openrabbit/tools/ai-review/agents/issue_agent.py

"""Issue Review Agent

AI agent for triaging, labeling, and responding to issues.
Handles issue.opened, issue.labeled, and issue_comment events.
"""

import logging
from dataclasses import dataclass

from agents.base_agent import AgentContext, AgentResult, BaseAgent


@dataclass
class TriageResult:
    """Result of issue triage analysis."""

    issue_type: str
    priority: str
    confidence: float
    summary: str
    suggested_labels: list[str]
    is_duplicate: bool
    duplicate_of: int | None
    needs_more_info: bool
    missing_info: list[str]
    components: list[str]
    reasoning: str


class IssueAgent(BaseAgent):
    """Agent for handling issue events."""

    # Marker specific to issue comments
    ISSUE_AI_MARKER = "<!-- AI_ISSUE_TRIAGE -->"

    def can_handle(self, event_type: str, event_data: dict) -> bool:
        """Check if this agent handles the given event."""
        # Check if agent is enabled
        agent_config = self.config.get("agents", {}).get("issue", {})
        if not agent_config.get("enabled", True):
            return False

        # Handle issue events
        if event_type == "issues":
            action = event_data.get("action", "")
            allowed_events = agent_config.get("events", ["opened", "labeled"])
            if action not in allowed_events:
                return False

            # Ignore our own codebase reports to prevent double-commenting
            issue = event_data.get("issue", {})
            title = issue.get("title", "")
            labels = [l.get("name") for l in issue.get("labels", [])]
            if "AI Codebase Report" in title or "ai-codebase-report" in labels:
                return False

            return True

        # Handle issue comment events (for @mentions)
        if event_type == "issue_comment":
            action = event_data.get("action", "")
            if action == "created":
                comment_body = event_data.get("comment", {}).get("body", "")
                mention_prefix = self.config.get("interaction", {}).get(
                    "mention_prefix", "@ai-bot"
                )
                return mention_prefix in comment_body

        return False

    def execute(self, context: AgentContext) -> AgentResult:
        """Execute the issue agent."""
        event_data = context.event_data
        action = event_data.get("action", "")

        if context.event_type == "issues":
            if action == "opened":
                return self._handle_issue_opened(context)
            elif action == "labeled":
                return self._handle_issue_labeled(context)

        if context.event_type == "issue_comment":
            return self._handle_issue_comment(context)

        return AgentResult(
            success=False,
            message=f"Unknown action: {action}",
        )

    def _handle_issue_opened(self, context: AgentContext) -> AgentResult:
        """Handle a newly opened issue."""
        issue = context.event_data.get("issue", {})
        issue_index = issue.get("number")
        title = issue.get("title", "")
        body = issue.get("body", "")
        author = issue.get("user", {}).get("login", "unknown")
        existing_labels = [l.get("name", "") for l in issue.get("labels", [])]

        self.logger.info(f"Triaging issue #{issue_index}: {title}")

        # Step 1: Triage the issue
        triage = self._triage_issue(title, body, author, existing_labels)

        actions_taken = []

        # Step 2: Apply labels if auto-label is enabled
        agent_config = self.config.get("agents", {}).get("issue", {})
        if agent_config.get("auto_label", True):
            labels_applied = self._apply_labels(
                context.owner, context.repo, issue_index, triage
            )
            if labels_applied:
                actions_taken.append(f"Applied labels: {labels_applied}")

        # Step 3: Post triage comment
        comment = self._generate_triage_comment(triage, issue)
        self.upsert_comment(
            context.owner,
            context.repo,
            issue_index,
            comment,
            marker=self.ISSUE_AI_MARKER,
        )
        actions_taken.append("Posted triage comment")

        return AgentResult(
            success=True,
            message=f"Triaged issue #{issue_index} as {triage.issue_type} ({triage.priority} priority)",
            data={
                "triage": {
                    "type": triage.issue_type,
                    "priority": triage.priority,
                    "confidence": triage.confidence,
                }
            },
            actions_taken=actions_taken,
        )

    def _handle_issue_labeled(self, context: AgentContext) -> AgentResult:
        """Handle label addition to an issue."""
        # Could be used for specific label-triggered actions
        issue = context.event_data.get("issue", {})
        label = context.event_data.get("label", {})

        return AgentResult(
            success=True,
            message=f"Noted label '{label.get('name')}' added to issue #{issue.get('number')}",
        )

    def _handle_issue_comment(self, context: AgentContext) -> AgentResult:
        """Handle @mention in issue comment."""
        issue = context.event_data.get("issue", {})
        comment = context.event_data.get("comment", {})
        issue_index = issue.get("number")
        comment_body = comment.get("body", "")

        # Parse command from mention
        command = self._parse_command(comment_body)

        if command:
            response = self._handle_command(context, issue, command)
            self.gitea.create_issue_comment(
                context.owner, context.repo, issue_index, response
            )
            return AgentResult(
                success=True,
                message=f"Responded to command: {command}",
                actions_taken=["Posted command response"],
            )

        return AgentResult(
            success=True,
            message="No actionable command found in mention",
        )

    def _triage_issue(
        self,
        title: str,
        body: str,
        author: str,
        existing_labels: list[str],
    ) -> TriageResult:
        """Use LLM to triage the issue."""
        prompt_template = self.load_prompt("issue_triage")
        prompt = prompt_template.format(
            title=title,
            body=body or "(no description provided)",
            author=author,
            existing_labels=", ".join(existing_labels) if existing_labels else "none",
        )

        try:
            result = self.call_llm_json(prompt)
            return TriageResult(
                issue_type=result.get("type", "question"),
                priority=result.get("priority", "medium"),
                confidence=result.get("confidence", 0.5),
                summary=result.get("summary", title),
                suggested_labels=result.get("suggested_labels", []),
                is_duplicate=result.get("is_duplicate", False),
                duplicate_of=result.get("duplicate_of"),
                needs_more_info=result.get("needs_more_info", False),
                missing_info=result.get("missing_info", []),
                components=result.get("components", []),
                reasoning=result.get("reasoning", ""),
            )
        except Exception as e:
            self.logger.warning(f"LLM triage failed: {e}")
            # Return default triage on failure
            return TriageResult(
                issue_type="question",
                priority="medium",
                confidence=0.3,
                summary=title,
                suggested_labels=[],
                is_duplicate=False,
                duplicate_of=None,
                needs_more_info=True,
                missing_info=["Unable to parse issue automatically"],
                components=[],
                reasoning="Automatic triage failed, needs human review",
            )

    def _apply_labels(
        self,
        owner: str,
        repo: str,
        issue_index: int,
        triage: TriageResult,
    ) -> list[str]:
        """Apply labels based on triage result."""
        labels_config = self.config.get("labels", {})

        # Get all repo labels
        try:
            repo_labels = self.gitea.get_repo_labels(owner, repo)
            label_map = {l["name"]: l["id"] for l in repo_labels}
        except Exception as e:
            self.logger.warning(f"Failed to get repo labels: {e}")
            return []

        labels_to_add = []

        # Map priority
        priority_labels = labels_config.get("priority", {})
        priority_label = priority_labels.get(triage.priority)
        if priority_label and priority_label in label_map:
            labels_to_add.append(label_map[priority_label])

        # Map type
        type_labels = labels_config.get("type", {})
        type_label = type_labels.get(triage.issue_type)
        if type_label and type_label in label_map:
            labels_to_add.append(label_map[type_label])

        # Add AI reviewed label
        status_labels = labels_config.get("status", {})
        reviewed_label = status_labels.get("ai_reviewed")
        if reviewed_label and reviewed_label in label_map:
            labels_to_add.append(label_map[reviewed_label])

        if labels_to_add:
            try:
                self.gitea.add_issue_labels(owner, repo, issue_index, labels_to_add)
                return [name for name, id in label_map.items() if id in labels_to_add]
            except Exception as e:
                self.logger.warning(f"Failed to add labels: {e}")

        return []

    def _generate_triage_comment(self, triage: TriageResult, issue: dict) -> str:
        """Generate a triage summary comment."""
        lines = [
            f"{self.AI_DISCLAIMER}",
            "",
            "## AI Issue Triage",
            "",
            f"| Field | Value |",
            f"|-------|--------|",
            f"| **Type** | {triage.issue_type.capitalize()} |",
            f"| **Priority** | {triage.priority.capitalize()} |",
            f"| **Confidence** | {triage.confidence:.0%} |",
            "",
        ]

        if triage.summary != issue.get("title"):
            lines.append(f"**Summary:** {triage.summary}")
            lines.append("")

        if triage.components:
            lines.append(f"**Components:** {', '.join(triage.components)}")
            lines.append("")

        if triage.needs_more_info and triage.missing_info:
            lines.append("### Additional Information Needed")
            lines.append("")
            for info in triage.missing_info:
                lines.append(f"- {info}")
            lines.append("")

        if triage.is_duplicate and triage.duplicate_of:
            lines.append(f"### Possible Duplicate")
            lines.append(f"This issue may be a duplicate of #{triage.duplicate_of}")
            lines.append("")

        lines.append("---")
        lines.append(f"*{triage.reasoning}*")

        return "\n".join(lines)

    def _parse_command(self, body: str) -> str | None:
        """Parse a command from a comment body."""
        mention_prefix = self.config.get("interaction", {}).get(
            "mention_prefix", "@ai-bot"
        )
        commands = self.config.get("interaction", {}).get(
            "commands", ["explain", "suggest", "security", "summarize"]
        )

        for command in commands:
            if f"{mention_prefix} {command}" in body.lower():
                return command

        return None

    def _handle_command(self, context: AgentContext, issue: dict, command: str) -> str:
        """Handle a command from an @mention."""
        title = issue.get("title", "")
        body = issue.get("body", "")

        if command == "summarize":
            return self._command_summarize(title, body)
        elif command == "explain":
            return self._command_explain(title, body)
        elif command == "suggest":
            return self._command_suggest(title, body)
        elif command == "triage":
            return self._command_triage(context, issue)

        return f"{self.AI_DISCLAIMER}\n\nSorry, I don't understand the command `{command}`."

    def _command_summarize(self, title: str, body: str) -> str:
        """Generate a summary of the issue."""
        prompt = f"""Summarize the following issue in 2-3 concise sentences:

Title: {title}
Body: {body}

Provide only the summary, no additional formatting."""

        try:
            response = self.call_llm(prompt)
            return f"{self.AI_DISCLAIMER}\n\n**Summary:**\n{response.content}"
        except Exception as e:
            return f"{self.AI_DISCLAIMER}\n\nSorry, I was unable to generate a summary. Error: {e}"

    def _command_explain(self, title: str, body: str) -> str:
        """Explain the issue in more detail."""
        prompt = f"""Analyze this issue and provide a clear explanation of what the user is asking for or reporting:

Title: {title}
Body: {body}

Provide:
1. What the issue is about
2. What the user expects
3. Any technical context that might be relevant

Be concise and helpful."""

        try:
            response = self.call_llm(prompt)
            return f"{self.AI_DISCLAIMER}\n\n**Explanation:**\n{response.content}"
        except Exception as e:
            return f"{self.AI_DISCLAIMER}\n\nSorry, I was unable to explain this issue. Error: {e}"

    def _command_suggest(self, title: str, body: str) -> str:
        """Suggest solutions for the issue."""
        prompt = f"""Based on this issue, suggest potential solutions or next steps:

Title: {title}
Body: {body}

Provide 2-3 actionable suggestions. If this is a bug, suggest debugging steps. If this is a feature request, suggest implementation approaches.

Be practical and concise."""

        try:
            response = self.call_llm(prompt)
            return f"{self.AI_DISCLAIMER}\n\n**Suggestions:**\n{response.content}"
        except Exception as e:
            return f"{self.AI_DISCLAIMER}\n\nSorry, I was unable to generate suggestions. Error: {e}"

    def _command_triage(self, context: AgentContext, issue: dict) -> str:
        """Perform full triage analysis on the issue."""
        title = issue.get("title", "")
        body = issue.get("body", "")
        author = issue.get("user", {}).get("login", "unknown")
        existing_labels = [l.get("name", "") for l in issue.get("labels", [])]
        issue_index = issue.get("number")

        try:
            # Perform triage analysis
            triage = self._triage_issue(title, body, author, existing_labels)

            # Apply labels if enabled
            agent_config = self.config.get("agents", {}).get("issue", {})
            labels_applied = []
            if agent_config.get("auto_label", True):
                labels_applied = self._apply_labels(
                    context.owner, context.repo, issue_index, triage
                )

            # Generate response
            response = self._generate_triage_comment(triage, issue)

            if labels_applied:
                response += f"\n\n**Labels Applied:** {', '.join(labels_applied)}"

            return response
        except Exception as e:
            return f"{self.AI_DISCLAIMER}\n\nSorry, I was unable to triage this issue. Error: {e}"