Add trademarking to Apache Airflow/Airflow where relevant (#328)

Airflow got its R this weekend :)

Devlist message:
https://lists.apache.org/thread/0fmp9olljfkm3vmg3ns5qd1o95sm7235
Guidelines: https://www.apache.org/foundation/marks/guide#naming

docs/README.md

@@ -4,7 +4,7 @@
 
 <br />
 
-Ask Astro is an open-source reference implementation of [Andreessen Horowitz's LLM Application Architecture](https://a16z.com/emerging-architectures-for-llm-applications/) built by [Astronomer](https://astronomer.io). It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow and Astronomer, including:
+Ask Astro is an open-source reference implementation of [Andreessen Horowitz's LLM Application Architecture](https://a16z.com/emerging-architectures-for-llm-applications/) built by [Astronomer](https://astronomer.io). It provides an end-to-end example of a Q&A LLM application used to answer questions about [Apache Airflow®](https://airflow.apache.org/) and Astronomer, including:
 
 - Airflow DAGs for data ingestion from various sources to a vector database
 - An API containing the business logic of taking a user's prompt and ultimately generating a response
@@ -25,11 +25,11 @@ In order to make the responses as factual and accurate as possible, it's general
 
 Ask Astro uses a set of Airflow DAGs that: ingest data from a source via an API or Python library, preprocesses and splits the data into smaller chunks, embeds those chunks, and writes the embeddings to Weaviate. As of today, Ask Astro retrieves data from the following sources:
 
-- [Apache Airflow docs](https://airflow.apache.org/docs/)
+- [Apache Airflow® docs](https://airflow.apache.org/docs/)
 - [Astronomer docs](https://docs.astronomer.io)
 - [Astronomer blog](https://www.astronomer.io/blog/)
 - [Astronomer Registry](https://registry.astronomer.io)
-- [Apache Airflow GitHub](https://github.com/apache/airflow) pull requests
+- [Apache Airflow® GitHub](https://github.com/apache/airflow) pull requests
 - [OpenLineage GitHub](https://github.com/OpenLineage/OpenLineage)
 - [OpenLineage GitHub docs](https://github.com/OpenLineage/docs)
 - [StackOverflow's Stack Exchange Data Dump](https://archive.org/details/stackexchange)
@@ -99,7 +99,7 @@ python3 scripts/local_dev.py run-airflow    # To run Airflow
 
 - [Run ask-astro UI](./ui/README.md)
 
-**Apache Airflow**
+**Apache Airflow®**
 
 - [Run airflow](./airflow/README.md)
 
@@ -113,8 +113,8 @@ The following sections describe how to deploy the various components of Ask Astr
 ### User Interface
 - [UI](./ui/README.md)
 
-### Apache Airflow
-- [Airflow](./airflow/README.md)
+### Apache Airflow®
+- [Apache Airflow®](./airflow/README.md)
 
 ## Slackbot
 - [Slackbot](./api/setup_slack_bot.md)

docs/airflow/README.md

@@ -6,23 +6,23 @@
 
 <br />
 
-# ask-astro Airflow
+# ask-astro Apache Airflow®
 
-Ask Astro is an open-source reference implementation of [Andreessen Horowitz's LLM Application Architecture](https://a16z.com/emerging-architectures-for-llm-applications/) built by [Astronomer](https://astronomer.io). It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow and Astronomer.
+Ask Astro is an open-source reference implementation of [Andreessen Horowitz's LLM Application Architecture](https://a16z.com/emerging-architectures-for-llm-applications/) built by [Astronomer](https://astronomer.io). It provides an end-to-end example of a Q&A LLM application used to answer questions about [Apache Airflow®](https://airflow.apache.org/) and Astronomer.
 
 ## Ingest and Embedding
 In order to make the responses as factual and accurate as possible, Ask Astro uses [Retrieval Augmented Generation (RAG)](https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html). Use the following setup procedures to ingest and embed documents for RAG with Apache Airflow and Astro.
 
 Ask Astro uses a set of Airflow DAGs that ingests data from a source via an API or Python library, preprocesses and splits the data into smaller chunks, embeds those chunks, and writes the embeddings to Weaviate. Ask Astro retrieves data from the following sources:
 
-- [Apache Airflow docs](https://airflow.apache.org/docs/)
+- [Apache Airflow® docs](https://airflow.apache.org/docs/)
 - [Astronomer docs](https://docs.astronomer.io)
 - [Astronomer blog](https://www.astronomer.io/blog/)
 - [Astronomer Registry](https://registry.astronomer.io)
-- [Apache Airflow GitHub](https://github.com/apache/airflow) issues and pull requests
+- [Apache Airflow® GitHub](https://github.com/apache/airflow) issues and pull requests
 - [OpenLineage GitHub](https://github.com/OpenLineage/OpenLineage)
 - [OpenLineage GitHub docs](https://github.com/OpenLineage/docs)
-- Apache Airflow Slack's [#troubleshooting channel](https://app.slack.com/client/TCQ18L22Z/CCQ7EGB1P)
+- Apache Airflow® Slack's [#troubleshooting channel](https://app.slack.com/client/TCQ18L22Z/CCQ7EGB1P)
 - [StackOverflow's Stack Exchange Data Dump](https://archive.org/details/stackexchange)
 - [astro-cli](https://docs.astronomer.io/astro/cli/overview)
 

docs/index.md

@@ -28,7 +28,7 @@ api/setup_slack_bot.md
 
 ```{toctree}
 :hidden:
-:caption: 💻 Develop Airflow DAGs
+:caption: 💻 Develop Apache Airflow® DAGs
 
 airflow/README.md
 ```

docs/local_development.md

@@ -40,7 +40,7 @@ $ poetry install
 
 ![docs.generate-tasks-help-screenshot](_static/images/task_help_message/docs-generate-tasks-help-screenshot.svg)
 
-### Airflow DAGs Tasks
+### Apache Airflow® DAGs Tasks
 
 - Run ask-astro airflow
 

docs/monitoring.md

@@ -6,12 +6,12 @@ This document outlines the monitoring and observability practices implemented fo
 
 ## Infrastructure Monitoring
 
-### Airflow DAGs
+### Apache Airflow® DAGs
 
 - **Monitoring DAG**: This [DAG](../airflow/dags/monitor/monitor.py) runs every 5 minutes to check the health of all infrastructure components.
 - **Ingestion DAG Monitoring**: This [DAG](../airflow/dags/monitor/monitor_ingestion_dags.py) monitors the health of ingestion processes.
 
-- ![Airflow DAGs](./_static/images/monitoring/airflow_dags.png)
+- ![Apache Airflow® DAGs](./_static/images/monitoring/airflow_dags.png)
 
 ### Alerting
 
@@ -31,7 +31,7 @@ This document outlines the monitoring and observability practices implemented fo
 4. **UI Monitoring**: Regular checks of the UI for a 200 response status.
    - UI Link: [https://ask.astronomer.io/](https://ask.astronomer.io/)
 
-5. **Airflow Data Ingestion and Feedback DAGs**: Monitors for completeness and errors.
+5. **Apache Airflow® Data Ingestion and Feedback DAGs**: Monitors for completeness and errors.
 
 6. **Open AI Integration**: Ensures the availability and response quality of Open AI services.
 

ui/src/routes/+layout.svelte

@@ -15,11 +15,11 @@
   let prompt = "";
   let showDescription = false;
 
-  let placeholderText = "Ask an Airflow or Astronomer question...";
+  let placeholderText = "Ask an Apache Airflow® or Astronomer question...";
   $: {
     placeholderText = !!$page.params?.request_id
       ? "Continue this conversation with another question..."
-      : "Ask an Airflow or Astronomer question...";
+      : "Ask an Apache Airflow® or Astronomer question...";
   }
 </script>
 
@@ -70,7 +70,7 @@
             target="_blank"
             >Andreessen Horowitz's LLM Application Architecture</a
           >. It's meant to showcase how to build an LLM application on top of
-          Apache Airflow. You can read more about the project and find the
+          Apache Airflow®. You can read more about the project and find the
           source code
           <a href="https://github.com/astronomer/ask-astro"> on GitHub</a>. Note
           that anything you ask may be publicly visible and should not include