Update docs for renamed products

contrib/job_with_ai_parse_document/README.md

@@ -63,7 +63,7 @@ Source Documents (UC Volume)
 
 5. **Upload documents** to your source volume
 
-6. **Run job** from the Databricks UI (Workflows)
+6. **Run job** from the Databricks UI (Jobs & Pipelines)
 
 ## Configuration
 
@@ -172,7 +172,7 @@ The included notebook visualizes parsing results with interactive bounding boxes
 ## Resources
 
 - [Declarative Automation Bundles](https://docs.databricks.com/dev-tools/bundles/)
-- [Databricks Workflows](https://docs.databricks.com/workflows/)
+- [Lakeflow Jobs](https://docs.databricks.com/aws/en/jobs/)
 - [Structured Streaming](https://docs.databricks.com/structured-streaming/)
 - [`ai_parse_document` Function](https://docs.databricks.com/aws/en/sql/language-manual/functions/ai_parse_document)
 - [`ai_query` Function](https://docs.databricks.com/aws/en/sql/language-manual/functions/ai_query)

contrib/templates/default-scala/template/{{.project_name}}/README.md.tmpl

@@ -21,7 +21,7 @@ The '{{.project_name}}' project was generated by using the default-scala templat
     This deploys everything that's defined for this project.
     For example, the default template would deploy a job called
     `[dev yourname] {{.project_name}}_job` to your workspace.
-    You can find that job by opening your workspace and clicking on **Workflows**.
+    You can find that job by opening your workspace and clicking on **Jobs & Pipelines**.
 
 4. Similarly, to deploy a production copy, type:
    ```

knowledge_base/job_backfill_data/README.md

@@ -60,7 +60,7 @@ with this project. You can also use the CLI:
    (Note: "dev" is the default target, so `--target` is optional.)
 
    This deploys everything defined for this project, including the job
-   `[dev yourname] sql_backfill_example`. You can find it under **Workflows** (or **Jobs & Pipelines**) in your workspace.
+   `[dev yourname] sql_backfill_example`. You can find it under **Jobs & Pipelines** in your workspace.
 
 3. To run the job with the default `run_date`:
    ```

knowledge_base/pipeline_with_schema/README.md

@@ -1,6 +1,6 @@
 # Pipeline with a dedicated Unity Catalog schema
 
-This example demonstrates how to define a Unity Catalog schema and a Delta Live Tables pipeline that uses it.
+This example demonstrates how to define a Unity Catalog schema and a [Lakeflow Spark Declarative Pipelines](https://docs.databricks.com/aws/en/dlt/) pipeline that uses it.
 
 ## Prerequisites
 

knowledge_base/serverless_job/README.md

@@ -2,7 +2,7 @@
 
 This Declarative Automation Bundles example demonstrates how to define a job that runs on serverless compute.
 
-For more information, please refer to the [documentation](https://docs.databricks.com/en/workflows/jobs/how-to/use-bundles-with-jobs.html#configure-a-job-that-uses-serverless-compute).
+For more information, please refer to the [documentation](https://docs.databricks.com/aws/en/dev-tools/bundles/jobs-tutorial).
 
 ## Prerequisites
 

knowledge_base/vector_search_product_discovery/README.md

@@ -1,8 +1,9 @@
-# Vector Search: Semantic Product Discovery
+# AI Search: Semantic Product Discovery
 
 A Declarative Automation Bundle demonstrating semantic product search using
-[Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html).
-It automates the full setup — the Unity Catalog schema, the Vector Search endpoint and
+[Databricks AI Search](https://docs.databricks.com/aws/en/ai-search/ai-search) (formerly
+Vector Search).
+It automates the full setup — the Unity Catalog schema, the AI Search endpoint and
 index, and the jobs that load and query the catalog — so a single `databricks bundle deploy`
 gives you a working semantic-search example to explore and adapt.
 
@@ -22,7 +23,7 @@ products in vector space.
 ```
 data/products.json  (synced to workspace by bundle deploy)
         ↓  embed descriptions → upsert_data()
-product_index  (Direct Access Vector Search index)
+product_index  (Direct Access AI Search index)
         ↓  embed query → similarity_search(query_vector=...)
 ranked results
 ```
@@ -36,7 +37,7 @@ ranked results
 │   └── products.json                    # Product catalog — synced to the workspace on deploy
 ├── resources/
 │   ├── schema.yml                       # Unity Catalog schema that namespaces the index
-│   ├── vector-search-endpoint.yml       # Vector Search endpoint (managed ANN serving)
+│   ├── vector-search-endpoint.yml       # AI Search endpoint (managed ANN serving)
 │   ├── vector-search-index.yml          # Direct Access index — schema defined inline
 │   ├── setup-job.yml                    # Job: embed product descriptions and upsert them
 │   └── query-job.yml                    # Job: embed a query and return ranked results
@@ -45,6 +46,9 @@ ranked results
     └── 02_query_demo.py                 # Semantic search — runs as a job or interactively
 ```
 
+Bundle resource types are unchanged by the rename to AI Search: the endpoint and index
+are still declared as `vector_search_endpoints` and `vector_search_indexes`.
+
 ## Prerequisites
 
 - Databricks workspace with Unity Catalog enabled
@@ -69,7 +73,7 @@ ranked results
    you — and several people can deploy into the same workspace without colliding. Use
    `databricks bundle deploy --target prod` for the shared production copy.
 
-   > Vector Search endpoint creation takes a few minutes to reach ONLINE status.
+   > AI Search endpoint creation takes a few minutes to reach ONLINE status.
 
 4. Load the catalog by running the bundle. This embeds all product descriptions and upserts them into the index.
    ```bash
@@ -103,7 +107,7 @@ databricks bundle deploy \
 |---|---|---|
 | `catalog` | `main` | Existing Unity Catalog catalog |
 | `schema` | `product_search` | Schema created by the bundle |
-| `endpoint_name` | `product-search-endpoint` | Vector Search endpoint name. Shared in prod; the `dev` target overrides it per user. |
+| `endpoint_name` | `product-search-endpoint` | AI Search endpoint name. Shared in prod; the `dev` target overrides it per user. |
 | `embedding_model` | `databricks-gte-large-en` | Foundation model used for embeddings |
 | `embedding_dimension` | `1024` | Vector dimension. Drives both the index and the embedding requests; immutable after the index is created. |
 
@@ -150,6 +154,6 @@ table and it keeps itself up to date. Replace `index_type: DIRECT_ACCESS` and
 
 ## Resources
 
-- [Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html)
+- [Databricks AI Search](https://docs.databricks.com/aws/en/ai-search/ai-search)
 - [Declarative Automation Bundles](https://docs.databricks.com/dev-tools/bundles/)
 - [Foundation Models — GTE Large](https://docs.databricks.com/en/machine-learning/foundation-models/supported-models.html)

knowledge_base/vector_search_product_discovery/databricks.yml

@@ -12,7 +12,7 @@ variables:
     description: Unity Catalog schema name for the product search use case
     default: product_search
   endpoint_name:
-    description: Name of the Vector Search endpoint
+    description: Name of the AI Search endpoint
     default: product-search-endpoint
   embedding_model:
     description: Model serving endpoint used to embed product descriptions

knowledge_base/vector_search_product_discovery/resources/setup-job.yml

@@ -24,7 +24,7 @@ resources:
 
       tasks:
         - task_key: upsert_products
-          description: Load products from JSON, embed descriptions, and upsert into the Vector Search index
+          description: Load products from JSON, embed descriptions, and upsert into the AI Search index
           environment_key: serverless_env
           notebook_task:
             notebook_path: ../src/01_upsert_products.py

knowledge_base/vector_search_product_discovery/src/01_upsert_products.py

@@ -1,10 +1,10 @@
 # Databricks notebook source
 # MAGIC %md
-# MAGIC # Upsert Products into Vector Search Index
+# MAGIC # Upsert Products into AI Search Index
 # MAGIC
 # MAGIC Reads the product catalog from the JSON file deployed with the bundle,
-# MAGIC embeds each product description, then upserts all records into the Vector
-# MAGIC Search index. Re-running is safe — upsert is idempotent on `product_id`.
+# MAGIC embeds each product description, then upserts all records into the AI Search
+# MAGIC index. Re-running is safe — upsert is idempotent on `product_id`.
 
 # COMMAND ----------
 

knowledge_base/vector_search_product_discovery/src/02_query_demo.py

@@ -2,7 +2,7 @@
 # MAGIC %md
 # MAGIC # Semantic Product Search Demo
 # MAGIC
-# MAGIC Queries the Vector Search index to find products that match a natural-language
+# MAGIC Queries the AI Search index to find products that match a natural-language
 # MAGIC description. Try queries that would fail keyword search — e.g. *"something to
 # MAGIC keep my coffee hot all day"* or *"gear for sleeping outside in freezing weather"*.
 

mlops_stacks/docs/ml-developer-guide.md

@@ -22,15 +22,15 @@ to use databricks CLI bundles to deploy ML code together with resource configs t
 
 This will allow you to develop locally and use databricks CLI bundles to deploy to your dev workspace to test out code and config changes.
 
-### Develop on Databricks using Databricks Repos
+### Develop on Databricks using Databricks Git folders
 
 #### Prerequisites
 You'll need:
 * Access to run commands on a cluster running Databricks Runtime ML version 11.0 or above in your dev Databricks workspace
-* To set up [Databricks Repos](https://learn.microsoft.com/azure/databricks/repos/index): see instructions below
+* To set up [Databricks Git folders](https://learn.microsoft.com/azure/databricks/repos/index): see instructions below
 
-#### Configuring Databricks Repos
-To use Repos, [set up git integration](https://learn.microsoft.com/azure/databricks/repos/repos-setup) in your dev workspace.
+#### Configuring Databricks Git folders
+To use Git folders, [set up git integration](https://learn.microsoft.com/azure/databricks/repos/repos-setup) in your dev workspace.
 
 If the current project has already been pushed to a hosted Git repo, follow the
 [UI workflow](https://learn.microsoft.com/azure/databricks/repos/git-operations-with-repos#add-a-repo-and-connect-remotely-later)
@@ -49,7 +49,7 @@ Otherwise, e.g. if iterating on ML code for a new project, follow the steps belo
 
 #### Running code on Databricks
 You can iterate on the sample ML code by running the provided `mlops_stacks/training/notebooks/Train.py` notebook on Databricks using
-[Repos](https://learn.microsoft.com/azure/databricks/repos/index). 
+[Git folders](https://learn.microsoft.com/azure/databricks/repos/index). 
 
 
 ## Next Steps

mlops_stacks/mlops_stacks/resources/README.md

@@ -74,7 +74,7 @@ Alternatively, you can use the other approaches described in the [databricks CLI
 ### Validate and provision ML resource configurations
 1. After installing the databricks CLI and creating the `DATABRICKS_TOKEN` env variable, change to the `mlops_stacks` directory.
 2. Run `databricks bundle validate` to validate the Databricks resource configurations. 
-3. Run `databricks bundle deploy` to provision the Databricks resource configurations to the dev workspace. The resource configurations and your ML code will be copied together to the dev workspace. The defined resources such as Databricks Workflows, MLflow Model and MLflow Experiment will be provisioned according to the config files under `mlops_stacks/resources`.
+3. Run `databricks bundle deploy` to provision the Databricks resource configurations to the dev workspace. The resource configurations and your ML code will be copied together to the dev workspace. The defined resources such as Lakeflow Jobs, MLflow Model and MLflow Experiment will be provisioned according to the config files under `mlops_stacks/resources`.
 4. Go to the Databricks dev workspace, check the defined model, experiment and workflows status, and interact with the created workflows.
 
 ### Destroy ML resource configurations