diff --git a/contrib/job_with_ai_parse_document/README.md b/contrib/job_with_ai_parse_document/README.md index d7e6ad2e..2bd3ba2d 100644 --- a/contrib/job_with_ai_parse_document/README.md +++ b/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) diff --git a/contrib/templates/default-scala/template/{{.project_name}}/README.md.tmpl b/contrib/templates/default-scala/template/{{.project_name}}/README.md.tmpl index cc4be258..80115834 100644 --- a/contrib/templates/default-scala/template/{{.project_name}}/README.md.tmpl +++ b/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: ``` diff --git a/knowledge_base/job_backfill_data/README.md b/knowledge_base/job_backfill_data/README.md index 760a1ba4..1f755070 100644 --- a/knowledge_base/job_backfill_data/README.md +++ b/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`: ``` diff --git a/knowledge_base/pipeline_with_schema/README.md b/knowledge_base/pipeline_with_schema/README.md index fa87aab4..d5d22df1 100644 --- a/knowledge_base/pipeline_with_schema/README.md +++ b/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 diff --git a/knowledge_base/serverless_job/README.md b/knowledge_base/serverless_job/README.md index 318c9311..b96b26b9 100644 --- a/knowledge_base/serverless_job/README.md +++ b/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 diff --git a/knowledge_base/vector_search_product_discovery/README.md b/knowledge_base/vector_search_product_discovery/README.md index 6c46e8d7..772db15a 100644 --- a/knowledge_base/vector_search_product_discovery/README.md +++ b/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) diff --git a/knowledge_base/vector_search_product_discovery/databricks.yml b/knowledge_base/vector_search_product_discovery/databricks.yml index 8a304671..73af3b8a 100644 --- a/knowledge_base/vector_search_product_discovery/databricks.yml +++ b/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 diff --git a/knowledge_base/vector_search_product_discovery/resources/setup-job.yml b/knowledge_base/vector_search_product_discovery/resources/setup-job.yml index f2e28739..a117fae6 100644 --- a/knowledge_base/vector_search_product_discovery/resources/setup-job.yml +++ b/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 diff --git a/knowledge_base/vector_search_product_discovery/src/01_upsert_products.py b/knowledge_base/vector_search_product_discovery/src/01_upsert_products.py index 0d16d556..0ab60c42 100644 --- a/knowledge_base/vector_search_product_discovery/src/01_upsert_products.py +++ b/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 ---------- diff --git a/knowledge_base/vector_search_product_discovery/src/02_query_demo.py b/knowledge_base/vector_search_product_discovery/src/02_query_demo.py index f938e3a3..8d821b9d 100644 --- a/knowledge_base/vector_search_product_discovery/src/02_query_demo.py +++ b/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"*.