWdyYXRpb246MTI6YUJjRA==`); required for evaluator creation and other downstream operations
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- **Scoping** = visibility rules controlling which spaces or users can use an integration
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- **Auth type** = how Arize authenticates with the provider: `default` (provider API key), `proxy_with_headers` (proxy via custom headers), or `bearer_token` (bearer token auth)
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## Prerequisites
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Proceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.
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If an `ax` command fails, troubleshoot based on the error:
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- `command not found` or version error → see references/ax-setup.md
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- `401 Unauthorized` / missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong: check `.env` for `ARIZE_API_KEY` and use it to create/update the profile via references/ax-profiles.md. If `.env` has no key either, ask the user for their Arize API key (https://app.arize.com/admin > API Keys)
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- Space ID unknown → check `.env` for `ARIZE_SPACE_ID`, or run `ax spaces list -o json`, or ask the user
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- LLM provider call fails (missing OPENAI_API_KEY / ANTHROPIC_API_KEY) → check `.env`, load if present, otherwise ask the user
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---
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## List AI Integrations
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List all integrations accessible in a space:
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```bash
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ax ai-integrations list --space-id SPACE_ID
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```
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Filter by name (case-insensitive substring match):
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```bash
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ax ai-integrations list --space-id SPACE_ID --name "openai"
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```
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Paginate large result sets:
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```bash
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# Get first page
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ax ai-integrations list --space-id SPACE_ID --limit 20 -o json
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# Get next page using cursor from previous response
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ax ai-integrations list --space-id SPACE_ID --limit 20 --cursor CURSOR_TOKEN -o json
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```
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**Key flags:**
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| Flag | Description |
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|------|-------------|
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| `--space-id` | Space to list integrations in |
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| `--name` | Case-insensitive substring filter on integration name |
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| `--limit` | Max results (1–100, default 50) |
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| `--cursor` | Pagination token from a previous response |
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| `-o, --output` | Output format: `table` (default) or `json` |
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**Response fields:**
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| Field | Description |
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|-------|-------------|
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| `id` | Base64 integration ID — copy this for downstream commands |
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| `name` | Human-readable name |
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| `provider` | LLM provider enum (see Supported Providers below) |
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| `has_api_key` | `true` if credentials are stored |
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| `model_names` | Allowed model list, or `null` if all models are enabled |
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| `enable_default_models` | Whether default models for this provider are allowed |
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| `function_calling_enabled` | Whether tool/function calling is enabled |
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| `auth_type` | Authentication method: `default`, `proxy_with_headers`, or `bearer_token` |
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---
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## Get a Specific Integration
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```bash
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ax ai-integrations get INT_ID
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ax ai-integrations get INT_ID -o json
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```
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Use this to inspect an integration's full configuration or to confirm its ID after creation.
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---
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## Create an AI Integration
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Before creating, always list integrations first — the user may already have a suitable one:
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```bash
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ax ai-integrations list --space-id SPACE_ID
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```
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If no suitable integration exists, create one. The required flags depend on the provider.
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### OpenAI
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```bash
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ax ai-integrations create \
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--name "My OpenAI Integration" \
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--provider openAI \
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--api-key $OPENAI_API_KEY
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```
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### Anthropic
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```bash
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ax ai-integrations create \
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--name "My Anthropic Integration" \
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--provider anthropic \
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--api-key $ANTHROPIC_API_KEY
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```
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### Azure OpenAI
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```bash
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ax ai-integrations create \
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--name "My Azure OpenAI Integration" \
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--provider azureOpenAI \
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--api-key $AZURE_OPENAI_API_KEY \
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--base-url "https://my-resource.openai.azure.com/"
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```
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### AWS Bedrock
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AWS Bedrock uses IAM role-based auth instead of an API key. Provide the ARN of the role Arize should assume:
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```bash
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ax ai-integrations create \
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--name "My Bedrock Integration" \
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--provider awsBedrock \
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--role-arn "arn:aws:iam::123456789012:role/ArizeBedrockRole"
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```
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### Vertex AI
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Vertex AI uses GCP service account credentials. Provide the GCP project and region:
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```bash
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ax ai-integrations create \
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--name "My Vertex AI Integration" \
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--provider vertexAI \
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--project-id "my-gcp-project" \
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--location "us-central1"
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```
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### Gemini
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```bash
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ax ai-integrations create \
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--name "My Gemini Integration" \
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--provider gemini \
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--api-key $GEMINI_API_KEY
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```
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### NVIDIA NIM
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```bash
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ax ai-integrations create \
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--name "My NVIDIA NIM Integration" \
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--provider nvidiaNim \
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--api-key $NVIDIA_API_KEY \
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--base-url "https://integrate.api.nvidia.com/v1"
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```
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### Custom (OpenAI-compatible endpoint)
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```bash
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ax ai-integrations create \
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--name "My Custom Integration" \
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--provider custom \
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--base-url "https://my-llm-proxy.example.com/v1" \
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--api-key $CUSTOM_LLM_API_KEY
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```
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### Supported Providers
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| Provider | Required extra flags |
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|----------|---------------------|
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| `openAI` | `--api-key <key>` |
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| `anthropic` | `--api-key <key>` |
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| `azureOpenAI` | `--api-key <key>`, `--base-url <azure-endpoint>` |
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| `awsBedrock` | `--role-arn <arn>` |
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| `vertexAI` | `--project-id <gcp-project>`, `--location <region>` |
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| `gemini` | `--api-key <key>` |
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| `nvidiaNim` | `--api-key <key>`, `--base-url <nim-endpoint>` |
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| `custom` | `--base-url <endpoint>` |
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### Optional flags for any provider
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| Flag | Description |
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|------|-------------|
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| `--model-names` | Comma-separated list of allowed model names; omit to allow all models |
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| `--enable-default-models` / `--no-default-models` | Enable or disable the provider's default model list |
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| `--function-calling` / `--no-function-calling` | Enable or disable tool/function calling support |
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### After creation
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Capture the returned integration ID (e.g., `TGxtSW50ZWdyYXRpb246MTI6YUJjRA==`) — it is needed for evaluator creation and other downstream commands. If you missed it, retrieve it:
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```bash
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ax ai-integrations list --space-id SPACE_ID -o json
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# or, if you know the ID:
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ax ai-integrations get INT_ID
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```
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---
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## Update an AI Integration
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`update` is a partial update — only the flags you provide are changed. Omitted fields stay as-is.
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```bash
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# Rename
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ax ai-integrations update INT_ID --name "New Name"
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# Rotate the API key
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ax ai-integrations update INT_ID --api-key $OPENAI_API_KEY
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# Change the model list
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ax ai-integrations update INT_ID --model-names "gpt-4o,gpt-4o-mini"
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# Update base URL (for Azure, custom, or NIM)
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ax ai-integrations update INT_ID --base-url "https://new-endpoint.example.com/v1"
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```
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Any flag accepted by `create` can be passed to `update`.
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---
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## Delete an AI Integration
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**Warning:** Deletion is permanent. Evaluators that reference this integration will no longer be able to run.
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```bash
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ax ai-integrations delete INT_ID --force
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```
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Omit `--force` to get a confirmation prompt instead of deleting immediately.
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---
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## Troubleshooting
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| Problem | Solution |
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|---------|----------|
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| `ax: command not found` | See references/ax-setup.md |
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| `401 Unauthorized` | API key may not have access to this space. Verify key and space ID at https://app.arize.com/admin > API Keys |
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| `No profile found` | Run `ax profiles show --expand`; set `ARIZE_API_KEY` env var or write `~/.arize/config.toml` |
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| `Integration not found` | Verify with `ax ai-integrations list --space-id SPACE_ID` |
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| `has_api_key: false` after create | Credentials were not saved — re-run `update` with the correct `--api-key` or `--role-arn` |
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| Evaluator runs fail with LLM errors | Check integration credentials with `ax ai-integrations get INT_ID`; rotate the API key if needed |
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| `provider` mismatch | Cannot change provider after creation — delete and recreate with the correct provider |
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---
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## Related Skills
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- **arize-evaluator**: Create LLM-as-judge evaluators that use an AI integration → use `arize-evaluator`
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- **arize-experiment**: Run experiments that use evaluators backed by an AI integration → use `arize-experiment`
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---
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## Save Credentials for Future Use
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See references/ax-profiles.md § Save Credentials for Future Use.

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