Documentation Update: 0.6.14

ai-assistance/llm-connections.mdx

@@ -10,6 +10,25 @@ Using [AI assistance](/ai-assistance/overview) in OpenOps requires enabling AI i
 
 ## Configuring an LLM connection
 
+To enable AI and configure an LLM connection:
+1. In the OpenOps left sidebar, click the **Settings** icon at the bottom:
+    <NarrowImage src="/images/access-llm-settings-icon.png" alt="Settings icon" widthPercent={30} />
+2. In the **Settings** view, click **OpenOps AI**:
+![OpenOps AI settings](/images/access-llm-ai-providers.png)
+3. Under **AI Connection**, click the dropdown and select **Create new connection**. The **Create AI Connection** view opens:
+![Create AI Connection](/images/access-llm-create-connection.png)
+---
+title: 'LLM Connections'
+description: 'How to integrate OpenOps with an LLM provider of your choice'
+icon: 'rectangle-list'
+---
+
+import { NarrowImage } from '/snippets/narrow-image.jsx'
+
+Using [AI assistance](/ai-assistance/overview) in OpenOps requires enabling AI in OpenOps settings and configuring at least one connection to a large language model (LLM) provider.
+
+## Configuring an LLM connection
+
 To enable AI and configure an LLM connection:
 1. In the OpenOps left sidebar, click the **Settings** icon at the bottom:
     <NarrowImage src="/images/access-llm-settings-icon.png" alt="Settings icon" widthPercent={30} />
@@ -18,7 +37,50 @@ To enable AI and configure an LLM connection:
 3. Under **AI Connection**, click the dropdown and select **Create new connection**. The **Create AI Connection** view opens:
 ![Create AI Connection](/images/access-llm-create-connection.png)
 4. In the **Provider** dropdown, select one of the supported LLM providers. Anthropic, Azure OpenAI, Cerebras, Cohere, Deep Infra, DeepSeek, Google Generative AI, Google Vertex AI, Groq, Mistral, OpenAI, OpenAI-compatible providers, Perplexity, Together.ai, and xAI Grok are currently supported.
-5. In the **Model** dropdown, select one of the models your LLM provider supports. (If you're configuring Azure OpenAI, select **Custom** instead of a model and complete the other [Azure OpenAI-specific steps](#azure-openai).)
+5. In the **Model** dropdown, select one of the models your LLM provider supports (including `gpt-5.2` when using OpenAI). (If you're configuring Azure OpenAI, select **Custom** instead of a model and complete the other [Azure OpenAI-specific steps](#azure-openai).)
+6. (Optional) If the model you're looking for is not listed, specify a custom model in the **Custom model** field. This overrides whatever you've selected under **Model**.
+7. Enter your API key for the selected LLM provider in the **API Key** field.
+8. (Optional) Enter a **Base URL** for the selected model. This is useful if you want to use a proxy or if your LLM provider does not use the default base URL. If you selected _OpenAI Compatible_ as the provider, then you are required to enter the base URL.
+9. (Optional) Use the **Provider settings** and **Model settings** fields to specify custom parameters as JSON. The JSON schema varies depending on the chosen provider and model:
+   * See the [Azure OpenAI](#azure-openai) and [Google Vertex AI](#google-vertex-ai) instructions for custom provider settings required by these providers.
+   * If you've selected OpenAI, use **Provider settings** for JSON you'd normally pass to the `createOpenAI` function, and **Model settings** for JSON you'd normally pass to the `streamText` function. For more details, see the [OpenAI documentation](https://platform.openai.com/docs/api-reference).
+10. Click **Save** to apply your changes in the **Create AI Connection** view.
+11. (Optional) Back in the **OpenOps AI** section, if you're working with AWS and you want your AI connection to have access to AWS MCP servers, go to the **MCP** section and select an [AWS connection](/cloud-access/access-levels-permissions/#aws-connections) in the **AWS Cost** dropdown:
+    ![AWS Cost MCP connection](/images/access-llm-mcp.png)
+    This enables access to AWS Cost Explorer MCP Server, AWS Pricing MCP Server, and AWS Billing and Cost Management MCP Server.
+
+
+Configuring an LLM connection enables all [AI assistance features](/ai-assistance/overview) in OpenOps.
+
+## Provider-specific settings
+
+Some LLM providers require additional configuration beyond the API key or may require non-standard settings. This section offers guidance for some of the most common providers in this category.
+
+### Azure OpenAI
+
+When configuring an AI connection where Azure OpenAI serves as the provider:
+1. In the **Model** dropdown, select **Custom**.
+2. In the **Custom model** field, enter the name of a model deployment in your Azure OpenAI resource (for example, `my-gpt-5`).
+3. In the **API Key** field, enter the API key from a service principal that has access to your Azure OpenAI resource.
+4. In the **Provider settings** field, enter the following JSON:
+    ```json
+    {"resourceName": "Azure AI resource name"}
+    ```
+   The value of `resourceName` must match the name of your Azure OpenAI resource — the same name that appears in the endpoint URL, e.g. `https://<resourceName>.openai.azure.com/`, or, if you're using Azure AI Foundry, `https://<resourceName>.services.ai.azure.com/`.
+
+### Google Vertex AI
+
+When configuring an AI connection where Google Vertex AI serves as the provider, add the following to the **Provider settings** field:
+
+```json
+{"project":"your-google-cloud-project-id","location":"global"}
+```
+
+In this JSON:
+* `your-google-cloud-project-id` is the ID of the Google Cloud project where your API key was created. You can look up the project ID in the Google Cloud Console by opening the project selector:
+    ![Google Cloud project ID](/images/access-llm-google-project-id.png)
+* Use `global` for `location` or, if needed, specify one of the [supported locations](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#google_model_endpoint_locations) for your chosen model.
+
 6. (Optional) If the model you're looking for is not listed, specify a custom model in the **Custom model** field. This overrides whatever you've selected under **Model**.
 7. Enter your API key for the selected LLM provider in the **API Key** field.
 8. (Optional) Enter a **Base URL** for the selected model. This is useful if you want to use a proxy or if your LLM provider does not use the default base URL. If you selected _OpenAI Compatible_ as the provider, then you are required to enter the base URL.

reporting-analytics/analytics.mdx

@@ -31,7 +31,11 @@ Behind every OpenOps table, there is a Postgres database view that can be used t
 
 When a new table is created in [OpenOps tables](/reporting-analytics/tables/), the system automatically creates a new database view with the naming convention _table name \_ table id \_ userfriendly_. You can use this view to create dashboards in OpenOps, as well as connect it to other BI systems.
 
-## Building new charts
+## Running without OpenOps Analytics
+
+OpenOps can run without the Analytics service.
+
+When OpenOps Analytics is not available, the **Analytics** view and admin portal are not available, but other OpenOps features (for example, tables, workflows, and workflow runs) continue to work.
 
 To configure a new chart based on an OpenOps table to display in the **Analytics** view in OpenOps, do the following: