feat(scrape_pacer_free_opinions): apply task recap_document_into_opinions

cl/corpus_importer/management/commands/recap_into_opinions.py

@@ -1,52 +1,12 @@
-from asgiref.sync import async_to_sync
 from django.conf import settings
 from django.core.management import BaseCommand
 from django.db.models import Q
-from eyecite.tokenizers import HyperscanTokenizer
-from httpx import (
-    HTTPStatusError,
-    NetworkError,
-    RemoteProtocolError,
-    Response,
-    TimeoutException,
-)
-
-from cl.corpus_importer.tasks import ingest_recap_document
+
+from cl.corpus_importer.tasks import recap_document_into_opinions
 from cl.lib.celery_utils import CeleryThrottle
 from cl.lib.command_utils import logger
-from cl.lib.decorators import retry
-from cl.lib.microservice_utils import microservice
 from cl.search.models import SOURCES, Court, OpinionCluster, RECAPDocument
 
-HYPERSCAN_TOKENIZER = HyperscanTokenizer(cache_dir=".hyperscan")
-
-
-@retry(
-    ExceptionToCheck=(
-        NetworkError,
-        TimeoutException,
-        RemoteProtocolError,
-        HTTPStatusError,
-    ),
-    tries=3,
-    delay=5,
-    backoff=2,
-    logger=logger,
-)
-def extract_recap_document(rd: RECAPDocument) -> Response:
-    """Call recap-extract from doctor with retries
-
-    :param rd: the recap document to extract
-    :return: Response object
-    """
-    response = async_to_sync(microservice)(
-        service="recap-extract",
-        item=rd,
-        params={"strip_margin": True},
-    )
-    response.raise_for_status()
-    return response
-
 
 def import_opinions_from_recap(
     jurisdiction: str | None = None,
@@ -89,6 +49,8 @@ def import_opinions_from_recap(
 
         # Manually select the replica db which has an addt'l index added to
         # improve this query.
+        # Since we don't have scrapers for FD courts, the last documents
+        # that are not from SOURCES.RECAP should be from Harvard or other import
         latest_date_filed = (
             OpinionCluster.objects.using(db_connection)
             .filter(docket__court=court)
@@ -97,7 +59,7 @@ def import_opinions_from_recap(
             .values_list("date_filed", flat=True)
             .first()
         )
-        if latest_date_filed == None:
+        if latest_date_filed is None:
             logger.error(
                 msg=f"Court {court.id} has no opinion clusters for recap import"
             )
@@ -121,8 +83,8 @@ def import_opinions_from_recap(
                 f"{count}: Importing rd {recap_document.id} in {court.id}"
             )
             throttle.maybe_wait()
-            ingest_recap_document.apply_async(
-                args=[recap_document.id, add_to_solr], queue=queue
+            recap_document_into_opinions.apply_async(
+                args=[{}, recap_document.id, add_to_solr], queue=queue
             )
             count += 1
             if total_count > 0 and count >= total_count:

cl/corpus_importer/management/commands/scrape_pacer_free_opinions.py

@@ -23,6 +23,7 @@
     get_and_save_free_document_report,
     mark_court_done_on_date,
     process_free_opinion_result,
+    recap_document_into_opinions,
 )
 from cl.corpus_importer.utils import CycleChecker
 from cl.lib.argparse_types import valid_date
@@ -339,7 +340,6 @@ def get_pdfs(
                 f"before starting the next cycle."
             )
             time.sleep(2)
-
         logger.info(f"Processing row id: {row.id} from {row.court_id}")
         c = chain(
             process_free_opinion_result.si(
@@ -348,8 +348,14 @@ def get_pdfs(
                 cnt,
             ).set(queue=q),
             get_and_process_free_pdf.s(row.pk, row.court_id).set(queue=q),
+            # `recap_document_into_opinions` uses a different doctor extraction
+            # endpoint, so it doesn't depend on the document's content
+            # being extracted on `get_and_process_free_pdf`, where it's
+            # only extracted if it doesn't require OCR
+            recap_document_into_opinions.s().set(queue=q),
             delete_pacer_row.s(row.pk).set(queue=q),
         )
+
         if index:
             c = c | add_items_to_solr.s("search.RECAPDocument").set(queue=q)
         c.apply_async()

cl/corpus_importer/tasks.py

@@ -2720,7 +2720,7 @@ def query_and_save_list_of_creditors(
     backoff=2,
     logger=logger,
 )
-def extract_recap_document(rd: RECAPDocument) -> Response:
+def extract_recap_document_for_opinions(rd: RECAPDocument) -> Response:
     """Call recap-extract from doctor with retries
 
     :param rd: the recap document to extract
@@ -2736,17 +2736,26 @@ def extract_recap_document(rd: RECAPDocument) -> Response:
 
 
 @app.task(bind=True, max_retries=5, ignore_result=True)
-def ingest_recap_document(
+def recap_document_into_opinions(
     self,
-    recap_document_id: int,
-    add_to_solr: bool,
-) -> None:
+    task_data: Optional[TaskData] = None,
+    recap_document_id: Optional[int] = None,
+    add_to_solr: bool = False,
+) -> Optional[TaskData]:
     """Ingest recap document into Opinions
 
+    :param task_data: dictionary that will contain the recap_document_id,
+        if called inside a chain() on the scraper_pacer_free_opinions
+        command. This task should be chained after the PDF has
+        been downloaded from PACER
     :param recap_document_id: The document id to inspect and import
     :param add_to_solr: Whether to add to solr
-    :return:None
+
+    :return: The same `task_data` that came as input
     """
+    if not recap_document_id and task_data:
+        recap_document_id = task_data["rd_pk"]
+
     logger.info(f"Importing recap document {recap_document_id}")
     recap_document = (
         RECAPDocument.objects.select_related("docket_entry__docket")
@@ -2763,17 +2772,27 @@ def ingest_recap_document(
         .get(id=recap_document_id)
     )
     docket = recap_document.docket_entry.docket
-    if recap_document.docket_entry.docket.court.jurisdiction == "FD":
+
+    jurisdiction = recap_document.docket_entry.docket.court.jurisdiction
+    court_id = recap_document.docket_entry.docket.court.id
+    # `dcd` has a regular juriscraper scraper. Avoid duplicates
+    if court_id in ["dcd", "orld"] or jurisdiction not in [
+        Court.FEDERAL_DISTRICT,
+        Court.FEDERAL_BANKRUPTCY,
+    ]:
+        return task_data
+
+    if jurisdiction == Court.FEDERAL_DISTRICT:
         if "cv" not in docket.docket_number.lower():
             logger.info("Skipping non-civil opinion in district court")
-            return
+            return task_data
 
     ops = Opinion.objects.filter(sha1=recap_document.sha1)
     if ops.count() > 0:
         logger.info(f"Skipping previously imported opinion: {ops[0].id}")
-        return
+        return task_data
 
-    response = extract_recap_document(rd=recap_document)
+    response = extract_recap_document_for_opinions(rd=recap_document)
     r = response.json()
 
     try:
@@ -2792,7 +2811,7 @@ def ingest_recap_document(
     case_law_citations = filter_out_non_case_law_citations(citations)
     if len(case_law_citations) == 0:
         logger.info(f"No citation found for rd: {recap_document.id}")
-        return
+        return task_data
 
     with transaction.atomic():
         cluster = OpinionCluster.objects.create(
@@ -2823,3 +2842,5 @@ def ingest_recap_document(
                 cluster.id
             )
         )
+    # Return input task data to preserve the chain in scrape_pacer_free_opinion
+    return task_data