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"*.