Use AskAstroWeaviateHook for ask-astro-load-github.py

airflow/.dockerignore

@@ -3,4 +3,3 @@ astro
 .env
 airflow_settings.yaml
 logs/
-dags/

airflow/Dockerfile

@@ -1,5 +1 @@
-# syntax=quay.io/astronomer/airflow-extensions:latest
-
-FROM quay.io/astronomer/astro-runtime:9.5.0-base
-
-COPY include/airflow_provider_weaviate-0.0.1-py3-none-any.whl /tmp
+FROM quay.io/astronomer/astro-runtime:9.5.0

airflow/dags/ingestion/ask-astro-load-airflow-docs.py

@@ -0,0 +1,47 @@
+import os
+from datetime import datetime
+
+from include.tasks import ingest, split
+from include.tasks.extract import airflow_docs
+
+from airflow.decorators import dag, task
+
+ask_astro_env = os.environ.get("ASK_ASTRO_ENV", "")
+
+_WEAVIATE_CONN_ID = f"weaviate_{ask_astro_env}"
+WEAVIATE_CLASS = os.environ.get("WEAVIATE_CLASS", "DocsProd")
+
+airflow_docs_base_url = "https://airflow.apache.org/docs/"
+
+default_args = {"retries": 3, "retry_delay": 30}
+
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
+
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
+def ask_astro_load_airflow_docs():
+    """
+    This DAG performs incremental load for any new Airflow docs. Initial load via ask_astro_load_bulk imported
+    data from a point-in-time data capture. By using the upsert logic of the weaviate_import decorator
+    any existing documents that have been updated will be removed and re-added.
+    """
+
+    extracted_airflow_docs = task(airflow_docs.extract_airflow_docs)(docs_base_url=airflow_docs_base_url)
+
+    split_md_docs = task(split.split_html).expand(dfs=[extracted_airflow_docs])
+
+    task.weaviate_import(
+        ingest.import_upsert_data,
+        weaviate_conn_id=_WEAVIATE_CONN_ID,
+    ).partial(
+        class_name=WEAVIATE_CLASS, primary_key="docLink"
+    ).expand(dfs=[split_md_docs])
+
+
+ask_astro_load_airflow_docs()

airflow/dags/ingestion/ask-astro-load-blogs.py

@@ -1,5 +1,5 @@
+import datetime
 import os
-from datetime import datetime
 
 from include.tasks import ingest, split
 from include.tasks.extract import blogs
@@ -11,27 +11,37 @@
 _WEAVIATE_CONN_ID = f"weaviate_{ask_astro_env}"
 WEAVIATE_CLASS = os.environ.get("WEAVIATE_CLASS", "DocsProd")
 
-blog_cutoff_date = datetime.strptime("2023-01-19", "%Y-%m-%d")
+blog_cutoff_date = datetime.date(2023, 1, 19)
 
+default_args = {"retries": 3, "retry_delay": 30}
 
-@dag(schedule_interval="0 5 * * *", start_date=datetime(2023, 9, 27), catchup=False, is_paused_upon_creation=True)
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
+
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime.datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
 def ask_astro_load_blogs():
     """
-    This DAG performs incremental load for any new docs.  Initial load via ask_astro_load_bulk imported
-    data from a point-in-time data capture.  By using the upsert logic of the weaviate_import decorator
+    This DAG performs incremental load for any new docs. Initial load via ask_astro_load_bulk imported
+    data from a point-in-time data capture. By using the upsert logic of the weaviate_import decorator
     any existing documents that have been updated will be removed and re-added.
     """
 
-    blogs_docs = task(blogs.extract_astro_blogs, retries=3)(blog_cutoff_date=blog_cutoff_date)
+    blogs_docs = task(blogs.extract_astro_blogs)(blog_cutoff_date=blog_cutoff_date)
 
     split_md_docs = task(split.split_markdown).expand(dfs=[blogs_docs])
 
     task.weaviate_import(
         ingest.import_upsert_data,
         weaviate_conn_id=_WEAVIATE_CONN_ID,
-        retries=10,
-        retry_delay=30,
-    ).partial(class_name=WEAVIATE_CLASS, primary_key="docLink").expand(dfs=[split_md_docs])
+    ).partial(
+        class_name=WEAVIATE_CLASS, primary_key="docLink"
+    ).expand(dfs=[split_md_docs])
 
 
 ask_astro_load_blogs()

airflow/dags/ingestion/ask-astro-load-github.py

@@ -1,79 +1,86 @@
+import datetime
 import os
-from datetime import datetime
 
-from include.tasks import ingest, split
+from dateutil.relativedelta import relativedelta
+from include.tasks import split
 from include.tasks.extract import github
+from include.tasks.extract.utils.weaviate.ask_astro_weaviate_hook import AskAstroWeaviateHook
 
 from airflow.decorators import dag, task
 
-ask_astro_env = os.environ.get("ASK_ASTRO_ENV", "")
+ask_astro_env = os.environ.get("ASK_ASTRO_ENV", "dev")
 
 _WEAVIATE_CONN_ID = f"weaviate_{ask_astro_env}"
 _GITHUB_CONN_ID = "github_ro"
-WEAVIATE_CLASS = os.environ.get("WEAVIATE_CLASS", "DocsProd")
+WEAVIATE_CLASS = os.environ.get("WEAVIATE_CLASS", "DocsDev")
+
+ask_astro_weaviate_hook = AskAstroWeaviateHook(_WEAVIATE_CONN_ID)
+
 markdown_docs_sources = [
     {"doc_dir": "learn", "repo_base": "astronomer/docs"},
     {"doc_dir": "astro", "repo_base": "astronomer/docs"},
     {"doc_dir": "", "repo_base": "OpenLineage/docs"},
     {"doc_dir": "", "repo_base": "OpenLineage/OpenLineage"},
 ]
-rst_docs_sources = [
-    {"doc_dir": "docs", "repo_base": "apache/airflow", "exclude_docs": ["changelog.rst", "commits.rst"]},
-]
 code_samples_sources = [
     {"doc_dir": "code-samples", "repo_base": "astronomer/docs"},
 ]
 issues_docs_sources = [
-    "apache/airflow",
+    {
+        "repo_base": "apache/airflow",
+        "cutoff_date": datetime.date.today() - relativedelta(months=1),
+        "cutoff_issue_number": 30000,
+    }
 ]
 
+default_args = {"retries": 3, "retry_delay": 30}
+
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
 
-@dag(schedule_interval="0 5 * * *", start_date=datetime(2023, 9, 27), catchup=False, is_paused_upon_creation=True)
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime.datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
 def ask_astro_load_github():
     """
-    This DAG performs incremental load for any new docs.  Initial load via ask_astro_load_bulk imported
-    data from a point-in-time data capture.  By using the upsert logic of the weaviate_import decorator
+    This DAG performs incremental load for any new docs. Initial load via ask_astro_load_bulk imported
+    data from a point-in-time data capture. By using the upsert logic of the weaviate_import decorator
     any existing documents that have been updated will be removed and re-added.
     """
 
     md_docs = (
-        task(github.extract_github_markdown, retries=3)
+        task(github.extract_github_markdown)
         .partial(github_conn_id=_GITHUB_CONN_ID)
         .expand(source=markdown_docs_sources)
     )
 
-    rst_docs = (
-        task(github.extract_github_rst, retries=3)
-        .partial(github_conn_id=_GITHUB_CONN_ID)
-        .expand(source=rst_docs_sources)
-    )
-
     issues_docs = (
-        task(github.extract_github_issues, retries=3)
-        .partial(github_conn_id=_GITHUB_CONN_ID)
-        .expand(repo_base=issues_docs_sources)
+        task(github.extract_github_issues).partial(github_conn_id=_GITHUB_CONN_ID).expand(source=issues_docs_sources)
     )
 
     code_samples = (
-        task(github.extract_github_python, retries=3)
-        .partial(github_conn_id=_GITHUB_CONN_ID)
-        .expand(source=code_samples_sources)
+        task(github.extract_github_python).partial(github_conn_id=_GITHUB_CONN_ID).expand(source=code_samples_sources)
     )
 
-    markdown_tasks = [md_docs, rst_docs, issues_docs]
-
-    split_md_docs = task(split.split_markdown).expand(dfs=markdown_tasks)
+    split_md_docs = task(split.split_markdown).expand(dfs=[md_docs, issues_docs])
 
     split_code_docs = task(split.split_python).expand(dfs=[code_samples])
 
-    task.weaviate_import(
-        ingest.import_upsert_data,
-        weaviate_conn_id=_WEAVIATE_CONN_ID,
-        retries=10,
-        retry_delay=30,
-    ).partial(class_name=WEAVIATE_CLASS, primary_key="docLink").expand(dfs=[split_md_docs, split_code_docs])
-
-    issues_docs >> md_docs >> rst_docs >> code_samples
+    _import_data = (
+        task(ask_astro_weaviate_hook.ingest_data, retries=10)
+        .partial(
+            class_name=WEAVIATE_CLASS,
+            existing="upsert",
+            doc_key="docLink",
+            batch_params={"batch_size": 1000},
+            verbose=True,
+        )
+        .expand(dfs=[split_md_docs, split_code_docs])
+    )
 
 
 ask_astro_load_github()

airflow/dags/ingestion/ask-astro-load-registry.py

@@ -11,18 +11,28 @@
 _WEAVIATE_CONN_ID = f"weaviate_{ask_astro_env}"
 WEAVIATE_CLASS = os.environ.get("WEAVIATE_CLASS", "DocsProd")
 
+default_args = {"retries": 3, "retry_delay": 30}
 
-@dag(schedule_interval="0 5 * * *", start_date=datetime(2023, 9, 27), catchup=False, is_paused_upon_creation=True)
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
+
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
 def ask_astro_load_registry():
     """
-    This DAG performs incremental load for any new docs.  Initial load via ask_astro_load_bulk imported
-    data from a point-in-time data capture.  By using the upsert logic of the weaviate_import decorator
+    This DAG performs incremental load for any new docs. Initial load via ask_astro_load_bulk imported
+    data from a point-in-time data capture. By using the upsert logic of the weaviate_import decorator
     any existing documents that have been updated will be removed and re-added.
     """
 
-    registry_cells_docs = task(registry.extract_astro_registry_cell_types, retries=3)()
+    registry_cells_docs = task(registry.extract_astro_registry_cell_types)()
 
-    registry_dags_docs = task(registry.extract_astro_registry_dags, retries=3)()
+    registry_dags_docs = task(registry.extract_astro_registry_dags)()
 
     split_md_docs = task(split.split_markdown).expand(dfs=[registry_cells_docs])
 
@@ -31,9 +41,9 @@ def ask_astro_load_registry():
     task.weaviate_import(
         ingest.import_upsert_data,
         weaviate_conn_id=_WEAVIATE_CONN_ID,
-        retries=10,
-        retry_delay=30,
-    ).partial(class_name=WEAVIATE_CLASS, primary_key="docLink").expand(dfs=[split_md_docs, split_code_docs])
+    ).partial(
+        class_name=WEAVIATE_CLASS, primary_key="docLink"
+    ).expand(dfs=[split_md_docs, split_code_docs])
 
 
 ask_astro_load_registry()

airflow/dags/ingestion/ask-astro-load-slack.py

@@ -20,25 +20,35 @@
     }
 ]
 
+default_args = {"retries": 3, "retry_delay": 30}
 
-@dag(schedule_interval="0 5 * * *", start_date=datetime(2023, 9, 27), catchup=False, is_paused_upon_creation=True)
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
+
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
 def ask_astro_load_slack():
     """
-    This DAG performs incremental load for any new slack threads.  The slack archive is a point-in-time capture.  This
-    DAG should run nightly to capture threads between archive periods.  By using the upsert logic of the
+    This DAG performs incremental load for any new slack threads. The slack archive is a point-in-time capture.  This
+    DAG should run nightly to capture threads between archive periods. By using the upsert logic of the
     weaviate_import decorator any existing documents that have been updated will be removed and re-added.
     """
 
-    slack_docs = task(slack.extract_slack, retries=3).expand(source=slack_channel_sources)
+    slack_docs = task(slack.extract_slack).expand(source=slack_channel_sources)
 
     split_md_docs = task(split.split_markdown).expand(dfs=[slack_docs])
 
     task.weaviate_import(
         ingest.import_upsert_data,
         weaviate_conn_id=_WEAVIATE_CONN_ID,
-        retries=10,
-        retry_delay=30,
-    ).partial(class_name=WEAVIATE_CLASS, primary_key="docLink").expand(dfs=[split_md_docs])
+    ).partial(
+        class_name=WEAVIATE_CLASS, primary_key="docLink"
+    ).expand(dfs=[split_md_docs])
 
 
 ask_astro_load_slack()

airflow/dags/ingestion/ask-astro-load-stackoverflow.py

@@ -16,17 +16,27 @@
     "airflow",
 ]
 
+default_args = {"retries": 3, "retry_delay": 30}
 
-@dag(schedule_interval=None, start_date=datetime(2023, 9, 27), catchup=False, is_paused_upon_creation=True)
+schedule_interval = "0 5 * * *" if ask_astro_env == "prod" else None
+
+
+@dag(
+    schedule_interval=schedule_interval,
+    start_date=datetime(2023, 9, 27),
+    catchup=False,
+    is_paused_upon_creation=True,
+    default_args=default_args,
+)
 def ask_astro_load_stackoverflow():
     """
-    This DAG performs incremental load for any new docs.  Initial load via ask_astro_load_bulk imported
-    data from a point-in-time data capture.  By using the upsert logic of the weaviate_import decorator
+    This DAG performs incremental load for any new docs. Initial load via ask_astro_load_bulk imported
+    data from a point-in-time data capture. By using the upsert logic of the weaviate_import decorator
     any existing documents that have been updated will be removed and re-added.
     """
 
     stack_overflow_docs = (
-        task(stack_overflow.extract_stack_overflow_archive, retries=3)
+        task(stack_overflow.extract_stack_overflow)
         .partial(stackoverflow_cutoff_date=stackoverflow_cutoff_date)
         .expand(tag=stackoverflow_tags)
     )
@@ -36,9 +46,9 @@ def ask_astro_load_stackoverflow():
     task.weaviate_import(
         ingest.import_upsert_data,
         weaviate_conn_id=_WEAVIATE_CONN_ID,
-        retries=10,
-        retry_delay=30,
-    ).partial(class_name=WEAVIATE_CLASS, primary_key="docLink").expand(dfs=[split_md_docs])
+    ).partial(
+        class_name=WEAVIATE_CLASS, primary_key="docLink"
+    ).expand(dfs=[split_md_docs])
 
 
 ask_astro_load_stackoverflow()