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feat(linear): add delete_linear_issue tool
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CREDO23
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1 changed files with 259 additions and 0 deletions
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import logging
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from typing import Any
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from langchain_core.tools import tool
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from langgraph.types import interrupt
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.connectors.linear_connector import LinearConnector
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from app.services.linear import LinearToolMetadataService
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logger = logging.getLogger(__name__)
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def create_delete_linear_issue_tool(
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db_session: AsyncSession | None = None,
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search_space_id: int | None = None,
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user_id: str | None = None,
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connector_id: int | None = None,
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):
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"""
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Factory function to create the delete_linear_issue tool.
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Args:
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db_session: Database session for accessing the Linear connector
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search_space_id: Search space ID to find the Linear connector
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user_id: User ID for finding the correct Linear connector
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connector_id: Optional specific connector ID (if known)
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Returns:
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Configured delete_linear_issue tool
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"""
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@tool
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async def delete_linear_issue(
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issue_ref: str,
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delete_from_kb: bool = False,
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) -> dict[str, Any]:
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"""Archive (delete) a Linear issue.
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Use this tool when the user asks to delete, remove, or archive a Linear issue.
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Note that Linear archives issues rather than permanently deleting them
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(they can be restored from the archive).
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Args:
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issue_ref: The issue to delete. Can be the issue title (e.g. "Fix login bug"),
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the identifier (e.g. "ENG-42"), or the full document title
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(e.g. "ENG-42: Fix login bug").
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delete_from_kb: Whether to also remove the issue from the knowledge base.
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Default is False. Set to True to remove from both Linear
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and the knowledge base.
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Returns:
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Dictionary with:
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- status: "success", "rejected", "not_found", or "error"
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- identifier: Human-readable ID like "ENG-42" (if success)
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- message: Success or error message
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- deleted_from_kb: Whether the issue was also removed from the knowledge base (if success)
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IMPORTANT:
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- If status is "rejected", the user explicitly declined the action.
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Respond with a brief acknowledgment (e.g., "Understood, I won't delete the issue.")
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and move on. Do NOT ask for alternatives or troubleshoot.
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- If status is "not_found", inform the user conversationally using the exact message
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provided. Do NOT treat this as an error. Simply relay the message and ask the user
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to verify the issue title or identifier, or check if it has been indexed.
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Examples:
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- "Delete the 'Fix login bug' Linear issue"
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- "Archive ENG-42"
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- "Remove the 'Old payment flow' issue from Linear"
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"""
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logger.info(
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f"delete_linear_issue called: issue_ref='{issue_ref}', delete_from_kb={delete_from_kb}"
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)
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if db_session is None or search_space_id is None or user_id is None:
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logger.error(
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"Linear tool not properly configured - missing required parameters"
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)
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return {
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"status": "error",
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"message": "Linear tool not properly configured. Please contact support.",
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}
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try:
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metadata_service = LinearToolMetadataService(db_session)
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context = await metadata_service.get_delete_context(
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search_space_id, user_id, issue_ref
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)
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if "error" in context:
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error_msg = context["error"]
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if "not found" in error_msg.lower():
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logger.warning(f"Issue not found: {error_msg}")
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return {"status": "not_found", "message": error_msg}
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else:
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logger.error(f"Failed to fetch delete context: {error_msg}")
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return {"status": "error", "message": error_msg}
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issue_id = context["issue"]["id"]
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document_id = context["issue"]["document_id"]
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connector_id_from_context = context.get("workspace", {}).get("id")
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logger.info(
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f"Requesting approval for deleting Linear issue: '{issue_ref}' "
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f"(id={issue_id}, delete_from_kb={delete_from_kb})"
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)
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approval = interrupt(
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{
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"type": "linear_issue_deletion",
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"action": {
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"tool": "delete_linear_issue",
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"params": {
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"issue_id": issue_id,
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"connector_id": connector_id_from_context,
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"delete_from_kb": delete_from_kb,
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},
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},
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"context": context,
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}
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)
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decisions_raw = (
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approval.get("decisions", []) if isinstance(approval, dict) else []
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)
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decisions = (
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decisions_raw if isinstance(decisions_raw, list) else [decisions_raw]
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)
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decisions = [d for d in decisions if isinstance(d, dict)]
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if not decisions:
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logger.warning("No approval decision received")
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return {"status": "error", "message": "No approval decision received"}
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decision = decisions[0]
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decision_type = decision.get("type") or decision.get("decision_type")
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logger.info(f"User decision: {decision_type}")
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if decision_type == "reject":
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logger.info("Linear issue deletion rejected by user")
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return {
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"status": "rejected",
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"message": "User declined. The issue was not deleted. Do not ask again or suggest alternatives.",
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}
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edited_action = decision.get("edited_action")
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final_params: dict[str, Any] = {}
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if isinstance(edited_action, dict):
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edited_args = edited_action.get("args")
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if isinstance(edited_args, dict):
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final_params = edited_args
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elif isinstance(decision.get("args"), dict):
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final_params = decision["args"]
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final_issue_id = final_params.get("issue_id", issue_id)
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final_connector_id = final_params.get(
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"connector_id", connector_id_from_context
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)
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final_delete_from_kb = final_params.get("delete_from_kb", delete_from_kb)
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logger.info(
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f"Deleting Linear issue with final params: issue_id={final_issue_id}, "
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f"connector_id={final_connector_id}, delete_from_kb={final_delete_from_kb}"
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)
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from sqlalchemy.future import select
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from app.db import SearchSourceConnector, SearchSourceConnectorType
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if final_connector_id:
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result = await db_session.execute(
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select(SearchSourceConnector).filter(
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SearchSourceConnector.id == final_connector_id,
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SearchSourceConnector.search_space_id == search_space_id,
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SearchSourceConnector.user_id == user_id,
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SearchSourceConnector.connector_type
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== SearchSourceConnectorType.LINEAR_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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logger.error(
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f"Invalid connector_id={final_connector_id} for search_space_id={search_space_id}"
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)
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return {
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"status": "error",
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"message": "Selected Linear connector is invalid or has been disconnected.",
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}
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actual_connector_id = connector.id
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logger.info(f"Validated Linear connector: id={actual_connector_id}")
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else:
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logger.error("No connector found for this issue")
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return {
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"status": "error",
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"message": "No connector found for this issue.",
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}
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linear_client = LinearConnector(
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session=db_session, connector_id=actual_connector_id
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)
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result = await linear_client.archive_issue(issue_id=final_issue_id)
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logger.info(
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f"archive_issue result: {result.get('status')} - {result.get('message', '')}"
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)
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deleted_from_kb = False
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if (
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result.get("status") == "success"
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and final_delete_from_kb
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and document_id
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):
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try:
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from app.db import Document
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doc_result = await db_session.execute(
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select(Document).filter(Document.id == document_id)
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)
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document = doc_result.scalars().first()
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if document:
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await db_session.delete(document)
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await db_session.commit()
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deleted_from_kb = True
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logger.info(
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f"Deleted document {document_id} from knowledge base"
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)
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else:
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logger.warning(f"Document {document_id} not found in DB")
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except Exception as e:
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logger.error(f"Failed to delete document from KB: {e}")
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result["warning"] = (
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f"Issue archived in Linear, but failed to remove from knowledge base: {e!s}"
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)
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if result.get("status") == "success":
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result["deleted_from_kb"] = deleted_from_kb
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if deleted_from_kb:
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result["message"] = (
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f"{result.get('message', '')} Also removed from the knowledge base."
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)
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return result
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except Exception as e:
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from langgraph.errors import GraphInterrupt
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if isinstance(e, GraphInterrupt):
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raise
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logger.error(f"Error deleting Linear issue: {e}", exc_info=True)
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return {
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"status": "error",
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"message": str(e)
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if isinstance(e, ValueError)
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else f"Unexpected error: {e!s}",
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}
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return delete_linear_issue
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