diff --git a/surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py b/surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py
index 3b46b6437..8f447b842 100644
--- a/surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py
+++ b/surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py
@@ -1,49 +1,74 @@
-"""
-Confluence connector indexer.
-
-Provides real-time document status updates during indexing using a two-phase approach:
-- Phase 1: Create all documents with PENDING status (visible in UI immediately)
-- Phase 2: Process each document one by one (PENDING → PROCESSING → READY/FAILED)
-"""
+"""Confluence connector indexer using the unified parallel indexing pipeline."""
import contextlib
-import time
from collections.abc import Awaitable, Callable
-from datetime import datetime
from sqlalchemy.exc import SQLAlchemyError
from sqlalchemy.ext.asyncio import AsyncSession
from app.connectors.confluence_history import ConfluenceHistoryConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
from app.services.llm_service import get_user_long_context_llm
from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
- create_document_chunks,
- embed_text,
- generate_content_hash,
- generate_document_summary,
- generate_unique_identifier_hash,
-)
from .base import (
calculate_date_range,
- check_document_by_unique_identifier,
check_duplicate_document_by_hash,
get_connector_by_id,
- get_current_timestamp,
logger,
- safe_set_chunks,
update_connector_last_indexed,
)
-# Type hint for heartbeat callback
HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds
HEARTBEAT_INTERVAL_SECONDS = 30
+def _build_connector_doc(
+ page: dict,
+ full_content: str,
+ *,
+ connector_id: int,
+ search_space_id: int,
+ user_id: str,
+ enable_summary: bool,
+) -> ConnectorDocument:
+ """Map a raw Confluence page dict to a ConnectorDocument."""
+ page_id = page.get("id", "")
+ page_title = page.get("title", "")
+ space_id = page.get("spaceId", "")
+ comment_count = len(page.get("comments", []))
+
+ metadata = {
+ "page_id": page_id,
+ "page_title": page_title,
+ "space_id": space_id,
+ "comment_count": comment_count,
+ "connector_id": connector_id,
+ "document_type": "Confluence Page",
+ "connector_type": "Confluence",
+ }
+
+ fallback_summary = (
+ f"Confluence Page: {page_title}\n\nSpace ID: {space_id}\n\n{full_content}"
+ )
+
+ return ConnectorDocument(
+ title=page_title,
+ source_markdown=full_content,
+ unique_id=page_id,
+ document_type=DocumentType.CONFLUENCE_CONNECTOR,
+ search_space_id=search_space_id,
+ connector_id=connector_id,
+ created_by_id=user_id,
+ should_summarize=enable_summary,
+ fallback_summary=fallback_summary,
+ metadata=metadata,
+ )
+
+
async def index_confluence_pages(
session: AsyncSession,
connector_id: int,
@@ -53,26 +78,9 @@ async def index_confluence_pages(
end_date: str | None = None,
update_last_indexed: bool = True,
on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
- """
- Index Confluence pages and comments.
-
- Args:
- session: Database session
- connector_id: ID of the Confluence connector
- search_space_id: ID of the search space to store documents in
- user_id: User ID
- start_date: Start date for indexing (YYYY-MM-DD format)
- end_date: End date for indexing (YYYY-MM-DD format)
- update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
- on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-
- Returns:
- Tuple containing (number of documents indexed, error message or None)
- """
+) -> tuple[int, int, str | None]:
+ """Index Confluence pages and comments."""
task_logger = TaskLoggingService(session, search_space_id)
-
- # Log task start
log_entry = await task_logger.log_task_start(
task_name="confluence_pages_indexing",
source="connector_indexing_task",
@@ -86,7 +94,6 @@ async def index_confluence_pages(
)
try:
- # Get the connector from the database
connector = await get_connector_by_id(
session, connector_id, SearchSourceConnectorType.CONFLUENCE_CONNECTOR
)
@@ -98,9 +105,8 @@ async def index_confluence_pages(
"Connector not found",
{"error_type": "ConnectorNotFound"},
)
- return 0, f"Connector with ID {connector_id} not found"
+ return 0, 0, f"Connector with ID {connector_id} not found"
- # Initialize Confluence OAuth client
await task_logger.log_task_progress(
log_entry,
f"Initializing Confluence OAuth client for connector {connector_id}",
@@ -114,7 +120,6 @@ async def index_confluence_pages(
)
)
- # Calculate date range
start_date_str, end_date_str = calculate_date_range(
connector, start_date, end_date, default_days_back=365
)
@@ -129,19 +134,14 @@ async def index_confluence_pages(
},
)
- # Get pages within date range
try:
pages, error = await confluence_client.get_pages_by_date_range(
start_date=start_date_str, end_date=end_date_str, include_comments=True
)
if error:
- # Don't treat "No pages found" as an error that should stop indexing
if "No pages found" in error:
logger.info(f"No Confluence pages found: {error}")
- logger.info(
- "No pages found is not a critical error, continuing with update"
- )
if update_last_indexed:
await update_connector_last_indexed(
session, connector, update_last_indexed
@@ -156,11 +156,10 @@ async def index_confluence_pages(
f"No Confluence pages found in date range {start_date_str} to {end_date_str}",
{"pages_found": 0},
)
- # Close client before returning
if confluence_client:
with contextlib.suppress(Exception):
await confluence_client.close()
- return 0, None
+ return 0, 0, None
else:
logger.error(f"Failed to get Confluence pages: {error}")
await task_logger.log_task_failure(
@@ -169,36 +168,35 @@ async def index_confluence_pages(
"API Error",
{"error_type": "APIError"},
)
- # Close client on error
if confluence_client:
with contextlib.suppress(Exception):
await confluence_client.close()
- return 0, f"Failed to get Confluence pages: {error}"
+ return 0, 0, f"Failed to get Confluence pages: {error}"
logger.info(f"Retrieved {len(pages)} pages from Confluence API")
except Exception as e:
logger.error(f"Error fetching Confluence pages: {e!s}", exc_info=True)
- # Close client on error
if confluence_client:
with contextlib.suppress(Exception):
await confluence_client.close()
- return 0, f"Error fetching Confluence pages: {e!s}"
+ return 0, 0, f"Error fetching Confluence pages: {e!s}"
+
+ if not pages:
+ logger.info("No Confluence pages found for the specified date range")
+ if update_last_indexed:
+ await update_connector_last_indexed(
+ session, connector, update_last_indexed
+ )
+ await session.commit()
+ if confluence_client:
+ with contextlib.suppress(Exception):
+ await confluence_client.close()
+ return 0, 0, None
- # =======================================================================
- # PHASE 1: Analyze all pages, create pending documents
- # This makes ALL documents visible in the UI immediately with pending status
- # =======================================================================
- documents_indexed = 0
documents_skipped = 0
- documents_failed = 0
duplicate_content_count = 0
-
- # Heartbeat tracking - update notification periodically to prevent appearing stuck
- last_heartbeat_time = time.time()
-
- pages_to_process = [] # List of dicts with document and page data
- new_documents_created = False
+ connector_docs: list[ConnectorDocument] = []
for page in pages:
try:
@@ -213,12 +211,10 @@ async def index_confluence_pages(
documents_skipped += 1
continue
- # Extract page content
page_content = ""
if page.get("body") and page["body"].get("storage"):
page_content = page["body"]["storage"].get("value", "")
- # Add comments to content
comments = page.get("comments", [])
comments_content = ""
if comments:
@@ -235,61 +231,25 @@ async def index_confluence_pages(
comments_content += f"**Comment by {comment_author}** ({comment_date}):\n{comment_body}\n\n"
- # Combine page content with comments
full_content = f"# {page_title}\n\n{page_content}{comments_content}"
- if not full_content.strip():
+ if not page_content.strip() and not comments:
logger.warning(f"Skipping page with no content: {page_title}")
documents_skipped += 1
continue
- # Generate unique identifier hash for this Confluence page
- unique_identifier_hash = generate_unique_identifier_hash(
- DocumentType.CONFLUENCE_CONNECTOR, page_id, search_space_id
+ doc = _build_connector_doc(
+ page,
+ full_content,
+ connector_id=connector_id,
+ search_space_id=search_space_id,
+ user_id=user_id,
+ enable_summary=connector.enable_summary,
)
- # Generate content hash
- content_hash = generate_content_hash(full_content, search_space_id)
-
- # Check if document with this unique identifier already exists
- existing_document = await check_document_by_unique_identifier(
- session, unique_identifier_hash
- )
-
- comment_count = len(comments)
-
- if existing_document:
- # Document exists - check if content has changed
- if existing_document.content_hash == content_hash:
- # Ensure status is ready (might have been stuck in processing/pending)
- if not DocumentStatus.is_state(
- existing_document.status, DocumentStatus.READY
- ):
- existing_document.status = DocumentStatus.ready()
- documents_skipped += 1
- continue
-
- # Queue existing document for update (will be set to processing in Phase 2)
- pages_to_process.append(
- {
- "document": existing_document,
- "is_new": False,
- "full_content": full_content,
- "page_content": page_content,
- "content_hash": content_hash,
- "page_id": page_id,
- "page_title": page_title,
- "space_id": space_id,
- "comment_count": comment_count,
- }
- )
- continue
-
- # Document doesn't exist by unique_identifier_hash
- # Check if a document with the same content_hash exists (from another connector)
with session.no_autoflush:
duplicate_by_content = await check_duplicate_document_by_hash(
- session, content_hash
+ session, compute_content_hash(doc)
)
if duplicate_by_content:
@@ -302,151 +262,29 @@ async def index_confluence_pages(
documents_skipped += 1
continue
- # Create new document with PENDING status (visible in UI immediately)
- document = Document(
- search_space_id=search_space_id,
- title=page_title,
- document_type=DocumentType.CONFLUENCE_CONNECTOR,
- document_metadata={
- "page_id": page_id,
- "page_title": page_title,
- "space_id": space_id,
- "comment_count": comment_count,
- "connector_id": connector_id,
- },
- content="Pending...", # Placeholder until processed
- content_hash=unique_identifier_hash, # Temporary unique value - updated when ready
- unique_identifier_hash=unique_identifier_hash,
- embedding=None,
- chunks=[], # Empty at creation - safe for async
- status=DocumentStatus.pending(), # Pending until processing starts
- updated_at=get_current_timestamp(),
- created_by_id=user_id,
- connector_id=connector_id,
- )
- session.add(document)
- new_documents_created = True
-
- pages_to_process.append(
- {
- "document": document,
- "is_new": True,
- "full_content": full_content,
- "page_content": page_content,
- "content_hash": content_hash,
- "page_id": page_id,
- "page_title": page_title,
- "space_id": space_id,
- "comment_count": comment_count,
- }
- )
+ connector_docs.append(doc)
except Exception as e:
- logger.error(f"Error in Phase 1 for page: {e!s}", exc_info=True)
- documents_failed += 1
+ logger.error(f"Error building ConnectorDocument for page: {e!s}", exc_info=True)
+ documents_skipped += 1
continue
- # Commit all pending documents - they all appear in UI now
- if new_documents_created:
- logger.info(
- f"Phase 1: Committing {len([p for p in pages_to_process if p['is_new']])} pending documents"
- )
- await session.commit()
+ pipeline = IndexingPipelineService(session)
+ await pipeline.migrate_legacy_docs(connector_docs)
- # =======================================================================
- # PHASE 2: Process each document one by one
- # Each document transitions: pending → processing → ready/failed
- # =======================================================================
- logger.info(f"Phase 2: Processing {len(pages_to_process)} documents")
+ async def _get_llm(s: AsyncSession):
+ return await get_user_long_context_llm(s, user_id, search_space_id)
- for item in pages_to_process:
- # Send heartbeat periodically
- if on_heartbeat_callback:
- current_time = time.time()
- if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
- await on_heartbeat_callback(documents_indexed)
- last_heartbeat_time = current_time
+ _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+ connector_docs,
+ _get_llm,
+ max_concurrency=3,
+ on_heartbeat=on_heartbeat_callback,
+ heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
+ )
- document = item["document"]
- try:
- # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
- document.status = DocumentStatus.processing()
- await session.commit()
-
- # Heavy processing (LLM, embeddings, chunks)
- user_llm = await get_user_long_context_llm(
- session, user_id, search_space_id
- )
-
- if user_llm and connector.enable_summary:
- document_metadata = {
- "page_title": item["page_title"],
- "page_id": item["page_id"],
- "space_id": item["space_id"],
- "comment_count": item["comment_count"],
- "document_type": "Confluence Page",
- "connector_type": "Confluence",
- }
- (
- summary_content,
- summary_embedding,
- ) = await generate_document_summary(
- item["full_content"], user_llm, document_metadata
- )
- else:
- summary_content = f"Confluence Page: {item['page_title']}\n\nSpace ID: {item['space_id']}\n\n{item['full_content']}"
- summary_embedding = embed_text(summary_content)
-
- # Process chunks - using the full page content with comments
- chunks = await create_document_chunks(item["full_content"])
-
- # Update document to READY with actual content
- document.title = item["page_title"]
- document.content = summary_content
- document.content_hash = item["content_hash"]
- document.embedding = summary_embedding
- document.document_metadata = {
- "page_id": item["page_id"],
- "page_title": item["page_title"],
- "space_id": item["space_id"],
- "comment_count": item["comment_count"],
- "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
- "connector_id": connector_id,
- }
- await safe_set_chunks(session, document, chunks)
- document.updated_at = get_current_timestamp()
- document.status = DocumentStatus.ready()
-
- documents_indexed += 1
-
- # Batch commit every 10 documents (for ready status updates)
- if documents_indexed % 10 == 0:
- logger.info(
- f"Committing batch: {documents_indexed} Confluence pages processed so far"
- )
- await session.commit()
-
- except Exception as e:
- logger.error(
- f"Error processing page {item.get('page_title', 'Unknown')}: {e!s}",
- exc_info=True,
- )
- # Mark document as failed with reason (visible in UI)
- try:
- document.status = DocumentStatus.failed(str(e))
- document.updated_at = get_current_timestamp()
- except Exception as status_error:
- logger.error(
- f"Failed to update document status to failed: {status_error}"
- )
- documents_failed += 1
- continue # Skip this page and continue with others
-
- # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
- # This ensures the UI shows "Last indexed" instead of "Never indexed"
await update_connector_last_indexed(session, connector, update_last_indexed)
- # Final commit to ensure all documents are persisted (safety net)
logger.info(
f"Final commit: Total {documents_indexed} Confluence pages processed"
)
@@ -456,7 +294,6 @@ async def index_confluence_pages(
"Successfully committed all Confluence document changes to database"
)
except Exception as e:
- # Handle any remaining integrity errors gracefully (race conditions, etc.)
if (
"duplicate key value violates unique constraint" in str(e).lower()
or "uniqueviolationerror" in str(e).lower()
@@ -467,11 +304,9 @@ async def index_confluence_pages(
f"Rolling back and continuing. Error: {e!s}"
)
await session.rollback()
- # Don't fail the entire task - some documents may have been successfully indexed
else:
raise
- # Build warning message if there were issues
warning_parts = []
if duplicate_content_count > 0:
warning_parts.append(f"{duplicate_content_count} duplicate")
@@ -479,7 +314,6 @@ async def index_confluence_pages(
warning_parts.append(f"{documents_failed} failed")
warning_message = ", ".join(warning_parts) if warning_parts else None
- # Log success
await task_logger.log_task_success(
log_entry,
f"Successfully completed Confluence indexing for connector {connector_id}",
@@ -490,22 +324,19 @@ async def index_confluence_pages(
"duplicate_content_count": duplicate_content_count,
},
)
-
logger.info(
f"Confluence indexing completed: {documents_indexed} ready, "
f"{documents_skipped} skipped, {documents_failed} failed "
f"({duplicate_content_count} duplicate content)"
)
- # Close the client connection
if confluence_client:
await confluence_client.close()
- return documents_indexed, warning_message
+ return documents_indexed, documents_skipped, warning_message
except SQLAlchemyError as db_error:
await session.rollback()
- # Close client if it exists
if confluence_client:
with contextlib.suppress(Exception):
await confluence_client.close()
@@ -516,10 +347,9 @@ async def index_confluence_pages(
{"error_type": "SQLAlchemyError"},
)
logger.error(f"Database error: {db_error!s}", exc_info=True)
- return 0, f"Database error: {db_error!s}"
+ return 0, 0, f"Database error: {db_error!s}"
except Exception as e:
await session.rollback()
- # Close client if it exists
if confluence_client:
with contextlib.suppress(Exception):
await confluence_client.close()
@@ -530,4 +360,4 @@ async def index_confluence_pages(
{"error_type": type(e).__name__},
)
logger.error(f"Failed to index Confluence pages: {e!s}", exc_info=True)
- return 0, f"Failed to index Confluence pages: {e!s}"
+ return 0, 0, f"Failed to index Confluence pages: {e!s}"
diff --git a/surfsense_backend/app/tasks/connector_indexers/jira_indexer.py b/surfsense_backend/app/tasks/connector_indexers/jira_indexer.py
index 25491a8f6..6e370fc35 100644
--- a/surfsense_backend/app/tasks/connector_indexers/jira_indexer.py
+++ b/surfsense_backend/app/tasks/connector_indexers/jira_indexer.py
@@ -1,49 +1,80 @@
-"""
-Jira connector indexer.
-
-Provides real-time document status updates during indexing using a two-phase approach:
-- Phase 1: Create all documents with PENDING status (visible in UI immediately)
-- Phase 2: Process each document one by one (PENDING → PROCESSING → READY/FAILED)
-"""
+"""Jira connector indexer using the unified parallel indexing pipeline."""
import contextlib
-import time
from collections.abc import Awaitable, Callable
-from datetime import datetime
from sqlalchemy.exc import SQLAlchemyError
from sqlalchemy.ext.asyncio import AsyncSession
from app.connectors.jira_history import JiraHistoryConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
from app.services.llm_service import get_user_long_context_llm
from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
- create_document_chunks,
- embed_text,
- generate_content_hash,
- generate_document_summary,
- generate_unique_identifier_hash,
-)
from .base import (
calculate_date_range,
- check_document_by_unique_identifier,
check_duplicate_document_by_hash,
get_connector_by_id,
- get_current_timestamp,
logger,
- safe_set_chunks,
update_connector_last_indexed,
)
-# Type hint for heartbeat callback
HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds - update notification every 30 seconds
HEARTBEAT_INTERVAL_SECONDS = 30
+def _build_connector_doc(
+ issue: dict,
+ formatted_issue: dict,
+ issue_content: str,
+ *,
+ connector_id: int,
+ search_space_id: int,
+ user_id: str,
+ enable_summary: bool,
+) -> ConnectorDocument:
+ """Map a raw Jira issue dict to a ConnectorDocument."""
+ issue_id = issue.get("key", "")
+ issue_identifier = issue.get("key", "")
+ issue_title = issue.get("id", "")
+ state = formatted_issue.get("status", "Unknown")
+ priority = formatted_issue.get("priority", "Unknown")
+ comment_count = len(formatted_issue.get("comments", []))
+
+ metadata = {
+ "issue_id": issue_id,
+ "issue_identifier": issue_identifier,
+ "issue_title": issue_title,
+ "state": state,
+ "priority": priority,
+ "comment_count": comment_count,
+ "connector_id": connector_id,
+ "document_type": "Jira Issue",
+ "connector_type": "Jira",
+ }
+
+ fallback_summary = (
+ f"Jira Issue {issue_identifier}: {issue_title}\n\n"
+ f"Status: {state}\n\n{issue_content}"
+ )
+
+ return ConnectorDocument(
+ title=f"{issue_identifier}: {issue_title}",
+ source_markdown=issue_content,
+ unique_id=issue_id,
+ document_type=DocumentType.JIRA_CONNECTOR,
+ search_space_id=search_space_id,
+ connector_id=connector_id,
+ created_by_id=user_id,
+ should_summarize=enable_summary,
+ fallback_summary=fallback_summary,
+ metadata=metadata,
+ )
+
+
async def index_jira_issues(
session: AsyncSession,
connector_id: int,
@@ -53,26 +84,9 @@ async def index_jira_issues(
end_date: str | None = None,
update_last_indexed: bool = True,
on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
- """
- Index Jira issues and comments.
-
- Args:
- session: Database session
- connector_id: ID of the Jira connector
- search_space_id: ID of the search space to store documents in
- user_id: User ID
- start_date: Start date for indexing (YYYY-MM-DD format)
- end_date: End date for indexing (YYYY-MM-DD format)
- update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
- on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-
- Returns:
- Tuple containing (number of documents indexed, error message or None)
- """
+) -> tuple[int, int, str | None]:
+ """Index Jira issues and comments."""
task_logger = TaskLoggingService(session, search_space_id)
-
- # Log task start
log_entry = await task_logger.log_task_start(
task_name="jira_issues_indexing",
source="connector_indexing_task",
@@ -86,7 +100,6 @@ async def index_jira_issues(
)
try:
- # Get the connector from the database
connector = await get_connector_by_id(
session, connector_id, SearchSourceConnectorType.JIRA_CONNECTOR
)
@@ -98,24 +111,15 @@ async def index_jira_issues(
"Connector not found",
{"error_type": "ConnectorNotFound"},
)
- return 0, f"Connector with ID {connector_id} not found"
+ return 0, 0, f"Connector with ID {connector_id} not found"
- # Initialize Jira client with internal refresh capability
- # Token refresh will happen automatically when needed
await task_logger.log_task_progress(
log_entry,
f"Initializing Jira client for connector {connector_id}",
{"stage": "client_initialization"},
)
-
- logger.info(f"Initializing Jira client for connector {connector_id}")
-
- # Create connector with session and connector_id for internal refresh
- # Token refresh will happen automatically when needed
jira_client = JiraHistoryConnector(session=session, connector_id=connector_id)
- # Calculate date range
- # Handle "undefined" strings from frontend
if start_date == "undefined" or start_date == "":
start_date = None
if end_date == "undefined" or end_date == "":
@@ -135,19 +139,14 @@ async def index_jira_issues(
},
)
- # Get issues within date range
try:
issues, error = await jira_client.get_issues_by_date_range(
start_date=start_date_str, end_date=end_date_str, include_comments=True
)
if error:
- # Don't treat "No issues found" as an error that should stop indexing
if "No issues found" in error:
logger.info(f"No Jira issues found: {error}")
- logger.info(
- "No issues found is not a critical error, continuing with update"
- )
if update_last_indexed:
await update_connector_last_indexed(
session, connector, update_last_indexed
@@ -162,7 +161,8 @@ async def index_jira_issues(
f"No Jira issues found in date range {start_date_str} to {end_date_str}",
{"issues_found": 0},
)
- return 0, None
+ await jira_client.close()
+ return 0, 0, None
else:
logger.error(f"Failed to get Jira issues: {error}")
await task_logger.log_task_failure(
@@ -171,29 +171,30 @@ async def index_jira_issues(
"API Error",
{"error_type": "APIError"},
)
- return 0, f"Failed to get Jira issues: {error}"
+ await jira_client.close()
+ return 0, 0, f"Failed to get Jira issues: {error}"
logger.info(f"Retrieved {len(issues)} issues from Jira API")
except Exception as e:
logger.error(f"Error fetching Jira issues: {e!s}", exc_info=True)
- return 0, f"Error fetching Jira issues: {e!s}"
+ await jira_client.close()
+ return 0, 0, f"Error fetching Jira issues: {e!s}"
- # =======================================================================
- # PHASE 1: Analyze all issues, create pending documents
- # This makes ALL documents visible in the UI immediately with pending status
- # =======================================================================
- documents_indexed = 0
+ if not issues:
+ logger.info("No Jira issues found for the specified date range")
+ if update_last_indexed:
+ await update_connector_last_indexed(
+ session, connector, update_last_indexed
+ )
+ await session.commit()
+ await jira_client.close()
+ return 0, 0, None
+
+ connector_docs: list[ConnectorDocument] = []
documents_skipped = 0
- documents_failed = 0
duplicate_content_count = 0
- # Heartbeat tracking - update notification periodically to prevent appearing stuck
- last_heartbeat_time = time.time()
-
- issues_to_process = [] # List of dicts with document and issue data
- new_documents_created = False
-
for issue in issues:
try:
issue_id = issue.get("key")
@@ -207,10 +208,7 @@ async def index_jira_issues(
documents_skipped += 1
continue
- # Format the issue for better readability
formatted_issue = jira_client.format_issue(issue)
-
- # Convert to markdown
issue_content = jira_client.format_issue_to_markdown(formatted_issue)
if not issue_content:
@@ -220,53 +218,19 @@ async def index_jira_issues(
documents_skipped += 1
continue
- # Generate unique identifier hash for this Jira issue
- unique_identifier_hash = generate_unique_identifier_hash(
- DocumentType.JIRA_CONNECTOR, issue_id, search_space_id
+ doc = _build_connector_doc(
+ issue,
+ formatted_issue,
+ issue_content,
+ connector_id=connector_id,
+ search_space_id=search_space_id,
+ user_id=user_id,
+ enable_summary=connector.enable_summary,
)
- # Generate content hash
- content_hash = generate_content_hash(issue_content, search_space_id)
-
- # Check if document with this unique identifier already exists
- existing_document = await check_document_by_unique_identifier(
- session, unique_identifier_hash
- )
-
- comment_count = len(formatted_issue.get("comments", []))
-
- if existing_document:
- # Document exists - check if content has changed
- if existing_document.content_hash == content_hash:
- # Ensure status is ready (might have been stuck in processing/pending)
- if not DocumentStatus.is_state(
- existing_document.status, DocumentStatus.READY
- ):
- existing_document.status = DocumentStatus.ready()
- documents_skipped += 1
- continue
-
- # Queue existing document for update (will be set to processing in Phase 2)
- issues_to_process.append(
- {
- "document": existing_document,
- "is_new": False,
- "issue_content": issue_content,
- "content_hash": content_hash,
- "issue_id": issue_id,
- "issue_identifier": issue_identifier,
- "issue_title": issue_title,
- "formatted_issue": formatted_issue,
- "comment_count": comment_count,
- }
- )
- continue
-
- # Document doesn't exist by unique_identifier_hash
- # Check if a document with the same content_hash exists (from another connector)
with session.no_autoflush:
duplicate_by_content = await check_duplicate_document_by_hash(
- session, content_hash
+ session, compute_content_hash(doc)
)
if duplicate_by_content:
@@ -279,160 +243,37 @@ async def index_jira_issues(
documents_skipped += 1
continue
- # Create new document with PENDING status (visible in UI immediately)
- document = Document(
- search_space_id=search_space_id,
- title=f"{issue_identifier}: {issue_title}",
- document_type=DocumentType.JIRA_CONNECTOR,
- document_metadata={
- "issue_id": issue_id,
- "issue_identifier": issue_identifier,
- "issue_title": issue_title,
- "state": formatted_issue.get("status", "Unknown"),
- "comment_count": comment_count,
- "connector_id": connector_id,
- },
- content="Pending...", # Placeholder until processed
- content_hash=unique_identifier_hash, # Temporary unique value - updated when ready
- unique_identifier_hash=unique_identifier_hash,
- embedding=None,
- chunks=[], # Empty at creation - safe for async
- status=DocumentStatus.pending(), # Pending until processing starts
- updated_at=get_current_timestamp(),
- created_by_id=user_id,
- connector_id=connector_id,
- )
- session.add(document)
- new_documents_created = True
-
- issues_to_process.append(
- {
- "document": document,
- "is_new": True,
- "issue_content": issue_content,
- "content_hash": content_hash,
- "issue_id": issue_id,
- "issue_identifier": issue_identifier,
- "issue_title": issue_title,
- "formatted_issue": formatted_issue,
- "comment_count": comment_count,
- }
- )
-
- except Exception as e:
- logger.error(f"Error in Phase 1 for issue: {e!s}", exc_info=True)
- documents_failed += 1
- continue
-
- # Commit all pending documents - they all appear in UI now
- if new_documents_created:
- logger.info(
- f"Phase 1: Committing {len([i for i in issues_to_process if i['is_new']])} pending documents"
- )
- await session.commit()
-
- # =======================================================================
- # PHASE 2: Process each document one by one
- # Each document transitions: pending → processing → ready/failed
- # =======================================================================
- logger.info(f"Phase 2: Processing {len(issues_to_process)} documents")
-
- for item in issues_to_process:
- # Send heartbeat periodically
- if on_heartbeat_callback:
- current_time = time.time()
- if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
- await on_heartbeat_callback(documents_indexed)
- last_heartbeat_time = current_time
-
- document = item["document"]
- try:
- # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
- document.status = DocumentStatus.processing()
- await session.commit()
-
- # Heavy processing (LLM, embeddings, chunks)
- user_llm = await get_user_long_context_llm(
- session, user_id, search_space_id
- )
-
- if user_llm and connector.enable_summary:
- document_metadata = {
- "issue_key": item["issue_identifier"],
- "issue_title": item["issue_title"],
- "status": item["formatted_issue"].get("status", "Unknown"),
- "priority": item["formatted_issue"].get("priority", "Unknown"),
- "comment_count": item["comment_count"],
- "document_type": "Jira Issue",
- "connector_type": "Jira",
- }
- (
- summary_content,
- summary_embedding,
- ) = await generate_document_summary(
- item["issue_content"], user_llm, document_metadata
- )
- else:
- summary_content = f"Jira Issue {item['issue_identifier']}: {item['issue_title']}\n\n{item['issue_content']}"
- summary_embedding = embed_text(summary_content)
-
- # Process chunks - using the full issue content with comments
- chunks = await create_document_chunks(item["issue_content"])
-
- # Update document to READY with actual content
- document.title = f"{item['issue_identifier']}: {item['issue_title']}"
- document.content = summary_content
- document.content_hash = item["content_hash"]
- document.embedding = summary_embedding
- document.document_metadata = {
- "issue_id": item["issue_id"],
- "issue_identifier": item["issue_identifier"],
- "issue_title": item["issue_title"],
- "state": item["formatted_issue"].get("status", "Unknown"),
- "comment_count": item["comment_count"],
- "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
- "connector_id": connector_id,
- }
- await safe_set_chunks(session, document, chunks)
- document.updated_at = get_current_timestamp()
- document.status = DocumentStatus.ready()
-
- documents_indexed += 1
-
- # Batch commit every 10 documents (for ready status updates)
- if documents_indexed % 10 == 0:
- logger.info(
- f"Committing batch: {documents_indexed} Jira issues processed so far"
- )
- await session.commit()
+ connector_docs.append(doc)
except Exception as e:
logger.error(
- f"Error processing issue {item.get('issue_identifier', 'Unknown')}: {e!s}",
+ f"Error building ConnectorDocument for issue {issue_identifier}: {e!s}",
exc_info=True,
)
- # Mark document as failed with reason (visible in UI)
- try:
- document.status = DocumentStatus.failed(str(e))
- document.updated_at = get_current_timestamp()
- except Exception as status_error:
- logger.error(
- f"Failed to update document status to failed: {status_error}"
- )
- documents_failed += 1
- continue # Skip this issue and continue with others
+ documents_skipped += 1
+ continue
+
+ pipeline = IndexingPipelineService(session)
+ await pipeline.migrate_legacy_docs(connector_docs)
+
+ async def _get_llm(s: AsyncSession):
+ return await get_user_long_context_llm(s, user_id, search_space_id)
+
+ _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+ connector_docs,
+ _get_llm,
+ max_concurrency=3,
+ on_heartbeat=on_heartbeat_callback,
+ heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
+ )
- # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
- # This ensures the UI shows "Last indexed" instead of "Never indexed"
await update_connector_last_indexed(session, connector, update_last_indexed)
- # Final commit to ensure all documents are persisted (safety net)
logger.info(f"Final commit: Total {documents_indexed} Jira issues processed")
try:
await session.commit()
logger.info("Successfully committed all JIRA document changes to database")
except Exception as e:
- # Handle any remaining integrity errors gracefully (race conditions, etc.)
if (
"duplicate key value violates unique constraint" in str(e).lower()
or "uniqueviolationerror" in str(e).lower()
@@ -447,7 +288,6 @@ async def index_jira_issues(
else:
raise
- # Build warning message if there were issues
warning_parts = []
if duplicate_content_count > 0:
warning_parts.append(f"{duplicate_content_count} duplicate")
@@ -455,7 +295,6 @@ async def index_jira_issues(
warning_parts.append(f"{documents_failed} failed")
warning_message = ", ".join(warning_parts) if warning_parts else None
- # Log success
await task_logger.log_task_success(
log_entry,
f"Successfully completed JIRA indexing for connector {connector_id}",
@@ -466,17 +305,13 @@ async def index_jira_issues(
"duplicate_content_count": duplicate_content_count,
},
)
-
logger.info(
f"JIRA indexing completed: {documents_indexed} ready, "
f"{documents_skipped} skipped, {documents_failed} failed "
f"({duplicate_content_count} duplicate content)"
)
-
- # Clean up the connector
await jira_client.close()
-
- return documents_indexed, warning_message
+ return documents_indexed, documents_skipped, warning_message
except SQLAlchemyError as db_error:
await session.rollback()
@@ -487,11 +322,10 @@ async def index_jira_issues(
{"error_type": "SQLAlchemyError"},
)
logger.error(f"Database error: {db_error!s}", exc_info=True)
- # Clean up the connector in case of error
if "jira_client" in locals():
with contextlib.suppress(Exception):
await jira_client.close()
- return 0, f"Database error: {db_error!s}"
+ return 0, 0, f"Database error: {db_error!s}"
except Exception as e:
await session.rollback()
await task_logger.log_task_failure(
@@ -501,8 +335,7 @@ async def index_jira_issues(
{"error_type": type(e).__name__},
)
logger.error(f"Failed to index JIRA issues: {e!s}", exc_info=True)
- # Clean up the connector in case of error
if "jira_client" in locals():
with contextlib.suppress(Exception):
await jira_client.close()
- return 0, f"Failed to index JIRA issues: {e!s}"
+ return 0, 0, f"Failed to index JIRA issues: {e!s}"
diff --git a/surfsense_backend/tests/unit/connector_indexers/test_confluence_parallel.py b/surfsense_backend/tests/unit/connector_indexers/test_confluence_parallel.py
new file mode 100644
index 000000000..d11c88b64
--- /dev/null
+++ b/surfsense_backend/tests/unit/connector_indexers/test_confluence_parallel.py
@@ -0,0 +1,373 @@
+"""Tests for Confluence indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.confluence_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.confluence_indexer import (
+ _build_connector_doc,
+ index_confluence_pages,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_page(
+ page_id: str = "p1",
+ title: str = "Home",
+ space_id: str = "S1",
+ body: str = "Hello
",
+ comments=None,
+):
+ return {
+ "id": page_id,
+ "title": title,
+ "spaceId": space_id,
+ "body": {"storage": {"value": body}},
+ "comments": comments or [],
+ }
+
+
+def _to_markdown(page: dict) -> str:
+ page_title = page.get("title", "")
+ page_content = page.get("body", {}).get("storage", {}).get("value", "")
+ comments = page.get("comments", [])
+ comments_content = ""
+ if comments:
+ comments_content = "\n\n## Comments\n\n"
+ for comment in comments:
+ comment_body = (
+ comment.get("body", {}).get("storage", {}).get("value", "")
+ )
+ comment_author = comment.get("version", {}).get("authorId", "Unknown")
+ comment_date = comment.get("version", {}).get("createdAt", "")
+ comments_content += (
+ f"**Comment by {comment_author}** ({comment_date}):\n"
+ f"{comment_body}\n\n"
+ )
+ return f"# {page_title}\n\n{page_content}{comments_content}"
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+ page = _make_page(
+ page_id="abc-123",
+ title="Engineering Handbook",
+ space_id="ENG",
+ comments=[{"id": "c1"}],
+ )
+ markdown = _to_markdown(page)
+
+ doc = _build_connector_doc(
+ page,
+ markdown,
+ connector_id=_CONNECTOR_ID,
+ search_space_id=_SEARCH_SPACE_ID,
+ user_id=_USER_ID,
+ enable_summary=True,
+ )
+
+ assert doc.title == "Engineering Handbook"
+ assert doc.unique_id == "abc-123"
+ assert doc.document_type == DocumentType.CONFLUENCE_CONNECTOR
+ assert doc.source_markdown == markdown
+ assert doc.search_space_id == _SEARCH_SPACE_ID
+ assert doc.connector_id == _CONNECTOR_ID
+ assert doc.created_by_id == _USER_ID
+ assert doc.should_summarize is True
+ assert doc.metadata["page_id"] == "abc-123"
+ assert doc.metadata["page_title"] == "Engineering Handbook"
+ assert doc.metadata["space_id"] == "ENG"
+ assert doc.metadata["comment_count"] == 1
+ assert doc.metadata["connector_id"] == _CONNECTOR_ID
+ assert doc.metadata["document_type"] == "Confluence Page"
+ assert doc.metadata["connector_type"] == "Confluence"
+ assert doc.fallback_summary is not None
+ assert "Engineering Handbook" in doc.fallback_summary
+ assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+ doc = _build_connector_doc(
+ _make_page(),
+ _to_markdown(_make_page()),
+ connector_id=_CONNECTOR_ID,
+ search_space_id=_SEARCH_SPACE_ID,
+ user_id=_USER_ID,
+ enable_summary=False,
+ )
+ assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-7
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+ c = MagicMock()
+ c.config = {"access_token": "tok"}
+ c.enable_summary = enable_summary
+ c.last_indexed_at = None
+ return c
+
+
+def _mock_confluence_client(pages=None, error=None):
+ client = MagicMock()
+ client.get_pages_by_date_range = AsyncMock(
+ return_value=(pages if pages is not None else [], error),
+ )
+ client.close = AsyncMock()
+ return client
+
+
+@pytest.fixture
+def confluence_mocks(monkeypatch):
+ mock_session = AsyncMock()
+ mock_session.no_autoflush = MagicMock()
+
+ mock_connector = _mock_connector()
+ monkeypatch.setattr(
+ _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+ )
+
+ confluence_client = _mock_confluence_client(pages=[_make_page()])
+ monkeypatch.setattr(
+ _mod, "ConfluenceHistoryConnector", MagicMock(return_value=confluence_client),
+ )
+
+ monkeypatch.setattr(
+ _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+ )
+ monkeypatch.setattr(
+ _mod, "update_connector_last_indexed", AsyncMock(),
+ )
+ monkeypatch.setattr(
+ _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+ )
+
+ mock_task_logger = MagicMock()
+ mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+ mock_task_logger.log_task_progress = AsyncMock()
+ mock_task_logger.log_task_success = AsyncMock()
+ mock_task_logger.log_task_failure = AsyncMock()
+ monkeypatch.setattr(
+ _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+ )
+
+ batch_mock = AsyncMock(return_value=([], 1, 0))
+ pipeline_mock = MagicMock()
+ pipeline_mock.index_batch_parallel = batch_mock
+ pipeline_mock.migrate_legacy_docs = AsyncMock()
+ monkeypatch.setattr(
+ _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+ )
+
+ return {
+ "session": mock_session,
+ "connector": mock_connector,
+ "confluence_client": confluence_client,
+ "task_logger": mock_task_logger,
+ "pipeline_mock": pipeline_mock,
+ "batch_mock": batch_mock,
+ }
+
+
+async def _run_index(mocks, **overrides):
+ return await index_confluence_pages(
+ session=mocks["session"],
+ connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+ search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+ user_id=overrides.get("user_id", _USER_ID),
+ start_date=overrides.get("start_date", "2025-01-01"),
+ end_date=overrides.get("end_date", "2025-12-31"),
+ update_last_indexed=overrides.get("update_last_indexed", True),
+ on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+ )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_page_calls_pipeline_and_returns_indexed_count(confluence_mocks):
+ indexed, skipped, warning = await _run_index(confluence_mocks)
+ assert indexed == 1
+ assert skipped == 0
+ assert warning is None
+
+ confluence_mocks["batch_mock"].assert_called_once()
+ connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert connector_docs[0].document_type == DocumentType.CONFLUENCE_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(confluence_mocks):
+ await _run_index(confluence_mocks)
+ call_kwargs = confluence_mocks["batch_mock"].call_args[1]
+ assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(confluence_mocks):
+ await _run_index(confluence_mocks)
+ confluence_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Page skipping (missing id/title/content)
+# ---------------------------------------------------------------------------
+
+
+async def test_pages_with_missing_id_are_skipped(confluence_mocks):
+ pages = [
+ _make_page(page_id="p1", title="Valid"),
+ _make_page(page_id="", title="Missing id"),
+ ]
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ pages,
+ None,
+ )
+ _, skipped, _ = await _run_index(confluence_mocks)
+ connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+async def test_pages_with_missing_title_are_skipped(confluence_mocks):
+ pages = [
+ _make_page(page_id="p1", title="Valid"),
+ _make_page(page_id="p2", title=""),
+ ]
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ pages,
+ None,
+ )
+ _, skipped, _ = await _run_index(confluence_mocks)
+ connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+async def test_pages_with_no_content_are_skipped(confluence_mocks):
+ pages = [
+ _make_page(page_id="p1", title="Valid", body="ok
"),
+ _make_page(page_id="p2", title="Empty", body=""),
+ ]
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ pages,
+ None,
+ )
+ _, skipped, _ = await _run_index(confluence_mocks)
+ connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_pages_are_skipped(confluence_mocks, monkeypatch):
+ pages = [
+ _make_page(page_id="p1", title="One"),
+ _make_page(page_id="p2", title="Two"),
+ ]
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ pages,
+ None,
+ )
+
+ call_count = 0
+
+ async def _check_dup(session, content_hash):
+ nonlocal call_count
+ call_count += 1
+ if call_count == 2:
+ dup = MagicMock()
+ dup.id = 99
+ dup.document_type = "OTHER"
+ return dup
+ return None
+
+ monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+ _, skipped, _ = await _run_index(confluence_mocks)
+ connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(confluence_mocks):
+ heartbeat_cb = AsyncMock()
+ await _run_index(confluence_mocks, on_heartbeat_callback=heartbeat_cb)
+ call_kwargs = confluence_mocks["batch_mock"].call_args[1]
+ assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Empty pages and no-data success tuple
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_pages_returns_zero_tuple(confluence_mocks):
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ [],
+ None,
+ )
+ indexed, skipped, warning = await _run_index(confluence_mocks)
+ assert indexed == 0
+ assert skipped == 0
+ assert warning is None
+ confluence_mocks["batch_mock"].assert_not_called()
+
+
+async def test_no_pages_error_message_returns_success_tuple(confluence_mocks):
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ [],
+ "No pages found in date range",
+ )
+ indexed, skipped, warning = await _run_index(confluence_mocks)
+ assert indexed == 0
+ assert skipped == 0
+ assert warning is None
+
+
+async def test_api_error_still_returns_3_tuple(confluence_mocks):
+ confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+ [],
+ "API exploded",
+ )
+ result = await _run_index(confluence_mocks)
+ assert len(result) == 3
+ assert result[0] == 0
+ assert result[1] == 0
+ assert "Failed to get Confluence pages" in result[2]
+
+
+# ---------------------------------------------------------------------------
+# Slice 7: Failed docs warning
+# ---------------------------------------------------------------------------
+
+
+async def test_failed_docs_warning_in_result(confluence_mocks):
+ confluence_mocks["batch_mock"].return_value = ([], 0, 2)
+ _, _, warning = await _run_index(confluence_mocks)
+ assert warning is not None
+ assert "2 failed" in warning
diff --git a/surfsense_backend/tests/unit/connector_indexers/test_jira_parallel.py b/surfsense_backend/tests/unit/connector_indexers/test_jira_parallel.py
new file mode 100644
index 000000000..e8699c158
--- /dev/null
+++ b/surfsense_backend/tests/unit/connector_indexers/test_jira_parallel.py
@@ -0,0 +1,372 @@
+"""Tests for Jira indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.jira_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.jira_indexer import (
+ _build_connector_doc,
+ index_jira_issues,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_issue(
+ issue_key: str = "ENG-1",
+ issue_id: str = "10001",
+ title: str = "Fix login",
+):
+ return {"key": issue_key, "id": issue_id, "title": title}
+
+
+def _make_formatted_issue(
+ issue_key: str = "ENG-1",
+ issue_id: str = "10001",
+ title: str = "Fix login",
+ status: str = "In Progress",
+ priority: str = "High",
+ comments=None,
+):
+ return {
+ "key": issue_key,
+ "id": issue_id,
+ "title": title,
+ "status": status,
+ "priority": priority,
+ "comments": comments or [],
+ }
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+ issue = _make_issue(issue_key="ENG-42", issue_id="4242", title="Fix auth bug")
+ formatted = _make_formatted_issue(
+ issue_key="ENG-42",
+ issue_id="4242",
+ title="Fix auth bug",
+ status="Done",
+ priority="Urgent",
+ comments=[{"id": "c1"}],
+ )
+ markdown = "# ENG-42: Fix auth bug\n\nBody"
+
+ doc = _build_connector_doc(
+ issue,
+ formatted,
+ markdown,
+ connector_id=_CONNECTOR_ID,
+ search_space_id=_SEARCH_SPACE_ID,
+ user_id=_USER_ID,
+ enable_summary=True,
+ )
+
+ assert doc.title == "ENG-42: 4242"
+ assert doc.unique_id == "ENG-42"
+ assert doc.document_type == DocumentType.JIRA_CONNECTOR
+ assert doc.source_markdown == markdown
+ assert doc.search_space_id == _SEARCH_SPACE_ID
+ assert doc.connector_id == _CONNECTOR_ID
+ assert doc.created_by_id == _USER_ID
+ assert doc.should_summarize is True
+ assert doc.metadata["issue_id"] == "ENG-42"
+ assert doc.metadata["issue_identifier"] == "ENG-42"
+ assert doc.metadata["issue_title"] == "4242"
+ assert doc.metadata["state"] == "Done"
+ assert doc.metadata["priority"] == "Urgent"
+ assert doc.metadata["comment_count"] == 1
+ assert doc.metadata["connector_id"] == _CONNECTOR_ID
+ assert doc.metadata["document_type"] == "Jira Issue"
+ assert doc.metadata["connector_type"] == "Jira"
+ assert doc.fallback_summary is not None
+ assert "ENG-42" in doc.fallback_summary
+ assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+ doc = _build_connector_doc(
+ _make_issue(),
+ _make_formatted_issue(),
+ "# content",
+ connector_id=_CONNECTOR_ID,
+ search_space_id=_SEARCH_SPACE_ID,
+ user_id=_USER_ID,
+ enable_summary=False,
+ )
+ assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-7
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+ c = MagicMock()
+ c.config = {"access_token": "tok"}
+ c.enable_summary = enable_summary
+ c.last_indexed_at = None
+ return c
+
+
+def _mock_jira_client(issues=None, error=None):
+ client = MagicMock()
+ client.get_issues_by_date_range = AsyncMock(
+ return_value=(issues if issues is not None else [], error),
+ )
+ client.format_issue = MagicMock(
+ side_effect=lambda i: _make_formatted_issue(
+ issue_key=i.get("key", ""),
+ issue_id=i.get("id", ""),
+ title=i.get("title", ""),
+ )
+ )
+ client.format_issue_to_markdown = MagicMock(
+ side_effect=lambda fi: f"# {fi.get('key', '')}: {fi.get('id', '')}\n\nContent"
+ )
+ client.close = AsyncMock()
+ return client
+
+
+@pytest.fixture
+def jira_mocks(monkeypatch):
+ mock_session = AsyncMock()
+ mock_session.no_autoflush = MagicMock()
+
+ mock_connector = _mock_connector()
+ monkeypatch.setattr(
+ _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+ )
+
+ jira_client = _mock_jira_client(issues=[_make_issue()])
+ monkeypatch.setattr(
+ _mod, "JiraHistoryConnector", MagicMock(return_value=jira_client),
+ )
+
+ monkeypatch.setattr(
+ _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+ )
+ monkeypatch.setattr(
+ _mod, "update_connector_last_indexed", AsyncMock(),
+ )
+ monkeypatch.setattr(
+ _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+ )
+
+ mock_task_logger = MagicMock()
+ mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+ mock_task_logger.log_task_progress = AsyncMock()
+ mock_task_logger.log_task_success = AsyncMock()
+ mock_task_logger.log_task_failure = AsyncMock()
+ monkeypatch.setattr(
+ _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+ )
+
+ batch_mock = AsyncMock(return_value=([], 1, 0))
+ pipeline_mock = MagicMock()
+ pipeline_mock.index_batch_parallel = batch_mock
+ pipeline_mock.migrate_legacy_docs = AsyncMock()
+ monkeypatch.setattr(
+ _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+ )
+
+ return {
+ "session": mock_session,
+ "connector": mock_connector,
+ "jira_client": jira_client,
+ "task_logger": mock_task_logger,
+ "pipeline_mock": pipeline_mock,
+ "batch_mock": batch_mock,
+ }
+
+
+async def _run_index(mocks, **overrides):
+ return await index_jira_issues(
+ session=mocks["session"],
+ connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+ search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+ user_id=overrides.get("user_id", _USER_ID),
+ start_date=overrides.get("start_date", "2025-01-01"),
+ end_date=overrides.get("end_date", "2025-12-31"),
+ update_last_indexed=overrides.get("update_last_indexed", True),
+ on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+ )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_issue_calls_pipeline_and_returns_indexed_count(jira_mocks):
+ indexed, skipped, warning = await _run_index(jira_mocks)
+ assert indexed == 1
+ assert skipped == 0
+ assert warning is None
+
+ jira_mocks["batch_mock"].assert_called_once()
+ connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert connector_docs[0].document_type == DocumentType.JIRA_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(jira_mocks):
+ await _run_index(jira_mocks)
+ call_kwargs = jira_mocks["batch_mock"].call_args[1]
+ assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(jira_mocks):
+ await _run_index(jira_mocks)
+ jira_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Issue skipping (missing key/title/content)
+# ---------------------------------------------------------------------------
+
+
+async def test_issues_with_missing_key_are_skipped(jira_mocks):
+ issues = [
+ _make_issue(issue_key="ENG-1", issue_id="10001"),
+ {"key": "", "id": "10002", "title": "No key"},
+ ]
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+ _, skipped, _ = await _run_index(jira_mocks)
+ connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+async def test_issues_with_missing_title_are_skipped(jira_mocks):
+ issues = [
+ _make_issue(issue_key="ENG-1", issue_id="10001"),
+ {"key": "ENG-2", "id": "", "title": "Missing id used as title"},
+ ]
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+ _, skipped, _ = await _run_index(jira_mocks)
+ connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+async def test_issues_with_no_content_are_skipped(jira_mocks):
+ issues = [
+ _make_issue(issue_key="ENG-1", issue_id="10001"),
+ _make_issue(issue_key="ENG-2", issue_id="10002"),
+ ]
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+ jira_mocks["jira_client"].format_issue_to_markdown.side_effect = [
+ "# ENG-1: 10001\n\nContent",
+ "",
+ ]
+ _, skipped, _ = await _run_index(jira_mocks)
+ connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_issues_are_skipped(jira_mocks, monkeypatch):
+ issues = [
+ _make_issue(issue_key="ENG-1", issue_id="10001"),
+ _make_issue(issue_key="ENG-2", issue_id="10002"),
+ ]
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+ call_count = 0
+
+ async def _check_dup(session, content_hash):
+ nonlocal call_count
+ call_count += 1
+ if call_count == 2:
+ dup = MagicMock()
+ dup.id = 99
+ dup.document_type = "OTHER"
+ return dup
+ return None
+
+ monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+ _, skipped, _ = await _run_index(jira_mocks)
+ connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+ assert len(connector_docs) == 1
+ assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(jira_mocks):
+ heartbeat_cb = AsyncMock()
+ await _run_index(jira_mocks, on_heartbeat_callback=heartbeat_cb)
+ call_kwargs = jira_mocks["batch_mock"].call_args[1]
+ assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Empty issues and no-data success tuple
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_issues_returns_zero_tuple(jira_mocks):
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = ([], None)
+ indexed, skipped, warning = await _run_index(jira_mocks)
+ assert indexed == 0
+ assert skipped == 0
+ assert warning is None
+ jira_mocks["batch_mock"].assert_not_called()
+
+
+async def test_no_issues_error_message_returns_success_tuple(jira_mocks):
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (
+ [],
+ "No issues found in date range",
+ )
+ indexed, skipped, warning = await _run_index(jira_mocks)
+ assert indexed == 0
+ assert skipped == 0
+ assert warning is None
+
+
+async def test_api_error_still_returns_3_tuple(jira_mocks):
+ jira_mocks["jira_client"].get_issues_by_date_range.return_value = (
+ [],
+ "API exploded",
+ )
+ result = await _run_index(jira_mocks)
+ assert len(result) == 3
+ assert result[0] == 0
+ assert result[1] == 0
+ assert "Failed to get Jira issues" in result[2]
+
+
+# ---------------------------------------------------------------------------
+# Slice 7: Failed docs warning
+# ---------------------------------------------------------------------------
+
+
+async def test_failed_docs_warning_in_result(jira_mocks):
+ jira_mocks["batch_mock"].return_value = ([], 0, 2)
+ _, _, warning = await _run_index(jira_mocks)
+ assert warning is not None
+ assert "2 failed" in warning