mirror of
https://github.com/MODSetter/SurfSense.git
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Fixed formatting and linting post Jira connector PR
This commit is contained in:
Utkarsh-Patel-13
commit
2827522ebc
30 changed files with 5428 additions and 3279 deletions
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@ -10,6 +10,7 @@ from sqlalchemy.future import select
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from app.config import config
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from app.connectors.discord_connector import DiscordConnector
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from app.connectors.github_connector import GitHubConnector
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from app.connectors.jira_connector import JiraConnector
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from app.connectors.linear_connector import LinearConnector
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from app.connectors.notion_history import NotionHistoryConnector
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from app.connectors.slack_history import SlackHistory
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@ -1374,9 +1375,9 @@ async def index_linear_issues(
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# Process each issue
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for issue in issues:
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try:
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issue_id = issue.get("id")
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issue_identifier = issue.get("identifier", "")
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issue_title = issue.get("title", "")
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issue_id = issue.get("key")
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issue_identifier = issue.get("id", "")
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issue_title = issue.get("key", "")
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if not issue_id or not issue_title:
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logger.warning(
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@ -1978,3 +1979,353 @@ async def index_discord_messages(
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)
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logger.error(f"Failed to index Discord messages: {e!s}", exc_info=True)
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return 0, f"Failed to index Discord messages: {e!s}"
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async def index_jira_issues(
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session: AsyncSession,
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connector_id: int,
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search_space_id: int,
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user_id: str,
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start_date: str | None = None,
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end_date: str | None = None,
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update_last_indexed: bool = True,
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) -> tuple[int, str | None]:
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"""
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Index Jira issues and comments.
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Args:
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session: Database session
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connector_id: ID of the Jira connector
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search_space_id: ID of the search space to store documents in
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user_id: User ID
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start_date: Start date for indexing (YYYY-MM-DD format)
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end_date: End date for indexing (YYYY-MM-DD format)
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update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
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Returns:
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Tuple containing (number of documents indexed, error message or None)
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"""
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task_logger = TaskLoggingService(session, search_space_id)
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# Log task start
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log_entry = await task_logger.log_task_start(
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task_name="jira_issues_indexing",
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source="connector_indexing_task",
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message=f"Starting Jira issues indexing for connector {connector_id}",
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metadata={
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"connector_id": connector_id,
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"user_id": str(user_id),
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"start_date": start_date,
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"end_date": end_date,
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},
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)
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try:
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# Get the connector from the database
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result = await session.execute(
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select(SearchSourceConnector).filter(
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SearchSourceConnector.id == connector_id,
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SearchSourceConnector.connector_type
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== SearchSourceConnectorType.JIRA_CONNECTOR,
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)
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)
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connector = result.scalars().first()
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if not connector:
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await task_logger.log_task_failure(
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log_entry,
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f"Connector with ID {connector_id} not found",
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"Connector not found",
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{"error_type": "ConnectorNotFound"},
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)
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return 0, f"Connector with ID {connector_id} not found"
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# Get the Jira credentials from the connector config
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jira_email = connector.config.get("JIRA_EMAIL")
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jira_api_token = connector.config.get("JIRA_API_TOKEN")
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jira_base_url = connector.config.get("JIRA_BASE_URL")
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if not jira_email or not jira_api_token or not jira_base_url:
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await task_logger.log_task_failure(
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log_entry,
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f"Jira credentials not found in connector config for connector {connector_id}",
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"Missing Jira credentials",
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{"error_type": "MissingCredentials"},
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)
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return 0, "Jira credentials not found in connector config"
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# Initialize Jira client
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await task_logger.log_task_progress(
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log_entry,
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f"Initializing Jira client for connector {connector_id}",
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{"stage": "client_initialization"},
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)
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jira_client = JiraConnector(
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base_url=jira_base_url, email=jira_email, api_token=jira_api_token
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)
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# Calculate date range
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if start_date is None or end_date is None:
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# Fall back to calculating dates based on last_indexed_at
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calculated_end_date = datetime.now()
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# Use last_indexed_at as start date if available, otherwise use 365 days ago
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if connector.last_indexed_at:
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# Convert dates to be comparable (both timezone-naive)
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last_indexed_naive = (
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connector.last_indexed_at.replace(tzinfo=None)
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if connector.last_indexed_at.tzinfo
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else connector.last_indexed_at
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)
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# Check if last_indexed_at is in the future or after end_date
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if last_indexed_naive > calculated_end_date:
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logger.warning(
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f"Last indexed date ({last_indexed_naive.strftime('%Y-%m-%d')}) is in the future. Using 365 days ago instead."
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)
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calculated_start_date = calculated_end_date - timedelta(days=365)
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else:
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calculated_start_date = last_indexed_naive
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logger.info(
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f"Using last_indexed_at ({calculated_start_date.strftime('%Y-%m-%d')}) as start date"
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)
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else:
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calculated_start_date = calculated_end_date - timedelta(
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days=365
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) # Use 365 days as default
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logger.info(
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f"No last_indexed_at found, using {calculated_start_date.strftime('%Y-%m-%d')} (365 days ago) as start date"
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)
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# Use calculated dates if not provided
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start_date_str = (
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start_date if start_date else calculated_start_date.strftime("%Y-%m-%d")
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)
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end_date_str = (
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end_date if end_date else calculated_end_date.strftime("%Y-%m-%d")
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)
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else:
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# Use provided dates
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start_date_str = start_date
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end_date_str = end_date
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await task_logger.log_task_progress(
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log_entry,
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f"Fetching Jira issues from {start_date_str} to {end_date_str}",
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{
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"stage": "fetching_issues",
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"start_date": start_date_str,
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"end_date": end_date_str,
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},
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)
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# Get issues within date range
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try:
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issues, error = jira_client.get_issues_by_date_range(
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start_date=start_date_str, end_date=end_date_str, include_comments=True
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)
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if error:
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logger.error(f"Failed to get Jira issues: {error}")
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# Don't treat "No issues found" as an error that should stop indexing
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if "No issues found" in error:
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logger.info(
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"No issues found is not a critical error, continuing with update"
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)
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if update_last_indexed:
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connector.last_indexed_at = datetime.now()
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await session.commit()
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logger.info(
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f"Updated last_indexed_at to {connector.last_indexed_at} despite no issues found"
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)
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await task_logger.log_task_success(
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log_entry,
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f"No Jira issues found in date range {start_date_str} to {end_date_str}",
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{"issues_found": 0},
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)
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return 0, None
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else:
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to get Jira issues: {error}",
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"API Error",
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{"error_type": "APIError"},
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)
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return 0, f"Failed to get Jira issues: {error}"
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logger.info(f"Retrieved {len(issues)} issues from Jira API")
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except Exception as e:
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logger.error(f"Error fetching Jira issues: {e!s}", exc_info=True)
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return 0, f"Error fetching Jira issues: {e!s}"
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# Process and index each issue
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documents_indexed = 0
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skipped_issues = []
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documents_skipped = 0
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for issue in issues:
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try:
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issue_id = issue.get("key")
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issue_identifier = issue.get("key", "")
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issue_title = issue.get("id", "")
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if not issue_id or not issue_title:
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logger.warning(
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f"Skipping issue with missing ID or title: {issue_id or 'Unknown'}"
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)
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skipped_issues.append(
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f"{issue_identifier or 'Unknown'} (missing data)"
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)
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documents_skipped += 1
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continue
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# Format the issue for better readability
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formatted_issue = jira_client.format_issue(issue)
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# Convert to markdown
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issue_content = jira_client.format_issue_to_markdown(formatted_issue)
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if not issue_content:
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logger.warning(
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f"Skipping issue with no content: {issue_identifier} - {issue_title}"
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)
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skipped_issues.append(f"{issue_identifier} (no content)")
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documents_skipped += 1
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continue
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# Create a simple summary
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summary_content = f"Jira Issue {issue_identifier}: {issue_title}\n\nStatus: {formatted_issue.get('status', 'Unknown')}\n\n"
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if formatted_issue.get("description"):
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summary_content += (
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f"Description: {formatted_issue.get('description')}\n\n"
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)
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# Add comment count
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comment_count = len(formatted_issue.get("comments", []))
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summary_content += f"Comments: {comment_count}"
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# Generate content hash
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content_hash = generate_content_hash(issue_content, search_space_id)
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# Check if document already exists
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existing_doc_by_hash_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document_by_hash = (
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existing_doc_by_hash_result.scalars().first()
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)
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if existing_document_by_hash:
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logger.info(
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f"Document with content hash {content_hash} already exists for issue {issue_identifier}. Skipping processing."
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)
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documents_skipped += 1
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continue
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# Generate embedding for the summary
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summary_embedding = config.embedding_model_instance.embed(
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summary_content
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)
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# Process chunks - using the full issue content with comments
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chunks = [
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Chunk(
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content=chunk.text,
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embedding=config.embedding_model_instance.embed(chunk.text),
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)
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for chunk in config.chunker_instance.chunk(issue_content)
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]
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# Create and store new document
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logger.info(
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f"Creating new document for issue {issue_identifier} - {issue_title}"
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)
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document = Document(
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search_space_id=search_space_id,
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title=f"Jira - {issue_identifier}: {issue_title}",
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document_type=DocumentType.JIRA_CONNECTOR,
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document_metadata={
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"issue_id": issue_id,
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"issue_identifier": issue_identifier,
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"issue_title": issue_title,
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"state": formatted_issue.get("status", "Unknown"),
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"comment_count": comment_count,
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"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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},
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content=summary_content,
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content_hash=content_hash,
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embedding=summary_embedding,
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chunks=chunks,
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)
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session.add(document)
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documents_indexed += 1
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logger.info(
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f"Successfully indexed new issue {issue_identifier} - {issue_title}"
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)
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except Exception as e:
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logger.error(
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f"Error processing issue {issue.get('identifier', 'Unknown')}: {e!s}",
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exc_info=True,
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)
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skipped_issues.append(
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f"{issue.get('identifier', 'Unknown')} (processing error)"
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)
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documents_skipped += 1
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continue # Skip this issue and continue with others
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# Update the last_indexed_at timestamp for the connector only if requested
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total_processed = documents_indexed
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if update_last_indexed:
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connector.last_indexed_at = datetime.now()
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logger.info(f"Updated last_indexed_at to {connector.last_indexed_at}")
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# Commit all changes
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await session.commit()
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logger.info("Successfully committed all JIRA document changes to database")
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# Log success
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await task_logger.log_task_success(
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log_entry,
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f"Successfully completed JIRA indexing for connector {connector_id}",
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{
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"issues_processed": total_processed,
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"documents_indexed": documents_indexed,
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"documents_skipped": documents_skipped,
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"skipped_issues_count": len(skipped_issues),
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},
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)
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logger.info(
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f"JIRA indexing completed: {documents_indexed} new issues, {documents_skipped} skipped"
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)
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return (
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total_processed,
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None,
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) # Return None as the error message to indicate success
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except SQLAlchemyError as db_error:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Database error during JIRA indexing for connector {connector_id}",
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str(db_error),
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{"error_type": "SQLAlchemyError"},
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)
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logger.error(f"Database error: {db_error!s}", exc_info=True)
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return 0, f"Database error: {db_error!s}"
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except Exception as e:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to index JIRA issues for connector {connector_id}",
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str(e),
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{"error_type": type(e).__name__},
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)
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logger.error(f"Failed to index JIRA issues: {e!s}", exc_info=True)
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return 0, f"Failed to index JIRA issues: {e!s}"
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