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docs/tutorials/use-databricks-model.md

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+# Using Databricks Models in AI DIAL
+
+From this document you can learn how to use Databricks models in AI DIAL.
+
+## Configure Databricks
+
+### Step 1 (Optional): Create Serving Endpoint
+
+Create a serving endpoint for a Databricks model unless you already have one:
+
+1.	In the **Machine Learning** section, navigate to the  **Serving** tab and click **Create serving endpoint**.
+
+    ![](img/databricks1.png)
+
+2. Enter a serving endpoint name (it will be used in the [Step 4](#step-4-configure-openai-adapter)) and select the served Entity (model).
+
+    ![](img/databricks2.png)
+
+3.	Click **Create**
+
+### Step 2: Create Access Token
+
+To generate access token, navigate to the **Developer** section in your **User Settings**.
+
+    ![](img/databricks4.png)
+
+## Configure AI DIAL
+
+### Step 3: Configure AI DIAL Model
+
+Use the serving endpoint and the access token you have created in two previous steps to add the following configuration in the [DIAL Core dynamic settings](https://github.com/epam/ai-dial-core?tab=readme-ov-file#dynamic-settings) to the `model` section: 
+
+```json
+"{dial-deployment-name}": 
+{
+    "type": "chat",
+    "displayName": "{Deployment name}",
+    "endpoint": "http://{open-ai-adapter-host}/openai/deployments/{databricks-deployment-name}/chat/completions",
+    "upstreams": [
+        {
+            "endpoint": "{databricks-account-address}/serving-endpoints/chat/completions",
+            "key": "access token"
+        }
+    ]
+}
+```
+### Step 4: Configure OpenAI Adapter
+
+1. Since Databricks serving endpoints utilize an authorization flow that differs from OpenAI's, it's necessary to specify the Databricks deployments in the OpenAI Adapter environment variable: `DATABRICKS_DEPLOYMENTS=databricks-deployment-name`. Refer to [AI DIAL OpenAI adapter documentation](https://github.com/epam/ai-dial-adapter-openai?tab=readme-ov-file#categories-of-deployments) for details.
+2. Restart AI DIAL OpenAI Adapter for changes to apply.

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