feature/auto-releaser-bot (#4)

* feature/auto-releaser-bot

* release review updates

.github/workflows/auto-release.yml

@@ -0,0 +1,13 @@
+name: 'auto release'
+on:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - main
+
+jobs:
+  call-workflow-passing-data:
+    if: github.event.pull_request.merged
+    uses: fivetran/dbt_package_automations/.github/workflows/auto-release.yml@main
+    secrets: inherit

README.md

@@ -21,11 +21,11 @@ The main focus of this dbt package is to generate an end model and [Cortex Searc
 - [Zendesk](https://fivetran.com/docs/connectors/applications/zendesk): Tickets  
 
 The following table provides a detailed list of all models materialized within this package by default. 
-> TIP: See more details about these models in the package's [dbt docs site](https://fivetran.github.io/dbt_unified_rag/#!/overview/package_name_here).
+> TIP: See more details about these models in the package's [dbt docs site](https://fivetran.github.io/dbt_unified_rag/#!/overview).
 
 | **Table**                 | **Description**                                                                                                    |
 | ------------------------- | ------------------------------------------------------------------------------------------------------------------ |
-| [rag__unified_document](https://github.com/fivetran/dbt_unified_rag/blob/main/models/rag__unified_document.sql)  | Each record represents a chunk of text prepared for semantic-search and additional fields for use in LLM workflows.   |
+| [rag__unified_document](https://fivetran.github.io/dbt_unified_rag/#!/model/model.unified_rag.rag__unified_document)  | Each record represents a chunk of text prepared for semantic-search and additional fields for use in LLM workflows.   |
 
 Additionally, for **Snowflake** destinations, a [Cortex Search Service](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview) will be generated as a result of this data model. The Cortex Search Service uses the results of the `rag__unified_document` and enables Snowflake users to take advantage of low-latency, high quality "fuzzy" search over their data for use in RAG applications leveraging LLMs. See the below table for details.
 
@@ -58,7 +58,7 @@ packages:
 
 ### Step 3: Define database and schema variables
 #### Single connector
-By default, this package looks for your ad platform data in your target database. If this is not where your app platform data is stored, add the relevant `<connector>_database` variables to your `dbt_project.yml` file (see below).
+By default, this package looks for your HubSpot, Jira, and/or Zendesk data in your target database. If this is not where your data is stored, add the relevant `<connector>_database` variables to your `dbt_project.yml` file (see below).
 
 ```yml
 # dbt_project.yml

integration_tests/dbt_project.yml

@@ -14,14 +14,6 @@ vars:
   rag__using_zendesk: True
   rag__using_hubspot: True
 
-  rag_jira_comment_identifier: jira_comment
-  rag_jira_issue_identifier: jira_issue
-  rag_jira_user_identifier: jira_user
-  rag_jira_priority_identifier: jira_priority
-  rag_jira_status_identifier: jira_status
-  rag_zendesk_ticket_identifier: zendesk_ticket
-  rag_zendesk_ticket_comment_identifier: zendesk_ticket_comment
-  rag_zendesk_user_identifier: zendesk_user
   rag_hubspot_engagement_note_identifier: "hubspot_engagement_note"
   rag_hubspot_engagement_email_identifier: "hubspot_engagement_email"
   rag_hubspot_engagement_company_identifier: "hubspot_engagement_company"