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feat(linear): add LinearToolMetadataService
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CREDO23
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1 changed files with 343 additions and 0 deletions
343
surfsense_backend/app/services/linear/tool_metadata_service.py
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343
surfsense_backend/app/services/linear/tool_metadata_service.py
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from dataclasses import dataclass
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from sqlalchemy import String, and_, cast, func, or_
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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from app.connectors.linear_connector import LinearConnector
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from app.db import (
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Document,
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DocumentType,
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SearchSourceConnector,
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SearchSourceConnectorType,
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)
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@dataclass
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class LinearWorkspace:
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"""Represents a Linear connector as a workspace for tool context."""
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id: int
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name: str
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organization_name: str
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@classmethod
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def from_connector(cls, connector: SearchSourceConnector) -> "LinearWorkspace":
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return cls(
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id=connector.id,
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name=connector.name,
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organization_name=connector.config.get(
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"organization_name", "Linear Workspace"
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),
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)
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"name": self.name,
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"organization_name": self.organization_name,
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}
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@dataclass
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class LinearIssue:
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"""Represents an indexed Linear issue resolved from the knowledge base."""
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id: str
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identifier: str
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title: str
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state: str
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connector_id: int
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document_id: int
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indexed_at: str | None
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@classmethod
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def from_document(cls, document: Document) -> "LinearIssue":
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meta = document.document_metadata or {}
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return cls(
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id=meta.get("issue_id", ""),
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identifier=meta.get("issue_identifier", ""),
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title=meta.get("issue_title", document.title),
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state=meta.get("state", ""),
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connector_id=document.connector_id,
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document_id=document.id,
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indexed_at=meta.get("indexed_at"),
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)
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"identifier": self.identifier,
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"title": self.title,
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"state": self.state,
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"connector_id": self.connector_id,
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"document_id": self.document_id,
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"indexed_at": self.indexed_at,
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}
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class LinearToolMetadataService:
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"""Builds interrupt context for Linear HITL tools.
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All context queries (GraphQL reads) live here.
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Write mutations live in LinearConnector.
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"""
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def __init__(self, db_session: AsyncSession):
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self._db_session = db_session
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async def get_creation_context(self, search_space_id: int, user_id: str) -> dict:
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"""Return context needed to create a new Linear issue.
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Fetches all teams with their states, members, and labels from the
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Linear API, along with workspace info from the DB connector.
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Returns a dict with keys: workspace, priorities, teams.
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Returns a dict with key 'error' on failure.
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"""
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connector = await self._get_linear_connector(search_space_id, user_id)
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if not connector:
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return {"error": "No Linear account connected"}
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workspace = LinearWorkspace.from_connector(connector)
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linear_client = LinearConnector(
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session=self._db_session, connector_id=connector.id
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)
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try:
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priorities = await self._fetch_priority_values(linear_client)
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teams_raw = await self._fetch_teams_context(linear_client)
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except Exception as e:
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return {"error": f"Failed to fetch Linear context: {e!s}"}
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return {
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"workspace": workspace.to_dict(),
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"priorities": priorities,
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"teams": teams_raw,
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}
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async def get_update_context(
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self, search_space_id: int, user_id: str, issue_ref: str
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) -> dict:
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"""Return context needed to update an indexed Linear issue.
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Resolves the issue from the KB (title → identifier → full title),
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then fetches its current state, assignee, labels, and team context
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from the Linear API.
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Returns a dict with keys: workspace, priorities, issue, team.
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Returns a dict with key 'error' if the issue is not found or API fails.
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"""
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document = await self._resolve_issue(search_space_id, user_id, issue_ref)
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if not document:
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return {
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"error": f"Issue '{issue_ref}' not found in your indexed Linear issues. "
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"This could mean: (1) the issue doesn't exist, (2) it hasn't been indexed yet, "
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"or (3) the title or identifier is different."
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}
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connector = await self._get_connector_for_document(document, user_id)
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if not connector:
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return {"error": "Connector not found or access denied"}
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workspace = LinearWorkspace.from_connector(connector)
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issue = LinearIssue.from_document(document)
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linear_client = LinearConnector(
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session=self._db_session, connector_id=connector.id
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)
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try:
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priorities = await self._fetch_priority_values(linear_client)
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issue_api = await self._fetch_issue_context(linear_client, issue.id)
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except Exception as e:
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return {"error": f"Failed to fetch Linear issue context: {e!s}"}
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if not issue_api:
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return {
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"error": f"Issue '{issue_ref}' could not be fetched from Linear API"
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}
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team_raw = issue_api.get("team") or {}
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labels_raw = issue_api.get("labels") or {}
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states_raw = team_raw.get("states") or {}
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members_raw = team_raw.get("members") or {}
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team_labels_raw = team_raw.get("labels") or {}
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return {
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"workspace": workspace.to_dict(),
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"priorities": priorities,
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"issue": {
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"id": issue_api.get("id"),
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"identifier": issue_api.get("identifier"),
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"title": issue_api.get("title"),
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"description": issue_api.get("description"),
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"priority": issue_api.get("priority"),
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"url": issue_api.get("url"),
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"current_state": issue_api.get("state"),
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"current_assignee": issue_api.get("assignee"),
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"current_labels": labels_raw.get("nodes", []),
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"team_id": team_raw.get("id"),
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"document_id": issue.document_id,
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"indexed_at": issue.indexed_at,
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},
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"team": {
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"id": team_raw.get("id"),
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"name": team_raw.get("name"),
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"key": team_raw.get("key"),
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"states": states_raw.get("nodes", []),
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"members": members_raw.get("nodes", []),
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"labels": team_labels_raw.get("nodes", []),
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},
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}
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async def get_delete_context(
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self, search_space_id: int, user_id: str, issue_ref: str
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) -> dict:
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"""Return context needed to archive an indexed Linear issue.
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Resolves the issue from the KB only — no Linear API call required.
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Returns a dict with keys: workspace, issue.
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Returns a dict with key 'error' if the issue is not found.
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"""
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document = await self._resolve_issue(search_space_id, user_id, issue_ref)
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if not document:
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return {
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"error": f"Issue '{issue_ref}' not found in your indexed Linear issues. "
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"This could mean: (1) the issue doesn't exist, (2) it hasn't been indexed yet, "
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"or (3) the title or identifier is different."
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}
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connector = await self._get_connector_for_document(document, user_id)
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if not connector:
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return {"error": "Connector not found or access denied"}
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workspace = LinearWorkspace.from_connector(connector)
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issue = LinearIssue.from_document(document)
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return {
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"workspace": workspace.to_dict(),
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"issue": {
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**issue.to_dict(),
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"url": f"https://linear.app/issue/{issue.identifier}",
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},
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}
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@staticmethod
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async def _fetch_priority_values(client: LinearConnector) -> list[dict]:
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"""Fetch Linear priority values (0-4) with their display labels."""
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query = "{ issuePriorityValues { priority label } }"
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result = await client.execute_graphql_query(query)
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return result.get("data", {}).get("issuePriorityValues", [])
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@staticmethod
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async def _fetch_teams_context(client: LinearConnector) -> list[dict]:
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"""Fetch all teams with their states, members, and labels."""
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query = """
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query {
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teams {
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nodes {
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id name key
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states { nodes { id name type color position } }
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members { nodes { id name displayName email avatarUrl active } }
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labels { nodes { id name color } }
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}
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}
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}
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"""
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result = await client.execute_graphql_query(query)
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return result.get("data", {}).get("teams", {}).get("nodes", [])
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@staticmethod
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async def _fetch_issue_context(
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client: LinearConnector, issue_id: str
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) -> dict | None:
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"""Fetch a single issue with its current state, assignee, labels, and team context."""
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query = """
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query LinearIssueContext($id: String!) {
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issue(id: $id) {
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id identifier title description priority url
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state { id name type color }
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assignee { id name displayName email }
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labels { nodes { id name color } }
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team {
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id name key
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states { nodes { id name type color position } }
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members { nodes { id name displayName email avatarUrl active } }
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labels { nodes { id name color } }
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}
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}
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}
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"""
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result = await client.execute_graphql_query(query, {"id": issue_id})
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return result.get("data", {}).get("issue")
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async def _resolve_issue(
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self, search_space_id: int, user_id: str, issue_ref: str
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) -> Document | None:
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"""Resolve an issue from the KB using a 3-step fallback.
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Order: issue_title (most natural) → issue_identifier (e.g. ENG-42) → document.title.
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All comparisons are case-insensitive.
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"""
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ref_lower = issue_ref.lower()
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result = await self._db_session.execute(
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select(Document)
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.join(
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SearchSourceConnector, Document.connector_id == SearchSourceConnector.id
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)
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.filter(
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and_(
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Document.search_space_id == search_space_id,
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Document.document_type == DocumentType.LINEAR_CONNECTOR,
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SearchSourceConnector.user_id == user_id,
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or_(
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func.lower(
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cast(Document.document_metadata["issue_title"], String)
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)
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== ref_lower,
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func.lower(
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cast(Document.document_metadata["issue_identifier"], String)
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)
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== ref_lower,
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func.lower(Document.title) == ref_lower,
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),
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)
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)
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.limit(1)
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)
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return result.scalars().first()
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async def _get_linear_connector(
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self, search_space_id: int, user_id: str
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) -> SearchSourceConnector | None:
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"""Fetch the first Linear connector for the given search space and user."""
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result = await self._db_session.execute(
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select(SearchSourceConnector).filter(
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and_(
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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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)
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return result.scalars().first()
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async def _get_connector_for_document(
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self, document: Document, user_id: str
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) -> SearchSourceConnector | None:
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"""Fetch the connector associated with a document, scoped to the user."""
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if not document.connector_id:
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return None
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result = await self._db_session.execute(
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select(SearchSourceConnector).filter(
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and_(
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SearchSourceConnector.id == document.connector_id,
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SearchSourceConnector.user_id == user_id,
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)
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)
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)
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return result.scalars().first()
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