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tools/ai-review/agents/base_agent.py

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+"""Base Agent
+
+Abstract base class for all AI agents. Provides common functionality
+for Gitea API interaction, LLM calls, logging, and rate limiting.
+"""
+
+import logging
+import os
+import time
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+import yaml
+
+from clients.gitea_client import GiteaClient
+from clients.llm_client import LLMClient, LLMResponse
+
+
+@dataclass
+class AgentContext:
+    """Context passed to agent during execution."""
+
+    owner: str
+    repo: str
+    event_type: str
+    event_data: dict
+    config: dict = field(default_factory=dict)
+
+
+@dataclass
+class AgentResult:
+    """Result from agent execution."""
+
+    success: bool
+    message: str
+    data: dict = field(default_factory=dict)
+    actions_taken: list[str] = field(default_factory=list)
+    error: str | None = None
+
+
+class BaseAgent(ABC):
+    """Abstract base class for AI agents."""
+
+    # Marker to identify AI-generated comments
+    AI_MARKER = "<!-- AI_CODE_REVIEW -->"
+
+    # Disclaimer text
+    AI_DISCLAIMER = (
+        "**Note:** This review was generated by an AI assistant. "
+        "While it aims to be accurate and helpful, it may contain mistakes "
+        "or miss important issues. Please verify all findings before taking action."
+    )
+
+    def __init__(
+        self,
+        config: dict | None = None,
+        gitea_client: GiteaClient | None = None,
+        llm_client: LLMClient | None = None,
+    ):
+        """Initialize the base agent.
+
+        Args:
+            config: Agent configuration dictionary.
+            gitea_client: Optional pre-configured Gitea client.
+            llm_client: Optional pre-configured LLM client.
+        """
+        self.config = config or self._load_config()
+        self.gitea = gitea_client or GiteaClient()
+        self.llm = llm_client or LLMClient.from_config(self.config)
+        self.logger = logging.getLogger(self.__class__.__name__)
+
+        # Rate limiting
+        self._last_request_time = 0.0
+        self._min_request_interval = 1.0  # seconds
+
+    @staticmethod
+    def _load_config() -> dict:
+        """Load configuration from config.yml."""
+        config_path = os.path.join(os.path.dirname(__file__), "..", "config.yml")
+        if os.path.exists(config_path):
+            with open(config_path) as f:
+                return yaml.safe_load(f)
+        return {}
+
+    def _rate_limit(self):
+        """Apply rate limiting between requests."""
+        elapsed = time.time() - self._last_request_time
+        if elapsed < self._min_request_interval:
+            time.sleep(self._min_request_interval - elapsed)
+        self._last_request_time = time.time()
+
+    def load_prompt(self, prompt_name: str) -> str:
+        """Load a prompt template from the prompts directory.
+
+        Args:
+            prompt_name: Name of the prompt file (without extension).
+
+        Returns:
+            Prompt template content.
+        """
+        prompt_path = os.path.join(
+            os.path.dirname(__file__), "..", "prompts", f"{prompt_name}.md"
+        )
+        if not os.path.exists(prompt_path):
+            raise FileNotFoundError(f"Prompt not found: {prompt_path}")
+        with open(prompt_path) as f:
+            return f.read()
+
+    def call_llm(self, prompt: str, **kwargs) -> LLMResponse:
+        """Make a rate-limited call to the LLM.
+
+        Args:
+            prompt: The prompt to send.
+            **kwargs: Additional LLM options.
+
+        Returns:
+            LLM response.
+        """
+        self._rate_limit()
+        return self.llm.call(prompt, **kwargs)
+
+    def call_llm_json(self, prompt: str, **kwargs) -> dict:
+        """Make a rate-limited call and parse JSON response.
+
+        Args:
+            prompt: The prompt to send.
+            **kwargs: Additional LLM options.
+
+        Returns:
+            Parsed JSON response.
+        """
+        self._rate_limit()
+        return self.llm.call_json(prompt, **kwargs)
+
+    def find_ai_comment(
+        self,
+        owner: str,
+        repo: str,
+        issue_index: int,
+        marker: str | None = None,
+    ) -> int | None:
+        """Find an existing AI comment by marker.
+
+        Args:
+            owner: Repository owner.
+            repo: Repository name.
+            issue_index: Issue or PR number.
+            marker: Custom marker to search for. Defaults to AI_MARKER.
+
+        Returns:
+            Comment ID if found, None otherwise.
+        """
+        marker = marker or self.AI_MARKER
+        comments = self.gitea.list_issue_comments(owner, repo, issue_index)
+
+        for comment in comments:
+            if marker in comment.get("body", ""):
+                return comment["id"]
+
+        return None
+
+    def upsert_comment(
+        self,
+        owner: str,
+        repo: str,
+        issue_index: int,
+        body: str,
+        marker: str | None = None,
+    ) -> dict:
+        """Create or update an AI comment.
+
+        Args:
+            owner: Repository owner.
+            repo: Repository name.
+            issue_index: Issue or PR number.
+            body: Comment body (marker will be prepended if not present).
+            marker: Custom marker. Defaults to AI_MARKER.
+
+        Returns:
+            Created or updated comment.
+        """
+        marker = marker or self.AI_MARKER
+
+        # Ensure marker is in the body
+        if marker not in body:
+            body = f"{marker}\n{body}"
+
+        # Check for existing comment
+        existing_id = self.find_ai_comment(owner, repo, issue_index, marker)
+
+        if existing_id:
+            return self.gitea.update_issue_comment(owner, repo, existing_id, body)
+        else:
+            return self.gitea.create_issue_comment(owner, repo, issue_index, body)
+
+    def format_with_disclaimer(self, content: str) -> str:
+        """Add AI disclaimer to content.
+
+        Args:
+            content: The main content.
+
+        Returns:
+            Content with disclaimer prepended.
+        """
+        return f"{self.AI_DISCLAIMER}\n\n{self.AI_MARKER}\n{content}"
+
+    @abstractmethod
+    def execute(self, context: AgentContext) -> AgentResult:
+        """Execute the agent's main task.
+
+        Args:
+            context: Execution context with event data.
+
+        Returns:
+            Result of the agent execution.
+        """
+        pass
+
+    @abstractmethod
+    def can_handle(self, event_type: str, event_data: dict) -> bool:
+        """Check if this agent can handle the given event.
+
+        Args:
+            event_type: Type of event (issue, pull_request, etc).
+            event_data: Event payload data.
+
+        Returns:
+            True if this agent can handle the event.
+        """
+        pass
+
+    def run(self, context: AgentContext) -> AgentResult:
+        """Run the agent with error handling.
+
+        Args:
+            context: Execution context.
+
+        Returns:
+            Agent result, including any errors.
+        """
+        try:
+            self.logger.info(
+                f"Running {self.__class__.__name__} for {context.owner}/{context.repo}"
+            )
+            result = self.execute(context)
+            self.logger.info(
+                f"Completed with success={result.success}: {result.message}"
+            )
+            return result
+        except Exception as e:
+            self.logger.exception(f"Agent execution failed: {e}")
+            return AgentResult(
+                success=False,
+                message="Agent execution failed",
+                error=str(e),
+            )